Keynotes

  • Susan Athey, The Economics of Technology Professor, Graduate School of Business, Stanford University
    • Keynote talk: “Data-driven market design”

    Market design decisions in digital marketplaces are typically informed by data. However, standard tools for short-term, user-level A/B testing have serious shortcomings for questions about market design, in particular when (as is typical) market design changes affect advertiser or seller behavior. This talk reviews several papers that take a variety of approaches for data-driven market design. A first approach is to estimate structural models rely on behavioral assumptions to predict advertiser behavior in counterfactual worlds. A second is to make use of “surrogate” short-term outcomes that can be mapped to long-term outcomes; recent work develops the statistical theory for this approach and suggest estimation approaches in big data settings. A third approach makes use of seller or advertiser-level experimentation; this requires confronting the fact that sellers compete with one another, so that the standard independence assumptions for analyzing experiments are violated; instead, sellers may be thought of as competing in a network.

    • Speaker's bio:

    Susan Athey is the Economics of Technology Professor at Stanford Graduate School of Business. Born in 1970, she received her bachelor's degree from Duke University and her PhD from Stanford, and she holds an honorary doctorate from Duke University. She previously taught at the economics departments at MIT, Stanford and Harvard.

    Her current research focuses on the economics of the internet, marketplace design, auction theory, the statistical analysis of auction data, and the intersection of econometrics and machine learning. She has focused on several applications, including timber auctions, internet search, online advertising, the news media, and virtual currency. She advises governments and businesses on the design of auction-based marketplaces. She has served as a long-term consultant for Microsoft Corporation since 2007, including a period as chief economist. She also serves as a long-term advisor to the British Columbia Ministry of Forests, helping to architect and implement their auction-based pricing system.

    At the age of 36, Professor Athey received the John Bates Clark Medal. The Clark Medal was awarded by the American Economic Association every other year to “that American economist under the age of forty who is adjudged to have made the most significant contribution to economic thought and knowledge.” She was elected to the National Academy of Sciences in 2012 and to the American Academy of Arts and Sciences in 2008. In 2000, she received the Elaine Bennett research award, given every other year to an outstanding young woman in any field of economics. She received continuous funding from the National Science Foundation from 1995 to 2008, including a prestigious Career Development award. In addition, she received the Sloan Foundation Research Fellowship for 2000-2002. She was elected as a fellow of the Econometric Society in 2004, and she is a Research Associate at the National Bureau of Economic Research. She was a National Fellow at the Hoover Institution in 2000-2001, and in 2004-2005 was a fellow at the Center for Advanced Studies in Behavioral Science at Stanford.

    Professor Athey is a member of the President's Committee on the National Medal of Science, as well as the Honors and Awards Committee of the American Economics Association. She served as an elected member of the executive committee of the American Economic Association; as an elected member of the Council of the Econometric Society, and an elected member of the Council of the Game Theory Society.

    She has served as co-editor of American Economic Journals: Microeconomics and Journal of Economics and Management Strategy, and as an associate editor of several leading journals, including the American Economic Review, Review of Economic Studies, Quarterly Journal of Economics, Theoretical Economics, Econometrica, and the RAND Journal of Economics, as well as the National Science Foundation economics panel. She was the chair of the program committee for the 2006 North American Winter Meetings, and she has served on numerous committees for the National Academy of Science, Econometric Society, the American Economic Association, and the Committee for the Status of Women in the Economics Profession. She has also served on program committees and delivered keynote addresses for numerous conferences in computer science.

    Non-academic honors include being named as a World Economic Forum Young Global Leader, Fast Company's 100 Most Creative People in Business, Diversity MBA's Top 100 under 50Diverse Executives, Kilby Award Foundation's Young Innovator Award, and the World Innovation Summit on Entrepreneurship and InnovationŐs World's Most Innovative People Award.


  • Guido Imbens, The Applied Econometrics Professor, Graduate School of Business, Stanford University
    • Keynote talk: “Exact P-values for Network Interference”

    We study the calculation of exact p-values for a large class of non-sharp null hypotheses about treatment effects in a setting with data from experiments involving members of a single connected network. The class includes null hypotheses that limit the effect of one unit's treatment status on another according to the distance between units; for example, the hypothesis might specify that the treatment status of immediate neighbors has no effect, or that units more than two edges away have no effect. We also consider hypotheses concerning the validity of sparsification of a network (for example based on the strength of ties) and hypotheses restricting heterogeneity in peer effects (so that, for example, only the number or fraction treated among neighboring units matters). Our general approach is to define an artificial experiment, such that the null hypothesis that was not sharp for the original experiment is sharp for the artificial experiment, and such that the randomization analysis for the artificial experiment is validated by the design of the original experiment.

