Sharad Goel

Welcome! I’m an Assistant Professor at Stanford in the Department of Management Science & Engineering (in the School of Engineering). I also have courtesy appointments in Sociology and Computer Science. My primary area of research is computational social science, an emerging discipline at the intersection of computer science, statistics, and the social sciences. I’m particularly interested in applying modern computational and statistical techniques to study and design public policy. For example, I’ve recently been looking at stop-and-frisk, swing voting, filter bubbles, do-not-track, and media bias.

I studied at the University of Chicago (B.S. in mathematics) and at Cornell (M.S. in computer science; Ph.D. in applied mathematics), and was a postdoc in the Stanford math department. Before joining the Stanford faculty, I worked at Microsoft Research in New York City.

If you would like to chat, please stop by my office (Huang 356), or send me an email.

1. Non-Representative Surveys: Fast, Cheap, and Mostly Accurate
   With Adam Obeng and David Rothschild. Under review.

2. Disentangling Total Error, Bias, and Variance in Election Polls
   With Houshmand Shirani-Mehr, David Rothschild, and Andrew Gelman. Under review.

3. Do-Not-Track and the Economics of Third-Party Advertising
   With Ceren Budak, Justin Rao, and Georgios Zervas. Working paper.

4. Personalized Risk Assessments in the Criminal Justice System
   With Justin Rao and Ravi Shroff.
   The American Economic Review (Papers and Proceedings). To appear.

5. The Effect of Recommendations on Network Structure
   With Jessica Su and Aneesh Sharma.
   Proceedings of the 25th International World Wide Web Conference (WWW 2016).

6. Precinct or Prejudice: Understanding Racial Disparities in New York City's Stop-and-Frisk Policy
   With Justin Rao and Ravi Shroff.
   Annals of Applied Statistics. To appear.
   Download the processed stop-and-frisk data as an RData file; see the original NYPD data.

7. High-Frequency Polling with Non-Representative Data
   With Andrew Gelman, David Rothschild, and Wei Wang.
   Routledge Studies in Global Information, Politics and Society (book chapter). To appear.

8. Fair and Balanced? Quantifying Media Bias through Crowdsourced Content Analysis
   With Ceren Budak and Justin Rao.
   Public Opinion Quarterly. To appear.

9. Filter Bubbles, Echo Chambers, and Online News Consumption
   With Seth Flaxman and Justin Rao.
   Public Opinion Quarterly. To appear.

10. The Mythical Swing Voter
    With David Rothschild, Andrew Gelman, and Doug Rivers.
    Quarterly Journal of Political Science. To appear.

11. The Structural Virality of Online Diffusion
    With Ashton Anderson, Jake Hofman, and Duncan J. Watts.
    Management Science, Vol. 62, 2016.

12. Forecasting Elections with Non-Representative Polls
    With Wei Wang, David Rothschild, and Andrew Gelman.
    International Journal of Forecasting, Vol 31, 2015.

13. Political Ideology and Racial Preferences in Online Dating
    With Ashton Anderson, Gregory Huber, Neil Malhotra, and Duncan J. Watts.
    Sociological Science, Vol. 1, 2014.
    (c.f., a rejoinder to a comment on our paper.)

14. Predicting Individual Behavior with Social Networks
    With Daniel G. Goldstein.
    Marketing Science, Vol. 33, 2014.

15. Sharding Social Networks
    With Quang Duong, Jake Hofman, and Sergei Vassilvitskii.
    Proceedings of the Fifth Conference on Web Search and Data Mining (WSDM 2012).

16. Respondent Driven Sampling—Where We Are and Where Should We be Going?
    With Richard White, Amy Lansky, David Wilson, Wolfgang Hladik, Avi Hakim and Simon DW Frost
    Sexually Transmitted Infections, Vol. 88, No. 6, 2012, 397-399.
    Supporting Information

17. The Structure of Online Diffusion Networks
    With Duncan J. Watts and Daniel G. Goldstein.
    Proceedings of the 13th ACM Conference on Economics & Computation (EC 2012).

18. Who Does What on the Web: Studying Web Browsing Behavior at Scale
    With Jake Hofman and M. Irmak Sirer
    Proceedings of the 6th International Conference on Weblogs and Social Media (ICWSM 2012).

19. Predicting Consumer Behavior with Web Search
    With Jake Hofman, Sébastien Lahaie, David Pennock, and Duncan Watts
    Proceedings of the National Academy of Sciences, Vol 107, No. 41, 2010, 17486-17490.

20. Real and Perceived Attitude Agreement in Social Networks
    With Winter Mason and Duncan Watts
    Journal of Personality and Social Psychology, Vol. 99, No. 4, 2010, 611-621.

