computational social science phd stanford

Computational Social Science

David Sebastian Broska

Amy L. Johnson

Sheridan Stewart

Jan G. Voelkel

Introduction to Computational Social Science

Daniel McFarland

Professor of education and, by courtesy, of sociology and of organizational behavior at the graduate school of business, graduate school of education, web page:  http://web.stanford.edu/people/dan.mcfarland.

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I study the intellectual, social and institutional dynamics of educational systems like schools, classrooms, universities and disciplines. In particular, I have performed a series of studies on classroom organization and interaction; on the formation of adolescent relationships, social structures, and identities; on interdisciplinary collaboration and intellectual innovation; and on the form, dynamics and innovation of scientific fields. I have broad research interests and have been drawn into a variety of interdisciplinary collaborations with linguists, computer scientists, and sociologists. This in turn has led to studies of big data and methodological advances in social networks, language modeling and the study of innovation.

Academic Appointments

• Professor, Graduate School of Education
• Professor (By courtesy), Sociology
• Professor (By courtesy), Organizational Behavior
• Faculty Affiliate, Institute for Human-Centered Artificial Intelligence (HAI)

Administrative Appointments

• Professor, Stanford Graduate School of Education (2000 - Present)
• Director, Stanford Center for Computational Social Science (2012 - 2016)
• Director, Stanford Center for Computational Social Science (2018 - 2020)

Honors & Awards

• Gould Award, American Journal of Sociology (2013-14)
• Friedrich Wilhelm Bessel Award, Alexander von Humboldt Foundation (2014-17)

Program Affiliations

• Science, Technology and Society

Professional Education

• PhD/MA, University of Chicago, Sociology, Sociology
• BA, University of Chicago, Philosophy
• BA, University of Chicago, Sociology
• School of Education
• CERAS, Rm. 419
• Stanford,  California  94305-3096 
• (650) 723-1761 (office)
• Admin Support Mitch Gilmer [email protected]

Additional Info

• Mail Code: 3096
• Profile: Professor in education and (by courtesy) sociology, business. Areas of study: networks, education, culture, interaction.

• Graduate School of Education Site
• Google Scholar

Research Interests

• Data Sciences
• Diversity and Identity
• Higher Education
• Leadership and Organization
• Research Methods

Current Research and Scholarly Interests

The majority of my current research projects concern the sociology of science and research innovation. Here are some examples of projects we are pursuing: 1. the process of intellectual jurisdiction across fields and disciplines 2. the process of knowledge innovation diffusion in science 3. the propagators of scientific careers and advance 4. the role of identity and diversity on the process of knowledge diffusion and career advance 5. the process of research translation across scientific fields and into practice 6. the formal properties and mechanisms of ideational change (network analysis, or holistic conceptions of scientific propositions and ideas) 7. developing methods for identifying the rediscovery of old ideas recast anew 8. investigating the process of scientific review I am also heavily involved in research on social networks and social network theory development. Some of my work concerns relational dynamics and cognitive networks as represented in communication. This often concerns the communication of children (in their writings and speech in classrooms) and academic scholars. I am also co-editing a special issue in Social Networks on "network ecology", and I am a coauthor on a social network methods textbook coming out with Cambridge Press (Forthcoming, by Craig Rawlings, Jeff Smith, James Moody and Daniel McFarland). Last, I am heavily involved in institutional efforts to develop computational social science, computational sociology, and education data science on Stanford's campus.

2023-24 Courses

• Computational Sociology EDUC 317, SOC 317W (Aut, Win, Spr)
• Social Network Methods EDUC 316, SOC 369 (Spr)
• Sociology of Science EDUC 120, EDUC 320, SOC 330, STS 200Q (Aut)
• Curricular Practical Training SOC 392 (Aut, Win, Spr, Sum)
• Directed Reading EDUC 480 (Aut, Win, Spr, Sum)
• Directed Reading in Education EDUC 180 (Aut, Win, Spr, Sum)
• Directed Reading in Environment and Resources ENVRES 398 (Aut, Win, Spr, Sum)
• Directed Research EDUC 490 (Aut, Win, Spr, Sum)
• Directed Research in Education EDUC 190 (Aut, Win, Spr, Sum)
• Directed Research in Environment and Resources ENVRES 399 (Aut, Win, Spr, Sum)
• Graduate Directed Research SOC 391 (Aut, Win, Spr, Sum)
• Graduate Individual Study SOC 390 (Aut, Win, Spr, Sum)
• Honors Research EDUC 140 (Aut)
• Master's Thesis EDUC 185 (Aut, Win, Sum)
• Ph.D. Research CME 400 (Win, Spr, Sum)
• Ph.D. Research Rotation CME 391 (Win)
• Practicum EDUC 470 (Aut, Win, Spr, Sum)
• Research Internship SOC 395 (Aut, Win)
• Senior Honors Thesis URBANST 199 (Win)
• Senior Thesis SOC 196 (Aut, Win, Spr, Sum)
• Supervised Internship EDUC 380 (Aut, Win, Spr, Sum)
• Undergraduate Directed Research SOC 191 (Aut, Win, Spr, Sum)
• Undergraduate Individual Study SOC 190 (Spr)
• Undergraduate Research Apprenticeship SOC 192 (Aut, Win, Spr, Sum)

2022-23 Courses

• Relational Sociology EDUC 312, SOC 224B (Win)
• Social Network Methods EDUC 316, SOC 369 (Win)

2021-22 Courses

• Education Data Science Seminar EDUC 259A (Aut)
• Education Data Science Seminar EDUC 259B (Win)
• Education Data Science Seminar EDUC 259C (Spr)

2020-21 Courses

• Sociology of Science EDUC 120, EDUC 320, SOC 330, STS 200Q (Win)

Stanford Advisees

• Doctoral Dissertation Reader (AC) ELISA KIM , Sheridan Stewart
• Postdoctoral Faculty Sponsor Daniel Scott Smith
• Doctoral Dissertation Advisor (AC) Hancheng Cao , Taylor LiCausi , Kemi Oyewole
• Master's Program Advisor Ruishi Chen, Kennedy Reed
• Doctoral (Program) Hancheng Cao , Mike Hardy , Hansol Lee , Haley Lepp, Taylor LiCausi , Kemi Oyewole

