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Mathematics and Statistics Theses and Dissertations

Theses/dissertations from 2024 2024.

The Effect of Fixed Time Delays on the Synchronization Phase Transition , Shaizat Bakhytzhan

On the Subelliptic and Subparabolic Infinity Laplacian in Grushin-Type Spaces , Zachary Forrest

Utilizing Machine Learning Techniques for Accurate Diagnosis of Breast Cancer and Comprehensive Statistical Analysis of Clinical Data , Myat Ei Ei Phyo

Quandle Rings, Idempotents and Cocycle Invariants of Knots , Dipali Swain

Comparative Analysis of Time Series Models on U.S. Stock and Exchange Rates: Bayesian Estimation of Time Series Error Term Model Versus Machine Learning Approaches , Young Keun Yang

Theses/Dissertations from 2023 2023

Classification of Finite Topological Quandles and Shelves via Posets , Hitakshi Lahrani

Applied Analysis for Learning Architectures , Himanshu Singh

Rational Functions of Degree Five That Permute the Projective Line Over a Finite Field , Christopher Sze

Theses/Dissertations from 2022 2022

New Developments in Statistical Optimal Designs for Physical and Computer Experiments , Damola M. Akinlana

Advances and Applications of Optimal Polynomial Approximants , Raymond Centner

Data-Driven Analytical Predictive Modeling for Pancreatic Cancer, Financial & Social Systems , Aditya Chakraborty

On Simultaneous Similarity of d-tuples of Commuting Square Matrices , Corey Connelly

Symbolic Computation of Lump Solutions to a Combined (2+1)-dimensional Nonlinear Evolution Equation , Jingwei He

Boundary behavior of analytic functions and Approximation Theory , Spyros Pasias

Stability Analysis of Delay-Driven Coupled Cantilevers Using the Lambert W-Function , Daniel Siebel-Cortopassi

A Functional Optimization Approach to Stochastic Process Sampling , Ryan Matthew Thurman

Theses/Dissertations from 2021 2021

Riemann-Hilbert Problems for Nonlocal Reverse-Time Nonlinear Second-order and Fourth-order AKNS Systems of Multiple Components and Exact Soliton Solutions , Alle Adjiri

Zeros of Harmonic Polynomials and Related Applications , Azizah Alrajhi

Combination of Time Series Analysis and Sentiment Analysis for Stock Market Forecasting , Hsiao-Chuan Chou

Uncertainty Quantification in Deep and Statistical Learning with applications in Bio-Medical Image Analysis , K. Ruwani M. Fernando

Data-Driven Analytical Modeling of Multiple Myeloma Cancer, U.S. Crop Production and Monitoring Process , Lohuwa Mamudu

Long-time Asymptotics for mKdV Type Reduced Equations of the AKNS Hierarchy in Weighted L 2 Sobolev Spaces , Fudong Wang

Online and Adjusted Human Activities Recognition with Statistical Learning , Yanjia Zhang

Theses/Dissertations from 2020 2020

Bayesian Reliability Analysis of The Power Law Process and Statistical Modeling of Computer and Network Vulnerabilities with Cybersecurity Application , Freeh N. Alenezi

Discrete Models and Algorithms for Analyzing DNA Rearrangements , Jasper Braun

Bayesian Reliability Analysis for Optical Media Using Accelerated Degradation Test Data , Kun Bu

On the p(x)-Laplace equation in Carnot groups , Robert D. Freeman

Clustering methods for gene expression data of Oxytricha trifallax , Kyle Houfek

Gradient Boosting for Survival Analysis with Applications in Oncology , Nam Phuong Nguyen

Global and Stochastic Dynamics of Diffusive Hindmarsh-Rose Equations in Neurodynamics , Chi Phan

Restricted Isometric Projections for Differentiable Manifolds and Applications , Vasile Pop

On Some Problems on Polynomial Interpolation in Several Variables , Brian Jon Tuesink

Numerical Study of Gap Distributions in Determinantal Point Process on Low Dimensional Spheres: L -Ensemble of O ( n ) Model Type for n = 2 and n = 3 , Xiankui Yang

Non-Associative Algebraic Structures in Knot Theory , Emanuele Zappala

Theses/Dissertations from 2019 2019

Field Quantization for Radiative Decay of Plasmons in Finite and Infinite Geometries , Maryam Bagherian

Probabilistic Modeling of Democracy, Corruption, Hemophilia A and Prediabetes Data , A. K. M. Raquibul Bashar

Generalized Derivations of Ternary Lie Algebras and n-BiHom-Lie Algebras , Amine Ben Abdeljelil

Fractional Random Weighted Bootstrapping for Classification on Imbalanced Data with Ensemble Decision Tree Methods , Sean Charles Carter

Hierarchical Self-Assembly and Substitution Rules , Daniel Alejandro Cruz

Statistical Learning of Biomedical Non-Stationary Signals and Quality of Life Modeling , Mahdi Goudarzi

Probabilistic and Statistical Prediction Models for Alzheimer’s Disease and Statistical Analysis of Global Warming , Maryam Ibrahim Habadi

Essays on Time Series and Machine Learning Techniques for Risk Management , Michael Kotarinos

The Systems of Post and Post Algebras: A Demonstration of an Obvious Fact , Daviel Leyva

Reconstruction of Radar Images by Using Spherical Mean and Regular Radon Transforms , Ozan Pirbudak

Analyses of Unorthodox Overlapping Gene Segments in Oxytricha Trifallax , Shannon Stich

An Optimal Medium-Strength Regularity Algorithm for 3-uniform Hypergraphs , John Theado

Power Graphs of Quasigroups , DayVon L. Walker

Theses/Dissertations from 2018 2018

Groups Generated by Automata Arising from Transformations of the Boundaries of Rooted Trees , Elsayed Ahmed

Non-equilibrium Phase Transitions in Interacting Diffusions , Wael Al-Sawai

A Hybrid Dynamic Modeling of Time-to-event Processes and Applications , Emmanuel A. Appiah

Lump Solutions and Riemann-Hilbert Approach to Soliton Equations , Sumayah A. Batwa

Developing a Model to Predict Prevalence of Compulsive Behavior in Individuals with OCD , Lindsay D. Fields

Generalizations of Quandles and their cohomologies , Matthew J. Green

Hamiltonian structures and Riemann-Hilbert problems of integrable systems , Xiang Gu

Optimal Latin Hypercube Designs for Computer Experiments Based on Multiple Objectives , Ruizhe Hou

Human Activity Recognition Based on Transfer Learning , Jinyong Pang

Signal Detection of Adverse Drug Reaction using the Adverse Event Reporting System: Literature Review and Novel Methods , Minh H. Pham

Statistical Analysis and Modeling of Cyber Security and Health Sciences , Nawa Raj Pokhrel

Machine Learning Methods for Network Intrusion Detection and Intrusion Prevention Systems , Zheni Svetoslavova Stefanova

Orthogonal Polynomials With Respect to the Measure Supported Over the Whole Complex Plane , Meng Yang

Theses/Dissertations from 2017 2017

Modeling in Finance and Insurance With Levy-It'o Driven Dynamic Processes under Semi Markov-type Switching Regimes and Time Domains , Patrick Armand Assonken Tonfack

Prevalence of Typical Images in High School Geometry Textbooks , Megan N. Cannon

On Extending Hansel's Theorem to Hypergraphs , Gregory Sutton Churchill

Contributions to Quandle Theory: A Study of f-Quandles, Extensions, and Cohomology , Indu Rasika U. Churchill

Linear Extremal Problems in the Hardy Space H p for 0 p , Robert Christopher Connelly

Statistical Analysis and Modeling of Ovarian and Breast Cancer , Muditha V. Devamitta Perera

Statistical Analysis and Modeling of Stomach Cancer Data , Chao Gao

Structural Analysis of Poloidal and Toroidal Plasmons and Fields of Multilayer Nanorings , Kumar Vijay Garapati

Dynamics of Multicultural Social Networks , Kristina B. Hilton

Cybersecurity: Stochastic Analysis and Modelling of Vulnerabilities to Determine the Network Security and Attackers Behavior , Pubudu Kalpani Kaluarachchi

Generalized D-Kaup-Newell integrable systems and their integrable couplings and Darboux transformations , Morgan Ashley McAnally

Patterns in Words Related to DNA Rearrangements , Lukas Nabergall

Time Series Online Empirical Bayesian Kernel Density Segmentation: Applications in Real Time Activity Recognition Using Smartphone Accelerometer , Shuang Na

Schreier Graphs of Thompson's Group T , Allen Pennington

Cybersecurity: Probabilistic Behavior of Vulnerability and Life Cycle , Sasith Maduranga Rajasooriya

Bayesian Artificial Neural Networks in Health and Cybersecurity , Hansapani Sarasepa Rodrigo

Real-time Classification of Biomedical Signals, Parkinson’s Analytical Model , Abolfazl Saghafi

Lump, complexiton and algebro-geometric solutions to soliton equations , Yuan Zhou

Theses/Dissertations from 2016 2016

A Statistical Analysis of Hurricanes in the Atlantic Basin and Sinkholes in Florida , Joy Marie D'andrea

Statistical Analysis of a Risk Factor in Finance and Environmental Models for Belize , Sherlene Enriquez-Savery

Putnam's Inequality and Analytic Content in the Bergman Space , Matthew Fleeman

On the Number of Colors in Quandle Knot Colorings , Jeremy William Kerr

Statistical Modeling of Carbon Dioxide and Cluster Analysis of Time Dependent Information: Lag Target Time Series Clustering, Multi-Factor Time Series Clustering, and Multi-Level Time Series Clustering , Doo Young Kim

Some Results Concerning Permutation Polynomials over Finite Fields , Stephen Lappano

Hamiltonian Formulations and Symmetry Constraints of Soliton Hierarchies of (1+1)-Dimensional Nonlinear Evolution Equations , Solomon Manukure

Modeling and Survival Analysis of Breast Cancer: A Statistical, Artificial Neural Network, and Decision Tree Approach , Venkateswara Rao Mudunuru

Generalized Phase Retrieval: Isometries in Vector Spaces , Josiah Park

Leonard Systems and their Friends , Jonathan Spiewak

Resonant Solutions to (3+1)-dimensional Bilinear Differential Equations , Yue Sun

Statistical Analysis and Modeling Health Data: A Longitudinal Study , Bhikhari Prasad Tharu

Global Attractors and Random Attractors of Reaction-Diffusion Systems , Junyi Tu

Time Dependent Kernel Density Estimation: A New Parameter Estimation Algorithm, Applications in Time Series Classification and Clustering , Xing Wang

On Spectral Properties of Single Layer Potentials , Seyed Zoalroshd

Theses/Dissertations from 2015 2015

Analysis of Rheumatoid Arthritis Data using Logistic Regression and Penalized Approach , Wei Chen

Active Tile Self-assembly and Simulations of Computational Systems , Daria Karpenko

Nearest Neighbor Foreign Exchange Rate Forecasting with Mahalanobis Distance , Vindya Kumari Pathirana

Statistical Learning with Artificial Neural Network Applied to Health and Environmental Data , Taysseer Sharaf

Radial Versus Othogonal and Minimal Projections onto Hyperplanes in l_4^3 , Richard Alan Warner

Ensemble Learning Method on Machine Maintenance Data , Xiaochuang Zhao

Theses/Dissertations from 2014 2014

Properties of Graphs Used to Model DNA Recombination , Ryan Arredondo

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Top 99+ Trending Statistics Research Topics for Students

Being a statistics student, finding the best statistics research topics is quite challenging. But not anymore; find the best statistics research topics now!!!

Statistics is one of the tough subjects because it consists of lots of formulas, equations and many more. Therefore the students need to spend their time to understand these concepts. And when it comes to finding the best statistics research project for their topics, statistics students are always looking for someone to help them. 

In this blog, we will share with you the most interesting and trending statistics research topics in 2023. It will not just help you to stand out in your class but also help you to explore more about the world.

If you face any problem regarding statistics, then don’t worry. You can get the best statistics assignment help from one of our experts.

As you know, it is always suggested that you should work on interesting topics. That is why we have mentioned the most interesting research topics for college students and high school students. Here in this blog post, we will share with you the list of 99+ awesome statistics research topics.

Why Do We Need to Have Good Statistics Research Topics?

Table of Contents

Having a good research topic will not just help you score good grades, but it will also allow you to finish your project quickly. Because whenever we work on something interesting, our productivity automatically boosts. Thus, you need not invest lots of time and effort, and you can achieve the best with minimal effort and time. 

What Are Some Interesting Research Topics?

If we talk about the interesting research topics in statistics, it can vary from student to student. But here are the key topics that are quite interesting for almost every student:-

• Literacy rate in a city.
• Abortion and pregnancy rate in the USA.
• Eating disorders in the citizens.
• Parent role in self-esteem and confidence of the student.
• Uses of AI in our daily life to business corporates.

Top 99+ Trending Statistics Research Topics For 2023

Here in this section, we will tell you more than 99 trending statistics research topics:

Sports Statistics Research Topics

• Statistical analysis for legs and head injuries in Football.
• Statistical analysis for shoulder and knee injuries in MotoGP.
• Deep statistical evaluation for the doping test in sports from the past decade.
• Statistical observation on the performance of athletes in the last Olympics.
• Role and effect of sports in the life of the student.

Psychology Research Topics for Statistics

• Deep statistical analysis of the effect of obesity on the student’s mental health in high school and college students.
• Statistical evolution to find out the suicide reason among students and adults.
• Statistics analysis to find out the effect of divorce on children in a country.
• Psychology affects women because of the gender gap in specific country areas.
• Statistics analysis to find out the cause of online bullying in students’ lives. 
• In Psychology, PTSD and descriptive tendencies are discussed.
• The function of researchers in statistical testing and probability.
• Acceptable significance and probability thresholds in clinical Psychology.
• The utilization of hypothesis and the role of P 0.05 for improved comprehension.
• What types of statistical data are typically rejected in psychology?
• The application of basic statistical principles and reasoning in psychological analysis.
• The role of correlation is when several psychological concepts are at risk.
• Actual case study learning and modeling are used to generate statistical reports.
• In psychology, naturalistic observation is used as a research sample.
• How should descriptive statistics be used to represent behavioral data sets?

Applied Statistics Research Topics

• Does education have a deep impact on the financial success of an individual?
• The investment in digital technology is having a meaningful return for corporations?
• The gap of financial wealth between rich and poor in the USA.
• A statistical approach to identify the effects of high-frequency trading in financial markets.
• Statistics analysis to determine the impact of the multi-agent model in financial markets. 

Personalized Medicine Statistics Research Topics

• Statistical analysis on the effect of methamphetamine on substance abusers.
• Deep research on the impact of the Corona vaccine on the Omnicrone variant. 
• Find out the best cancer treatment approach between orthodox therapies and alternative therapies.
• Statistics analysis to identify the role of genes in the child’s overall immunity.
• What factors help the patients to survive from Coronavirus .

Experimental Design Statistics Research Topics

• Generic vs private education is one of the best for the students and has better financial return.
• Psychology vs physiology: which leads the person not to quit their addictions?
• Effect of breastmilk vs packed milk on the infant child overall development
• Which causes more accidents: male alcoholics vs female alcoholics.
• What causes the student not to reveal the cyberbullying in front of their parents in most cases. 

Easy Statistics Research Topics

• Application of statistics in the world of data science
• Statistics for finance: how statistics is helping the company to grow their finance
• Advantages and disadvantages of Radar chart
• Minor marriages in south-east Asia and African countries.
• Discussion of ANOVA and correlation.
• What statistical methods are most effective for active sports?
• When measuring the correctness of college tests, a ranking statistical approach is used.
• Statistics play an important role in Data Mining operations.
• The practical application of heat estimation in engineering fields.
• In the field of speech recognition, statistical analysis is used.
• Estimating probiotics: how much time is necessary for an accurate statistical sample?
• How will the United States population grow in the next twenty years?
• The legislation and statistical reports deal with contentious issues.
• The application of empirical entropy approaches with online grammar checking.
• Transparency in statistical methodology and the reporting system of the United States Census Bureau.

Statistical Research Topics for High School

• Uses of statistics in chemometrics
• Statistics in business analytics and business intelligence
• Importance of statistics in physics.
• Deep discussion about multivariate statistics
• Uses of Statistics in machine learning

Survey Topics for Statistics

• Gather the data of the most qualified professionals in a specific area.
• Survey the time wasted by the students in watching Tvs or Netflix.
• Have a survey the fully vaccinated people in the USA 
• Gather information on the effect of a government survey on the life of citizens
• Survey to identify the English speakers in the world.

Statistics Research Paper Topics for Graduates

• Have a deep decision of Bayes theorems
• Discuss the Bayesian hierarchical models
• Analysis of the process of Japanese restaurants. 
• Deep analysis of Lévy’s continuity theorem
• Analysis of the principle of maximum entropy

AP Statistics Topics

• Discuss about the importance of econometrics
• Analyze the pros and cons of Probit Model
• Types of probability models and their uses
• Deep discussion of ortho stochastic matrix
• Find out the ways to get an adjacency matrix quickly

Good Statistics Research Topics 

• National income and the regulation of cryptocurrency.
• The benefits and drawbacks of regression analysis.
• How can estimate methods be used to correct statistical differences?
• Mathematical prediction models vs observation tactics.
• In sociology research, there is bias in quantitative data analysis.
• Inferential analytical approaches vs. descriptive statistics.
• How reliable are AI-based methods in statistical analysis?
• The internet news reporting and the fluctuations: statistics reports.
• The importance of estimate in modeled statistics and artificial sampling.

Business Statistics Topics

• Role of statistics in business in 2023
• Importance of business statistics and analytics
• What is the role of central tendency and dispersion in statistics
• Best process of sampling business data.
• Importance of statistics in big data.
• The characteristics of business data sampling: benefits and cons of software solutions.
• How may two different business tasks be tackled concurrently using linear regression analysis?
• In economic data relations, index numbers, random probability, and correctness are all important.
• The advantages of a dataset approach to statistics in programming statistics.
• Commercial statistics: how should the data be prepared for maximum accuracy?

Statistical Research Topics for College Students

• Evaluate the role of John Tukey’s contribution to statistics.
• The role of statistics to improve ADHD treatment.
• The uses and timeline of probability in statistics.
• Deep analysis of Gertrude Cox’s experimental design in statistics.
• Discuss about Florence Nightingale in statistics.
• What sorts of music do college students prefer?
• The Main Effect of Different Subjects on Student Performance.
• The Importance of Analytics in Statistics Research.
• The Influence of a Better Student in Class.
• Do extracurricular activities help in the transformation of personalities?
• Backbenchers’ Impact on Class Performance.
• Medication’s Importance in Class Performance.
• Are e-books better than traditional books?
• Choosing aspects of a subject in college

How To Write Good Statistics Research Topics?

So, the main question that arises here is how you can write good statistics research topics. The trick is understanding the methodology that is used to collect and interpret statistical data. However, if you are trying to pick any topic for your statistics project, you must think about it before going any further. 

As a result, it will teach you about the data types that will be researched because the sample will be chosen correctly. On the other hand, your basic outline for choosing the correct topics is as follows:

• Introduction of a problem
• Methodology explanation and choice. 
• Statistical research itself is in the main part (Body Part). 
• Samples deviations and variables. 
• Lastly, statistical interpretation is your last part (conclusion). 

Note:   Always include the sources from which you obtained the statistics data.

Top 3 Tips to Choose Good Statistics Research Topics

It can be quite easy for some students to pick a good statistics research topic without the help of an essay writer. But we know that it is not a common scenario for every student. That is why we will mention some of the best tips that will help you choose good statistics research topics for your next project. Either you are in a hurry or have enough time to explore. These tips will help you in every scenario.

1. Narrow down your research topic

We all start with many topics as we are not sure about our specific interests or niche. The initial step to picking up a good research topic for college or school students is to narrow down the research topic.

For this, you need to categorize the matter first. And then pick a specific category as per your interest. After that, brainstorm about the topic’s content and how you can make the points catchy, focused, directional, clear, and specific. 

2. Choose a topic that gives you curiosity

After categorizing the statistics research topics, it is time to pick one from the category. Don’t pick the most common topic because it will not help your grades and knowledge. Instead of it, please choose the best one, in which you have little information, or you are more likely to explore it.

In a statistics research paper, you always can explore something beyond your studies. By doing this, you will be more energetic to work on this project. And you will also feel glad to get them lots of information you were willing to have but didn’t get because of any reasons.

It will also make your professor happy to see your work. Ultimately it will affect your grades with a positive attitude.

3. Choose a manageable topic

Now you have decided on the topic, but you need to make sure that your research topic should be manageable. You will have limited time and resources to complete your project if you pick one of the deep statistics research topics with massive information.

Then you will struggle at the last moment and most probably not going to finish your project on time. Therefore, spend enough time exploring the topic and have a good idea about the time duration and resources you will use for the project. 

Statistics research topics are massive in numbers. Because statistics operations can be performed on anything from our psychology to our fitness. Therefore there are lots more statistics research topics to explore. But if you are not finding it challenging, then you can take the help of our statistics experts . They will help you to pick the most interesting and trending statistics research topics for your projects. 