    Joint with Susan Athey and Dean Eckles

    • Speaker's bio:

    Guido Imbens does research in econometrics and statistics. His research focuses on developing methods for drawing causal inferences in observational studies, using matching, instrumental variables, and regression discontinuity designs.

    Guido Imbens is Professor of Economics at the Stanford Graduate School of Business. After graduating from Brown University Guido taught at Harvard University, UCLA, and UC Berkeley. He holds an honorary degree from the University of St Gallen. Professor Imbens joined the GSB in 2012 where he specializes in econometrics, and in particular methods for drawing causal inferences. Guido Imbens is a fellow of the Econometric Society and the American Academy of Arts and Sciences.


  • Michael Kearns, Professor and National Center Chair, Department of Computer and Information Science, University of Pennsylvania
    • Keynote talk: “Private Search in Social Networks”

    Motivated by tensions between data privacy for individual citizens and societal priorities such as counterterrorism and the containment of infectious disease, we introduce a computational model that distinguishes between parties for whom privacy is explicitly protected, and those for whom it is not (the targeted subpopulation).
    The goal is the development of algorithms that can effectively identify and take action upon members of the targeted subpopulation in a way that minimally compromises the privacy of the protected, while simultaneously limiting the expense of distinguishing members of the two groups via costly mechanisms such as surveillance, background checks, or medical testing. Within this framework, we provide provably privacy-preserving algorithms for targeted search in social networks. These algorithms are natural variants of common graph search methods, and ensure privacy for the protected by the careful injection of noise in the prioritization of potential targets. We validate the utility of our algorithms with extensive computational experiments on two large-scale social network datasets.

    Joint research with Aaron Roth, Zhiwei Steven Wu, and Grigory Yaroslavtsev.

    • Speaker's bio:

    Michael Kearns is Professor and National Center Chair in the Computer and Information Science department at the University of Pennsylvania. His research interests include topics in machine learning, algorithmic game theory, and computational social science. Prior to joining the Penn faculty, he spent a decade at AT&T/Bell Labs, where he was head of AI Research. He is the founding director of both Penn's Warren Center for Network and Data Sciences (warrencenter.upenn.edu), and Penn's Networked and Social Systems Engineering (NETS) undergraduate program (www.nets.upenn.edu). Kearns consults extensively in technology and finance, and is a Fellow of the American Academy of Arts and Science, the Association for Computing Machinery, and the Association for the Advancement of Artificial Intelligence.


  • Jean Walrand, Professor of Electrical Engineering and Computer Science, UC Berkeley
    • Keynote talk: “Discounted regret minimization and expert selection”

    We design an adaptive strategy that minimizes the worst-case total expected discounted loss compared to the best constant action in a two-player repeated game. The technique is a vector-valued value iteration. The method is illustrated with the problem of expert selection.

    This is joint work with Vijay Kamble and Patrick Loiseau.

    • Speaker's bio:

    Jean Walrand received his Ph.D. in EECS from UC Berkeley and has been on the faculty of that department since 1982. He is the author of An Introduction to Queueing Networks (Prentice Hall, 1988) and Communication Networks: A First Course (2nd ed. McGraw-Hill,1998) and co-author of High-Performance Communication Networks (2nd ed, Morgan Kaufman, 2000), Communication Networks: A Concise Introduction (Morgan & Claypool, 2010), Scheduling and Congestion Control for Communication and Processing networks (Morgan & Claypool, 2010), and Probability in Electrical Engineering and Computer Science (Amazon, 2014).

    His research interests include stochastic processes, queuing theory, communication networks, game theory and the economics of the Internet.

    Prof. Walrand is a Fellow of the Belgian American Education Foundation and of the IEEE and a recipient of the Lanchester Prize, the Stephen O. Rice Prize, the IEEE Kobayashi Award and the ACM Sigmetrics Achievement Award.