All Publications

View details for DOI 10.1016/j.socnet.2022.04.002

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View details for DOI 10.1080/00221546.2024.2301912

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View details for DOI 10.1177/00031224231166955

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View details for DOI 10.1016/j.socnet.2022.06.006

View details for Web of Science ID 000932028700003

This article systematically investigates the technology licensing by Stanford University. We analyzed all the inventions marketed by Stanford's Office of Technology Licensing (OTL) between 1970 to 2020, with 4,512 inventions from 6,557 inventors. We quantified how the innovation landscape at Stanford changed over time and examined factors that correlate with commercial success. We found that the most profitable inventions are predominantly licensed by inventors' own startups, inventions have involved larger teams over time, and the proportion of female inventors has tripled over the past 25 years. We also identified linguistic features in how the inventors and OTL describe the inventions that significantly correlate with the invention's future revenue. Interestingly, inventions with more adjectives in their abstracts have worse net income. Our study opens up a new perspective for analyzing the translation of research into practice and commercialization using large-scale computational and linguistics analysis.

View details for DOI 10.1016/j.patter.2022.100584

View details for PubMedID 36124300

View details for DOI 10.1007/s10734-021-00810-8

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View details for DOI 10.1177/23780231221085118

View details for Web of Science ID 000846864100001

BACKGROUND: In recent years, interest has grown in whether and to what extent demographic diversity sparks discovery and innovation in research. At the same time, topic modeling has been employed to discover differences in what women and men write about. This study engages these two strands of scholarship to explore associations between changing researcher demographics and research questions asked in the discipline of history. Specifically, we analyze developments in history as women entered the field.METHODS: We focus on author gender in diachronic analysis of history dissertations from 1980 (when online data is first available) to 2015 and a select set of general history journals from 1950 to 2015. We use correlated topic modeling and network visualizations to map developments in research agendas over time and to examine how women and men have contributed to these developments.RESULTS: Our summary snapshot of aggregate interests of women and men for the period 1950 to 2015 identifies new topics associated with women authors: gender and women's history, body history, family and households, consumption and consumerism, and sexuality. Diachronic analysis demonstrates that while women pioneered topics such as gender and women's history or the history of sexuality, these topics broaden over time to become methodological frameworks that historians widely embraced and that changed in interesting ways as men engaged with them. Our analysis of history dissertations surface correlations between advisor/advisee gender pairings and choice of dissertation topic.CONCLUSIONS: Overall, this quantitative longitudinal study suggests that the growth in women historians has coincided with the broadening of research agendas and an increased sensitivity to new topics and methodologies in the field.

View details for DOI 10.1371/journal.pone.0262027

View details for PubMedID 35045091

View details for DOI 10.1016/j.respol.2021.104411

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View details for DOI 10.1177/00031224211056267

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View details for DOI 10.1177/23328584211052055

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View details for DOI 10.15195/v7.a18

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Prior work finds a diversity paradox: Diversity breeds innovation, yet underrepresented groups that diversify organizations have less successful careers within them. Does the diversity paradox hold for scientists as well? We study this by utilizing a near-complete population of 1.2 million US doctoral recipients from 1977 to 2015 and following their careers into publishing and faculty positions. We use text analysis and machine learning to answer a series of questions: How do we detect scientific innovations? Are underrepresented groups more likely to generate scientific innovations? And are the innovations of underrepresented groups adopted and rewarded? Our analyses show that underrepresented groups produce higher rates of scientific novelty. However, their novel contributions are devalued and discounted: For example, novel contributions by gender and racial minorities are taken up by other scholars at lower rates than novel contributions by gender and racial majorities, and equally impactful contributions of gender and racial minorities are less likely to result in successful scientific careers than for majority groups. These results suggest there may be unwarranted reproduction of stratification in academic careers that discounts diversity's role in innovation and partly explains the underrepresentation of some groups in academia.

View details for DOI 10.1073/pnas.1915378117

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View details for DOI 10.1007/s11024-019-09381-6

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• The Patterning of Collaborative Behavior and Knowledge Culminations in Interdisciplinary Research Centers Minerva Mäkinen, E. I., Evans, E. D., McFarland, D. A. 2019 : 1-25

View details for DOI 10.1016/j.respol.2018.01.014

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• Measuring the evolution of a scientific field through citation frames Transactions of the Association for Computational Linguistics Jurgens, D., Kumar, S., Hoover, R., McFarland, D., Jurafsky, D. 2018 ; 6 : 391-406

View details for DOI 10.1145/3132847.3132923

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• Descriptive Analysis in Education: A Guide for Researchers. NCEE 2017-4023. National Center for Education Evaluation and Regional Assistance Loeb, S., Dynarski, S., McFarland, D., Morris, P., Reardon, S., Reber, S. 2017

Ethnically diverse settings provide opportunities for interethnic friendship but can also increase the preference for same-ethnic friendship. Therefore, same-ethnic friendship preferences, or ethnic homophily, can work at cross-purposes with policy recommendations to diversify ethnic representation in social settings. In order to effectively overcome ethnic segregation, we need to identify those factors within diverse settings that exacerbate the tendency toward ethnic homophily. Using unique data and multiple network analyses, the authors examine 529 adolescent friendship networks in English, German, Dutch, and Swedish schools and find that the ethnic composition of school classes relates differently to immigrant and native homophily. Immigrant homophily disproportionately increases as immigrants see more same-ethnic peers, and friendship density among natives has no effect on this. By contrast, native homophily remains relatively low until natives see dense groups of immigrants. The authors' results suggest that theories of interethnic competition and contact opportunities apply differently to ethnic majority and minority groups.

