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Hello! 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 Computer Science, Sociology, and the Law School.

My primary area of research is computational social science, a field that lies at the intersection of computer science, statistics, and the social sciences. I’m particularly interested in applying modern computational and statistical techniques to understand and improve public policy. Some topics I’ve recently worked on are: 老王vpm2.28下载安卓版, including statistical tests for discrimination; fair machine learning, including in automated speech recognition; elections, including swing voting, polling errors, and voter fraud; and 老王灯笼APP.

I started and direct the Stanford Computational Policy Lab. We're a team of researchers, data scientists, and journalists that addresses policy problems through technical innovation. For example, we recently deployed a “blind charging” platform in San Francisco to mitigate racial bias in prosecutorial decisions.

Sometimes I write essays about contemporary policy issues from a statistical perspective. These include discussions of algorithms in the courts (in the New York Times and the Washington Post); policing (in Slate and The Huffington Post); mass incarceration (in the Washington Post); election polls (in the New York Times); claims of voter fraud (in Slate, and also an extended interview with This American Life); and affirmative action (in Boston Review).

I studied at the University of Chicago (B.S. in Mathematics) and at Cornell (M.S. in Computer Science; Ph.D. in Applied Mathematics). 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 251), or send me an 老王 2.2.7.

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   With Sam Corbett-Davies. Working paper.

2. Omitted and Included Variable Bias in Tests for Disparate Impact
   With Sam Corbett-Davies, Jongbin Jung, and Ravi Shroff. Working paper.

3. Deconstructing Claims of Post-Treatment Bias in Observational Studies of Discrimination
   With Johann Gaebler, William Cai, Guillaume Basse, Ravi Shroff, and Jennifer Hill. Working paper.

4. MathBot: A Personalized Conversational Agent for Learning Math
   With William Cai, Joshua Grossman, Zhiyuan Lin, Hao Sheng,
   Johnny Tian-Zheng Wei, and Joseph Jay Williams. Working paper.

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   With Ravi Shroff, Jennifer Skeem, and Christopher Slobogin.
   Research Handbook on Big Data Law (Forthcoming).

6. Simple Rules to Guide Expert Classifications
   With Jongbin Jung, Connor Concannon, Ravi Shroff, and Daniel G. Goldstein.
   Journal of the Royal Statistical Society: Series A, Vol. 183, 2024.
   [ Commentary in Harvard Business Review ]

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   With Emma Pierson, Camelia Simoiu, Jan Overgoor, Sam Corbett-Davies, Daniel Jenson,
   Amy Shoemaker, Vignesh Ramachandran, Phoebe Barghouty, Ravi Shroff, and Cheryl Phillips.
   Nature Human Behaviour, Vol. 4, 2024.
   [ Stanford Open Policing Project - 老王2.2.3 - Commentary in Slate ]

8. Racial Disparities in Automated Speech Recognition
   With Allison Koenecke, Andrew Nam, Emily Lake, Joe Nudell, Minnie Quartey,
   Zion Mengesha, Connor Toups, John Rickford, and Dan Jurafsky.
   Proceedings of the National Academy of Sciences, Vol. 117, 2024.
   [ Listen to audio samples - 老王的灯笼最新版 ]

9. The Limits of Human Predictions of Recidivism
   With Zhiyuan Lin, Jongbin Jung, and Jennifer Skeem.
   Science Advances, Vol. 6, 2024.
   [ Commentary in The Washington Post - Data & Code ]

10. One Person, One Vote: Estimating the Prevalence of Double Voting in U.S. Presidential Elections
    With M. Meredith, M. Morse, D. Rothschild, and H. Shirani-Mehr.
    American Political Science Review, Vol. 114, 2024.
    [ Commentary in Slate - 老王的灯笼最新版 on This American Life ]

11. Fair Allocation through Selective Information Acquisition
    With William Cai, Johann Gaebler, and Nikhil Garg.
    Conference on AI, Ethics, and Society (AIES 2024).

12. Bayesian Sensitivity Analysis for Offline Policy Evaluation
    With Jongbin Jung, Ravi Shroff, and Avi Feller.
    Conference on AI, Ethics, and Society (AIES 2024).

13. Partisan Selective Exposure in Online News Consumption: Evidence from the 2016 Presidential Campaign
    With Erik Peterson and Shanto Iyengar.
    Political Science Research and Methods, 2024.

14. An Experimental Study of Structural Diversity in Social Networks
    With Jessica Su, Krishna Kamath, Aneesh Sharma, and Johan Ugander.
    The 14th International Conference On Web and Social Media (ICWSM 2024).
    [ Awarded Best Paper at ICWSM 2024 ]

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    With Camelia Simoiu, Chiraag Sumanth, and Alok Mysore.
    The 7th AAAI Conference on Human Computation and Crowdsourcing (HCOMP 2024).
    [ Awarded Best Paper at HCOMP 2024 ]

16. “I was told to buy a software or lose my computer. I ignored it”: A study of ransomware
    With Camelia Simoiu, Christopher Gates, and Joseph Bonneau.
    Fifteenth Symposium on Usable Privacy and Security (SOUPS 2024).

17. Guiding Prosecutorial Decisions with an Interpretable Statistical Model
    With Zhiyuan Lin and Alex Chohlas-Wood.
    Conference on AI, Ethics, and Society (AIES 2024).