With this help, you can also save your precious time to invest it in something else. You can also come up with a plethora of topics of your choice and we will help you to pick the best one among them. Apart from that, if you are working on a project and you are not sure whether that is the topic that excites you to work on it or not. Then we can also help you to clear all your doubts on the statistics research topic. 

Frequently Asked Questions

Q1. what are some good topics for the statistics project.

Have a look at some good topics for statistics projects:- 1. Research the average height and physics of basketball players. 2. Birth and death rate in a specific city or country. 3. Study on the obesity rate of children and adults in the USA. 4. The growth rate of China in the past few years 5. Major causes of injury in Football

Q2. What are the topics in statistics?

Statistics has lots of topics. It is hard to cover all of them in a short answer. But here are the major ones: conditional probability, variance, random variable, probability distributions, common discrete, and many more. 

Q3. What are the top 10 research topics?

Here are the top 10 research topics that you can try in 2023:

1. Plant Science 2. Mental health 3. Nutritional Immunology 4. Mood disorders 5. Aging brains 6. Infectious disease 7. Music therapy 8. Political misinformation 9. Canine Connection 10. Sustainable agriculture

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120 Statistical Research Topics: Explore Up-to-date Trends

Researchers and statistics teachers are often tasked with writing an article or paper on a given stats project idea. One of the most crucial things in writing an outstanding and well-composed statistics research project, paper, or essay is to come up with a very interesting topic that will captivate your reader’s minds and provoke their thoughts.

What Are the Best Statistical Research Topics Worth Writing On?

Leading statistical research topics for college students that will interest you, project topics in statistics worth considering, the best idea for statistics project you can focus on, good experiments for statistics topics you should be writing on, what are the best ap statistics project ideas that will be of keen interest to you, good statistics project ideas suitable for our modern world, some of the most crucial survey topics for statistics project, statistical projects topics every researcher wants to write on, statistical research topics you can focus your research on.

Students often find it difficult to come up with well-composed statistical research project topics that take the format of argumentative essay topics to pass across their message. In this essay, we will look at some of the most interesting statistics research topics to focus your research on.

Here are some of the best statistical research topics worth writing on:

• Predictive Healthcare Modeling with Machine Learning
• Analyzing Online Education During COVID-19 Epidemic
• Modeling How Climate Change Affects Natural Disasters
• Essential Elements Influencing Personnel Productivity
• Social Media Influence on Customer Choices and Behavior
• Can Geographical Statistics Aid In Analyzing Crime Trends and Patterns?
• Financial Markets and Stock Price Predictions
• Statistical Analysis of Voting-related Behaviors
• An Analysis of Public Transportation Usage Trends in Urban Areas
• How Can Public Health Education Reduce Air Pollution?
• Statistical Analysis of Suicide In Adolescents and Adults
• A Review of Divorce and How It Affects Children

As a college student, here are the best statistical projects for high school students to focus your research on, especially if you need social media research topics .

• Major Factors Influencing College Students’ Academic Performance
• Social Media and How It Defines thee Mental Health of Students
• Evaluation of the Elements Influencing Student Engagement and Retention
• An Examination of Extracurricular Activities On Academic Success
• Does Parental Involvement Determine Academic Achievement of Kids?
• Examining How Technology Affects Improving Educational Performance
• Factors That Motivate Students’ Involvement In Online Learning
• The Impact of Socioeconomic Status On Academic Performance
• Does Criticism Enhance Student Performance?
• Student-Centered Learning and Improved Performance
• A Cursory Look At Students’ Career Goals and Major Life Decisions
• Does Mental Health Impact Academic Achievement?

Are you a student tasked with writing a project but can’t come up with befitting stats research topics? Here are the best ideas for statistical projects worth considering:

• Financial Data And Stock Price Forecasting
• Investigation of Variables Influencing Students’ Grades
• What Causes Traffic Flow and Congestion In Urban Areas?
• How to Guarantee Customer Retention In the Retail Sector
• Using Epidemiological Data to Model the Spread of Infectious Diseases
• Does Direct Advertisement Affect Consumer Preferences and Behavior?
• How to Predict and Adapt to Climate Change
• Using Spatial Statistics to Analyze Trends and Patterns In Crime
• Examination of the Elements Influencing Workplace Morale and Productivity
• Understanding User Behavior and Preferences Through Statistical Analysis of Social Media Data
• How Many Percent Get Married After Their Degree Programs?
• A Comparative Analysis of Different Academic Fee Payments

If you have been confused based on the availability of different statistics project topics to choose from, here are some of the best thesis statement about social media to choose from:

• Analysis of the Variables Affecting A Startup’s Success
• The Valid Connection Between Mental Health and Social Media Use
• Different Teaching Strategies and Academic Performance
• Factors Influencing Employee Satisfaction In Different Work Environments
• The Impact of Public Policy On Different Population Groups
• Reviewing Different Health Outcomes and Incomes
• Different Marketing Tactics for Good Service Promotion
• What Influences Results In Different Sports Competitions?
• Differentiating Elements Affecting Students’ Performance In A Given Subject
• Internal Communication and Building An Effective Workplace
• Does the Use of Business Technologies Boost Workers’ Output?
• The Role of Modern Communication In An Effective Company Management

Are you a student tasked with writing an essay on social issues research topics but having challenges coming up with a topic? Here are some amazing statistical experiments ideas you can center your research on.

• How Global Pandemic Affects Local Businesses
• Investigating the Link Between Income and Health Outcomes In a Demography
• Key Motivators for Student’s Performance In a Particular Academic Program
• Evaluating the Success of a Promotional Plan Over Others
• Continuous Social Media Use and Impact On Mental Health
• Does Culture Impact the Religious Beliefs of Certain Groups?
• Key Indicators of War and How to Manage These Indicators
• An Overview of War As a Money Laundering Scheme
• How Implementations Guarantee Effectiveness of Laws In Rural Areas
• Performance of Students In War-torn Areas
• Key Indicators For Measuring the Success of Your Venture
• How Providing FAQs Can Help a Business Scale

The best AP statistic project ideas every student especially those interested in research topics for STEM students  will want to write in include:

• The Most Affected Age Demography By the Covid-19 Pandemic
• The Health Outcomes Peculiar to a Specific Demography
• Unusual Ways to Enhance Student Performance In a Classroom
• How Marketing Efforts Can Determine Promotional Outputs
• Can Mental Health Solutions Be Provided On Social Media?
• Assessing How Certain Species Are Affected By Climate Change.
• What Influences Voter Turnouts In Different Elections?
• How Many People Have Used Physical Exercises to Improve Mental Health
• How Financial Circumstances Can Determine Criminal Activities
• Ways DUI Laws Can Reduce Road Accidents
• Examining the Connection Between Corruption and Underdevelopment In Africa
• What Key Elements Do Top Global Firms Engage for Success?

If you need some of the best economics research paper topics , here are the best statistics experiment ideas you can write research on:

• Retail Client Behaviors and Weather Trends
• The Impact of Marketing Initiatives On Sales and Customer Retention
• How Socioeconomic Factors Determine Crime Rates In Different Locations
• Public and Private School Students: Who Performs Better?
• How Fitness Affects the Mental Health of People In Different Ages
• Focus On the Unbanked Employees Globally
• Does Getting Involve In a Kid’s Life Make Them Better?
• Dietary Decisions and a Healthy Life
• Managing Diabetes and High Blood Pressure of a Specific Group
• How to Engage Different Learning Methods for Effectiveness
• Understudying the Sleeping Habits of Specific Age Groups
• How the Numbers Can Help You Create a Brand Recognition

As a student who needs fresh ideas relating to the topic for a statistics project to write on, here are crucial survey topics for statistics that will interest you.

• Understanding Consumer Spending and Behavior In Different Regions
• Why Some People in Certain Areas Live Longer than Others
• Comparative Analysis of Different Customer Behaviors
• Do Social Media Businesses Benefit More than Physical Businesses?
• Does a Healthy Work Environment Guarantee Productivity?
• The Impact of Ethnicity and Religion On Voting Patterns
• Does Financial Literacy Guarantee Better Money Management?
• Cultural Identities and Behavioral Patterns
• How Religious Orientation Determines Social Media Use
• The Growing Need for Economists Globally
• Getting Started with Businesses On Social Media
• Which Is Better: A 9-5 or An Entrepreneurial Job?

Do you want to write on unique statistical experiment ideas? Here are some topics you do not want to miss out on:

• Consumer Satisfaction-Related Variables on E-Commerce Websites
• Obesity Rates and Socioeconomic Status In Developed Countries
• How Marketing Strategies Can Make or Mar Sales Performance
• The Correlation Between Increased Income and Happiness In Various Nations
• Regression Models and Forecasting Home Prices
• Climate Change Affecting Agricultural Production In Specific Areas
• A Study of Employee Satisfaction In the Healthcare Industry
• Social Media, Marketing Tactics, and Consumer Behavior In the Fashion Industry
• Predicting the Risk of Default Among Credit Card Holders In Different Regions
• Why Crime Rates Are Increasing In Urban Areas than Rural Areas
• Statistical Evaluation of Methamphetamine’s Impact On Drug Users
• Genes and a Child’s Total Immunity

Here are some of the most carefully selected stat research topics you can focus on.

• Social Media’s Effects On Consumer Behavior
• The Correlation Between Urban Crime Rates and Poverty Levels
• Physical Exercise and Mental Health Consequences
• Predictive Modeling In the Financial Markets
• How Minimum Wage Regulations Impact Employment Rates
• Healthcare Outcomes and Access Across Various Socioeconomic Groups
• How High School Students’ Environment Affect Academic Performance
• Automated Technology and Employment Loss
• Environmental Elements and Their Effects On Public Health
• Various Advertising Tactics and How They Influence Customer Behavior
• Political Polarization And Economic Inequality
• Climate Change and Agricultural Productivity

The above statistics final project examples will stimulate your curiosity and test your abilities, and they can even be linked to some biochemistry topics and anatomy research paper topics . Writing about these statistics project ideas helps provide a deeper grasp of the natural and social phenomena that affect our lives and the environment by studying these subjects.

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Praphruetpong (Ben) Athiwaratkun - "Density representations for words and hierarchical data"

Dissertation Advisor: Andrew Wilson

Initial job placement: AI Scientist - AWS AI Labs

Yiming Sun - “High dimensional data analysis with dependency and under limited memory”

Dissertation Advisor: Sumanta Basu and Madeleine Udell

Initial job placement: Applied Scientist - Amazon

Zi Ye - “Functional single index model and jensen effect"

Dissertation Advisor: Giles Hooker 

Initial job placement: Data & Applied Scientist - Microsoft

Hui Fen (Sarah) Tan - “Interpretable approaches to opening up black-box models”

Dissertation Advisor: Giles Hooker and Martin Wells

Initial job placement: Microsoft

Daniel E. Gilbert - “Luck, fairness and Bayesian tensor completion”

Dissertation Advisor: Martin Wells

Yichen Zhou - “Asymptotics and interpretability of decision trees and decision tree ensemblesg”

Dissertation Advisor: Giles Hooker

Initial job placement: Data Scientist - Google

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What do senior theses in Statistics look like?

This is a brief overview of thesis writing; for more information, please see our website here . Senior theses in Statistics cover a wide range of topics, across the spectrum from applied to theoretical. Typically, senior theses are expected to have one of the following three flavors:                                                                                                            

1. Novel statistical theory or methodology, supported by extensive mathematical and/or simulation results, along with a clear account of how the research extends or relates to previous related work.

2. An analysis of a complex data set that advances understanding in a related field, such as public health, economics, government, or genetics. Such a thesis may rely entirely on existing methods, but should give useful results and insights into an interesting applied problem.                                                                                 

3. An analysis of a complex data set in which new methods or modifications of published methods are required. While the thesis does not necessarily contain an extensive mathematical study of the new methods, it should contain strong plausibility arguments or simulations supporting the use of the new methods.

A good thesis is clear, readable, and well-motivated, justifying the applicability of the methods used rather than, for example, mechanically running regressions without discussing the assumptions (and whether they are plausible), performing diagnostics, and checking whether the conclusions make sense. 

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Home > FACULTIES > Statistical and Actuarial Sciences > STATS-ETD

Statistics and Actuarial Sciences Theses and Dissertations

This collection contains theses and dissertations from the Department of Statistics and Actuarial Sciences, collected from the Scholarship@Western Electronic Thesis and Dissertation Repository

Theses/Dissertations from 2024 2024

Studies of compound risk models with dependence and parameter uncertainty , Dechen Gao

Theses/Dissertations from 2023 2023

Parameter Estimation for Normally Distributed Grouped Data and Clustering Single-Cell RNA Sequencing Data via the Expectation-Maximization Algorithm , Zahra Aghahosseinalishirazi

Statistical modelling and applications for sustainable-development goals , Yiyang Chen

Multivariate Regression Analysis for Data with Measurement Error, Missing Values, and/or Sparsity Structures , Jingyu Cui

Addressing the Impact of Time-Dependent Social Groupings on Animal Survival and Recapture Rates in Mark-Recapture Studies , Alexandru M. Draghici

Generalized Poisson random variables: Their distributional properties and actuarial applications , Pouya Faroughi

Optimizing Dynamic Treatment Regimes with Q-Learning: Complications due to Error-Prone Data and Applications to COVID-19 Data , Yasin Khadem Charvadeh

Estimating the spatial correlation structure of measurement error in functional magnetic resonance imaging (fMRI) to improve multivariate inference , Lingling Lin

Cyber risk valuation via a hidden Markov-modulated modelling approach , Yuying Li

Advances in Copula Estimation and Distribution Theory , Yishan Zang

Modelling long-term security returns , XINGHAN ZHU

Theses/Dissertations from 2022 2022

Efficiency Improvements in the Least-Squares Monte Carlo Algorithm , François-Michel Boire

Portfolio Optimization Analysis in the Family of 4/2 Stochastic Volatility Models , Yuyang Cheng

Early-Warning Alert Systems for Financial-Instability Detection: An HMM-Driven Approach , Xing Gu

The Analysis of Mark-recapture Data with Individual Heterogeneity via the H-likelihood , Han-na Kim

Statistical Applications to the Management of Intensive Care and Step-down Units , Yawo Mamoua Kobara

Regression-based Methods for Dynamic Treatment Regimes with Mismeasured Covariates or Misclassified Response , Dan Liu

Statistical Roles of the G-expectation Framework in Model Uncertainty: the Semi-G-structure as a Stepping Stone , Yifan Li

Risk theory: data-driven models , Yang Miao

New Developments on the Estimability and the Estimation of Phase-Type Actuarial Models , Cong Nie

Copulas, maximal dependence, and anomaly detection in bi-variate time series , Ning Sun

Interdisciplinary Knowledge Exchange in Statistics with Applications in Fire Science and Statistical Education , Chelsea Uggenti

On the Geometry of Multi-Affine Polynomials , Junquan Xiao

Understanding Deep Learning with Noisy Labels , Li Yi

An Analysis of Weighted Least Squares Monte Carlo , Xiaotian Zhu

Application Of A Polynomial Affine Method In Dynamic Portfolio Choice , Yichen Zhu

Theses/Dissertations from 2021 2021

A class of phase-type ageing models and their lifetime distributions , Boquan Cheng

Application of Stochastic Control to Portfolio Optimization and Energy Finance , Junhe Chen

Making Sense of Noisy Data: Theory and Applications , Lingzhi Chen

The Mean-Reverting 4/2 Stochastic Volatility Model: Properties And Financial Applications , Zhenxian Gong

Compound Sums, Their Distributions, and Actuarial Pricing , Ang Li

On the Estimation of Heston-Nandi GARCH Using Returns and/or Options: A Simulation-based Approach , Xize Ye

Theses/Dissertations from 2020 2020

A Treatise of PD-LGD Correlation Modelling , Wisdom S. Avusuglo WSA

Visualization and Joint Analysis of Monitored Multivariate Spatio-Temporal Data with Applications to Forest Fire Modelling and Sports Analytics , Devan Becker

Generalized 4/2 Factor Model , Yuyang Cheng

Renewable-energy resources, economic growth and their causal link , Yiyang Chen

Some Insurance Options on Stochastic Drawdowns , Filip Dikic

Extensions of Classification Method Based on Quantiles , Yuanhao Lai

Point Process Modelling of Objects in the Star Formation Complexes of the M33 Galaxy , Dayi Li

Classification-based method for estimating dynamic treatment regimes , Junwei Shen

Statistical Methods with a Focus on Joint Outcome Modeling and on Methods for Fire Science , Da Zhong Xi

Ranking comments: An Entropy-based Method with Word Embedding Clustering , Yuyang Zhang

Theses/Dissertations from 2019 2019

A computationally efficient methodology in pricing a guaranteed minimum accumulation benefit , Yiming Huang

Some Recent Developments on Pareto-optimal Reinsurance , Wenjun Jiang

Classification with Measurement Error in Covariates Or Response, with Application to Prostate Cancer Imaging Study , Kexin Luo

Exploring the Estimability of Mark-Recapture Models with Individual, Time-Varying Covariates using the Scaled Logit Link Function , Jiaqi Mu

Split credibility: A two-dimensional semi-linear credibility model , Jingbing Qiu

Advances in Moment-Based Distributional Methodologies , Yishan Zang

How to Rank Answers in Text Mining , Guandong Zhang

On the Sparre-Andersen Risk Models , Ruixi Zhang

Valuation and Risk Management of Some Longevity and P&C Insurance Products , Yixing Zhao

Theses/Dissertations from 2018 2018

Modelling the Common Risk among Equities Using a New Time Series Model , Jingjia Chu

Stochastic modelling of implied correlation index and herd behavior index. Evidence, properties and pricing. , Lin Fang

Optimal Trading of a Storable Commodity via Forward Markets , Behzad Ghafouri

Statistical Modeling of CO2 Flux Data , Fang He

Advances in the Modeling of Heavy-tailed Distributions , Sang Jin Kang

The Statistical Exploration in the $G$-expectation Framework: The Pseudo Simulation and Estimation of Variance Uncertainty , Yifan Li

Statistical tools for assessment of spatial properties of mutations observed under the microarray platform , Bin Luo

Valuation of Multiple Exercise Option Using a Modified Longstaff and Schwartz Approach , Rahim Mohammadhasani Khorasany

Statistical Applications in Healthcare Systems , Maryam Mojalal

Exact Box-Cox Analysis , Samira Soleymani

Anisotropic kernel smoothing for change-point data with an analysis of fire spread rate variability , John Ronald James Thompson

Some applications of higher-order hidden Markov models in the exotic commodity markets , Heng Xiong

Advances in Semi-Nonparametric Density Estimation and Shrinkage Regression , Hossein Zareamoghaddam

Analysis Challenges for High Dimensional Data , Bangxin Zhao

Theses/Dissertations from 2017 2017

Properties of k-isotropic functions , Tianpei Jiang

Data-Adaptive Kernel Support Vector Machine , Xin Liu

Annuity Product Valuation and Risk Measurement under Correlated Financial and Longevity Risks , Soohong Park

Statistical Modelling, Optimal Strategies and Decisions in Two-Period Economies , Jiang Wu

Theses/Dissertations from 2016 2016

Joint Models for Spatial and Spatio-Temporal Point Processes , Alisha Albert-Green

Applications of Credit Scoring Models , Mimi Mei Ling Chong

Joint Analysis of Zero-heavy Longitudinal Outcomes: Models and Comparison of Study Designs , Erin R. Lundy

Data Smoothing Techniques: Historical and Modern , Lori L. Murray

Joint Modelling in Liver Transplantation , Elizabeth M. Renouf

Probability Models for Health Care Operations with Application to Emergency Medicine , Azaz Bin Sharif

Advances in Portmanteau Diagnostic Tests , Jinkun Xiao

Actuarial Modelling with Mixtures of Markov Chains , Yuzhou Zhang

Theses/Dissertations from 2015 2015

Healthy And Unhealthy Statistics: Examining The Impact Of Erroneous Statistical Analyses In Health-Related Research , Britney Allen

Recent Advances in Accumulating Priority Queues , Na Li

Quantitative Techniques for Spread Trading in Commodity Markets , Mir Hashem Moosavi Avonleghi

A Novel Method for Assessing Co-monotonicity: an Interplay between Mathematics and Statistics with Applications , Danang T. Qoyyimi

Completely monotone and Bernstein functions with convexity properties on their measures , Shen Shan

Online Nonparametric Estimation of Stochastic Differential Equations , Xin Wang

On the Dual Risk Models , Chen Yang

Theses/Dissertations from 2014 2014

Statistical methods for the analysis of RNA sequencing data , Man-Kee Maggie Chu

Valuation and Risk Measurement of Guaranteed Annuity Options under Stochastic Environment , Huan Gao

Statistical Applications in Wildfire Management and Prediction , Lengyi Han

Computing and Approximation Methods for the Distribution of Multivariate Aggregate Claims , Tao Jin

The Doubly Adaptive LASSO Methods for Time Series Analysis , Zi Zhen Liu

Risk models with dependence and perturbation , Zhong Li

Censored Time Series Analysis , Nagham Muslim Mohammad

A Spatial Analysis of Forest Fire Survival and a Marked Cluster Process for Simulating Fire Load , Amy A. Morin

Estimation of Hidden Markov Models and Their Applications in Finance , Anton Tenyakov

Perfect and Nearly Perfect Sampling of Work-conserving Queues , Yaofei Xiong

Decision Theory Based Models in Insurance and Beyond , Raymond Ye Zhang

Theses/Dissertations from 2013 2013

Seasonal Decomposition for Geographical Time Series using Nonparametric Regression , Hyukjun Gweon

Stochastic simulation and spatial statistics of large datasets using parallel computing , Jonathan SW Lee

Flexible Partially Linear Single Index Regression Models for Multivariate Survival Data , Na Lei

Joint outcome modeling using shared frailties with application to temporal streamflow data , Lihua Li

Asymptotic Theory for GARCH-in-mean Models , Weiwei Liu

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Research Topics & Ideas: Data Science

50 Topic Ideas To Kickstart Your Research Project

If you’re just starting out exploring data science-related topics for your dissertation, thesis or research project, you’ve come to the right place. In this post, we’ll help kickstart your research by providing a hearty list of data science and analytics-related research ideas , including examples from recent studies.