View details for Web of Science ID 000369717400006

• Sociology in the era of big data: The ascent of forensic social science The American Sociologist McFarland, D. A., Lewis, K., Goldberg, A. 2016 ; 47 (1) : 12-35
• Community (in) colleges: The relationship between online network involvement and academic outcomes at a community college Community College Review Evans, E. D., McFarland, D. A., Rios-Aguilar, C., Deil-Amen, R. 2016 ; 44 (3) : 232-254
• Measuring paradigmaticness of disciplines using text Sociological Science Evans, E. D., Gomez, C. J., McFarland, D. A. 2016 ; 3 : 757-778
• Citation classification for behavioral analysis of a scientific field arXiv preprint arXiv:1609.00435 Jurgens, D., Kumar, S., Hoover, R., McFarland, D., Jurafsky, D. 2016

View details for DOI 10.1093/sf/sov004

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• The Organization of Schools and Classrooms Emerging Trends in the Social and Behavioral Sciences: An Interdisciplinary, Searchable, and Linkable Resource Diehl, D., McFarland, D. A. 2015 : 1-15
• Big data and the danger of being precisely inaccurate Big Data & Society McFarland, D. A., McFarland, R. 2015 ; 2 (2) : 2053951715602495
• Social Informatics: SocInfo 2014 International Workshops, Barcelona, Spain, November 11, 2014, Revised Selected Papers Aiello, L. M., McFarland, D. Springer. 2015

Adolescent societies-whether arising from weak, short-term classroom friendships or from close, long-term friendships-exhibit various levels of network clustering, segregation, and hierarchy. Some are rank-ordered caste systems and others are flat, cliquish worlds. Explaining the source of such structural variation remains a challenge, however, because global network features are generally treated as the agglomeration of micro-level tie-formation mechanisms, namely balance, homophily, and dominance. How do the same micro-mechanisms generate significant variation in global network structures? To answer this question we propose and test a network ecological theory that specifies the ways features of organizational environments moderate the expression of tie-formation processes, thereby generating variability in global network structures across settings. We develop this argument using longitudinal friendship data on schools (Add Health study) and classrooms (Classroom Engagement study), and by extending exponential random graph models to the study of multiple societies over time.

View details for DOI 10.1177/0003122414554001

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View details for DOI 10.1287/orsc.2013.0882

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• Encouraging Forum Participation in Online Courses with Collectivist, Individualist, and Neutral Motivational Framings. eLearning Papers Kizilcec, R., Schneider, E., Cohen, G., McFarland, D. A. 2014 ; 37
• The Semi-Formal Organization Organization Science, Permalink: http://dx.doi.org/10.1287/orsc.2013.0882 Biancani, S., Dahlander, L., McFarland, D. A. 2014

View details for DOI 10.1016/j.jmp.2013.04.001

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View details for DOI 10.1016/j.poetic.2013.06.004

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Medical researchers have called for new forms of translational science that can solve complex medical problems. Mainstream science has made complementary calls for heterogeneous teams of collaborators who conduct transdisciplinary research so as to solve complex social problems. Is transdisciplinary translational science what the medical community needs? What challenges must the medical community overcome to successfully implement this new form of translational science? This article makes several contributions. First, it clarifies the concept of transdisciplinary research and distinguishes it from other forms of collaboration. Second, it presents an example of a complex medical problem and a concrete effort to solve it through transdisciplinary collaboration: for example, the problem of preterm birth and the March of Dimes effort to form a transdisciplinary research center that synthesizes knowledge on it. The presentation of this example grounds discussion on new medical research models and reveals potential means by which they can be judged and evaluated. Third, this article identifies the challenges to forming transdisciplines and the practices that overcome them. Departments, universities and disciplines tend to form intellectual silos and adopt reductionist approaches. Forming a more integrated (or 'constructionist'), problem-based science reflective of transdisciplinary research requires the adoption of novel practices to overcome these obstacles.

View details for DOI 10.1038/jp.2012.133

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• Social Networks Research in Higher Education. Higher Education: Handbook of Theory and Research Biancani, S., McFarland, D. A. 2013 ; 28 : 151-215

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View details for DOI 10.1016/j.ssresearch.2010.10.002

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• The Ties that Influence: How Social Networks Channel Faculty Grant Productivity. Social Science Research Rawlings, C., McFarland, D. A. 2011 ; 40 : 1001-1017

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• The Art and Science of Dynamic Network Visualization. Journal of Social Structure, Permalink: http://www.cmu.edu/joss/content/articles/volume7/deMollMcFarland/ Bender-deMoll , S., McFarland, D. A. 2006 ; 7 (2)

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Research Areas

Main navigation.

Research in MS&E spans a broad intellectual spectrum, and our work often involves cross-disciplinary integrations across areas of the department and Stanford as a whole.

MS&E students and faculty work within and across these research areas:

Computational Social Science Decision and Risk Analysis Operations Research Organizations, Technology and Entrepreneurship Policy and Strategy Quantitative Finance

Computational Social Science

The design and operation of networks requires technical concepts such as information theory, algorithms, and optimization, but also depends on economic, social, and even political factors.

One example is the concept of net neutrality, in which infrastructure providers seeking to recoup their investments by charging differently based on bandwidth consumption come into conflict with users who oppose making economic distinctions related to information content. The department has deep expertise in all facets of these and other problems of technology implementation and usage in the real world.

The Society & Algorithms Lab (SOAL) combines computer science and algorithms with social science and economics in response to two recent trends: the rise of more powerful information technology and algorithms, and an increase in networked social and economic interactions.

Decision and Risk Analysis

MS&E Professor Ron Howard pioneered the field of decision analysis starting in 1964. And MS&E continues to lead in the field today.

Although many people make personal decisions instinctively and haphazardly, a more serious approach essential for management is to analyze the problem as a three-legged structure of preferences, alternatives, and available information in the context of a frame.

The mission of the Engineering Risk Research Group (ERRG) , led by department Professor M. Elisabeth Pate-Cornell, is the analysis, mathematical modeling, and management of the safety of engineering systems using probabilistic methods and systems analysis. Recently, the members of the ERRG have extended the application of these methods to the domains of medical operations and devices, national security, and the strategy of firms in the financial industry with the objective of identifying the most cost-effective risk-reduction measures within these complex systems. Technical, organizational, and strategic solutions are considered, and decision analysis is often used to make the final choice among a spectrum of risk-mitigation options.