21. Assessing Respondent-Driven Sampling
    With Matthew Salganik
    Proceedings of the National Academy of Sciences, Vol. 107, No. 15, 2010, 6743-6747.
    Supporting Information - Project 90 Data

22. Prediction Without Markets
    With Daniel Reeves, Duncan Watts, and David Pennock
    Proceedings of the 11th ACM Conference on Economics & Computation (EC 2010).

23. Anatomy of the Long Tail: Ordinary People With Extraordinary Tastes
    With Andrei Broder, Evgeniy Gabrilovich, and Bo Pang
    Proceedings of the Third Conference on Web Search and Data Mining (WSDM 2010).

24. Contract Auctions for Sponsored Search
    With Sébastien Lahaie and Sergei Vassilvitskii
    Proceedings of the 5th Workshop on Internet and Network Economics (WINE 2009).
    Short version in SIGecom Exchanges

25. Collective Revelation: A Mechanism for Self-Verified, Weighted, and Truthful Predictions
    With Daniel Reeves and David Pennock
    Proceedings of the 10th ACM Conference on Economics & Computation (EC 2009).

26. CentMail: Rate Limiting via Certified Micro-Donations
    With Jake Hofman, John Langford, David Pennock, and Daniel Reeves
    Proceedings of the 6th Conference on Email and Anti-Spam (CEAS 2009).
    Short version at WWW 2009, Developer's Track

27. Respondent-Driven Sampling as Markov Chain Monte Carlo
    With Matthew Salganik
    Statistics in Medicine, Vol. 28, No. 17, 2009, 2202-2229.

28. Social Search in “Small-World” Experiments
    With Roby Muhamad and Duncan Watts
    Proceedings of the 18th International World Wide Web Conference (WWW 2009).

29. Predictive Indexing for Fast Search
    With John Langford and Alex Strehl
    Advances in Neural Information Processing Systems (NIPS 2008).

30. Pricing Combinatorial Markets for Tournaments
    With Yiling Chen and David Pennock
    Proceedings of the 40th ACM Symposium on Theory of Computing (STOC 2008).

31. Horseshoes in Multidimensional Scaling and Local Kernel Methods
    With Persi Diaconis and Susan Holmes
    Annals of Applied Statistics, Vol. 2, No. 3, 2008, 777-807.

32. An Invisible Minority: Asian-Americans in Mathematics
    Notices of the American Mathematical Society, Vol. 53, No. 8, 2006, 878-882.

33. Analysis of Top to Bottom-k Shuffles
    Annals of Applied Probability, Vol. 16, No. 1, 2006, 30-55.

34. Mixing Time Bounds via the Spectral Profile
    With Ravi Montenegro and Prasad Tetali
    Electronic Journal of Probability, Vol. 11, 2006, 1-26.

35. Eluding Carnivores: File Sharing with Strong Anonymity
    With Emin Gün Sirer, Mark Robson, and Doğan Engin
    Proceedings of the 11th ACM SIGOPS European Workshop. 2004.

36. Modified Logarithmic Sobolev Inequalities for Some Models of Random Walk
    Stochastic Processes and Their Applications, Vol. 114, 2004, 51-79.

MS&E 125: Introduction to Applied Statistics — Winter 2016

An increasing amount of data is now generated in a variety of disciplines, ranging from finance and economics, to the natural and social sciences. Making use of this information requires both statistical tools and an understanding of how the substantive scientific questions should drive the analysis. In this hands-on course, we learn to explore and analyze real-world datasets. We cover techniques for summarizing and describing data, methods for statistical inference, and principles for effectively communicating results.

MS&E 231: Computational Social Science (SOC 278) — Fall 2015

With a vast amount of information now collected on our online and offline actions — from what we buy, to where we travel, to who we interact with — we have an unprecedented opportunity to study complex social systems. This opportunity, however, comes with scientific, engineering, and ethical challenges. In this hands-on course, we develop ideas from computer science and statistics to address problems in sociology, economics, political science, and beyond. We cover techniques for collecting and parsing data, methods for large-scale machine learning, and principles for effectively communicating results. To see how these techniques are applied in practice, we discuss recent research findings in a variety of areas.

MS&E 330: Law, Order & Algorithms (SOC 279) — Winter 2016

Data and algorithms are rapidly transforming law enforcement and criminal justice, including how police officers are deployed, how discrimination is detected, and how sentencing, probation, and parole terms are set. Modern computational and statistical methods offer the promise of greater efficiency, equity, and transparency, but their use also raises complex legal, social, and ethical questions. In this course, we analyze recent court decisions, discuss methods from machine learning and game theory, and examine the often subtle relationship between law, public policy and statistics. Students work in interdisciplinary teams to explore these issues in a data-driven project of their choice.