PS – This is just the start…

We know it’s exciting to run through a list of research topics, but please keep in mind that this list is just a starting point . These topic ideas provided here are intentionally broad and generic , so keep in mind that you will need to develop them further. Nevertheless, they should inspire some ideas for your project.

To develop a suitable research topic, you’ll need to identify a clear and convincing research gap , and a viable plan to fill that gap. If this sounds foreign to you, check out our free research topic webinar that explores how to find and refine a high-quality research topic, from scratch. Alternatively, consider our 1-on-1 coaching service .

Data Science-Related Research Topics

• Developing machine learning models for real-time fraud detection in online transactions.
• The use of big data analytics in predicting and managing urban traffic flow.
• Investigating the effectiveness of data mining techniques in identifying early signs of mental health issues from social media usage.
• The application of predictive analytics in personalizing cancer treatment plans.
• Analyzing consumer behavior through big data to enhance retail marketing strategies.
• The role of data science in optimizing renewable energy generation from wind farms.
• Developing natural language processing algorithms for real-time news aggregation and summarization.
• The application of big data in monitoring and predicting epidemic outbreaks.
• Investigating the use of machine learning in automating credit scoring for microfinance.
• The role of data analytics in improving patient care in telemedicine.
• Developing AI-driven models for predictive maintenance in the manufacturing industry.
• The use of big data analytics in enhancing cybersecurity threat intelligence.
• Investigating the impact of sentiment analysis on brand reputation management.
• The application of data science in optimizing logistics and supply chain operations.
• Developing deep learning techniques for image recognition in medical diagnostics.
• The role of big data in analyzing climate change impacts on agricultural productivity.
• Investigating the use of data analytics in optimizing energy consumption in smart buildings.
• The application of machine learning in detecting plagiarism in academic works.
• Analyzing social media data for trends in political opinion and electoral predictions.
• The role of big data in enhancing sports performance analytics.
• Developing data-driven strategies for effective water resource management.
• The use of big data in improving customer experience in the banking sector.
• Investigating the application of data science in fraud detection in insurance claims.
• The role of predictive analytics in financial market risk assessment.
• Developing AI models for early detection of network vulnerabilities.

Data Science Research Ideas (Continued)

• The application of big data in public transportation systems for route optimization.
• Investigating the impact of big data analytics on e-commerce recommendation systems.
• The use of data mining techniques in understanding consumer preferences in the entertainment industry.
• Developing predictive models for real estate pricing and market trends.
• The role of big data in tracking and managing environmental pollution.
• Investigating the use of data analytics in improving airline operational efficiency.
• The application of machine learning in optimizing pharmaceutical drug discovery.
• Analyzing online customer reviews to inform product development in the tech industry.
• The role of data science in crime prediction and prevention strategies.
• Developing models for analyzing financial time series data for investment strategies.
• The use of big data in assessing the impact of educational policies on student performance.
• Investigating the effectiveness of data visualization techniques in business reporting.
• The application of data analytics in human resource management and talent acquisition.
• Developing algorithms for anomaly detection in network traffic data.
• The role of machine learning in enhancing personalized online learning experiences.
• Investigating the use of big data in urban planning and smart city development.
• The application of predictive analytics in weather forecasting and disaster management.
• Analyzing consumer data to drive innovations in the automotive industry.
• The role of data science in optimizing content delivery networks for streaming services.
• Developing machine learning models for automated text classification in legal documents.
• The use of big data in tracking global supply chain disruptions.
• Investigating the application of data analytics in personalized nutrition and fitness.
• The role of big data in enhancing the accuracy of geological surveying for natural resource exploration.
• Developing predictive models for customer churn in the telecommunications industry.
• The application of data science in optimizing advertisement placement and reach.

Recent Data Science-Related Studies

While the ideas we’ve presented above are a decent starting point for finding a research topic, they are fairly generic and non-specific. So, it helps to look at actual studies in the data science and analytics space to see how this all comes together in practice.

Below, we’ve included a selection of recent studies to help refine your thinking. These are actual studies,  so they can provide some useful insight as to what a research topic looks like in practice.

• Data Science in Healthcare: COVID-19 and Beyond (Hulsen, 2022)
• Auto-ML Web-application for Automated Machine Learning Algorithm Training and evaluation (Mukherjee & Rao, 2022)
• Survey on Statistics and ML in Data Science and Effect in Businesses (Reddy et al., 2022)
• Visualization in Data Science VDS @ KDD 2022 (Plant et al., 2022)
• An Essay on How Data Science Can Strengthen Business (Santos, 2023)
• A Deep study of Data science related problems, application and machine learning algorithms utilized in Data science (Ranjani et al., 2022)
• You Teach WHAT in Your Data Science Course?!? (Posner & Kerby-Helm, 2022)
• Statistical Analysis for the Traffic Police Activity: Nashville, Tennessee, USA (Tufail & Gul, 2022)
• Data Management and Visual Information Processing in Financial Organization using Machine Learning (Balamurugan et al., 2022)
• A Proposal of an Interactive Web Application Tool QuickViz: To Automate Exploratory Data Analysis (Pitroda, 2022)
• Applications of Data Science in Respective Engineering Domains (Rasool & Chaudhary, 2022)
• Jupyter Notebooks for Introducing Data Science to Novice Users (Fruchart et al., 2022)
• Towards a Systematic Review of Data Science Programs: Themes, Courses, and Ethics (Nellore & Zimmer, 2022)
• Application of data science and bioinformatics in healthcare technologies (Veeranki & Varshney, 2022)
• TAPS Responsibility Matrix: A tool for responsible data science by design (Urovi et al., 2023)
• Data Detectives: A Data Science Program for Middle Grade Learners (Thompson & Irgens, 2022)
• MACHINE LEARNING FOR NON-MAJORS: A WHITE BOX APPROACH (Mike & Hazzan, 2022)
• COMPONENTS OF DATA SCIENCE AND ITS APPLICATIONS (Paul et al., 2022)
• Analysis on the Application of Data Science in Business Analytics (Wang, 2022)

As you can see, these research topics are a lot more focused than the generic topic ideas we presented earlier. So, for you to develop a high-quality research topic, you’ll need to get specific and laser-focused on a specific context with specific variables of interest.  In the video below, we explore some other important things you’ll need to consider when crafting your research topic.

Get 1-On-1 Help

If you’re still unsure about how to find a quality research topic, check out our Research Topic Kickstarter service, which is the perfect starting point for developing a unique, well-justified research topic.

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Home > Statistics > Dissertations, Theses, and Student Work

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Department of statistics: dissertations, theses, and student work.

Examining the Effect of Word Embeddings and Preprocessing Methods on Fake News Detection , Jessica Hauschild

Exploring Experimental Design and Multivariate Analysis Techniques for Evaluating Community Structure of Bacteria in Microbiome Data , Kelsey Karnik

Human Perception of Exponentially Increasing Data Displayed on a Log Scale Evaluated Through Experimental Graphics Tasks , Emily Robinson

Factors Influencing Student Outcomes in a Large, Online Simulation-Based Introductory Statistics Course , Ella M. Burnham

Comparing Machine Learning Techniques with State-of-the-Art Parametric Prediction Models for Predicting Soybean Traits , Susweta Ray

Using Stability to Select a Shrinkage Method , Dean Dustin

Statistical Methodology to Establish a Benchmark for Evaluating Antimicrobial Resistance Genes through Real Time PCR assay , Enakshy Dutta

Group Testing Identification: Objective Functions, Implementation, and Multiplex Assays , Brianna D. Hitt

Community Impact on the Home Advantage within NCAA Men's Basketball , Erin O'Donnell

Optimal Design for a Causal Structure , Zaher Kmail

Role of Misclassification Estimates in Estimating Disease Prevalence and a Non-Linear Approach to Study Synchrony Using Heart Rate Variability in Chickens , Dola Pathak

A Characterization of a Value Added Model and a New Multi-Stage Model For Estimating Teacher Effects Within Small School Systems , Julie M. Garai

Methods to Account for Breed Composition in a Bayesian GWAS Method which Utilizes Haplotype Clusters , Danielle F. Wilson-Wells

Beta-Binomial Kriging: A New Approach to Modeling Spatially Correlated Proportions , Aimee Schwab

Simulations of a New Response-Adaptive Biased Coin Design , Aleksandra Stein

MODELING THE DYNAMIC PROCESSES OF CHALLENGE AND RECOVERY (STRESS AND STRAIN) OVER TIME , Fan Yang

A New Approach to Modeling Multivariate Time Series on Multiple Temporal Scales , Tucker Zeleny

A Reduced Bias Method of Estimating Variance Components in Generalized Linear Mixed Models , Elizabeth A. Claassen

NEW STATISTICAL METHODS FOR ANALYSIS OF HISTORICAL DATA FROM WILDLIFE POPULATIONS , Trevor Hefley

Informative Retesting for Hierarchical Group Testing , Michael S. Black

A Test for Detecting Changes in Closed Networks Based on the Number of Communications Between Nodes , Christopher S. Wichman

GROUP TESTING REGRESSION MODELS , Boan Zhang

A Comparison of Spatial Prediction Techniques Using Both Hard and Soft Data , Megan L. Liedtke Tesar

STUDYING THE HANDLING OF HEAT STRESSED CATTLE USING THE ADDITIVE BI-LOGISTIC MODEL TO FIT BODY TEMPERATURE , Fan Yang

Estimating Teacher Effects Using Value-Added Models , Jennifer L. Green

SEQUENCE COMPARISON AND STOCHASTIC MODEL BASED ON MULTI-ORDER MARKOV MODELS , Xiang Fang

DETECTING DIFFERENTIALLY EXPRESSED GENES WHILE CONTROLLING THE FALSE DISCOVERY RATE FOR MICROARRAY DATA , SHUO JIAO

Spatial Clustering Using the Likelihood Function , April Kerby

FULLY EXPONENTIAL LAPLACE APPROXIMATION EM ALGORITHM FOR NONLINEAR MIXED EFFECTS MODELS , Meijian Zhou

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• Starting the research process

How to Choose a Dissertation Topic | 8 Steps to Follow

Published on November 11, 2022 by Shona McCombes and Tegan George. Revised on November 20, 2023.

Choosing your dissertation topic is the first step in making sure your research goes as smoothly as possible. When choosing a topic, it’s important to consider:

• Your institution and department’s requirements
• Your areas of knowledge and interest
• The scientific, social, or practical relevance
• The availability of data and resources
• The timeframe of your dissertation
• The relevance of your topic

You can follow these steps to begin narrowing down your ideas.

Table of contents

Step 1: check the requirements, step 2: choose a broad field of research, step 3: look for books and articles, step 4: find a niche, step 5: consider the type of research, step 6: determine the relevance, step 7: make sure it’s plausible, step 8: get your topic approved, other interesting articles, frequently asked questions about dissertation topics.

The very first step is to check your program’s requirements. This determines the scope of what it is possible for you to research.

• Is there a minimum and maximum word count?
• When is the deadline?
• Should the research have an academic or a professional orientation?
• Are there any methodological conditions? Do you have to conduct fieldwork, or use specific types of sources?

Some programs have stricter requirements than others. You might be given nothing more than a word count and a deadline, or you might have a restricted list of topics and approaches to choose from. If in doubt about what is expected of you, always ask your supervisor or department coordinator.

Start by thinking about your areas of interest within the subject you’re studying. Examples of broad ideas include:

• Twentieth-century literature
• Economic history
• Health policy

To get a more specific sense of the current state of research on your potential topic, skim through a few recent issues of the top journals in your field. Be sure to check out their most-cited articles in particular. For inspiration, you can also search Google Scholar , subject-specific databases , and your university library’s resources.

As you read, note down any specific ideas that interest you and make a shortlist of possible topics. If you’ve written other papers, such as a 3rd-year paper or a conference paper, consider how those topics can be broadened into a dissertation.

After doing some initial reading, it’s time to start narrowing down options for your potential topic. This can be a gradual process, and should get more and more specific as you go. For example, from the ideas above, you might narrow it down like this:

• Twentieth-century literature   Twentieth-century Irish literature   Post-war Irish poetry
• Economic history   European economic history   German labor union history
• Health policy   Reproductive health policy   Reproductive rights in South America

All of these topics are still broad enough that you’ll find a huge amount of books and articles about them. Try to find a specific niche where you can make your mark, such as: something not many people have researched yet, a question that’s still being debated, or a very current practical issue.

At this stage, make sure you have a few backup ideas — there’s still time to change your focus. If your topic doesn’t make it through the next few steps, you can try a different one. Later, you will narrow your focus down even more in your problem statement and research questions .

There are many different types of research , so at this stage, it’s a good idea to start thinking about what kind of approach you’ll take to your topic. Will you mainly focus on:

• Collecting original data (e.g., experimental or field research)?
• Analyzing existing data (e.g., national statistics, public records, or archives)?
• Interpreting cultural objects (e.g., novels, films, or paintings)?
• Comparing scholarly approaches (e.g., theories, methods, or interpretations)?

Many dissertations will combine more than one of these. Sometimes the type of research is obvious: if your topic is post-war Irish poetry, you will probably mainly be interpreting poems. But in other cases, there are several possible approaches. If your topic is reproductive rights in South America, you could analyze public policy documents and media coverage, or you could gather original data through interviews and surveys .

You don’t have to finalize your research design and methods yet, but the type of research will influence which aspects of the topic it’s possible to address, so it’s wise to consider this as you narrow down your ideas.

It’s important that your topic is interesting to you, but you’ll also have to make sure it’s academically, socially or practically relevant to your field.

• Academic relevance means that the research can fill a gap in knowledge or contribute to a scholarly debate in your field.
• Social relevance means that the research can advance our understanding of society and inform social change.
• Practical relevance means that the research can be applied to solve concrete problems or improve real-life processes.

The easiest way to make sure your research is relevant is to choose a topic that is clearly connected to current issues or debates, either in society at large or in your academic discipline. The relevance must be clearly stated when you define your research problem .

Before you make a final decision on your topic, consider again the length of your dissertation, the timeframe in which you have to complete it, and the practicalities of conducting the research.

Will you have enough time to read all the most important academic literature on this topic? If there’s too much information to tackle, consider narrowing your focus even more.

Will you be able to find enough sources or gather enough data to fulfil the requirements of the dissertation? If you think you might struggle to find information, consider broadening or shifting your focus.

Do you have to go to a specific location to gather data on the topic? Make sure that you have enough funding and practical access.

Last but not least, will the topic hold your interest for the length of the research process? To stay motivated, it’s important to choose something you’re enthusiastic about!

Most programmes will require you to submit a brief description of your topic, called a research prospectus or proposal .

Remember, if you discover that your topic is not as strong as you thought it was, it’s usually acceptable to change your mind and switch focus early in the dissertation process. Just make sure you have enough time to start on a new topic, and always check with your supervisor or department.

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

Methodology

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

Formulating a main research question can be a difficult task. Overall, your question should contribute to solving the problem that you have defined in your problem statement .

However, it should also fulfill criteria in three main areas:

• Researchability
• Feasibility and specificity
• Relevance and originality

All research questions should be:

• Focused on a single problem or issue
• Researchable using primary and/or secondary sources
• Feasible to answer within the timeframe and practical constraints
• Specific enough to answer thoroughly
• Complex enough to develop the answer over the space of a paper or thesis
• Relevant to your field of study and/or society more broadly

You can assess information and arguments critically by asking certain questions about the source. You can use the CRAAP test , focusing on the currency , relevance , authority , accuracy , and purpose of a source of information.

Ask questions such as:

• Who is the author? Are they an expert?
• Why did the author publish it? What is their motivation?
• How do they make their argument? Is it backed up by evidence?

A dissertation prospectus or proposal describes what or who you plan to research for your dissertation. It delves into why, when, where, and how you will do your research, as well as helps you choose a type of research to pursue. You should also determine whether you plan to pursue qualitative or quantitative methods and what your research design will look like.

It should outline all of the decisions you have taken about your project, from your dissertation topic to your hypotheses and research objectives , ready to be approved by your supervisor or committee.

Note that some departments require a defense component, where you present your prospectus to your committee orally.

The best way to remember the difference between a research plan and a research proposal is that they have fundamentally different audiences. A research plan helps you, the researcher, organize your thoughts. On the other hand, a dissertation proposal or research proposal aims to convince others (e.g., a supervisor, a funding body, or a dissertation committee) that your research topic is relevant and worthy of being conducted.

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Updated: April 2024

Math/Stats Thesis and Colloquium Topics 2024- 2025

The degree with honors in Mathematics or Statistics is awarded to the student who has demonstrated outstanding intellectual achievement in a program of study which extends beyond the requirements of the major. The principal considerations for recommending a student for the degree with honors will be: Mastery of core material and skills, breadth and, particularly, depth of knowledge beyond the core material, ability to pursue independent study of mathematics or statistics, originality in methods of investigation, and, where appropriate, creativity in research.

An honors program normally consists of two semesters (MATH/STAT 493 and 494) and a winter study (WSP 031) of independent research, culminating in a thesis and a presentation. Under certain circumstances, the honors work can consist of coordinated study involving a one semester (MATH/STAT 493 or 494) and a winter study (WSP 030) of independent research, culminating in a “minithesis” and a presentation. At least one semester should be in addition to the major requirements, and thesis courses do not count as 400-level senior seminars.

An honors program in actuarial studies requires significant achievement on four appropriate examinations of the Society of Actuaries.

Highest honors will be reserved for the rare student who has displayed exceptional ability, achievement or originality. Such a student usually will have written a thesis, or pursued actuarial honors and written a mini-thesis. An outstanding student who writes a mini-thesis, or pursues actuarial honors and writes a paper, might also be considered. In all cases, the award of honors and highest honors is the decision of the Department.

Here is a list of possible colloquium topics that different faculty are willing and eager to advise. You can talk to several faculty about any colloquium topic, the sooner the better, at least a month or two before your talk. For various reasons faculty may or may not be willing or able to advise your colloquium, which is another reason to start early.

RESEARCH INTERESTS OF MATHEMATICS AND STATISTICS FACULTY

Here is a list of faculty interests and possible thesis topics.  You may use this list to select a thesis topic or you can use the list below to get a general idea of the mathematical interests of our faculty.

Colin Adams (On Leave 2024 – 2025)

Research interests:   Topology and tiling theory.  I work in low-dimensional topology.  Specifically, I work in the two fields of knot theory and hyperbolic 3-manifold theory and develop the connections between the two. Knot theory is the study of knotted circles in 3-space, and it has applications to chemistry, biology and physics.  I am also interested in tiling theory and have been working with students in this area as well.

Hyperbolic 3-manifold theory utilizes hyperbolic geometry to understand 3-manifolds, which can be thought of as possible models of the spatial universe.

Possible thesis topics:

• Investigate various aspects of virtual knots, a generalization of knots.
• Consider hyperbolicity of virtual knots, building on previous SMALL work. For which virtual knots can you prove hyperbolicity?
• Investigate why certain virtual knots have the same hyperbolic volume.
• Consider the minimal Turaev volume of virtual knots, building on previous SMALL work.
• Investigate which knots have totally geodesic Seifert surfaces. In particular, figure out how to interpret this question for virtual knots.
• Investigate n-crossing number of knots. An n-crossing is a crossing with n strands of the knot passing through it. Every knot can be drawn in a picture with only n-crossings in it. The least number of n-crossings is called the n-crossing number. Determine the n-crossing number for various n and various families of knots.
• An übercrossing projection of a knot is a projection with just one n-crossing. The übercrossing number of a knot is the least n for which there is such an übercrossing projection. Determine the übercrossing number for various knots, and see how it relates to other traditional knot invariants.
• A petal projection of a knot is a projection with just one n-crossing such that none of the loops coming out of the crossing are nested. In other words, the projection looks like a daisy. The petal number of a knot is the least n for such a projection. Determine petal number for various knots, and see how it relates to other traditional knot invariants.
• In a recent paper, we extended petal number to virtual knots. Show that the virtual petal number of a classical knot is equal to the classical petal number of the knot (This is a GOOD question!)
• Similarly, show that the virtual n-crossing number of a classical knot is equal to the classical n-crossing number. (This is known for n = 2.)
• Find tilings of the branched sphere by regular polygons. This would extend work of previous research students. There are lots of interesting open problems about something as simple as tilings of the sphere.
• Other related topics.