Operations Research

Research focuses on developing and applying analytical, computational, and economic tools to address a wide variety of problems in business, government, and society.

The area is characterized by its mathematical depth, broad applicability, and interdisciplinary nature and has a particular emphasis in developing and applying models and algorithms to gain new insights and make better decisions across multiple domains.

Operations research is distinguished by its combination of foundational methodological research with applications and translation to practice.  The methodological foundations of operations research include: linear and nonlinear optimization; applied probability and stochastic modeling; simulation; statistical methodology; algorithms; dynamic programming and reinforcement learning; and game theory, market design, and microeconomic theory.  Research in this area often combines ideas across these methodological foundations to develop new techniques that are matched to emerging needs driven by applications; for example, many modern advances in machine learning are driven by advances at the interface of optimization, algorithms, and stochastic analysis, and a rich frontier in market design involves the interface of computational and economic theories.

In turn, these methodological foundations have significant impact on practice.  Faculty in operations research have consistently contributed to both methodological innovation, and instantiation of those methodological innovations in applied domains.  Interaction with applications further inspires novel frontiers for methodological research.  Significant areas of application include: school choice; design of kidney exchanges; design of pricing and matching algorithms for online platforms; operations management for health care; computational methods for social choice and collective decision-making; analysis and control of epidemics; and energy efficient management of buildings.

Research activities in the  operations research group  are broad and extensive. They include projects in optimization, stochastics, operations management, reinforcement learning, algorithms, market design and more.

Organizations, Technology and Entrepreneurship

Research spans the study of technical work, technology's effects on individuals and teams, the formation and growth of entrepreneurial firms, and strategy and innovation in technology-based firms.

Some projects examine the role of technology in the work of engineers, including the interplay between workplace technologies and engineering knowledge, on-the-job learning, problem-solving, and coordination. Other research investigates the dynamics of globally distributed work teams, the implications of contracting and outsourcing, human-robot interaction in the workplace, and evidence-based management.

At the firm-level, research examines how entrepreneurial firms gain financing, build alliance networks, and grow. Other investigations center on established firms, including creating successful R&D collaborations across businesses, effectively competing against other firms, and entering new markets. Researchers also study strategies that enable established organizations to discover, develop, and commercialize technologies.

The department is home to premier teaching programs in these areas including Stanford Technology Ventures Program (STVP) and the Center for Work, Technology and Organizations (WTO) , a world-class center dedicated to understanding how work is changing and how to design more effective organizations and technologies.

Policy and Strategy

Research and teaching in this area focus on the design and analysis of public policies and corporate strategies, especially those with technology-based issues.  Sub-areas include Energy and Environment, Health Systems Modeling and Policy, and National Security Policy

It features a grounding in microeconomics and modeling approaches. Courses with a policy focus include such topics such as national security, energy and environment, and health care. Courses with a strategy focus cover topics such as entrepreneurship, innovation, and product development.

The Energy Modeling Forum (EMF) seeks to improve the use of energy and environmental policy models for making important corporate and government decisions. Systems Utilization Research For Stanford Medicine (SURF Stanford Medicine) facilitates the delivery of world class advances in medical care through world class advances in hospital operations.

Quantitative Finance

Research and teaching in the department cover a range of topics including investments, economic growth, natural resources and energy, entrepreneurship, and microeconomics.

Through systematic analysis and application of sophisticated mathematical tools, engineers make vital contributions to understanding commodities, credit, currencies, derivatives, equities, pricing, profits, resources, and other core economic and financial concepts.

The  Advanced Financial Technologies Laboratory  at Stanford University pioneers financial models, statistical tools, computational algorithms, and software to address the challenges that arise in this context. Faculty and doctoral students of AFT combine expertise in core areas such as stochastics, optimization, data science, and networks and algorithms with a deep understanding of financial markets and institutions to make fundamental advances of broad relevance.

The Economics of Technology Professor, Stanford Graduate School of Business

Integrating explanation and prediction in computational social science.

Jake M. Hofman, Duncan J. Watts Nature (2021). https://doi.org/10.1038/s41586-021-03659-0 . June 2021

URL: https://www.nature.com/articles/s41586-021-03659-0

CS 224C: NLP for Computational Social Science

Stanford / spring 2024, instructors.

We live in an era where many aspects of our social interactions are recorded as textual data, from social media posts to medical and financial records. This course is about using a variety of techniques from machine learning and theories from social science to study human behaviors and important societal questions at scale. Topics will include methods for natural language processing and causal inference, and their applications to important societal questions around hate speech, misinformation, and social movements.

Note: tentative schedule is subject to change.

Course Info

• Class time: Turing Auditorium, Tuesday/Thursday 4:30 PM - 5:50 PM Pacific Time, from 04/01/2024 to 06/05/2024 with Yang, D. (PI).
• Contact : Students should ask all course-related questions in the Ed Discussion Forum, where you will also find announcements. For personal matters that you don't wish to put in a private post, you can email us at [email protected] .

Office Hours

• Diyi Yang: Tuesdays 3:00-4:00pm Pacific Time, Gates 342 or Zoom if needed.
• Jing Huang: Thursday 2:00-3:00 PM Pacific Time, Gates 3A.
• Weixin Liang: Monday 9:35-10:35 AM Pacific Time, Packard 353.

Prerequisites

• Completion of CS106B is strictly required. PhD students from other departments are welcome.

Academic Accommodations

From Stanford's Office of Accessible Education : Students who may need an academic accommodation based on the impact of a disability must initiate the request with the Office of Accessible Education (OAE). Professional staff will evaluate the request with required documentation, recommend reasonable accommodations, and prepare an Accommodation Letter for faculty dated in the current quarter in which the request is being made. Students should contact the OAE as soon as possible since timely notice is needed to coordinate accommodations. The OAE is located at 563 Salvatierra Walk (phone: 723-1066, URL: http://oae.stanford.edu ).

If you need an academic accommodation based on a disability, you should initiate the request with the OAE before the end of the second week of classes.