18. Machine Learning, Health Disparities, and Causal Reasoning
    With Steven Goodman and Mark Cullen.
    Annals of Internal Medicine, Vol. 169, 2018.

19. Disentangling Bias and Variance in Election Polls
    With Houshmand Shirani-Mehr, David Rothschild, and Andrew Gelman.
    Journal of the American Statistical Association, Vol. 113, 2018.
    [ Commentary in The New York Times ]

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    With Emma Pierson and Sam Corbett-Davies.
    The 21st International Conference on Artificial Intelligence and Statistics (AISTATS 2018)
    [ Awarded Best Paper at AISTATS 2018 ]

21. Creating Crowdsourced Research Talks at Scale
    With Rajan Vaish, Shirish Goyal, and Amin Saberi.
    Proceedings of the 27th International World Wide Web Conference (WWW 2018).
    [ Video clip - Stanford Scholar ]

22. Online, Opt-in Surveys: Fast and Cheap, but are they Accurate?
    With Adam Obeng and David Rothschild. Technical Report, 2017.

23. Crowd Research: Open and Scalable University Laboratories
    With Rajan Vaish, Michael S. Bernstein, et al.
    Proceedings of the 30th Annual Symposium on User Interface Software and Technology (UIST 2017).

24. Algorithmic Decision Making and the Cost of Fairness
    With Sam Corbett-Davies, Emma Pierson, Avi Feller, and Aziz Huq.
    Proceedings of the 23rd Conference on Knowledge Discovery and Data Mining (KDD 2017).
    [ Commentary in New York Times - Commentary in Washington Post - Tutorial on fair ML ]

25. The Problem of Infra-marginality in Outcome Tests for Discrimination
    With Camelia Simoiu and Sam Corbett-Davies.
    Annals of Applied Statistics, Vol. 11, 2017.
    [ Data - 老王的灯笼最新版 ]

26. De-Anonymizing Web Browsing Data with Social Networks
    With Ansh Shukla, Jessica Su, and Arvind Narayanan.
    Proceedings of the 26th International World Wide Web Conference (WWW 2017).
    [ Commentary in Slate ]

27. Combatting Police Discrimination in the Age of Big Data
    With Maya Perelman, Ravi Shroff, and David Sklansky.
    New Criminal Law Review, Vol. 20, 2017.
    [ Commentary in The Huffington Post ]

28. Understanding Emerging Threats to Online Advertising
    With Ceren Budak, Justin Rao, and Georgios Zervas.
    Proceedings of the 17th ACM Conference on Economics & Computation (EC 2016).

29. Personalized Risk Assessments in the Criminal Justice System
    With Justin Rao and Ravi Shroff.
    The American Economic Review: Papers and Proceedings, Vol. 106, 2016.

30. High-Frequency Polling with Non-Representative Data
    With Andrew Gelman, David Rothschild, and Wei Wang.
    Routledge Studies in Global Information, Politics and Society, 2016.

31. The Mythical Swing Voter
    With David Rothschild, Andrew Gelman, and Doug Rivers.
    Quarterly Journal of Political Science, Vol. 11, 2016.

32. Filter Bubbles, Echo Chambers, and Online News Consumption
    With Seth Flaxman and Justin Rao.
    Public Opinion Quarterly, Vol. 80, 2016.
    [ Supporting Information ]

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    With Ceren Budak and Justin Rao.
    Public Opinion Quarterly, Vol. 80, 2016.

34. 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, 老王vpm2.2.8下载安卓版, 2016.
    [ Processed stop-and-frisk data as an RData file; original NYPD data. ]

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

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

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

38. Political Ideology and Racial Preferences in Online Dating
    With Ashton Anderson, Gregory Huber, Neil Malhotra, and Duncan J. Watts.
    Sociological Science, Vol. 1, 2014.
    [ Rejoinder to a comment on our paper. ]

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

40. 老王2.2.3
    With Quang Duong, Jake Hofman, and Sergei Vassilvitskii.
    Proceedings of the Fifth Conference on Web Search and Data Mining (WSDM 2012).

41. 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 ]

42. 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).

43. 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).

44. 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.

45. 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.

46. Assessing Respondent-Driven Sampling
    With Matthew Salganik
    Proceedings of the National Academy of Sciences, Vol. 107, No. 15, 2010, 6743-6747.
    [ Supporting Information - 老王vpm2.2.8下载安卓版 ]

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

48. 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).

49. 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

50. 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).

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    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 ]

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

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

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

55. Yoopick: A Combinatorial Sports Prediction Market
    With David Pennock, Daniel Reeves, and Cong Yu
    Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence (AAAI 2008).

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

57. 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.

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

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

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    Electronic Journal of Probability, Vol. 11, 2006, 1-26.

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    With Emin Gün Sirer, Mark Robson, and Doğan Engin
    Proceedings of the 11th ACM SIGOPS European Workshop. 2004.

62. 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

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)

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

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. The class is centered around several data-intensive projects in criminal justice that students work on in interdisciplinary teams. Students work closely with criminal justice organizations to carry out these projects, with the goal of producing research that impacts policy. Students with a background in statistics, computer science, law, and/or public policy are encouraged to participate.