Possible colloquium topics : Particularly interested in topology, knot theory, graph theory, tiling theory and geometry but will consider other topics.

Christina Athanasouli

Research Interests:   Differential equations, dynamical systems (both smooth and non-smooth), mathematical modeling with applications in biological and mechanical systems

My research focuses on analyzing mathematical models that describe various phenomena in Mathematical Neuroscience and Engineering. In particular, I work on understanding 1) the underlying mechanisms of human sleep (e.g. how sleep patterns change with development or due to perturbations), and 2) potential design or physical factors that may influence the dynamics in vibro-impact mechanical systems for the purpose of harvesting energy. Mathematically, I use various techniques from dynamical systems and incorporate both numerical and analytical tools in my work. 

Possible colloquium topics:   Topics in applied mathematics, such as:

• Mathematical modeling of sleep-wake regulation
• Mathematical modeling vibro-impact systems
• Bifurcations/dynamics of mathematical models in Mathematical Neuroscience and Engineering
• Bifurcations in piecewise-smooth dynamical systems

Julie Blackwood

Research Interests:   Mathematical modeling, theoretical ecology, population biology, differential equations, dynamical systems.

My research uses mathematical models to uncover the complex mechanisms generating ecological dynamics, and when applicable emphasis is placed on evaluating intervention programs. My research is in various ecological areas including ( I ) invasive species management by using mathematical and economic models to evaluate the costs and benefits of control strategies, and ( II ) disease ecology by evaluating competing mathematical models of the transmission dynamics for both human and wildlife diseases.

• Mathematical modeling of invasive species
• Mathematical modeling of vector-borne or directly transmitted diseases
• Developing mathematical models to manage vector-borne diseases through vector control
• Other relevant topics of interest in mathematical biology

Each topic (1-3) can focus on a case study of a particular invasive species or disease, and/or can investigate the effects of ecological properties (spatial structure, resource availability, contact structure, etc.) of the system.

Possible colloquium topics:   Any topics in applied mathematics, such as:

Research Interest :  Statistical methodology and applications.  One of my research topics is variable selection for high-dimensional data.  I am interested in traditional and modern approaches for selecting variables from a large candidate set in different settings and studying the corresponding theoretical properties. The settings include linear model, partial linear model, survival analysis, dynamic networks, etc.  Another part of my research studies the mediation model, which examines the underlying mechanism of how variables relate to each other.  My research also involves applying existing methods and developing new procedures to model the correlated observations and capture the time-varying effect.  I am also interested in applications of data mining and statistical learning methods, e.g., their applications in analyzing the rhetorical styles in English text data.

• Variable selection uses modern techniques such as penalization and screening methods for several different parametric and semi-parametric models.
• Extension of the classic mediation models to settings with correlated, longitudinal, or high-dimensional mediators. We could also explore ways to reduce the dimensionality and simplify the structure of mediators to have a stable model that is also easier to interpret.
• We shall analyze the English text dataset processed by the Docuscope environment with tools for corpus-based rhetorical analysis. The data have a hierarchical structure and contain rich information about the rhetorical styles used. We could apply statistical models and statistical learning algorithms to reduce dimensions and gain a more insightful understanding of the text.

Possible colloquium topics:  I am open to any problems in statistical methodology and applications, not limited to my research interests and the possible thesis topics above.

Richard De Veaux 

Research interests: Statistics.

My research interests are in both statistical methodology and in statistical applications.  For the first, I look at different methods and try to understand why some methods work well in particular settings, or more creatively, to try to come up with new methods.  For the second, I work in collaboration with an investigator (e.g. scientist, doctor, marketing analyst) on a particular statistical application.  I have been especially interested in problems dealing with large data sets and the associated modeling tools that work for these problems.

• Human Performance and Aging.I have been working on models for assessing the effect of age on performance in running and swimming events. There is still much work to do. So far I’ve looked at masters’ freestyle swimming and running data and a handicapped race in California, but there are world records for each age group and other events in running and swimming that I’ve not incorporated. There are also many other types of events.
• Variable Selection.  How do we choose variables when we have dozens, hundreds or even thousands of potential predictors? Various model selection strategies exist, but there is still a lot of work to be done to find out which ones work under what assumptions and conditions.
• Problems at the interface.In this era of Big Data, not all methods of classical statistics can be applied in practice. What methods scale up well, and what advances in computer science give insights into the statistical methods that are best suited to large data sets?
• Applying statistical methods to problems in science or social science.In collaboration with a scientist or social scientist, find a problem for which statistical analysis plays a key role.

Possible colloquium topics:

• Almost any topic in statistics that extends things you’ve learned in courses —  specifically topics in Experimental design, regression techniques or machine learning
• Model selection problems

Thomas Garrity (On Leave 2024 – 2025)

Research interest:   Number Theory and Dynamics.

My area of research is officially called “multi-dimensional continued fraction algorithms,” an area that touches many different branches of mathematics (which is one reason it is both interesting and rich).  In recent years, students writing theses with me have used serious tools from geometry, dynamics, ergodic theory, functional analysis, linear algebra, differentiability conditions, and combinatorics.  (No single person has used all of these tools.)  It is an area to see how mathematics is truly interrelated, forming one coherent whole.

While my original interest in this area stemmed from trying to find interesting methods for expressing real numbers as sequences of integers (the Hermite problem), over the years this has led to me interacting with many different mathematicians, and to me learning a whole lot of math.  My theses students have had much the same experiences, including the emotional rush of discovery and the occasional despair of frustration.  The whole experience of writing a thesis should be intense, and ultimately rewarding.   Also, since this area of math has so many facets and has so many entrance points, I have had thesis students from wildly different mathematical backgrounds do wonderful work; hence all welcome.

• Generalizations of continued fractions.
• Using algebraic geometry to study real submanifolds of complex spaces.

Possible colloquium topics:   Any interesting topic in mathematics.

Leo Goldmakher

Research interests:   Number theory and arithmetic combinatorics.

I’m interested in quantifying structure and randomness within naturally occurring sets or sequences, such as the prime numbers, or the sequence of coefficients of a continued fraction, or a subset of a vector space. Doing so typically involves using ideas from analysis, probability, algebra, and combinatorics.

Possible thesis topics:  

Anything in number theory or arithmetic combinatorics.

Possible colloquium topics:   I’m happy to advise a colloquium in any area of math.

Susan Loepp

Research interests: Commutative Algebra.  I study algebraic structures called commutative rings.  Specifically, I have been investigating the relationship between local rings and their completion.  One defines the completion of a ring by first defining a metric on the ring and then completing the ring with respect to that metric.  I am interested in what kinds of algebraic properties a ring and its completion share.  This relationship has proven to be intricate and quite surprising.  I am also interested in the theory of tight closure, and Homological Algebra.

Topics in Commutative Algebra including:

• Using completions to construct Noetherian rings with unusual prime ideal structures.
• What prime ideals of C[[ x 1 ,…, x n ]] can be maximal in the generic formal fiber of a ring? More generally, characterize what sets of prime ideals of a complete local ring can occur in the generic formal fiber.
• Characterize what sets of prime ideals of a complete local ring can occur in formal fibers of ideals with height n where n ≥1.
• Characterize which complete local rings are the completion of an excellent unique factorization domain.
• Explore the relationship between the formal fibers of R and S where S is a flat extension of R .
• Determine which complete local rings are the completion of a catenary integral domain.
• Determine which complete local rings are the completion of a catenary unique factorization domain.

Possible colloquium topics:   Any topics in mathematics and especially commutative algebra/ring theory.

Steven Miller

For more information and references, see http://www.williams.edu/Mathematics/sjmiller/public_html/index.htm

Research interests :  Analytic number theory, random matrix theory, probability and statistics, graph theory.

My main research interest is in the distribution of zeros of L-functions.  The most studied of these is the Riemann zeta function, Sum_{n=1 to oo} 1/n^s.  The importance of this function becomes apparent when we notice that it can also be written as Prod_{p prime} 1 / (1 – 1/p^s); this function relates properties of the primes to those of the integers (and we know where the integers are!).  It turns out that the properties of zeros of L-functions are extremely useful in attacking questions in number theory.  Interestingly, a terrific model for these zeros is given by random matrix theory: choose a large matrix at random and study its eigenvalues.  This model also does a terrific job describing behavior ranging from heavy nuclei like Uranium to bus routes in Mexico!  I’m studying several problems in random matrix theory, which also have applications to graph theory (building efficient networks).  I am also working on several problems in probability and statistics, especially (but not limited to) sabermetrics (applying mathematical statistics to baseball) and Benford’s law of digit bias (which is often connected to fascinating questions about equidistribution).  Many data sets have a preponderance of first digits equal to 1 (look at the first million Fibonacci numbers, and you’ll see a leading digit of 1 about 30% of the time).  In addition to being of theoretical interest, applications range from the IRS (which uses it to detect tax fraud) to computer science (building more efficient computers).  I’m exploring the subject with several colleagues in fields ranging from accounting to engineering to the social sciences.

Possible thesis topics: 

• Theoretical models for zeros of elliptic curve L-functions (in the number field and function field cases).
• Studying lower order term behavior in zeros of L-functions.
• Studying the distribution of eigenvalues of sets of random matrices.
• Exploring Benford’s law of digit bias (both its theory and applications, such as image, voter and tax fraud).
• Propagation of viruses in networks (a graph theory / dynamical systems problem). Sabermetrics.
• Additive number theory (questions on sum and difference sets).

Possible colloquium topics: 

Plus anything you find interesting.  I’m also interested in applications, and have worked on subjects ranging from accounting to computer science to geology to marketing….

Ralph Morrison

Research interests:   I work in algebraic geometry, tropical geometry, graph theory (especially chip-firing games on graphs), and discrete geometry, as well as computer implementations that study these topics. Algebraic geometry is the study of solution sets to polynomial equations.  Such a solution set is called a variety.  Tropical geometry is a “skeletonized” version of algebraic geometry. We can take a classical variety and “tropicalize” it, giving us a tropical variety, which is a piecewise-linear subset of Euclidean space.  Tropical geometry combines combinatorics, discrete geometry, and graph theory with classical algebraic geometry, and allows for developing theory and computations that tell us about the classical varieties.  One flavor of this area of math is to study chip-firing games on graphs, which are motivated by (and applied to) questions about algebraic curves.

Possible thesis topics : Anything related to tropical geometry, algebraic geometry, chip-firing games (or other graph theory topics), and discrete geometry.  Here are a few specific topics/questions:

• Study the geometry of tropical plane curves, perhaps motivated by results from algebraic geometry.  For instance:  given 5 (algebraic) conics, there are 3264 conics that are tangent to all 5 of them.  What if we look at tropical conics–is there still a fixed number of tropical conics tangent to all of them?  If so, what is that number?  How does this tropical count relate to the algebraic count?
• What can tropical plane curves “look like”?  There are a few ways to make this question precise.  One common way is to look at the “skeleton” of a tropical curve, a graph that lives inside of the curve and contains most of the interesting data.  Which graphs can appear, and what can the lengths of its edges be?  I’ve done lots of work with students on these sorts of questions, but there are many open questions!
• What can tropical surfaces in three-dimensional space look like?  What is the version of a skeleton here?  (For instance, a tropical surface of degree 4 contains a distinguished polyhedron with at most 63 facets. Which polyhedra are possible?)
• Study the geometry of tropical curves obtained by intersecting two tropical surfaces.  For instance, if we intersect a tropical plane with a tropical surface of degree 4, we obtain a tropical curve whose skeleton has three loops.  How can those loops be arranged?  Or we could intersect degree 2 and degree 3 tropical surfaces, to get a tropical curve with 4 loops; which skeletons are possible there?
• One way to study tropical geometry is to replace the usual rules of arithmetic (plus and times) with new rules (min and plus).  How do topics like linear algebra work in these fields?  (It turns out they’re related to optimization, scheduling, and job assignment problems.)
• Chip-firing games on graphs model questions from algebraic geometry.  One of the most important comes in the “gonality” of a graph, which is the smallest number of chips on a graph that could eliminate (via a series of “chip-firing moves”) an added debt of -1 anywhere on the graph.  There are lots of open questions for studying the gonality of graphs; this include general questions, like “What are good lower bounds on gonality?” and specific ones, like “What’s the gonality of the n-dimensional hypercube graph?”
• We can also study versions of gonality where we place -r chips instead of just -1; this gives us the r^th gonality of a graph.  Together, the first, second, third, etc. gonalities form the “gonality sequence” of a graph.  What sequences of integers can be the gonality sequence of some graph?  Is there a graph whose gonality sequence starts 3, 5, 8?
• There are many computational and algorithmic questions to ask about chip-firing games.  It’s known that computing the gonality of a general graph is NP-hard; what if we restrict to planar graphs?  Or graphs that are 3-regular? And can we implement relatively efficient ways of computing these numbers, at least for small graphs?
• What if we changed our rules for chip-firing games, for instance by working with chips modulo N?  How can we “win” a chip-firing game in that context, since there’s no more notion of debt?
• Study a “graph throttling” version of gonality.  For instance, instead of minimizing the number of chips we place on the graph, maybe we can also try to decrease the number of chip-firing moves we need to eliminate debt.
• Chip-firing games lead to interesting questions on other topics in graph theory.  For instance, there’s a conjectured upper bound of (|E|-|V|+4)/2 on the gonality of a graph; and any graph is known to have gonality at least its tree-width.  Can we prove the (weaker) result that (|E|-|V|+4)/2 is an upper bound on tree-width?  (Such a result would be of interest to graph theorists, even the idea behind it comes from algebraic geometry!)
• Topics coming from discrete geometry.  For example:  suppose you want to make “string art”, where you have one shape inside of another with string weaving between the inside and the outside shapes.  For which pairs of shapes is this possible?

Possible Colloquium topics:   I’m happy to advise a talk in any area of math, but would be especially excited about talks related to algebra, geometry, graph theory, or discrete mathematics.

Shaoyang Ning (On Leave 2024 – 2025)

Research Interest :  Statistical methodologies and applications. My research focuses on the study and design of statistical methods for integrative data analysis, in particular, to address the challenges of increasing complexity and connectivity arising from “Big Data”. I’m interested in innovating statistical methods that efficiently integrate multi-source, multi-resolution information to solve real-life problems. Instances include tracking localized influenza with Google search data and predicting cancer-targeting drugs with high-throughput genetic profiling data. Other interests include Bayesian methods, copula modeling, and nonparametric methods.

• Digital (disease) tracking: Using Internet search data to track and predict influenza activities at different resolutions (nation, region, state, city); Integrating other sources of digital data (e.g. Twitter, Facebook) and/or extending to track other epidemics and social/economic events, such as dengue, presidential approval rates, employment rates, and etc.
• Predicting cancer drugs with multi-source profiling data: Developing new methods to aggregate genetic profiling data of different sources (e.g., mutations, expression levels, CRISPR knockouts, drug experiments) in cancer cell lines to identify potential cancer-targeting drugs, their modes of actions and genetic targets.
• Social media text mining: Developing new methods to analyze and extract information from social media data (e.g. Reddit, Twitter). What are the challenges in analyzing the large-volume but short-length social media data? Can classic methods still apply? How should we innovate to address these difficulties?
• Copula modeling: How do we model and estimate associations between different variables when they are beyond multivariate Normal? What if the data are heavily dependent in the tails of their distributions (commonly observed in stock prices)? What if dependence between data are non-symmetric and complex? When the size of data is limited but the dimension is large, can we still recover their correlation structures? Copula model enables to “link” the marginals of a multivariate random variable to its joint distribution with great flexibility and can just be the key to the questions above.
• Other cross-disciplinary, data-driven projects: Applying/developing statistical methodology to answer an interesting scientific question in collaboration with a scientist or social scientist.

Possible colloquium topics:   Any topics in statistical methodology and application, including but not limited to: topics in applied statistics, Bayesian methods, computational biology, statistical learning, “Big Data” mining, and other cross-disciplinary projects.

Anna Neufeld

Research interests:  My research is motivated by the gap between classical statistical tools and practical data analysis. Classic statistical tools are designed for testing a single hypothesis about a single, pre-specified model. However, modern data analysis is an adaptive process that involves exploring the data, fitting several models, evaluating these models, and then testing a potentially large number of hypotheses about one or more selected models. With this in mind, I am interested in topics such as (1) methods for model validation and selection, (2) methods for testing data-driven hypotheses (post-selection inference), and (3) methods for testing a large number of hypotheses. I am also interested in any applied project where I can help a scientist rigorously answer an important question using data. 

• Cross-validation for unsupervised learning. Cross-validation is one of the most widely-used tools for model validation, but, in its typical form, it cannot be used for unsupervised learning problems. Numerous ad-hoc proposals exist for validating unsupervised learning models, but there is a need to compare and contrast these proposals and work towards a unified approach.
• Identifying the number of cell types in single-cell genomics datasets. This is an application of the topic above, since the cell types are typically estimated via unsupervised learning.
• There is growing interest in “post-prediction inference”, which is the task of doing valid statistical inference when some inputs to your statistical model are the outputs of other statistical models (i.e. predictions). Frameworks have recently been proposed for post-prediction inference in the setting where you have access to a gold-standard dataset where the true inputs, rather than the predicted inputs, have been observed. A thesis could explore the possibility of post-prediction inference in the absence of this gold-standard dataset.
• Any other topic of student interest related to selective inference, multiple testing, or post-prediction inference.
• Any collaborative project in which we work with a scientist to identify an interesting question in need of non-standard statistics.
• I am open to advising colloquia in almost any area of statistical methodology or applications, including but not limited to: multiple testing, post-selection inference, post-prediction inference, model selection, model validation, statistical machine learning, unsupervised learning, or genomics.

Allison Pacelli

Research interests:   Math Education, Math & Politics, and Algebraic Number Theory.

Math Education.  Math education is the study of the practice of teaching and learning mathematics, at all levels. For example, do high school calculus students learn best from lecture or inquiry-based learning? What mathematical content knowledge is critical for elementary school math teachers? Is a flipped classroom more effective than a traditional learning format? Many fascinating questions remain, at all levels of education. We can talk further to narrow down project ideas.

Math & Politics.  The mathematics of voting and the mathematics of fair division are two fascinating topics in the field of mathematics and politics. Research questions look at types of voting systems, and the properties that we would want a voting system to satisfy, as well as the idea of fairness when splitting up a single object, like cake, or a collection of objects, such as after a divorce or a death.

Algebraic Number Theory.  The Fundamental Theorem of Arithmetic states that the ring of integers is a unique factorization domain, that is, every integer can be uniquely factored into a product of primes. In other rings, there are analogues of prime numbers, but factorization into primes is not necessarily unique!

In order to determine whether factorization into primes is unique in the ring of integers of a number field or function field, it is useful to study the associated class group – the group of equivalence classes of ideals. The class group is trivial if and only if the ring is a unique factorization domain. Although the study of class groups dates back to Gauss and played a key role in the history of Fermat’s Last Theorem, many basic questions remain open.

  Possible thesis topics:

• Topics in math education, including projects at the elementary school level all the way through college level.
• Topics in voting and fair division.
• Investigating the divisibility of class numbers or the structure of the class group of quadratic fields and higher degree extensions.
• Exploring polynomial analogues of theorems from number theory concerning sums of powers, primes, divisibility, and arithmetic functions.

Possible colloquium topics:   Anything in number theory, algebra, or math & politics.

Anna Plantinga

Research interests:   I am interested in both applied and methodological statistics. My research primarily involves problems related to statistical analysis within genetics, genomics, and in particular the human microbiome (the set of bacteria that live in and on a person).  Current areas of interest include longitudinal data, distance-based analysis methods such as kernel machine regression, high-dimensional data, and structured data.

• Impacts of microbiome volatility. Sometimes the variability of a microbial community is more indicative of an unhealthy community than the actual bacteria present. We have developed an approach to quantifying microbiome variability (“volatility”). This project will use extensive simulations to explore the impact of between-group differences in volatility on a variety of standard tests for association between the microbiome and a health outcome.
• Accounting for excess zeros (sparse feature matrices). Often in a data matrix with many zeros, some of the zeros are “true” or “structural” zeros, whereas others are simply there because we have fewer observations for some subjects. How we account for these zeros affects analysis results. Which methods to account for excess zeros perform best for different analyses?
• Longitudinal methods for compositional data. When we have longitudinal data, we assume the same variables are measured at every time point. For high-dimensional compositions, this may not be the case. We would generally assume that the missing component was absent at any time points for which it was not measured. This project will explore alternatives to making that assumption.
• Applied statistics research. In collaboration with a scientist or social scientist, use appropriate statistical methodology (or variations on existing methods) to answer an interesting scientific question.

Any topics in statistical application, education, or methodology, including but not restricted to:

• Topics in applied statistics.
• Methods for microbiome data analysis.
• Statistical genetics.
• Electronic health records.
• Variable selection and statistical learning.
• Longitudinal methods.