Well-Being, Stress Management, & Mental Health

If you are experiencing personal, academic, or relationship problems and would like someone to talk to, reach out to the Counseling and Psychological Services (CAPS) on campus. CAPS is the university’s counseling center dedicated to student mental health and wellbeing. Phone assessment appointments can be made at CAPS by calling 650-723- 3785, or by accessing the VadenPatient portal through the Vaden website. For more information, visit: https://vaden.stanford.edu/caps-and-wellness .

Previous offerings

Below you can find archived websites from previous years.

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Assistant Professor of Marketing

Research statement, research interests.

• Misinformation
• Social Media

Teaching Statement

Ding is an assistant professor of marketing at Stanford Graduate School of Business. He received his PhD in marketing from Columbia University. His research has been published in top marketing, management, psychology, and interdisciplinary journals, such as the Journal of Consumer Research, Journal of Marketing Research, Organizational Behavior and Human Decision Processes, Journal of Personality and Social Psychology, and PNAS Nexus . He has been awarded prestigious fellowships and awards, including the Computational Social Science Grant from Russell Sage Foundation (2020), ACR-Sheth Foundation Dissertation Award (2020), John A. Howard/AMA Doctoral Dissertation Award (2022), and the American Marketing Association’s Rising Star Award (2022).

Academic Degrees

• PhD in Marketing, Columbia University, 2022
• MS in Statistics, National University of Singapore, 2016
• BA (with Honors) in Marketing, Fudan University, 2013

Awards and Honors

• John A. Howard/AMA Doctoral Dissertation Award, 2022
• AMA-CBSIG Rising Star Award, 2022
• ACR-Sheth Foundation Dissertation Award, 2020
• Computational Social Science Grant, Russell Sage Foundation, 2020
• AMA-Sheth Doctoral Symposium Fellow, Indiana University, 2020
• Chazen Doctoral Research Grant, Columbia Business School, 2020
• Deming Doctoral Fellow Grant, Columbia Business School, 2019
• University of Houston Marketing Doctoral Symposium Fellow, 2019
• Luxury Education Foundation Doctoral Fellow, Columbia Business School, 2018
• LVMH-SMU Luxury Research Conference Special Award, 2018
• Chazen Global Research Grant, Columbia Business School, 2017
• Behavioral Research Lab Fellowship, Columbia Business School, 2016
• Doctoral Fellowship, Columbia Business School, 2016-2021

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Shortly after Kathleen Creel started her position at Stanford as the inaugural Embedded EthiCS fellow some two years ago, a colleague sent her a 1989 newspaper clipping about the launch of Stanford’s first computer ethics course to show her how the university has long been committed to what Creel was tasked with: helping Stanford students understand the moral and ethical dimensions of technology .

Kathleen Creel is training the next generation of entrepreneurs and engineers to identify and work through various ethical and moral problems they will encounter in their careers. (Image credit: Courtesy Kathleen Creel)

While much has changed since the article was first published in the San Jose Mercury News , many of the issues that reporter Tom Philp discussed with renowned Stanford computer scientist Terry Winograd in the article remain relevant.

Describing some of the topics Stanford students were going to deliberate in Winograd’s course – a period Philp described as “rapidly changing” – he wrote: “Should students freely share copyrighted software? Should they be concerned if their work has military applications? Should they submit a project on deadline if they are concerned that potential bugs could ruin peoples’ work?”

Three decades later, Winograd’s course on computer ethics has evolved , but now it is joined by a host of other efforts to expand ethics curricula at Stanford. Indeed, one of the main themes of the university’s Long Range Vision is embedding ethics across research and education. In 2020, the university launched the Ethics, Society, and Technology (EST) Hub , whose goal is to help ensure that technological advances born at Stanford address the full range of ethical and societal implications.

That same year, the EST Hub, in collaboration with Stanford Institute for Human-Centered Artificial Intelligence (HAI), the McCoy Family Center for Ethics in Society , and the Computer Science Department, created the Embedded EthiCS program, which will embed ethics modules into core computer science courses. Creel is Embedded EthiCS’ first fellow.

Stanford University, situated in the heart of Silicon Valley and intertwined with the influence and impact inspired by technological innovations in the region and beyond, is a vital place for future engineers and technologists to think through their societal responsibilities, Creel said.

“I think teaching ethics specifically at Stanford is very important because many Stanford students go on to be very influential in the world of tech,” said Creel, whose own research explores the moral, political, and epistemic implications of how machine learning is used in the world.

“If we can make any difference in the culture of tech, Stanford is a good place to be doing it,” she said.

Establishing an ethical mindset

Creel is both a computer scientist and a philosopher. After double-majoring in both fields at Williams College in Massachusetts, she worked as a software engineer at MIT Lincoln Laboratory on a large-scale satellite project. There, she found herself asking profound, philosophical questions about the dependence on technology in high-stake situations, particularly when it comes to how AI-based systems have evolved to inform people’s decision-making. She wondered, how do people know they can trust these tools and what information do they need to have in order to believe that it can be a reliable addition or substitution for human judgment?

Creel decided to confront these questions head-on at graduate school, and in 2020, she earned her PhD in history and the philosophy of science at the University of Pittsburgh.

During her time at Stanford, Creel has collaborated with faculty and lecturers across Stanford’s Computer Science department to identify various opportunities for students to think through the social consequences of technology – even if it’s just one or five minutes at a time.

Rather than have ethics be its own standalone seminar or dedicated class topic that is often presented at either the beginning or end of a course, the Embedded EthiCS program aims to intersperse ethics throughout the quarter by integrating it into core course assignments, class discussions, and lectures.

“The objective is to weave ethics into the curriculum organically so that it feels like a natural part of their practice,” said Creel. Creel has worked with professors on nine computer science courses, including: CS106A: Programming Methodology ; CS106B: Programming Abstractions ; CS107: Computer Organization and Systems ; CS109: Introduction to Probability for Computer Scientists ; CS221: Artificial Intelligence: Principles and Techniques ; CS161: Design and Analysis of Algorithms; and CS47B: Design for Behavior Change.