Cesar Silva

Research interests :  Ergodic theory and measurable dynamics; in particular mixing properties and rank one examples, and infinite measure-preserving and nonsingular transformations and group actions.  Measurable dynamics of transformations defined on the p-adic field.  Measurable sensitivity.  Fractals.  Fractal Geometry.

Possible thesis topics:    Ergodic Theory.   Ergodic theory studies the probabilistic behavior of abstract dynamical systems.  Dynamical systems are systems that change with time, such as the motion of the planets or of a pendulum.  Abstract dynamical systems represent the state of a dynamical system by a point in a mathematical space (phase space).  In many cases this space is assumed to be the unit interval [0,1) with Lebesgue measure.  One usually assumes that time is measured at discrete intervals and so the law of motion of the system is represented by a single map (or transformation) of the phase space [0,1).  In this case one studies various dynamical behaviors of these maps, such as ergodicity, weak mixing, and mixing.  I am also interested in studying the measurable dynamics of systems defined on the p-adics numbers.  The prerequisite is a first course in real analysis.  Topological Dynamics.  Dynamics on compact or locally compact spaces.

Topics in mathematics and in particular:

• Any topic in measure theory.  See for example any of the first few chapters in “Measure and Category” by J. Oxtoby. Possible topics include the Banach-Tarski paradox, the Banach-Mazur game, Liouville numbers and s-Hausdorff measure zero.
• Topics in applied linear algebra and functional analysis.
• Fractal sets, fractal generation, image compression, and fractal dimension.
• Dynamics on the p-adic numbers.
• Banach-Tarski paradox, space filling curves.

Mihai Stoiciu

Research interests: Mathematical Physics and Functional Analysis. I am interested in the study of the spectral properties of various operators arising from mathematical physics – especially the Schrodinger operator. In particular, I am investigating the distribution of the eigenvalues for special classes of self-adjoint and unitary random matrices.

Topics in mathematical physics, functional analysis and probability including:

• Investigate the spectrum of the Schrodinger operator. Possible research topics: Find good estimates for the number of bound states; Analyze the asymptotic growth of the number of bound states of the discrete Schrodinger operator at large coupling constants.
• Study particular classes of orthogonal polynomials on the unit circle.
• Investigate numerically the statistical distribution of the eigenvalues for various classes of random CMV matrices.
• Study the general theory of point processes and its applications to problems in mathematical physics.

Possible colloquium topics:  

Any topics in mathematics, mathematical physics, functional analysis, or probability, such as:

• The Schrodinger operator.
• Orthogonal polynomials on the unit circle.
• Statistical distribution of the eigenvalues of random matrices.
• The general theory of point processes and its applications to problems in mathematical physics.

Elizabeth Upton

Research Interests: My research interests center around network science, with a focus on regression methods for network-indexed data. Networks are used to capture the relationships between elements within a system. Examples include social networks, transportation networks, and biological networks. I also enjoy tackling problems with pragmatic applications and am therefore interested in applied interdisciplinary research.

• Regression models for network data: how can we incorporate network structure (and dependence) in our regression framework when modeling a vertex-indexed response?
• Identify effects shaping network structure. For example, in social networks, the phrase “birds of a feather flock together” is often used to describe homophily. That is, those who have similar interests are more likely to become friends. How can we capture or test this effect, and others, in a regression framework when modeling edge-indexed responses?
• Extending models for multilayer networks. Current methodologies combine edges from multiple networks in some sort of weighted averaging scheme. Could a penalized multivariate approach yield a more informative model?
• Developing algorithms to make inference on large networks more efficient.
• Any topic in linear or generalized linear modeling (including mixed-effects regression models, zero-inflated regressions, etc.).
• Applied statistics research. In collaboration with a scientist or social scientist, use appropriate statistical methodology to answer an interesting scientific question.
• Any applied statistics research project/paper
• Topics in linear or generalized linear modeling
• Network visualizations and statistics

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Qualitative & Quantitative data analysis

Best Statistics Research Topics & Ideas For 2021-22

Date published October 7 2021 by Jacob Miller

Statistics is a demanding subject that deals with the collection, analysis, interpretation, evaluation, and management of numeric data. The topic selection of the statistics dissertation can involve the subfields of statistics, i.e. Probability Theory, Mathematical Statistics, Design of Experiments, Sampling, Classification, and Time Series.

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Complications in statistics researches:

This subject is much complicated, further, the implication of the proportions in large quantities under complex theories contribute to the difficulties concerning the subject. That’s why it is hard to find considerable statistics dissertation topics. Moreover, the multiple dimensions of the subject make it more problematic to come up with a focused and comprehensive topic.

Why Choosing a Statistics Dissertation Topic is Hard for Students?

While selecting a topic for a statistics dissertation, you must consider the fundamental idea of statistics, i.e. variation and uncertainty. Certain statistical frameworks and methods are applied to get the results.

The topic of the statistics dissertation should be so close to the subject that you will be able the statistical method in the dissertation and presentation of findings.

There are several reasons which together make it a difficult task for the students to select a worthwhile topic for their statistics dissertation.

Shortage of Ideas

Students usually lack in generating potential ideas concerning different areas and aspects of the subject. That’s why they face difficulty in listing out the suitable statistics topics for the dissertation.

Wider Scope

Statistics has a wide scope. It holds a relation with scientific, industrial, and social problems. So, a dissertation topic for this subject can never stand out alone. Due to this reason, students find it difficult to determine their direction and fail to select a potential topic.

Irrelevant or diversified knowledge

Somehow, if students manage to come up with some understandable topics for their dissertation, the uncertainty of the context or the background leads them towards the confusion. They are unable to find a purpose and the background on which they can base their research.

While this all seems a pretty tough task, so then you may take inspiration from our free dissertation topics, and even better you can get the professional on those each topic.

How Do We Help You Select a Statistics Dissertation Topic?

We have skilled and professional subject experts, who bring the best ideas for your statistics dissertation selection. They are well aware of how to meet your subject requirements and professors’ expectations. Through their expertise, they help you select the most significant topics for your dissertation.

By selecting one of the strong statistics research topics we propose, you may contribute to the subject through your intellectual capabilities and unique ideas. While preparing a list of topic suggestions for you, we focus on the following points.

• Your level of Education
• Subject Domain
• Area of Interest
• Prerequisite Guidelines by the University (if any)

What do our experts say about the Statistics Topic Selection?

Our statistics dissertation experts are well-equipped with dense knowledge in the subject. They know which topic is worthy to be chosen for your dissertation. According to our experts, your topic must involve data collection, data analysis, and data synthesis.

You also must have to go through with several previous dissertations and research papers regarding the subject so that you can come up with a topic having fine scope, context, relevancy, and accuracy. Further, it should be concise and manageable so that you can complete a dissertation on it within the deadline.

You can avoid all these complexities by hiring our statistics dissertation topic selection services. Our experts have produced hundreds of successful works for the satisfaction of the customers. With vast experience in the world of academics and command of statistics dissertations, they have prepared the list of most suitable statistics dissertation topics.

Bayesian Methods for Functional and Time Series

Kernel regression using the four fourier transform, assessing and accounting for correlation in rna-seq data analysis., a guide to doing statistics in second language research using spss, prediction interval methods for reliability data, relevance of tests of significances uses and limitations., interaction forward selection in ultra-high-dimension functional linear models..

To know the details of the above-mentioned topics and have an idea about their aims and objectives, you can consult with our team. You are welcomed 24/7 to get our consultancy. Further, you can have more potential topics for your statistics dissertation topics by hiring our services.

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List of Best Statistics Research Topics with Objectives

Objectives:

• To explore all new bayesian methods which are used in statistical analysis.
• To introduce new methodology of bayesian which are suitable  for functional and time series data.
• To exhibit the functional challenges provided by the methodology. 

To explore the methods of kernel  regression

To demonstrate  the method  of speeding up the computation of kernel.

To analyse the FFT to improve the computation of kernel.

Difficulties in Learning Basic Concepts in Probability and Statistics: Implications of Research.

To explore the importance of statistics and probability.

To examine the different methods of statistics and probability used in education system. 

To provide the need for collaborative and cross-disciplinary in researches. 

To explore the concepts behind the usage of statistics in different domains.

To examine the concept of statistics in Second Language.

To study and implement the SPSS software in statistics.

To study the importance of Prediction in statistics.

To analyse the statistical Prediction methods in statistics theory.

To examine the different methods of Prediction interval under the parametric framework. 

To study the importance of statistical tools and significance test both in parametric and nonparametric test.

To examine the statistical tools significance in decision making.

To evaluate the statistical significance test in information retrieval.

To study the statistical methods for the variable selection in ultra-high dimensional functional linear models.

To propose two forward selection procedures on the basis of coefficients approximation.

To demonstrate the application of the proposed methodologies.

Bayes Methods for Biclustering and Vector Data with Binary Coordinates.

To explore the different method of Bayes and its applications.

To examine the Bayes method for the purpose of biclustering and inference for mixture models.

To represent the performance of model through the simulation and applications to real datasets.

To study the concept behind the RNA- sequence data analysis and its procedure.

To examine the papers on the analysis of RNA- sequence data analysis.

To perform a simulation and validate the proposed methods on the basis of results.

An Exploration of Techniques Used in Data Analytics to Produce Analysed Data in Graphical Format.

To explore the techniques used in data analytics used for various purposes in order to produce visual charts.

To demonstrate the use of python language as a main feature in Data analytics.

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500+ Statistics Research Topics

Statistics is a branch of mathematics that deals with the collection, analysis, interpretation, presentation, and organization of data . It is a fundamental tool used in various fields such as business, social sciences, engineering, healthcare, and many more. As a research topic , statistics can be a fascinating subject to explore, as it allows researchers to investigate patterns, trends, and relationships within data. With the help of statistical methods, researchers can make informed decisions and draw valid conclusions based on empirical evidence. In this post, we will explore some interesting statistics research topics that can be pursued by researchers to further expand our understanding of this field.

Statistics Research Topics

Statistics Research Topics are as follows:

• Analysis of the effectiveness of different marketing strategies on consumer behavior.
• An investigation into the relationship between economic growth and environmental sustainability.
• A study of the effects of social media on mental health and well-being.
• A comparative analysis of the educational outcomes of public and private schools.
• The impact of climate change on agriculture and food security.
• A survey of the prevalence and causes of workplace stress in different industries.
• A statistical analysis of crime rates in urban and rural areas.
• An evaluation of the effectiveness of alternative medicine treatments.
• A study of the relationship between income inequality and health outcomes.
• A comparative analysis of the effectiveness of different weight loss programs.
• An investigation into the factors that affect job satisfaction among employees.
• A statistical analysis of the relationship between poverty and crime.
• A study of the factors that influence the success of small businesses.
• A survey of the prevalence and causes of childhood obesity.
• An evaluation of the effectiveness of drug addiction treatment programs.
• A statistical analysis of the relationship between gender and leadership in organizations.
• A study of the relationship between parental involvement and academic achievement.
• An investigation into the causes and consequences of income inequality.
• A comparative analysis of the effectiveness of different types of therapy for mental health conditions.
• A survey of the prevalence and causes of substance abuse among teenagers.
• An evaluation of the effectiveness of online education compared to traditional classroom learning.
• A statistical analysis of the impact of globalization on different industries.
• A study of the relationship between social media use and political polarization.
• An investigation into the factors that influence customer loyalty in the retail industry.
• A comparative analysis of the effectiveness of different types of advertising.
• A survey of the prevalence and causes of workplace discrimination.
• An evaluation of the effectiveness of different types of employee training programs.
• A statistical analysis of the relationship between air pollution and health outcomes.
• A study of the factors that affect employee turnover rates.
• An investigation into the causes and consequences of income mobility.
• A comparative analysis of the effectiveness of different types of leadership styles.
• A survey of the prevalence and causes of mental health disorders among college students.
• An evaluation of the effectiveness of different types of cancer treatments.
• A statistical analysis of the impact of social media influencers on consumer behavior.
• A study of the factors that influence the adoption of renewable energy sources.
• An investigation into the relationship between alcohol consumption and health outcomes.
• A comparative analysis of the effectiveness of different types of conflict resolution strategies.
• A survey of the prevalence and causes of childhood poverty.
• An evaluation of the effectiveness of different types of diversity training programs.
• A statistical analysis of the relationship between immigration and economic growth.
• A study of the factors that influence customer satisfaction in the service industry.
• An investigation into the causes and consequences of urbanization.
• A comparative analysis of the effectiveness of different types of economic policies.
• A survey of the prevalence and causes of elder abuse.
• An evaluation of the effectiveness of different types of rehabilitation programs for prisoners.
• A statistical analysis of the impact of automation on different industries.
• A study of the factors that influence employee productivity in the workplace.
• An investigation into the causes and consequences of gentrification.
• A comparative analysis of the effectiveness of different types of humanitarian aid.
• A survey of the prevalence and causes of homelessness.
• Exploring the relationship between socioeconomic status and access to healthcare services
• An analysis of the relationship between parental education level and children’s academic performance.
• Exploring the effects of different statistical models on prediction accuracy in machine learning.
• The Impact of Social Media on Consumer Behavior: A Statistical Analysis
• Bayesian hierarchical modeling for network data analysis
• Spatial statistics and modeling for environmental data
• Nonparametric methods for time series analysis
• Bayesian inference for high-dimensional data analysis
• Multivariate analysis for genetic data
• Machine learning methods for predicting financial markets
• Causal inference in observational studies
• Sampling design and estimation for complex surveys
• Robust statistical methods for outlier detection
• Statistical inference for large-scale simulations
• Survival analysis and its applications in medical research
• Mixture models for clustering and classification
• Time-varying coefficient models for longitudinal data
• Multilevel modeling for complex data structures
• Graphical modeling and Bayesian networks
• Experimental design for clinical trials
• Inference for network data using stochastic block models
• Nonlinear regression modeling for data with complex structures
• Statistical learning for social network analysis
• Time series forecasting using deep learning methods
• Model selection and variable importance in high-dimensional data
• Spatial point process modeling for environmental data
• Bayesian spatial modeling for disease mapping
• Functional data analysis for longitudinal studies
• Bayesian network meta-analysis
• Statistical methods for big data analysis
• Mixed-effects models for longitudinal data
• Clustering algorithms for text data
• Bayesian modeling for spatiotemporal data
• Multivariate analysis for ecological data
• Statistical analysis of genomic data
• Bayesian network inference for gene regulatory networks
• Principal component analysis for high-dimensional data
• Time series analysis of financial data
• Multivariate survival analysis for complex outcomes
• Nonparametric estimation of causal effects
• Bayesian network analysis of complex systems
• Statistical inference for multilevel network data
• Generalized linear mixed models for non-normal data
• Bayesian inference for dynamic systems
• Latent variable modeling for categorical data
• Statistical inference for social network data
• Regression models for panel data
• Bayesian spatiotemporal modeling for climate data
• Predictive modeling for customer behavior analysis
• Nonlinear time series analysis for ecological systems
• Statistical modeling for image analysis
• Bayesian hierarchical modeling for longitudinal data
• Network-based clustering for high-dimensional data
• Bayesian spatial modeling for ecological systems.
• Analysis of the Effect of Climate Change on Crop Yields: A Case Study
• Examining the Relationship Between Physical Activity and Mental Health in Young Adults
• A Comparative Study of Crime Rates in Urban and Rural Areas Using Statistical Methods
• Investigating the Effect of Online Learning on Student Performance in Mathematics
• A Statistical Analysis of the Relationship Between Economic Growth and Environmental Sustainability
• Evaluating the Effectiveness of Different Marketing Strategies for E-commerce Businesses
• Identifying the Key Factors Affecting Customer Loyalty in the Hospitality Industry
• An Analysis of the Factors Influencing Student Dropout Rates in Higher Education
• Examining the Impact of Gender on Salary Disparities in the Workplace Using Statistical Methods
• Investigating the Relationship Between Physical Fitness and Academic Performance in High School Students
• Analyzing the Effect of Social Support on Mental Health in Elderly Populations
• A Comparative Study of Different Methods for Forecasting Stock Prices
• Investigating the Effect of Online Reviews on Consumer Purchasing Decisions
• Identifying the Key Factors Affecting Employee Turnover Rates in the Technology Industry
• Analyzing the Effect of Advertising on Brand Awareness and Purchase Intentions
• A Study of the Relationship Between Health Insurance Coverage and Healthcare Utilization
• Examining the Effect of Parental Involvement on Student Achievement in Elementary School
• Investigating the Impact of Social Media on Political Campaigns Using Statistical Methods
• A Comparative Analysis of Different Methods for Detecting Fraud in Financial Transactions
• Analyzing the Relationship Between Entrepreneurial Characteristics and Business Success
• Investigating the Effect of Job Satisfaction on Employee Performance in the Service Industry
• Identifying the Key Factors Affecting the Adoption of Renewable Energy Technologies
• A Study of the Relationship Between Personality Traits and Academic Achievement
• Examining the Impact of Social Media on Body Image and Self-Esteem in Adolescents
• Investigating the Effect of Mobile Advertising on Consumer Behavior
• Analyzing the Relationship Between Healthcare Expenditures and Health Outcomes Using Statistical Methods
• A Comparative Study of Different Methods for Analyzing Customer Satisfaction Data
• Investigating the Impact of Economic Factors on Voter Behavior Using Statistical Methods
• Identifying the Key Factors Affecting Student Retention Rates in Community Colleges
• Analyzing the Relationship Between Workplace Diversity and Organizational Performance
• Investigating the Effect of Gamification on Learning and Motivation in Education
• A Study of the Relationship Between Social Support and Depression in Cancer Patients
• Examining the Impact of Technology on the Travel Industry Using Statistical Methods
• Investigating the Effect of Customer Service Quality on Customer Loyalty in the Retail Industry
• Analyzing the Relationship Between Internet Usage and Social Isolation in Older Adults
• A Comparative Study of Different Methods for Predicting Customer Churn in Telecommunications
• Investigating the Impact of Social Media on Consumer Attitudes Towards Brands Using Statistical Methods
• Identifying the Key Factors Affecting Student Success in Online Learning Environments
• Analyzing the Relationship Between Employee Engagement and Organizational Commitment
• Investigating the Effect of Customer Reviews on Sales in E-commerce Businesses
• A Study of the Relationship Between Political Ideology and Attitudes Towards Climate Change
• Examining the Impact of Technological Innovations on the Manufacturing Industry Using Statistical Methods
• Investigating the Effect of Social Support on Postpartum Depression in New Mothers
• Analyzing the Relationship Between Cultural Intelligence and Cross-Cultural Adaptation
• Investigating the relationship between socioeconomic status and health outcomes using statistical methods.
• Analyzing trends in crime rates and identifying factors that contribute to them using statistical methods.
• Examining the effectiveness of different advertising strategies using statistical analysis of consumer behavior.
• Identifying factors that influence voting behavior and election outcomes using statistical methods.
• Investigating the relationship between employee satisfaction and productivity in the workplace using statistical methods.
• Developing new statistical models to better understand the spread of infectious diseases.
• Analyzing the impact of climate change on global food production using statistical methods.
• Identifying patterns and trends in social media data using statistical methods.
• Investigating the relationship between social networks and mental health using statistical methods.
• Developing new statistical models to predict financial market trends and identify investment opportunities.
• Analyzing the effectiveness of different educational programs and interventions using statistical methods.
• Investigating the impact of environmental factors on public health using statistical methods.
• Developing new statistical models to analyze complex biological systems and identify new drug targets.
• Analyzing trends in consumer spending and identifying factors that influence buying behavior using statistical methods.
• Investigating the relationship between diet and health outcomes using statistical methods.
• Developing new statistical models to analyze gene expression data and identify biomarkers for disease.
• Analyzing patterns in crime data to predict future crime rates and improve law enforcement strategies.
• Investigating the effectiveness of different medical treatments using statistical methods.
• Developing new statistical models to analyze the impact of air pollution on public health.
• Analyzing trends in global migration and identifying factors that influence migration patterns using statistical methods.
• Investigating the impact of automation on the job market using statistical methods.
• Developing new statistical models to analyze climate data and predict future climate trends.
• Analyzing trends in online shopping behavior and identifying factors that influence consumer decisions using statistical methods.
• Investigating the impact of social media on political discourse using statistical methods.
• Developing new statistical models to analyze gene-environment interactions and identify new disease risk factors.
• Analyzing trends in the stock market and identifying factors that influence investment decisions using statistical methods.
• Investigating the impact of early childhood education on long-term academic and social outcomes using statistical methods.
• Developing new statistical models to analyze the relationship between human behavior and the environment.
• Analyzing trends in the use of renewable energy and identifying factors that influence adoption rates using statistical methods.
• Investigating the impact of immigration on labor market outcomes using statistical methods.
• Developing new statistical models to analyze the relationship between social determinants and health outcomes.
• Analyzing patterns in customer churn to predict future customer behavior and improve business strategies.
• Investigating the effectiveness of different marketing strategies using statistical methods.
• Developing new statistical models to analyze the relationship between air pollution and climate change.
• Analyzing trends in global tourism and identifying factors that influence travel behavior using statistical methods.
• Investigating the impact of social media on mental health using statistical methods.
• Developing new statistical models to analyze the impact of transportation on the environment.
• Analyzing trends in global trade and identifying factors that influence trade patterns using statistical methods.
• Investigating the impact of social networks on political participation using statistical methods.
• Developing new statistical models to analyze the relationship between climate change and biodiversity loss.
• Analyzing trends in the use of alternative medicine and identifying factors that influence adoption rates using statistical methods.
• Investigating the impact of technological change on the labor market using statistical methods.
• Developing new statistical models to analyze the impact of climate change on agriculture.
• Investigating the impact of social media on mental health: A longitudinal study.
• A comparison of the effectiveness of different types of teaching methods on student learning outcomes.
• Examining the relationship between sleep duration and productivity among college students.
• A study of the factors that influence employee job satisfaction in the tech industry.
• Analyzing the relationship between income level and health outcomes among low-income populations.
• Investigating the effectiveness of online learning platforms for high school students.
• A study of the factors that contribute to success in online entrepreneurship.
• Analyzing the impact of climate change on agricultural productivity in developing countries.
• A comparison of different statistical models for predicting stock market trends.
• Examining the impact of sports on mental health: A cross-sectional study.
• A study of the factors that influence employee retention in the hospitality industry.
• Analyzing the impact of cultural differences on international business negotiations.
• Investigating the effectiveness of different weight loss interventions for obese individuals.
• A study of the relationship between personality traits and academic achievement.
• Examining the impact of technology on job displacement: A longitudinal study.
• A comparison of the effectiveness of different types of advertising strategies on consumer behavior.
• Analyzing the impact of environmental regulations on corporate profitability.
• Investigating the effectiveness of different types of therapy for treating depression.
• A study of the factors that contribute to success in e-commerce.
• Examining the relationship between social support and mental health in the elderly population.
• A comparison of different statistical methods for analyzing complex survey data.
• Analyzing the impact of employee diversity on organizational performance.
• Investigating the effectiveness of different types of exercise for improving cardiovascular health.
• A study of the relationship between emotional intelligence and job performance.
• Examining the impact of work-life balance on employee well-being.
• A comparison of the effectiveness of different types of financial education programs for low-income populations.
• Analyzing the impact of air pollution on respiratory health in urban areas.
• Investigating the relationship between personality traits and leadership effectiveness.
• A study of the factors that influence consumer behavior in the luxury goods market.
• Examining the impact of social networks on political participation: A cross-sectional study.
• A comparison of different statistical methods for analyzing survival data.
• Analyzing the impact of government policies on income inequality.
• Investigating the effectiveness of different types of counseling for substance abuse.
• A study of the relationship between cultural values and consumer behavior.
• Examining the impact of technology on privacy: A longitudinal study.
• A comparison of the effectiveness of different types of online marketing strategies.
• Analyzing the impact of the gig economy on job satisfaction: A cross-sectional study.
• Investigating the effectiveness of different types of education interventions for improving financial literacy.
• A study of the factors that contribute to success in social entrepreneurship.
• Examining the impact of gender diversity on board performance in publicly-traded companies.
• A comparison of different statistical methods for analyzing panel data.
• Analyzing the impact of employee involvement in decision-making on organizational performance.
• Investigating the effectiveness of different types of treatment for anxiety disorders.
• A study of the relationship between cultural values and entrepreneurial success.
• Examining the impact of technology on the labor market: A longitudinal study.
• A comparison of the effectiveness of different types of direct mail campaigns.
• Analyzing the impact of telecommuting on employee productivity: A cross-sectional study.
• Investigating the effectiveness of different types of retirement planning interventions for low-income individuals.
• Analyzing the effectiveness of different educational interventions in improving student performance
• Investigating the impact of climate change on food production and food security
• Identifying factors that influence employee satisfaction and productivity in the workplace
• Examining the prevalence and causes of mental health disorders in different populations
• Evaluating the effectiveness of different marketing strategies in promoting consumer behavior
• Analyzing the prevalence and consequences of substance abuse in different communities
• Investigating the relationship between social media use and mental health outcomes
• Examining the role of genetics in the development of different diseases
• Identifying factors that contribute to the gender wage gap in different industries
• Analyzing the effectiveness of different policing strategies in reducing crime rates
• Investigating the impact of immigration on economic growth and development
• Examining the prevalence and causes of domestic violence in different populations
• Evaluating the effectiveness of different interventions for treating addiction
• Analyzing the prevalence and impact of childhood obesity on health outcomes
• Investigating the relationship between diet and chronic diseases such as diabetes and heart disease
• Examining the effects of different types of exercise on physical and mental health outcomes
• Identifying factors that influence voter behavior and political participation
• Analyzing the prevalence and impact of sleep disorders on health outcomes
• Investigating the effectiveness of different educational interventions in improving health outcomes
• Examining the impact of environmental pollution on public health outcomes
• Evaluating the effectiveness of different interventions for reducing opioid addiction and overdose rates
• Analyzing the prevalence and causes of homelessness in different communities
• Investigating the relationship between race and health outcomes
• Examining the impact of social support networks on health outcomes
• Identifying factors that contribute to income inequality in different regions
• Analyzing the prevalence and impact of workplace stress on employee health outcomes
• Investigating the relationship between education and income levels in different communities
• Examining the effects of different types of technology on mental health outcomes
• Evaluating the effectiveness of different interventions for reducing healthcare costs
• Analyzing the prevalence and impact of chronic pain on health outcomes
• Investigating the relationship between urbanization and public health outcomes
• Examining the effects of different types of drugs on health outcomes
• Identifying factors that contribute to educational attainment in different populations
• Analyzing the prevalence and causes of food insecurity in different communities
• Investigating the relationship between race and crime rates
• Examining the impact of social media on political participation and engagement
• Evaluating the effectiveness of different interventions for reducing poverty levels
• Analyzing the prevalence and impact of stress on mental health outcomes
• Investigating the relationship between religion and health outcomes
• Examining the effects of different types of parenting styles on child development outcomes
• Identifying factors that contribute to political polarization in different regions
• Analyzing the prevalence and causes of teenage pregnancy in different communities
• Investigating the impact of globalization on economic growth and development
• Examining the prevalence and impact of social isolation on mental health outcomes
• Evaluating the effectiveness of different interventions for reducing gun violence
• Analyzing the prevalence and impact of bullying on mental health outcomes
• Investigating the relationship between immigration and crime rates
• Examining the effects of different types of diets on health outcomes
• Identifying factors that contribute to social inequality in different regions
• Bayesian inference for high-dimensional models
• Analysis of longitudinal data with missing values
• Nonparametric regression with functional predictors
• Estimation and inference for copula models
• Statistical methods for neuroimaging data analysis
• Robust methods for high-dimensional data analysis
• Analysis of spatially correlated data
• Bayesian nonparametric modeling
• Statistical methods for network data
• Optimal experimental design for nonlinear models
• Multivariate time series analysis
• Inference for partially identified models
• Statistical learning for personalized medicine
• Statistical inference for rare events
• High-dimensional mediation analysis
• Analysis of multi-omics data
• Nonparametric regression with mixed types of predictors
• Estimation and inference for graphical models
• Statistical inference for infectious disease dynamics
• Robust methods for high-dimensional covariance matrix estimation
• Analysis of spatio-temporal data
• Bayesian modeling for ecological data
• Multivariate spatial point pattern analysis
• Statistical methods for functional magnetic resonance imaging (fMRI) data
• Nonparametric estimation of conditional distributions
• Statistical methods for spatial econometrics
• Inference for stochastic processes
• Bayesian spatiotemporal modeling
• High-dimensional causal inference
• Analysis of data from complex survey designs
• Bayesian nonparametric survival analysis
• Statistical methods for fMRI connectivity analysis
• Spatial quantile regression
• Statistical modeling for climate data
• Estimation and inference for item response models
• Bayesian model selection and averaging
• High-dimensional principal component analysis
• Analysis of data from clinical trials with noncompliance
• Nonparametric regression with censored data
• Statistical methods for functional data analysis
• Inference for network models
• Bayesian nonparametric clustering
• High-dimensional classification
• Analysis of ecological network data
• Statistical modeling for time-to-event data with multiple events
• Estimation and inference for nonparametric density estimation
• Bayesian nonparametric regression with time-varying coefficients
• Statistical methods for functional magnetic resonance spectroscopy (fMRS) data

About the author

Muhammad Hassan

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School of Mathematics & Statistics

• Postgraduate research study
• Statistics Thesis Topics
• About the School
• Postgraduate Research Courses
• Mathematics Thesis Topics

Statistics thesis topics

Below are sample topics available for prospective postgraduate research students. These sample topics do not contain every possible project; they are aimed at giving an impression of the breadth of different topics available. Most prospective supervisors would be more than happy to discuss projects not listed below.

Funded projects are projects with project-specific funding. Funding for other projects is usally available on a competitive basis.

Modelling in Space and Time - Example Research Projects

Information about postgraduate research opportunities and how to apply can be found on the  Postgraduate Research Study page . Below is a selection of projects that could be undertaken with our group.

Evaluating probabilistic forecasts in high-dimensional settings (PhD)

Supervisors:   Jethro Browell Relevant research groups:  Modelling in Space and Time , Computational Statistics , Applied Probability and Stochastic Processes

Many decisions are informed by forecasts, and almost all forecasts are uncertain to some degree. Probabilistic forecasts quantify uncertainty to help improve decision-making and are playing an important role in fields including weather forecasting, economics, energy, and public policy. Evaluating the quality of past forecasts is essential to give forecasters and forecast users confidence in their current predictions, and to compare the performance of forecasting systems.

While the principles of probabilistic forecast evaluation have been established over the past 15 years, most notably that of “ sharpness subject to calibration/reliability” , we lack a complete toolkit for applying these principles in many situations, especially those that arise in high-dimensional settings. Furthermore, forecast evaluation must be interpretable by forecast users as well as expert forecasts, and assigning value to marginal improvements in forecast quality remains a challenge in many sectors.

This PhD will develop new statistical methods for probabilistic forecast evaluation considering some of the following issues:

• Verifying probabilistic calibration conditional on relevant covariates
• Skill scores for multivariate probabilistic forecasts where “ideal” performance is unknowable
• Assigning value to marginal forecast improvement though the convolution of utility functions and Murphey Diagrams
• Development of the concept of “anticipated verification” and “predicting the of uncertainty of future forecasts”
• Decomposing forecast misspecification (e.g. into spatial and temporal components)
• Evaluation of  Conformal Predictions

Good knowledge of multivariate statistics is essential, prior knowledge of probabilistic forecasting and forecast evaluation would be an advantage.

Adaptive probabilistic forecasting (PhD)

Supervisors:   Jethro Browell Relevant research groups:   Modelling in Space and Time , Computational Statistics , Applied Probability and Stochastic Processes

Data-driven predictive models depend on the representativeness of data used in model selection and estimation. However, many processes change over time meaning that recent data is more representative than old data. In this situation, predictive models should track these changes, which is the aim of “online” or “adaptive” algorithms. Furthermore, many users of forecasts require probabilistic forecasts, which quantify uncertainty, to inform their decision-making. Existing adaptive methods such as Recursive Least Squares, the Kalman Filter have been very successful for adaptive point forecasting, but adaptive probabilistic forecasting has received little attention. This PhD will develop methods for adaptive probabilistic forecasting from a theoretical perspective and with a view to apply these methods to problems in at least one application area to be determined.

In the context of adaptive probabilistic forecasting, this PhD may consider:

• Online estimation of Generalised Additive Models for Location Scale and Shape
• Online/adaptive (multivariate) time series prediction
• Online aggregation (of experts, or hierarchies)

A good knowledge of methods for time series analysis and regression is essential, familiarity with flexible regression (GAMs) and distributional regression (GAMLSS/quantile regression) would be an advantage.

The evolution of shape (PhD)

Supervisors:   Vincent Macaulay Relevant research groups:   Bayesian Modelling and Inference , Modelling in Space and Time , Statistical Modelling for Biology, Genetics and *omics

Shapes of objects change in time. Organisms evolve and in the process change form: humans and chimpanzees derive from some common ancestor presumably different from either in shape. Designed objects are no different: an Art Deco tea pot from the 1920s might share some features with one from Ikea in 2010, but they are different. Mathematical models of evolution for certain data types, like the strings of As, Gs , Cs and Ts in our evolving DNA, are quite mature and allow us to learn about the relationships of the objects (their phylogeny or family tree), about the changes that happen to them in time (the evolutionary process) and about the ways objects were configured in the past (the ancestral states), by statistical techniques like phylogenetic analysis. Such techniques for shape data are still in their infancy. This project will develop novel statistical inference approaches (in a Bayesian context) for complex data objects, like functions, surfaces and shapes, using Gaussian-process models, with potential application in fields as diverse as language evolution, morphometrics and industrial design.

New methods for analysis of migratory navigation (PhD)

Supervisors:   Janine Illian Relevant research groups:   Modelling in Space and Time , Bayesian Modelling and Inference , Computational Statistics , Environmental, Ecological Sciences and Sustainability

Joint project with Dr Urška Demšar (University of St Andrews)

Migratory birds travel annually across vast expanses of oceans and continents to reach their destination with incredible accuracy. How they are able to do this using only locally available cues is still not fully understood. Migratory navigation consists of two processes: birds either identify the direction in which to fly (compass orientation) or the location where they are at a specific moment in time (geographic positioning). One of the possible ways they do this is to use information from the Earth’s magnetic field in the so-called geomagnetic navigation (Mouritsen 2018). While there is substantial evidence (both physiological and behavioural) that they do sense magnetic field (Deutschlander and Beason 2014), we however still do not know exactly which of the components of the field they use for orientation or positioning. We also do not understand how rapid changes in the field affect movement behaviour.

There is a possibility that birds can sense these rapid large changes and that this may affect their navigational process. To study this, we need to link accurate data on Earth’s magnetic field with animal tracking data. This has only become possible very recently through new spatial data science advances:  we developed the MagGeo tool, which links contemporaneous geomagnetic data from Swarm satellites of the European Space Agency with animal tracking data (Benitez Paez et al. 2021).

Linking geomagnetic data to animal tracking data however creates a highly-dimensional data set, which is difficult to explore. Typical analyses of contextual environmental information in ecology include representing contextual variables as co-variates in relatively simple statistical models (Brum Bastos et al. 2021), but this is not sufficient for studying detailed navigational behaviour. This project will analyse complex spatio-temporal data using computationally efficient statistical model fitting approches in a Bayesian context.

This project is fully based on open data to support reproducibility and open science. We will test our new methods by annotating publicly available bird tracking data (e.g. from repositories such as Movebank.org), using the open MagGeo tool and implementing our new methods as Free and Open Source Software (R/Python).

Benitez Paez F, Brum Bastos VdS, Beggan CD, Long JA and Demšar U, 2021. Fusion of wildlife tracking and satellite geomagnetic data for the study of animal migration.  Movement Ecology , 9:31.  https://doi.org/10.1186/s40462-021-00268-4

Brum Bastos VdS, Łos M, Long JA, Nelson T and Demšar U, 2021, Context-aware movement analysis in ecology: a systematic review.  International Journal of Geographic Information Science ,  https://doi.org/10.1080/13658816.2021.1962528

Deutschlander ME and Beason RC, 2014. Avian navigation and geographic positioning.  Journal of Field Ornithology , 85(2):111–133. https://doi.org/10.1111/jofo.12055

Integrated spatio-temporal modelling for environmental data (PhD)

Supervisors:   Janine Illian Relevant research groups:   Modelling in Space and Time ,  Bayesian Modelling and Inference ,  Computational Statistics ,  Environmental, Ecological Sciences and Sustainability

(Jointly supervised by Peter Henrys, CEH)

The last decade has seen a proliferation of environmental data with vast quantities of information available from various sources. This has been due to a number of different factors including: the advent of sensor technologies; the provision of remotely sensed data from both drones and satellites; and the explosion in citizen science initiatives. These data represent a step change in the resolution of available data across space and time - sensors can be streaming data at a resolution of seconds whereas citizen science observations can be in the hundreds of thousands.  

Over the same period, the resources available for traditional field surveys have decreased dramatically whilst logistical issues (such as access to sites, ) have increased. This has severely impacted the ability for field survey campaigns to collect data at high spatial and temporal resolutions. It is exactly this sort of information that is required to fit models that can quantify and predict the spread of invasive species, for example. 

Whilst we have seen an explosion of data across various sources, there is no single source that provides both the spatial and temporal intensity that may be required when fitting complex spatio-temporal models (cf invasive species example) - each has its own advantages and benefits in terms of information content. There is therefore potentially huge benefit in beginning together data from these different sources within a consistent framework to exploit the benefits each offers and to understand processes at unprecedented resolutions/scales that would be impossible to monitor. 

Current approaches to combining data in this way are typically very bespoke and involve complex model structures that are not reusable outside of the particular application area. What is needed is an overarching generic methodological framework and associated software solutions to implement such analyses. Not only would such a framework provide the methodological basis to enable researchers to benefit from this big data revolution, but also the capability to change such analyses from being stand alone research projects in their own right, to more operational, standard analytical routines. 

FInally, such dynamic, integrated analyses could feedback into data collection initiatives to ensure optimal allocation of effort for traditional surveys or optimal power management for sensor networks. The major step change being that this optimal allocation of effort is conditional on other data that is available. So, for example, given the coverage and intensity of the citizen science data, where should we optimally send our paid surveyors? The idea is that information is collected at times and locations that provide the greatest benefit in understanding the underpinning stochastic processes. These two major issues - integrated analyses and adaptive sampling - ensure that environmental monitoring is fit for purpose and scientists, policy and industry can benefit from the big data revolution. 

This project will develop an integrated statistical modelling strategy that provides a single modelling framework for enabling quantification of ecosystem goods and services while accounting for the fundamental differences in different data streams. Data collected at different spatial resolutions can be used within the same model through projecting it into continuous space and projecting it back into the landscape level of interest.  As a result, decisions can be made at the relevant spatial scale and uncertainty is propagated through, facilitating appropriate decision making.

Statistical methodology for assessing the impacts of offshore renewable developments on marine wildlife (PhD)

(jointly supervised by Esther Jones and Adam Butler, BIOSS)

Assessing the impacts of offshore renewable developments on marine wildlife is a critical component of the consenting process. A NERC-funded project, ECOWINGS, will provide a step-change in analysing predator-prey dynamics in the marine environment, collecting data across trophic levels against a backdrop of developing wind farms and climate change. Aerial survey and GPS data from multiple species of seabirds will be collected contemporaneously alongside prey data available over the whole water column from an automated surface vehicle and underwater drone.

These methods of data collection will generate 3D space and time profiles of predators and prey, creating a rich source of information and enormous potential for modelling and interrogation. The data present a unique opportunity for experimental design across a dynamic and changing marine ecosystem, which is heavily influenced by local and global anthropogenic activities. However, these data have complex intrinsic spatio-temporal properties, which are challenging to analyse. Significant statistical methods development could be achieved using this system as a case study, contributing to the scientific knowledge base not only in offshore renewables but more generally in the many circumstances where patchy ecological spatio-temporal data are available. 

This PhD project will develop spatio-temporal modelling methodology that will allow user to anaylse these exciting - and complex - data sets and help inform our knowledge on the impact of off-shore renewable on wildlife. 

Analysis of spatially correlated functional data objects (PhD)

Supervisors:   Surajit Ray Relevant research groups:   Modelling in Space and Time ,  Computational Statistics ,  Nonparametric and Semi-parametric Statistics ,  Imaging, Image Processing and Image Analysis

Historically, functional data analysis techniques have widely been used to analyze traditional time series data, albeit from a different perspective. Of late, FDA techniques are increasingly being used in domains such as environmental science, where the data are spatio-temporal in nature and hence is it typical to consider such data as functional data where the functions are correlated in time or space. An example where modeling the dependencies is crucial is in analyzing remotely sensed data observed over a number of years across the surface of the earth, where each year forms a single functional data object. One might be interested in decomposing the overall variation across space and time and attribute it to covariates of interest. Another interesting class of data with dependence structure consists of weather data on several variables collected from balloons where the domain of the functions is a vertical strip in the atmosphere, and the data are spatially correlated. One of the challenges in such type of data is the problem of missingness, to address which one needs develop appropriate spatial smoothing techniques for spatially dependent functional data. There are also interesting design of experiment issues, as well as questions of data calibration to account for the variability in sensing instruments. Inspite of the research initiative in analyzing dependent functional data there are several unresolved problems, which the student will work on:

• robust statistical models for incorporating temporal and spatial dependencies in functional data
• developing reliable prediction and interpolation techniques for dependent functional data
• developing inferential framework for testing hypotheses related to simplified dependent structures
• analysing sparsely observed functional data by borrowing information from neighbours
• visualisation of data summaries associated with dependent functional data
• Clustering of functional data

Estimating the effects of air pollution on human health (PhD)

Supervisors:   Duncan Lee Relevant research groups:   Modelling in Space and Time ,  Biostatistics, Epidemiology and Health Applications

The health impact of exposure to air pollution is thought to reduce average life expectancy by six months, with an estimated equivalent health cost of 19 billion each year (from DEFRA). These effects have been estimated using statistical models, which quantify the impact on human health of exposure in both the short and the long term. However, the estimation of such effects is challenging, because individual level measures of health and pollution exposure are not available. Therefore, the majority of studies are conducted at the population level, and the resulting inference can only be made about the effects of pollution on overall population health. However, the data used in such studies are spatially misaligned, as the health data relate to extended areas such as cities or electoral wards, while the pollution concentrations are measured at individual locations. Furthermore, pollution monitors are typically located where concentrations are thought to be highest, known as preferential sampling, which is likely to result in overly high measurements being recorded. This project aims to develop statistical methodology to address these problems, and thus provide a less biased estimate of the effects of pollution on health than are currently produced.