During her fellowship, Creel gave engaging lectures about specific ethical issues and worked with professors to develop new coursework that demonstrates how the choices students will make as engineers carry broader implications for society.

One of the instructors Creel worked with was Nick Troccoli , a lecturer in the Computer Science Department. Troccoli teaches CS 107: Computer Organization & Systems , the third course in Stanford’s introductory programming sequence, which focuses mostly on how computer systems execute programs. Although some initially wondered how ethics would fit into such a technical curriculum, Creel and Troccoli, along with course assistant Brynne Hurst, found clear hooks for ethics discussions in assignments, lectures, and labs throughout the course.

For example, they refreshed a classic assignment about how to figure out a program’s behavior without seeing its code (“reverse engineering”). Students were asked to imagine they were security researchers hired by a bank to discover how a data breach had occurred, and how the hacked information could be combined with other publicly-available information to discover bank customers’ secrets.

Creel talked about how anonymized datasets can be reverse engineered to reveal identifying information and why that is a problem. She introduced the students to different models of privacy, including differential privacy, a technique that can make privacy in a database more robust by minimizing identifiable information.

Students were then tasked to provide recommendations to further anonymize or obfuscate data to avoid breaches.

“Katie helped students understand what potential scenarios may arise as a result of programming and how ethics can be a tool to allow you to better understand those kinds of issues,” Troccoli said.

Another instructor Creel worked with was Assistant Professor Aviad Rubinstein , who teaches CS161: Design and Analysis of Algorithms .

Creel and Rubinstein, joined by research assistant Ananya Karthik and course assistant Golrokh Emami, came up with an assignment where students were asked to create an algorithm that would help a popular distributor decide the locations of their warehouses and determine which customers received one versus two-day delivery.

Students worked through the many variables to determine warehouse location, such as optimizing cost with existing customer demand and driver route efficiency. If the algorithm prioritized these features, closer examination would reveal that historically redlined Black American neighborhoods would be excluded from receiving one-day delivery.

Students were then asked to develop another algorithm that would address the delivery issue while also optimizing even coverage and cost.

The goal of the exercise was to show students that as engineers, they are also decision-makers whose choices carry real-world consequences that can affect equity and inclusion in communities across the country. Students were asked to also share what those concepts mean to them.

“The hope is to show them this is a problem they might genuinely face and that they might use algorithms to solve, and that ethics will guide them in making this choice,” Creel said. “Using the tools that we’ve taught them in the ethics curriculum, they will now be able to understand that choosing an algorithm is indeed a moral choice that they are making, not only a technical one.”

Developing moral co urage

Some students have shared with Creel how they themselves have been subject to algorithmic biases.

For example, when the pandemic shuttered high schools across the country, some school districts turned to online proctoring services to help them deliver exams remotely. These services automated the supervision of students and their space while they take a test.

However, these AI-driven services have come under criticism, particularly around issues concerning privacy and racial bias. For example, the scanning software sometimes fails to detect students with darker skin, Creel said.

Sometimes, there are just glitches in the computer system and the AI will flag a student even though no offense has taken place. But because of the proprietary nature of the technology, how the algorithm came to its decision is not always entirely apparent.

“Students really understand how if these services were more transparent, they could have pointed to something that could prove why an automated flag that may have gone up was wrong,” said Creel.

Overall, Creel said, students have been eager to develop the skillset to help them discuss and deliberate on the ethical dilemmas they could encounter in their professional careers.

“I think they are very aware that they, as young engineers, could be in a situation where someone above them asks them to do something that they don’t think is right,” she added. “They want tools to figure out what is right, and I think they also want help building the moral courage to figure out how to say no and to interact in an environment where they may not have a lot of power. For many of them, it feels very important and existential.”

Creel is now transitioning from her role at Stanford to Northeastern University where she will hold a joint appointment as an assistant professor of philosophy and computer science.

Social Science Research Coordinator (On-Site Only)

🔍 School of Humanities and Sciences, Stanford, California, United States

The School of Humanities and Sciences (H&S) is the foundation of a liberal arts education at Stanford. The school encompasses 24 departments and 25 interdisciplinary programs. H&S is home to fundamental and applied research, where free, open, and critical inquiry is pursued across disciplines. As the university’s largest school, H&S serves as the foundation of a Stanford undergraduate education no matter which discipline students pursue as a major. Graduate students work alongside world-renowned faculty to pursue and shape foundational research that leads to breakthroughs and discoveries that shed new light on the past, influence the present, and shape the future. Together, faculty and students in H&S engage in inspirational teaching, learning, and research every day.

Position Summary:

The (brand-new) Laboratory for Computation and Language in Minds and Brains (CLiMB Lab, PI Cory Shain) is seeking a full-time research coordinator to play an integral role in the lab’s mission to study the computational and neural underpinnings of the human ability to learn, comprehend, and use language. This position will involve close collaboration with and supervision by PI Shain to (i) design and implement research protocols, (ii) recruit and consent participants and ensure their safety and comfort, (iii) collect experimental data (with a special focus on functional magnetic resonance imaging or fMRI), (iii) design and implement data management protocols, and (iv) analyze data. In the course of these duties, the research coordinator will have opportunities to co-author any research articles to which they make a substantive contribution, acquire new skills (e.g., in linguistics, cognitive neuroscience, experimental design, data analytics, and/or computational methods), collaborate with others (e.g., student researchers and postdocs) inside and outside the lab, and even potentially lead their own studies with support from the PI. Applicants with an appropriate computational background may also work with artificial intelligence tools (e.g., large language models) for research purposes.

This is a 2-year position ideal for applicants considering pursuing PhD training in linguistics, cognitive (neuro)science, or artificial intelligence. Strong coding stills and previous fMRI data collection experience are strongly preferred.

This is a 100% FTE, fixed-term, non-exempt position. This position will be based on the Stanford campus.

If you believe that this opportunity is a match for your knowledge, skills and abilities, we encourage you to apply. Thank you for considering employment opportunities with the School of Humanities and Sciences.

CORE DUTIES:

· Prepare correspondence, documents and reports.

· Maintain and file forms and documents, including consent forms and master subject logs.

· Assist with the screening, recruiting, and obtaining consent of study participants. Perform telephone or in-person interviews to gather data, as needed. Schedule and/or call subjects for appointments.

· Prepare, distribute, administer and process questionnaires and tests, score test measurements and questionnaires, and code data for computer entry. Perform quantitative review of forms, tests, and other measurements for completeness and accuracy.

· Prepare data for input into statistical databases by typing, editing, and organizing data.

· Apply formulas and calculations to research data using basic statistical programs. Review and verify accuracy of database information and assist in making necessary corrections according to specific guidelines.

· Order and maintain equipment and supplies.

· Process study compensation payments and thank you letters to subjects upon completion of trial activities.

· Assist with post-study activities, as needed.

* Other duties may also be assigned.

EDUCATION AND EXPERIENCE:

Two year college degree and one year of relevant experience or an equivalent combination of experience, education, and training.

KNOWLEDGE, SKILLS, AND ABILITIES:

· General understanding of scientific theory and methods.

· General computer skills and ability to quickly learn and master computer programs.

· Ability to work under deadlines with general guidance.

· Excellent organizational skills and demonstrated ability to complete detailed work accurately.

· Effective oral and written communication skills.

· Ability to work with human study participants

In addition, preferred requirements include:

● Strong programming skills (most of the coding in the lab is done in Python, with some MATLAB and R)

● Previous experience with fMRI data collection

● Previous experience with fMRI data analysis

● Training background in at least one of the following:

o Linguistics

o Neuroimaging

o Cognitive science

o Computer science

o Statistics

WORKING CONDITIONS:

· May be exposed to blood borne pathogens.

· May be required to work non-standard, extended or weekend hours in support of research work.

The expected pay range for this position is $24.04 to $34.13 per hour.

Stanford University provides pay ranges representing its good faith estimate of what the university reasonably expects to pay for a position. The pay offered to a selected candidate will be determined based on factors such as (but not limited to) the scope and responsibilities of the position, the qualifications of the selected candidate, departmental budget availability, internal equity, geographic location and external market pay for comparable jobs.

At Stanford University, base pay represents only one aspect of the comprehensive rewards package. The Cardinal at Work website (https://cardinalatwork.stanford.edu/benefits-rewards) provides detailed information on Stanford’s extensive range of benefits and rewards offered to employees. Specifics about the rewards package for this position may be discussed during the hiring process.

Why Stanford is for You

Imagine a world without search engines or social platforms. Consider lives saved through first-ever organ transplants and research to cure illnesses. Stanford University has revolutionized the way we live and enrich the world. Supporting this mission is our diverse and dedicated 17,000 staff. We seek talent driven to impact the future of our legacy. Our culture and unique perks empower you with:

● Freedom to grow. We offer career development programs, tuition reimbursement, or audit a course. Join a film screening, or listen to a renowned author or global leader speak.

● A caring culture. We provide superb retirement plans, generous time-off, and family care resources.

● A healthier you. Climb our rock wall, or choose from hundreds of health or fitness classes at our world-class exercise facilities. We also provide excellent health care benefits.

● Discovery and fun. Stroll through historic sculptures, trails, and museums.

● Enviable resources. Enjoy free commuter programs, ridesharing incentives, discounts and more.

The job duties listed are typical examples of work performed by positions in this job classifications and are not designed to contain or be interpreted as a comprehensive inventory of all duties, tasks and responsibilities. Specific duties and responsibilities may vary depending on department or program needs without changing the general nature and scope of the job or level of responsibility. Employees may also perform other duties as assigned.

Consistent with its obligations under the law, the University will provide reasonable accommodations to applicants and employees with disabilities. Applicants

requiring a reasonable accommodation for any part of the application or hiring process should contact Stanford University Human Resources at [email protected]. For all other inquiries, please submit a contact form.

Stanford is an equal employment opportunity and affirmative action employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex,

sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic protected by law.

• Schedule: Full-time
• Job Code: 4233
• Employee Status: Fixed-Term
• Requisition ID: 103259
• Work Arrangement : On Site

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Janabel Xia: Algorithms, dance rhythms, and the drive to succeed

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Senior math major Janabel Xia is a study of a person in constant motion.

When she isn’t sorting algorithms and improving traffic control systems for driverless vehicles, she’s dancing as a member of at least four dance clubs. She’s joined several social justice organizations, worked on cryptography and web authentication technology, and created a polling app that allows users to vote anonymously.

In her final semester, she’s putting the pedal to the metal, with a green light to lessen the carbon footprint of urban transportation by using sensors at traffic light intersections.

First steps

Growing up in Lexington, Massachusetts, Janabel has been competing on math teams since elementary school. On her math team, which met early mornings before the start of school, she discovered a love of problem-solving that challenged her more than her classroom “plug-and-chug exercises.”

At Lexington High School, she was math team captain, a two-time Math Olympiad attendee, and a silver medalist for Team USA at the  European Girls' Mathematical Olympiad.

As a math major, she studies combinatorics and theoretical computer science, including theoretical and applied cryptography. In her sophomore year, she was a researcher in the Cryptography and Information Security Group at the MIT Computer Science and Artificial Intelligence Laboratory, where she conducted cryptanalysis research under Professor  Vinod Vaikuntanathan .

Part of her interests in cryptography stem from the beauty of the underlying mathematics itself — the field feels like clever engineering with mathematical tools. But another part of her interest in cryptography stems from its political dimensions, including its potential to fundamentally change existing power structures and governance. Xia and students at the University of California at Berkeley and Stanford University created  zkPoll , a private polling app written with the Circom programming language, that allows users to create polls for specific sets of people, while generating a zero-knowledge proof that keeps personal information hidden to decrease negative voting influences from public perception.

Her participation in the PKG Center’s  Active Community Engagement Freshman Pre-Orientation Program introduced her to local community organizations focusing on food security, housing for formerly incarcerated individuals, and access to health care. She is also part of  Reading for Revolution , a student book club that discusses race, class, and working-class movements within MIT and the Greater Boston area.