Mapping disease risk in space and time (PhD)

Disease risk varies over space and time, due to similar variation in environmental exposures such as air pollution and risk inducing behaviours such as smoking.  Modelling the spatio-temporal pattern in disease risk is known as disease mapping, and the aims are to: quantify the spatial pattern in disease risk to determine the extent of health inequalities,  determine whether there has been any increase or reduction in the risk over time, identify the locations of clusters of areas at elevated risk, and quantify the impact of exposures, such as air pollution, on disease risk. I am working on all these related problems at present, and I have PhD projects in all these areas.

Bayesian Mixture Models for Spatio-Temporal Data (PhD)

Supervisors:   Craig Anderson Relevant research groups:   Modelling in Space and Time , Bayesian Modelling and Inference , Biostatistics, Epidemiology and Health Applications

The prevalence of disease is typically not constant across space – instead the risk tends to vary from one region to another.  Some of this variability may be down to environmental conditions, but many of them are driven by socio-economic differences between regions, with poorer regions tending to have worse health than wealthier regions.  For example, within the the Greater Glasgow and Clyde region, where the World Health Organisation noted that life expectancy ranges from 54 in Calton to 82 in Lenzie, despite these areas being less than 10 miles apart. There is substantial value to health professionals and policymakers in identifying some of the causes behind these localised health inequalities.

Disease mapping is a field of statistical epidemiology which focuses on estimating the patterns of disease risk across a geographical region. The main goal of such mapping is typically to identify regions of high disease risk so that relevant public health interventions can be made. This project involves the development of statistical models which will enhance our understanding regional differences in the risk of suffering from major diseases by focusing on these localised health inequalities.

Standard Bayesian hierarchical models with a conditional autoregressive prior are frequently used for risk estimation in this context, but these models assume a smooth risk surface which is often not appropriate in practice. In reality, it will often be the case that different regions have vastly different risk profiles and require different data generating functions as a result.

In this work we propose a mixture model based approach which allows different sub-populations to be represented by different underlying statistical distributions within a single modelling framework. By integrating CAR models into mixture models, researchers can simultaneously account for spatial dependencies and identify distinct disease patterns within subpopulations.

Bayesian Modelling and Inference - Example Research Projects

Modelling genetic variation (msc/phd).

Supervisors:   Vincent Macaulay Relevant research groups:   Bayesian Modelling and Inference ,  Statistical Modelling for Biology, Genetics and *omics

Variation in the distribution of different DNA sequences across individuals has been shaped by many processes which can be modelled probabilistically, processes such as demographic factors like prehistoric population movements, or natural selection. This project involves developing new techniques for teasing out information on those processes from the wealth of raw data that is now being generated by high-throughput genetic assays, and is likely to involve computationally-intensive sampling techniques to approximate the posterior distribution of parameters of interest. The characterization of the amount of population structure on different geographical scales will influence the design of experiments to identify the genetic variants that increase risk of complex diseases, such as diabetes or heart disease.

The evolution of shape (PhD)

Supervisors:   Vincent Macaulay Relevant research groups:   Bayesian Modelling and Inference ,  Modelling in Space and Time , Statistical Modelling for Biology, Genetics and *omics

New methods for analysis of migratory navigation (PhD)

Integrated spatio-temporal modelling for environmental data (phd), statistical methodology for assessing the impacts of offshore renewable developments on marine wildlife (phd).

This PhD project will develop spatio-temporal modelling methodology that will allow user to anaylse these exciting - and complex - data sets and help inform our knowledge on the impact of off-shore renewable on wildlife.

Bayesian variable selection for genetic and genomic studies (PhD)

Supervisors:   Mayetri Gupta Relevant research groups:   Bayesian Modelling and Inference ,  Computational Statistics ,  Statistical Modelling for Biology, Genetics and *omics

An important issue in high-dimensional regression problems is the accurate and efficient estimation of models when, compared to the number of data points, a substantially larger number of potential predictors are present. Further complications arise with correlated predictors, leading to the breakdown of standard statistical models for inference; and the uncertain definition of the outcome variable, which is often a varying composition of several different observable traits. Examples of such problems arise in many scenarios in genomics- in determining expression patterns of genes that may be responsible for a type of cancer; and in determining which genetic mutations lead to higher risks for occurrence of a disease. This project involves developing broad and improved Bayesian methodologies for efficient inference in high-dimensional regression-type problems with complex multivariate outcomes, with a focus on genetic data applications.

The successful candidate should have a strong background in methodological and applied Statistics, expert skills in relevant statistical software or programming languages (such as R, C/C++/Python), and also have a deep interest in developing knowledge in cross-disciplinary topics in genomics. The candidate will be expected to consolidate and master an extensive range of topics in modern Statistical theory and applications during their PhD, including advanced Bayesian modelling and computation, latent variable models, machine learning, and methods for Big Data. The successful candidate will be considered for funding to cover domestic tuition fees, as well as paying a stipend at the Research Council rate for four years.

Bayesian statistical data integration of single-cell and bulk “OMICS” datasets with clinical parameters for accurate prediction of treatment outcomes in Rheumatoid Arthritis (PhD)

Supervisors:   Mayetri Gupta Relevant research groups:   Bayesian Modelling and Inference ,  Computational Statistics ,  Statistical Modelling for Biology, Genetics and *omics ,  Biostatistics, Epidemiology and Health Applications

In recent years, many different computational methods to analyse biological data have been established: including DNA (Genomics), RNA (Transcriptomics), Proteins (proteomics) and Metabolomics, that captures more dynamic events. These methods were refined by the advent of single cell technology, where it is now possible to capture the transcriptomics profile of single cells, spatial arrangements of cells from flow methods or imaging methods like functional magnetic resonance imaging. At the same time, these OMICS data can be complemented with clinical data – measurement of patients, like age, smoking status, phenotype of disease or drug treatment. It is an interesting and important open statistical question how to combine data from different “modalities” (like transcriptome with clinical data or imaging data) in a statistically valid way, to compare different datasets and make justifiable statistical inferences. This PhD project will be jointly supervised with  Dr. Thomas Otto  and  Prof. Stefan Siebert  from the  Institute of Infection, Immunity & Inflammation ), you will explore how to combine different datasets using Bayesian latent variable modelling, focusing on clinical datasets from Rheumatoid Arthritis.

Funding Notes

The successful candidate will be considered for funding to cover domestic tuition fees, as well as paying a stipend at the Research Council rate for four years.

Scalable Bayesian models for inferring evolutionary traits of plants (PhD)

Supervisors:   Vinny Davies ,  Richard Reeve Relevant research groups:   Bayesian Modelling and Inference ,  Computational Statistics ,  Environmental, Ecological Sciences and Sustainability ,  Statistical Modelling for Biology, Genetics and *omics

The functional traits and environmental preferences of plant species determine how they will react to changes resulting from global warming. The main global biodiversity repositories, such as the Global Biodiversity Information Facility ( GBIF ), contain hundreds of millions of records from hundreds of thousands of species in the plant kingdom alone, and the spatiotemporal data in these records can be associated with soil, climate or other environmental data from other databases. Combining these records allow us to identify environmental preferences, especially for common species where many records exist. Furthermore, in a previous PhD studentship we showed that these traits are highly evolutionarily conserved ( Harris et al., 2022 ), so it is possible to impute the preferences for rare species where little data exists using phylogenetic inference techniques.

The aim of this PhD project is to investigate the application of Bayesian variable selection methods to identify these evolutionarily conserved traits more effectively, and to quantify these traits and their associated uncertainty for all plant species for use in a plant ecosystem digital twin that we are developing separately to forecast the impact of climate change on biodiversity. In another PhD studentship, we previously developed similar methods for trait inference in viral evolution ( Davies et al., 2017 ;  Davies et al., 2019 ), but due to the scale of the data here, these methods will need to be significantly enhanced. We therefore propose a project to investigate extensions to methods for phylogenetic trait inference to handle datasets involving hundreds of millions of records in phylogenies with hundreds of thousands of tips, potentially through either sub-sampling ( Quiroz et al, 2018 ) or modelling splitting and recombination ( Nemeth & Sherlock, 2018 ).

Computational Statistics - Example Research Projects

Supervisors:   Jethro Browell Relevant research groups:  Modelling in Space and Time ,  Computational Statistics ,  Applied Probability and Stochastic Processes

Supervisors:   Jethro Browell Relevant research groups:   Modelling in Space and Time ,  Computational Statistics ,  Applied Probability and Stochastic Processes

This project will develop an integrated statistical modelling strategy that provides a single modelling framework for enabling quantification of ecosystem goods and services while accounting for the fundamental differences in different data streams. Data collected at different spatial resolutions can be used within the same model through projecting it into continuous space and projecting it back into the landscape level of interest.  As a result, decisions can be made at the relevant spatial scale and uncertainty is propagated through, facilitating appropriate decision making. 

Statistical methodology for assessing the impacts of offshore renewable developments on marine wildlife (PhD)

Bayesian variable selection for genetic and genomic studies (phd), bayesian statistical data integration of single-cell and bulk “omics” datasets with clinical parameters for accurate prediction of treatment outcomes in rheumatoid arthritis (phd), scalable bayesian models for inferring evolutionary traits of plants (phd).

The aim of this PhD project is to investigate the application of Bayesian variable selection methods to identify these evolutionarily conserved traits more effectively, and to quantify these traits and their associated uncertainty for all plant species for use in a plant ecosystem digital twin that we are developing separately to forecast the impact of climate change on biodiversity. In another PhD studentship, we previously developed similar methods for trait inference in viral evolution ( Davies et al., 2017 ;  Davies et al., 2019 ), but due to the scale of the data here, these methods will need to be significantly enhanced. We therefore propose a project to investigate extensions to methods for phylogenetic trait inference to handle datasets involving hundreds of millions of records in phylogenies with hundreds of thousands of tips, potentially through either sub-sampling ( Quiroz et al, 2018 ) or modelling splitting and recombination ( Nemeth & Sherlock, 2018 ).

Multi objective Bayesian optimisation for in silico  to real metabolomics experiments    (PhD/MSc)

Supervisors:   Vinny Davies ,  Craig Alexander Relevant research groups:   Computational Statistics ,  Machine Learning and AI ,  Emulation and Uncertainty Quantification ,  Statistical Modelling for Biology, Genetics and *omics ,  Statistics in Chemistry/Physics

Untargeted metabolomics experiments aim to  identify  the small molecules that make up a particular sample  (e.g. ,  blood), allowing   us to  identify  biomarkers, discover new chemicals, or understand the  metabolism  ( Smith et al., 2014 ) .  Data Dependent Acquisition  (DDA)  methods  are used to collect  the information needed to  identify  the metabolites ,  and various more advanced  DDA  methods have  recently  been designed to improve this process  ( Davies et al. (2021) ;  McBride et al. (2023) ) . Each of  these methods , however,  ha s  parameters that must be  chosen   in order to  maximise the amount of relevant data  (metabolite spectra)  that is collected . Our recent work  led to the design of  a Virtual Metabolomics Mass Spectrometer ( ViMMS ) in which we can run  computer simulations of experiments  and test different parameter  settings  ( Wandy et al., 2019 ,  2022 ). Previously this has involve d  running a  pre-determined set of parameters as part of a grid search  in  ViMMS ,  and then choosing the best parameter settings  based on a single measure of performance. The proposed  M . Res .  (or Ph . D . ) will  extend this appro ach by using  multi objective  Bayesian Optimisation  to  adapt simulations and optimise over  multiple  different  measurements of quality . By  optimising parameters in this  manner,  we can help improve real experiments currently underway at the University of Glasgow and beyond.

Analysis of spatially correlated functional data objects (PhD)

Nonparametric and semi-parametric statistics - example research projects, modality of mixtures of distributions (phd).

Supervisors:   Surajit Ray Relevant research groups:   Nonparametric and Semi-parametric Statistics ,  Applied Probability and Stochastic Processes ,  Statistical Modelling for Biology, Genetics and *omics ,  Biostatistics, Epidemiology and Health Applications

Finite mixtures provide a flexible and powerful tool for fitting univariate and multivariate distributions that cannot be captured by standard statistical distributions. In particular, multivariate mixtures have been widely used to perform modeling and cluster analysis of high-dimensional data in a wide range of applications. Modes of mixture densities have been used with great success for organizing mixture components into homogenous groups. But the results are limited to normal mixtures. Beyond the clustering application existing research in this area has provided fundamental results regarding the upper bound of the number of modes, but they too are limited to normal mixtures. In this project, we wish to explore the modality of non-normal distributions and their application to real life problems.

Applied Probability and Stochastic Processes - Example Research Projects

Modality of mixtures of distributions (phd).

Finite mixtures provide a flexible and powerful tool for fitting univariate and multivariate distributions that cannot be captured by standard statistical distributions. In particular, multivariate mixtures have been widely used to perform modeling and cluster analysis of high-dimensional data in a wide range of applications. Modes of mixture densities have been used with great success for organizing mixture components into homogenous groups. But the results are limited to normal mixtures. Beyond the clustering application existing research in this area has provided fundamental results regarding the upper bound of the number of modes, but they too are limited to normal mixtures. In this project, we wish to explore the modality of non-normal distributions and their application to real life problems.

Machine Learning and AI - Example Research Projects

Estimating false discovery rates in metabolite identification using generative ai  (phd).

Supervisors:   Vinny Davies , Andrew Elliott ,  Justin J.J. van der Hooft (Wageningen University) Relevant research groups:   Machine Learning and AI ,  Emulation and Uncertainty Quantification ,  Statistical Modelling for Biology, Genetics and *omics ,  Statistics in Chemistry/Physics

Metabolomics is the study field that aims to map all molecules that are part of an organism, which can help us understand its metabolism and how it can be affected by disease, stress, age, or other factors. During metabolomics experiments, mass spectra of the metabolites are collected and then annotated by comparison against spectral databases such as METLIN ( Smith et al., 2005 ) or GNPS ( Wang et al., 2016 ). Generally, however, these spectral databases do not contain the mass spectra of a large proportion of metabolites, so the best matching spectrum from the database is not always the correct identification. Matches can be scored using cosine similarity, or more advanced methods such as Spec2Vec ( Huber et al., 2021 ), but these scores do not provide any statement about the statistical accuracy of the match. Creating decoy spectral libraries, specifically a large database of fake spectra, is one potential way of estimating False Discovery Rates (FDRs), allowing us to quantify the probability of a spectrum match being correct ( Scheubert et al., 2017 ). However, these methods are not widely used, suggesting there is significant scope to improve their performance and ease of use. In this project, we will use the code framework from our recently developed Virtual Metabolomics Mass Spectrometer (ViMMS) ( Wandy et al., 2019 ,  2022 ) to systematically evaluate existing methods and identify possible improvements. We will then explore how we can use generative AI, e.g., Generative Adversarial Networks or Variational Autoencoders, to train a deep neural network that can create more realistic decoy spectra, and thus improve our estimation of FDRs.

Medical image segmentation and uncertainty quantification (PhD)

Supervisors:  Surajit Ray Relevant research groups:   Machine Learning and AI ,  Imaging, Image Processing and Image Analysis

This project focuses on the application of medical imaging and uncertainty quantification for the detection of tumours. The project aims to provide clinicians with accurate, non-invasive methods for detecting and classifying the presence of malignant and benign tumours. It seeks to combine advanced medical imaging technologies such as ultrasound, computed tomography (CT) and magnetic resonance imaging (MRI) with the latest artificial intelligence algorithms. These methods will automate the detection process and may be used for determining malignancy with a high degree of accuracy. Uncertainty quantification (UQ) techniques will help generate a more precise prediction for tumour malignancy by providing a characterisation of the degree of uncertainty associated with the diagnosis. The combination of medical imaging and UQ will significantly decrease the requirement for performing invasive medical procedures such as biopsies. This will improve the accuracy of the tumour detection process and reduce the duration of diagnosis. The project will also benefit from the development of novel image processing algorithms (e.g. deep learning) and machine learning models. These algorithms and models will help improve the accuracy of the tumour detection process and assist clinicians in making the best treatment decisions.

Generating deep fake left ventricles: a step towards personalised heart treatments (PhD)

Supervisors:  Andrew Elliott , Vinny Davies , Hao Gao Relevant research groups:  Machine Learning and AI , Emulation and Uncertainty Quantification , Biostatistics, Epidemiology and Health Applications , Imaging, Image Processing and Image Analysis

Personalised medicine is an exciting avenue in the field of cardiac healthcare where an understanding of patient-specific mechanisms can lead to improved treatments ( Gao et al., 2017 ). The use of mathematical models to link the underlying properties of the heart with cardiac imaging offers the possibility of obtaining important parameters of heart function non-invasively ( Gao et al., 2015 ). Unfortunately, current estimation methods rely on complex mathematical forward simulations, resulting in a solution taking hours, a time frame not suitable for real-time treatment decisions. To increase the applicability of these methods, statistical emulation methods have been proposed as an efficient way of estimating the parameters ( Davies et al., 2019 ;  Noè et al., 2019 ). In this approach, simulations of the mathematical model are run in advance and then machine learning based methods are used to estimate the relationship between the cardiac imaging and the parameters of interest. These methods are, however, limited by our ability to understand the how cardiac geometry varies across patients which is in term limited by the amount of data available ( Romaszko et al., 2019 ). In this project we will look at AI based methods for generating fake cardiac geometries which can be used to increase the amount of data ( Qiao et al., 2023 ). We will explore different types of AI generation, including Generative Adversarial Networks or Variational Autoencoders, to understand how we can generate better 3D and 4D models of the fake left ventricles and create an improved emulation strategy that can make use of them.

Emulation and Uncertainty Quantification - Example Research Projects

Metabolomics is the study field that aims to map all molecules that are part of an organism, which can help us understand its metabolism and how it can be affected by disease, stress, age, or other factors. During metabolomics experiments, mass spectra of the metabolites are collected and then annotated by comparison against spectral databases such as METLIN ( Smith et al., 2005 ) or GNPS ( Wang et al., 2016 ). Generally, however, these spectral databases do not contain the mass spectra of a large proportion of metabolites, so the best matching spectrum from the database is not always the correct identification. Matches can be scored using cosine similarity, or more advanced methods such as Spec2Vec ( Huber et al., 2021 ), but these scores do not provide any statement about the statistical accuracy of the match. Creating decoy spectral libraries, specifically a large database of fake spectra, is one potential way of estimating False Discovery Rates (FDRs), allowing us to quantify the probability of a spectrum match being correct ( Scheubert et al., 2017 ). However, these methods are not widely used, suggesting there is significant scope to improve their performance and ease of use. In this project, we will use the code framework from our recently developed Virtual Metabolomics Mass Spectrometer (ViMMS) ( Wandy et al., 2019 ,  2022 ) to systematically evaluate existing methods and identify possible improvements. We will then explore how we can use generative AI, e.g., Generative Adversarial Networks or Variational Autoencoders, to train a deep neural network that can create more realistic decoy spectra, and thus improve our estimation of FDRs.

Supervisors: Andrew Elliott , Vinny Davies , Hao Gao Relevant research groups:  Machine Learning and AI ,  Emulation and Uncertainty Quantification ,  Biostatistics, Epidemiology and Health Applications ,  Imaging, Image Processing and Image Analysis

Environmental, Ecological Sciences and Sustainability - Example Research Projects

Statistical methodology for assessing the impacts of offshore renewable developments on marine wildlife (phd), statistical modelling for biology, genetics and *omics - example research projects, modelling genetic variation (msc/phd).