Xia’s educational journey led to her ongoing pursuit of combining mathematical and computational methods in areas adjacent to urban planning.  “When I realized how much planning was concerned with social justice as it was concerned with design, I became more attracted to the field.”

Going on autopilot

She took classes with the  Department of Urban Studies and Planning and is currently working on an Undergraduate Research Opportunities Program (UROP) project with Professor Cathy Wu in the Institute for Data, Systems, and Society.

Recent work on eco-driving by Wu and doctoral student  Vindula Jayawardana investigated semi-autonomous vehicles that communicate with sensors localized at traffic intersections, which in theory could reduce carbon emissions by up to 21 percent.

Xia aims to optimize the implementation scheme for these sensors at traffic intersections, considering a graded scheme where perhaps only 20 percent of all sensors are initially installed, and more sensors get added in waves. She wants to maximize the emission reduction rates at each step of the process, as well as ensure there is no unnecessary installation and de-installation of such sensors.  

Dance numbers

Meanwhile, Xia has been a member of MIT’s  Fixation ,  Ridonkulous , and MissBehavior groups, and as a traditional Chinese dance choreographer for the  MIT Asian Dance Team . 

A dancer since she was 3, Xia started with Chinese traditional dance, and later added ballet and jazz. Because she is as much of a dancer as a researcher, she has figured out how to make her schedule work.

“Production weeks are always madness, with dancers running straight from class to dress rehearsals and shows all evening and coming back early next morning to take down lights and roll up marley [material that covers the stage floor],” she says. “As busy as it keeps me, I couldn’t have survived MIT without dance. I love the discipline, creativity, and most importantly the teamwork that dance demands of us. I really love the dance community here with my whole heart. These friends have inspired me and given me the love to power me through MIT.”

Xia lives with her fellow Dance Team members at the off-campus  Women's Independent Living Group (WILG).  “I really value WILG's culture of independence, both in lifestyle — cooking, cleaning up after yourself, managing house facilities, etc. — and thought — questioning norms, staying away from status games, finding new passions.”

In addition to her UROP, she’s wrapping up some graduation requirements, finishing up a research paper on  sorting algorithms from her summer at the University of Minnesota Duluth Research Experience for Undergraduates in combinatorics, and deciding between PhD programs in math and computer science.  

“My biggest goal right now is to figure out how to combine my interests in mathematics and urban studies, and more broadly connect technical perspectives with human-centered work in a way that feels right to me,” she says.

“Overall, MIT has given me so many avenues to explore that I would have never thought about before coming here, for which I’m infinitely grateful. Every time I find something new, it’s hard for me not to find it cool. There’s just so much out there to learn about. While it can feel overwhelming at times, I hope to continue that learning and exploration for the rest of my life.”

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• MIT Asian Dance Team
• Women’s Independent Living Group
• Active Community Engagement
• Institute for Data, Systems, and Society
• Department of Mathematics
• Department of Urban Studies and Planning
• School of Architecture and Planning

Related Topics

• Undergraduate
• Mathematics
• Urban studies and planning
• Computer science and technology
• Cryptography
• Transportation
• Autonomous vehicles
• Electrical Engineering & Computer Science (eecs)
• Computer Science and Artificial Intelligence Laboratory (CSAIL)

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Computational Social Science Fellowship

The css fellowship program is on hiatus for the 2023–24 academic year..

The Computational Social Science Fellowship is designed to provide students with extra funds to conduct innovative research that applies computational techniques to social science research.

Eligibility

Stanford graduate students from the six social science departments (Sociology, Anthropology, Economics, Political Science, Communication, & Psychology) and the Graduate School of Education are eligible to receive these fellowships.

Award levels

Stipends are provided at 3 tiers.  You must indicate which funding level you are applying for in your application.  If you apply at the $10,000 level, you will not be considered for the $5000 level and vice versa, so please apply for the level of funding that best fits your research needs. In 2023, the following will be awarded:

• 2 awards of $10,000 
• 3 awards of $5,000   
• 4 awards of $2,500

What can you use the money for?

Funds can pay for software, computing, transcribing, translating, surveys, data acquisition, and other research costs.  

Data acquisition includes purchasing data sets and paying for participants (including MTurk).

Funds (at any level)  cannot  pay for travel, conferences, hardware or for hiring students.

To find out if your budget items are allowed, contact cmfraga [at] stanford.edu (subject: CSS%20Fellowship) (Christopher Fraga) .

All application materials should be submitted using this Application Link, which will be active only during the application period . Once you apply, faculty will receive a request to complete a recommendation form, including a paragraph of support. Please let them know to expect this request.

Applications should include a 1–2-page description of a research project that applies computational methods to social science research, and a budget which can be on a 3rd page if needed.

• Identify a faculty recommender who will advise your project and provide a brief paragraph of support. The advisor may be asked to help review proposals in May or June. They would be reviewing 2-4 proposals but would not be reviewing yours. Please let them know they will receive a link to complete the recommendation form once you complete your application.
• Include a budget listing specific items or services that will be purchased with approximate prices, links to any relevant websites or references, and a brief justification of how the item will be used in your project if it is not clear from your project description.
• Application materials must be submitted as a single PDF, with your name at the top of each page.

If the Fellowship Program is reinstated for the 2023–24 academic year, the deadline will likely be in Winter Quarter 2024.

If you are awarded funding

Recipients of funding must report on research activity at the end of the academic year, as well as indicate progress toward publications and/or obtaining external support for the project.

Previous Projects

More information on past projects can be found at the CSS Fellows page . 

Stanford Causal Science Center Conference on Experimentation

Event details:, this event is open to:.

This one-day-long event brings together top experts in experimentation and causal inference—researchers, practitioners, and industry professionals—and it is a unique opportunity to discuss the latest trends, cutting-edge methodologies and their real-world applications. This event is in-person only.

We are thrilled to have an excellent lineup of speakers who are leading figures in the tech industry and academia. 

Related Topics

Explore more events, cores annual symposium 2024, women in data science: health frontiers, inaugural berkeley-stanford workshop on veridical data science.