Supervisors:   Vincent Macaulay Relevant research groups:   Bayesian Modelling and Inference ,  Modelling in Space and Time ,  Statistical Modelling for Biology, Genetics and *omics

Bayesian statistical data integration of single-cell and bulk “OMICS” datasets with clinical parameters for accurate prediction of treatment outcomes in Rheumatoid Arthritis (PhD)

Supervisors:   Vinny Davies ,  Richard Reeve ,  Claire Harris (BIOSS) Relevant research groups:   Bayesian Modelling and Inference ,  Computational Statistics ,  Environmental, Ecological Sciences and Sustainability ,  Statistical Modelling for Biology, Genetics and *omics

Supervisors:   Vinny Davies , Andrew Elliott ,  Justin J.J. van der Hooft (Wageningen University) Relevant research groups:   Machine Learning and AI ,  Emulation and Uncertainty Quantification ,  Statistical Modelling for Biology, Genetics and *omics , Statistics in Chemistry/Physics

Multi objective Bayesian optimisation for in silico  to real metabolomics experiments  (PhD/MSc)

Finite mixtures provide a flexible and powerful tool for fitting univariate and multivariate distributions that cannot be captured by standard statistical distributions. In particular, multivariate mixtures have been widely used to perform modeling and cluster analysis of high-dimensional data in a wide range of applications. Modes of mixture densities have been used with great success for organizing mixture components into homogenous groups. But the results are limited to normal mixtures. Beyond the clustering application existing research in this area has provided fundamental results regarding the upper bound of the number of modes, but they too are limited to normal mixtures. In this project, we wish to explore the modality of non-normal distributions and their application to real life problems

Implementing a biology-empowered statistical framework to detect rare varient risk factors for complex diseases in whole genome sequence cohorts (PhD)

Supervisors:   Vincent Macaulay , Luísa Pereira (Geneticist, i3s ) Relevant research groups:  Statistical Modelling for Biology, Genetics and *omics ,  Biostatistics, Epidemiology and Health Applications

The traditional genome-wide association studies to detect candidate genetic risk factors for complex diseases/phenotypes (GWAS) recur largely to the microarray technology, genotyping at once thousands or millions of variants regularly spaced across the genome. These microarrays include mostly common variants (minor allele frequency, MAF>5%), missing candidate rare variants which are the more likely to be deleterious [ 1 ]. Currently, the best strategy to genotype low-frequency (1%<MAF<5%) and rare (MAF<1%) variants is through next generation sequencing, and the increasingly availability of whole genome sequences (WGS) places us in the brink of detecting rare variants associated with complex diseases [ 2 ]. Statistically, this detection constitutes a challenge, as the massive number of rare variants in genomes (for example, 64.7M in 150 Iberian WGSs) would imply genotyping millions/billions of individuals to attain statistical power. In the last couple years, several statistical methods have being tested in the context of association of rare variants with complex traits [ 2 , 3 , 4 ], largely testing strategies to aggregate the rare variants. These works have not yet tested the statistical empowerment that can be gained by incorporating reliable biological evidence on the aggregation of rare variants in the most probable functional regions, such as non-coding regulatory regions that control the expression of genes [ 4 ]. In fact, it has been demonstrated that even for common candidate variants, most of these variants (around 88%; [ 5 ]) are located in non-coding regions. If this is true for the common variants detected by the traditional GWAS, it is highly probable to be also true for rare variants.

In this work, we will implement a biology-empowered statistical framework to detect rare variant risk factors for complex diseases in WGS cohorts. We will recur to the 200,000 WGSs from UK Biobank database [ 6 ], that will be available to scientists before the end of 2023. Access to clinical information of these >40 years old UK residents is also provided. We will build our framework around type-2 diabetes (T2D), a common complex disease for which thousands of common variant candidates have been found [ 7 ]. Also, the mapping of regulatory elements is well known for the pancreatic beta cells that play a leading role in T2D [ 8 ]. We will use this mapping in guiding the rare variants’ aggregation and test it against a random aggregation across the genome. Of course, the framework rationale will be appliable to any other complex disease. We will browse literature for aggregation methods available at the beginning of this work, but we already selected the method SKAT (sequence kernel association test; [ 3 ]) to be tested. SKAT fits a random-effects model to the set of variants within a genomic interval or biologically-meaningful region (such as a coding or regulatory region) and computes variant-set level p-values, while permitting correction for covariates (such as the principal components mentioned above that can account for population stratification between cases and controls).

Biostatistics, Epidemiology and Health Applications - Example Research Projects

Bayesian statistical data integration of single-cell and bulk “omics” datasets with clinical parameters for accurate prediction of treatment outcomes in rheumatoid arthritis (phd).

Supervisors:   Mayetri Gupta Relevant research groups:   Bayesian Modelling and Inference ,  Computational Statistics ,  Vincent Macaulay ,  Biostatistics, Epidemiology and Health Applications

Supervisors: Andrew Elliott , Vinny Davies , Hao Gao Relevant research groups:  Machine Learning and AI ,  Emulation and Uncertainty Quantification ,  Biostatistics, Epidemiology and Health Applications ,  Statistical Modelling for Biology, Genetics and *omics

Supervisors:   Craig Anderson Relevant research groups: Modelling in Space and Time , Bayesian Modelling and Inference , Biostatistics, Epidemiology and Health Applications

Implementing a biology-empowered statistical framework to detect rare varient risk factors for complex diseases in whole genome sequence cohorts (PhD)

Supervisors:   Vincent Macaulay , Luísa Pereira (Geneticist,  i3s ) Relevant research groups:  Statistical Modelling for Biology, Genetics and *omics ,  Biostatistics, Epidemiology and Health Applications

The traditional genome-wide association studies to detect candidate genetic risk factors for complex diseases/phenotypes (GWAS) recur largely to the microarray technology, genotyping at once thousands or millions of variants regularly spaced across the genome. These microarrays include mostly common variants (minor allele frequency, MAF>5%), missing candidate rare variants which are the more likely to be deleterious [ 1 ]. Currently, the best strategy to genotype low-frequency (1%<MAF<5%) and rare (MAF<1%) variants is through next generation sequencing, and the increasingly availability of whole genome sequences (WGS) places us in the brink of detecting rare variants associated with complex diseases [ 2 ]. Statistically, this detection constitutes a challenge, as the massive number of rare variants in genomes (for example, 64.7M in 150 Iberian WGSs) would imply genotyping millions/billions of individuals to attain statistical power. In the last couple years, several statistical methods have being tested in the context of association of rare variants with complex traits [ 2 ,  3 ,  4 ], largely testing strategies to aggregate the rare variants. These works have not yet tested the statistical empowerment that can be gained by incorporating reliable biological evidence on the aggregation of rare variants in the most probable functional regions, such as non-coding regulatory regions that control the expression of genes [ 4 ]. In fact, it has been demonstrated that even for common candidate variants, most of these variants (around 88%; [ 5 ]) are located in non-coding regions. If this is true for the common variants detected by the traditional GWAS, it is highly probable to be also true for rare variants.

Social and Urban Studies - Example Research Projects

Our group has an active PhD student community, and every year we admit new PhD students. We welcome applications from across the world. Further information can be found here .

Imaging, Image Processing and Image Analysis - Example Research Projects

Supervisors:  Andrew Elliott , Vinny Davies , Hao Gao Relevant research groups:  Machine Learning and AI ,  Emulation and Uncertainty Quantification ,  Biostatistics, Epidemiology and Health Applications ,  Imaging, Image Processing and Image Analysis

Statistics in Chemistry/Physics - Example Research Projects

Statistics and data analytics education - example research projects.

Our group has an active PhD student community, and every year we admit new PhD students. We welcome applications from across the world. Further information can be found here .

Doctoral Program

Program summary.

Students are required to

• master the material in the prerequisite courses ;
• pass the first-year core program;
• attempt all three parts of the qualifying examinations and show acceptable performance in at least two of them (end of 1st year);
• satisfy the depth and breadth requirements (2nd/3rd/4th year);
• successfully complete the thesis proposal meeting and submit the Dissertation Reading Committee form (winter quarter of the 3rd year);
• present a draft of their dissertation and pass the university oral examination (4th/5th year).

The PhD requires a minimum of 135 units. Students are required to take a minimum of nine units of advanced topics courses (for depth) offered by the department (not including literature, research, consulting or Year 1 coursework), and a minimum of nine units outside of the Statistics Department (for breadth). Courses for the depth and breadth requirements must equal a combined minimum of 24 units. In addition, students must enroll in STATS 390 Statistical Consulting, taking it at least twice.

All students who have passed the qualifying exams but have not yet passed the Thesis Proposal Meeting must take STATS 319 at least once each year. For example, a student taking the qualifying exams in the summer after Year 1 and having the dissertation proposal meeting in Year 3, would take 319 in Years 2 and 3. Students in their second year are strongly encouraged to take STATS 399 with at least one faculty member. All details of program requirements can be found in our PhD handbook (available to Stanford affiliates only, using Stanford authentication. Requests for access from non-affiliates will not be approved).

Statistics Department PhD Handbook

All students are expected to abide by the Honor Code and the Fundamental Standard .

Doctoral and Research Advisors

During the first two years of the program, students' academic progress is monitored by the department's Graduate Director. Each student should meet at least once a quarter with the Graduate Director to discuss their academic plans and their progress towards choosing a thesis advisor (before the final study list deadline of spring of the second year). From the third year onward students are advised by their selected advisor.

Qualifying Examinations

Qualifying examinations are part of most PhD programs in the United States. At Stanford these exams are intended to test the student's level of knowledge when the first-year program, common to all students, has been completed. There are separate examinations in the three core subjects of statistical theory and methods, applied statistics, and probability theory, which are typically taken during the summer at the end of the student's first year. Students are expected to attempt all three examinations and show acceptable performance in at least two of them. Letter grades are not given. Qualifying exams may be taken only once. After passing the qualifying exams, students must file for Ph.D. Candidacy, a university milestone, by the end of spring quarter of their second year.

While nearly all students pass the qualifying examinations, those who do not can arrange to have their financial support continued for up to three quarters while alternative plans are made. Usually students are able to complete the requirements for the M.S. degree in Statistics in two years or less, whether or not they have passed the PhD qualifying exams.

Thesis Proposal Meeting and Dissertation Reading Committee 

The thesis proposal meeting is intended to demonstrate a student's depth in some areas of statistics, and to examine the general plan for their research. In the meeting the student gives a 60-minute presentation involving ideas developed to date and plans for completing a PhD dissertation, and for another 60 minutes answers questions posed by the committee. which consists of their advisor and two other members. The meeting must be successfully completed by the end of winter quarter of the third year. If a student does not pass, the exam must be repeated. Repeated failure can lead to a loss of financial support.

The Dissertation Reading Committee consists of the student’s advisor plus two faculty readers, all of whom are responsible for reading the full dissertation. Of these three, at least two must be members of the Statistics Department (faculty with a full or joint appointment in Statistics but excluding for this purpose those with only a courtesy or adjunct appointment). Normally, all committee members are members of the Stanford University Academic Council or are emeritus Academic Council members; the principal dissertation advisor must be an Academic Council member. 

The Doctoral Dissertation Reading Committee form should be completed and signed at the Dissertation Proposal Meeting. The form must be submitted before approval of TGR status or before scheduling a University Oral Examination.

 For further information on the Dissertation Reading Committee, please see the Graduate Academic Policies and Procedures (GAP) Handbook section 4.8.

University Oral Examinations

The oral examination consists of a public, approximately 60-minute, presentation on the thesis topic, followed by a 60 minute question and answer period attended only by members of the examining committee. The questions relate to the student's presentation and also explore the student's familiarity with broader statistical topics related to the thesis research. The oral examination is normally completed during the last few months of the student's PhD period. The examining committee typically consists of four faculty members from the Statistics Department and a fifth faculty member from outside the department serving as the committee chair. Four out of five passing votes are required and no grades are given. Nearly all students can expect to pass this examination, although it is common for specific recommendations to be made regarding completion of the thesis.

The Dissertation Reading Committee must also read and approve the thesis.

For further information on university oral examinations and committees, please see the Graduate Academic Policies and Procedures (GAP) Handbook section 4.7 .

Dissertation

The dissertation is the capstone of the PhD degree. It is expected to be an original piece of work of publishable quality. The research advisor and two additional faculty members constitute the student's dissertation reading committee.

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Statistics PhD theses

2015 onwards.

Applied Statistics

Current/past master's theses.

• Gabriel Marie Falconer-Stout Differentiating large mining projects in SSA to produce varying impacts on measured health outcomes in progress, Master's thesis in Biostatistics 
• Zhixuan Li Extending the eggCounts Package for Censored Data Analysis of Anthelmintic Resistance in progress, Master's thesis in Biostatistics 
• Delia Luana Schüpbach Analysis of spatio-temporal fly distribution patterns and influential factors 2024, Master's thesis in Mathematics 
• Georgy Astakhov Sample size calculation: Implementation of different scenarios for the online tool SampleSizeR 2024, Master's thesis in Biostatistics  PDF
• Jonas Raphael Füglistaler Estimating the non-reporting rate of animals used in preclinical research using meta-analytical approaches 2023, Master's thesis in Biostatistics  PDF
• Isidro Gonzalez Scale space multiresolution decomposition: an implementation for raster data with the Google Earth engine 2023, Master's thesis in Mathematics  PDF
• Lukas Nägeli Differential growth analysis of agricultural Aureobasidium pullulas isolates 2023, Master's thesis in Biostatistics  PDF
• Anouk Petitpierre Fusion of heterogeneous data sources in preclinical research – evaluation of ordinary and weighted least squares regression and a Bayesian hierarchical modeling approach in R and STAN 2023, Master's thesis in Biostatistics 
• Céline Fabienne Hofmann An EM Algorithm approach to GLM modeling with varying dispersion under censoring and truncation with an application in insurance claims 2023, Master's thesis in Mathematics  PDF
• Chiara Aruanno Analysis of estimation and optimization approaches for Spatial Processes 2022, Master's thesis in Mathematics 
• Ruben Scherrer Algorithms for the Simulation of Isotropic Gaussian Random Fields on the Sphere 2022, Master's thesis in Mathematics  PDF
• Annina Cincera The Consequences of Misspecification in Exponential Random Graph Models 2022, Master's thesis in Mathematics  PDF
• Thomas Fischer Direct Misspecification Using a Fully Parametrized Generalized Wendland Covariance Function 2022, Master's thesis in Biostatistics 
• Tengyingzi Ma Visual Streak Localization in Spectral Domain Optical Coherence Tomography Images of Minipics 2022, Master's thesis in Mathematics 
• Nicolas Huber Avoiding overfitting in additive Bayesian networks 2021, Master's thesis in Mathematics  PDF
• Stefan Willi Extensions of Backfitting to Mixed and Spatial Models 2021, Master's thesis in Mathematics 
• Oliver John Identification of Potential Risk Factors for Back Pain in Horses: An Analysis Using Additive Bayesian Networks 2021, Master's thesis in Biostatistics  PDF
• David Markwalder A comparison of information theoretic feature selection- and extraction methods 2020, Master's thesis in Mathematics 
• Audrey Yeo Clustering analysis of longitudinal data set 2020, Master's thesis in Biostatistics 
• Uriah Daugaard Analysis of Behaviors Affecting Predation Success in a Ciliate Predator-Prey System 2020, Master's thesis in Biostatistics 
• Sandar Felicity Lim Dynamic network analysis on social behavior of wild house mice 2019, Master's thesis in Biostatistics  PDF
• Josef Stocker Estimation of Gaussian Random Fields Using Generalized Wendland Functions 2019, Master's thesis in Mathematics 
• Natacha Bodenhausen Predicting fungal community composition based on soil properties 2019, Master's thesis in Biostatistics  PDF
• Jonas Fürstenberger Models for Short-Term Forecast of River Flooding 2019, Master's thesis in Mathematics 
• Tea Isler Small sample considerations for anthelmintic resistance tests 2018, Master's thesis in Biostatistics  PDF
• Cynthia Dukic An R Package Comparative Analysis Between bnlearn and abn 2018, Master's thesis in Mathematics 
• Christos Polysopoulos Cardiac Surgery and Blood Transfusion Products. What Does Really Matter? 2017, Master's thesis in Biostatistics  PDF
• Pierre Häberli Statistical Data Analysis and Forecasting of a Multinational Company's Production 2017, Master's thesis in Mathematics 
• Roman Flury Multiresolution Decomposition of Incomplete Random Signals - A Statistical Application of Sparse Matrix Calculus 2017, Master's thesis in Biostatistics  PDF
• Marco Reto Schleiniger Stan implementation of a parametric bootstrapping procedure for additive Bayesian network analysis 2017, Master's thesis in Biostatistics 
• Anja Fallegger Extension of the zero-inflated hierarchical models in the eggCounts package 2017, Master's thesis in Mathematics  PDF
• Servan Grüninger Does the Blue Bird Get the Flu? - Using Twitter for Flu Surveillance 2017, Master's thesis in Biostatistics  PDF
• Samuel Noll Two- and three-class ROC analysis. A comparison of statistical tests. 2017, Master's thesis in Mathematics  PDF
• Ursina Brunnhofer Identification of key data in stationary hospital bed-utilisation and application of queueing theory on optimal bed-occupancy 2017, Master's thesis in Mathematics 
• Andrea Meier Risk factor study of pododermatitis in rabbits using additive Bayesian networks 2017, Master's thesis in Biostatistics  PDF
• Thimo Schuster mrbsizerR - Scale space multiresolution analysis in R 2017, Master's thesis in Biostatistics  PDF
• Fabienne Schürch Spatial Interpolation for Huge Datasets: Concepts, Implementations and Illustrations 2016, Master's thesis in Mathematics 
• Fabian Frei Estimation by Transformation 2016, Master's thesis in Mathematics 
• Carina Schneider A spate of statistical tests to climate data validation 2016, Master's thesis in Mathematics  PDF
• Carlos Ochoa Pereira Novel semiparametric estimation method for the analysis of zero-inflated data: an application to the young forest records of the Swiss NFI 3 2015, Master's thesis in Biostatistics 
• Linna Du Vulnerability Analysis of European Windstorms 2015, Master's thesis in Mathematics  PDF
• Markus Hirschbühl Problem Analysis - MMANOVA Framework and Unbalanced Designs 2015, Master's thesis in Mathematics 
• Kaspar Mösinger An R implementation for huge spatiotemporal covariance matrices 2015, Master's thesis in Mathematics 
• Gabrielle Elaine Moser Spatial Aspects of Forest Monitoring Data and Surface Estimation: An Analysis of the Swiss NFI 2014, Master's thesis in Biostatistics 
• Sabine Güsewell Phenological responses to changing temperatures: representativeness and precision of results from the Swiss Phenological Network 2014, Master's thesis in Biostatistics 
• Maria Sereina Graber Phylogenetic comparative methods for discrete responses in evolutionary biology 2013, Master's thesis in Biostatistics  PDF
• Florian Gerber MSc thesis (Biostatistics, University of Zurich, 2013): Disease mapping with the Besag-York-Mollié model applied to a cancer and a worm infections dataset 2013, Master's thesis in Biostatistics 
• Stefan Purtschert Construction of bathymetric charts using spatial statistics 2012, Master's thesis in Mathematics 
• Rebekka Schibli Spatio-temporal homogeneity of a satellite-derived global radiation climatology 2011, Master's thesis in Biostatistics  PDF

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© Universität Zürich | Jun 26, 2024

Department of Statistics

Research topics in probability and statistics, problem solving in mathematics and statistics is inspiring and enjoyable. but are achievements in mathematics and statistics any of use in the so-called real world , researchers in the department of statistics at warwick are developing and utilising modern statistics, mathematics, and computing to solve practical problems., examples of themes for undergraduate research projects:.

• Discovering which genes can discriminate between diseased and healthy patients
• Modelling and detecting asset price bubbles while they are happening and before they burst
• Modelling infectious diseases and identifying localized outbreaks
• Developing a fast algorithm through probabilistic modeling for compression of sound data
• Automatically diagnosing diseases with large-scale image data utilizing crime data for crime prevention and optimal allocation of police resources
• Predicting the outcome of elections based on exit poll data
• Computed Tomography validation of complex structures in Additive Layer Manufacturing

Probability of containment for multitype branching process models for emerging epidemics

Non-stationary statistical modeling and inference for circadian oscillations for research in cancer chronotherapy

Bayesian Models of Category-Specific Emotional Brain Responses

Decision focused inference on Networked Proabilistic Systems: with applications to food security

Rotationally invariant statistics for examining the evidence from the pores in fingerprints

Dynamic Uncertainty Handling for Coherent Decision Making in Nuclear Emergency Response

Study of Key Interventions into Terrorism using Bayesian Networks

Assessing the risk of subsequent tonic-clonic seizures in patients with a history of simple or complex partial seizures

Multidimensional Markov-functional Interest Rate Models

Prospect Theory, Liquidation and the Disposition Effect

Dynamic Bradley-Terry modelling of sports tournaments

Further information on the wide range of research opportunities open to you as an Undergraduate or Postgraduate Taught student in the Department of Statistics can be found on at our Student Research Opportunities webpage.

More information about research in the Department of Statistics, both applied and theoretical, can be found at the departmental research pages .

Mathematics as bridge

The work of mathematicians and statisticians often turns out useful and essential, but typically in a less concrete manner than say the work of a scientists or a physician. David Hilbert, in his now historical address to scientists and physicians, put it this way:

"The instrument that mediates between theory and practice, between thought and observation, is mathematics; it builds the connecting bridge and makes it stronger and stronger. Thus it happens that our entire present-day culture, insofar as it rests on intellectual insight into and harnessing of nature, is founded on mathematics"

Probability and Statistics in the 21st century

Almost a century after Hilbert's words, the mathematical fundations of sciences and social sciences, and the evidence based approach in medicine are often being taken for granted. In the 21st century we are facing complex big data sets with unknown structures from manifold aspecs of the 'real world' as well as fascinating discourses about objective and subjective notions of risk and uncertainty.

Probability and statistics are mathematical disciplines for modelling and analysing theoretical and practical aspects of these burning questions.

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