Sanmay Das
Teaching Papers Data CV
Sanmay Associate Professor
Department of Computer Science and Engineering
Washington University in St. Louis

Office: Jolley 512
e-mail: sanmay at wustl dot edu
Phone: 314-935-4274 
Fax: 314-935-7302 
            




Department of Computer Science and Engineering
Washington University in St. Louis
Campus Box 1045, Jolley Hall Suite 304
One Brookings Dr.
St. Louis MO 63130

Contact

If you are interested in grad school at Wash U, or potential postdoc / visitor / intern positions in my group, please read this page before contacting me.

Research

I am part of the fantastic and growing Machine Learning and AI group at Wash U CSE. I have broad interests across AI, machine learning, and computational social science. Recently I have worked mainly on designing effective algorithms for agents in complex, uncertain environments, and on understanding the social or collective outcomes of individual behavior. My research spans market microstructure, matching markets, social networks, reinforcement learning, sequential decision-making, supervised learning, and data mining. For more details, you can read some of my papers.

Professional Bio (for talk announcements, etc.)

Sanmay Das is an associate professor in Computer Science and Engineering and the chair of the steering committee of the newly formed Division of Computational and Data Sciences at Washington University in St. Louis. He has broad interests across AI, machine learning, and computational social science. His research interests are in designing effective algorithms for agents in complex, uncertain environments, and in understanding the social or collective outcomes of individual behavior. His recent work focuses on algorithmic allocation of scarce societal resources, with an eye towards the distributive justice implications of different policies and mechanisms. Dr. Das is chair of the ACM Special Interest Group on Artificial Intelligence, a member of the board of directors of the International Foundation for Autonomous Agents and Multiagent Systems, and serves as an associate editor of the ACM Transactions on Economics and Computation and of the Journal of Artificial Intelligence Research. Dr. Das has served as program co-chair of the AAMAS and AMMA conferences and area chair for AAAI, in addition to regularly serving as a senior program committee member of major conferences including IJCAI, AAAI, EC, and AAMAS. He has been recognized with awards for research and teaching, including an NSF CAREER Award and the Department Chair Award for Outstanding Teaching at Washington University. He has worked with the US Treasury department on machine learning approaches to credit risk analysis, and occasionally consults in the areas of technology and finance. He holds a Ph.D. from MIT, and a Bachelor's degree from Harvard.

News

  • September 2018 (sticky!): I am super-excited to announce the new Division of Computational and Data Sciences at WashU. I am the steering committee chair, and chair of the Computational Methodologies track for the division. The first cohort is starting in Fall of 2019, and we are looking for strong Ph.D. applicants for our second cohort to enter in Fall of 2020!

  • September 2019: Congratulations to Hao Yan for defending his dissertation, and getting ready to embark on his next adventure at Facebook!

  • August 2019: I'm excited that Patrick Fowler and I will get to work more on algorithmic approaches to allocation of social services in the context of homelessness and child welfare thanks to two new NSF grants, one from the AI and Society competition and one from the Robust Intelligence core program. Sincerely grateful for all the NSF does to support research (and research in the service of society)!

  • July 2019: I am honored and excited to have been elected chair of ACM SIGAI and I'm looking forward to working with John Dickerson and Nick Mattei, and our amazing past chair Sven Koenig on strengthening and furthering the mission of the SIG and the work we do in the AI community.

  • Summer 2019: Our REU Site (Big Data Analytics) was renewed, yay! Another great cohort for the overall WUSTL CSE REU.

  • Spring 2019: Very much enjoyed visiting Rutgers, Johns Hopkins (the Sawyer Seminar), Stevens, and Georgetown Law (NSF Workshop on Computing, Data Science, and Access to Civil Justice) to talk about our work on allocation of scarce societal resources and hear about what others are up to.

  • November 2018: Looking forward to visiting USC CAIS to hear more about all the amazing work they are doing, and give a seminar.

  • November 2018: Happy to have been named a Distinguished Senior Program Committee Member for IJCAI 2018!

  • October 2018: Two one-day trips to Boston in two weeks, one for the MIT LFE conference on Interpretable Machine-Learning Models and Financial Applications (where I talked about measuring partisanship from text data!), and one to attend GroszFest, celebrating the life and work of the amazing Barbara Grosz, one of my undergraduate mentors, first co-author, and mentor to so many in our field.

  • September 2018: Zhuoshu defended her terrific thesis! Very proud of her and her work.

  • August 2018: I joined the editorial board of the ACM Transactions on Economics and Computation as an associate editor.

  • June 2018: I enjoyed meeting the young scholars of the macro financial modeling project and talking to them about machine learning and finance at the summer session.

  • May 2018: Honored to be elected to a 6-year term on the board of directors of IFAAMAS.

  • May 2018: Had a great visit to RPI to give a colloquium talk, and hear what the terrific machine learning / EconCS faculty and students there are up to.

  • February 2018: I gave a What's Hot Talk at AAAI, highlighting some of the best work presented at AAMAS 2017

  • January 2018: Gave a really fun tech talk on machine learning to 400+ folks at Mastercard!

  • Recent service: I was PC co-chair of AAMAS 2017, an Area Chair for AAAI 2018, and on the SPC for AAMAS, IJCAI, and EC.

  • April 2017: Mithun just defended his terrific dissertation!

  • Assorted Audio/Video and Media Coverage

  • Recording of an interview with Jay Kanzler on KTRS where I talk about search, bias, algorithms, and society.

  • I talked about machine learning for credit risk on a techemergence podcast.

  • Videos of a session in which I gave the second talk and interview I gave on prediction markets at the Microsoft Research Faculty Summit are online (along with a ton of other interesting talks and interviews!)

  • The Wikimedia research newsletter of September 2013 discussed some of our work on manipulation in Wikipedia

  • An INFORMS Daily Report blog post from 2008 on some of our Wikipedia research

  • An article from The Economist in 2003, describing some of my research on modeling financial markets

  • Current Ph.D. Students and Postdocs

  • Sujoy Sikdar (postdoc)
  • Hao Yan
  • Amanda Kube
  • Andrew Estornell (co-advised with Eugene Vorobeychik)
  • Zehao Dong (co-advised with Chien-Ju Ho)
  • Former Ph.D. Students

  • Zhuoshu Li (Ph.D. WashU 2018) → Google
  • Mithun Chakraborty (Ph.D. WashU 2017) → Postdoc at NUS
  • Allen Lavoie (Ph.D, WashU 2016) → Google Brain
  • Meenal Chhabra (Ph.D., VT 2014) → Square, Inc.
  • Selected Service and Organization

    Service:

    ACM SIGAI Chair (2019--); Vice-Chair (2013-2019)
    IFAAMAS Board Member (2018-2024)
    ACM TEAC Associate Editor (2018-)
    JAIR Associate Editor (2019-)
    IJCAI Sister Track Co-Chair 2015; Senior PC 2019, 2018, 2016, 2013, 2011.
    AAMAS Program Co-Chair 2017; Sponsorships Co-Chair 2013; Senior PC 2012, 2018; PC 2013-2015.
    ACM EC Workshops Chair 2011; Senior PC 2018; PC 2019, 2016, 2012-2014.
    AAAI Area Chair 2020, 2018, Senior PC 2019, 2016, 2012-2014; PC 2015
    NetEcon PC 2017.
    AMMA PC Co-Chair 2009; General Co-Chair 2011; PC 2015.
    ICML PC 2012, 2016.
    NeurIPS Reviewer 2012.
    ICDM PC 2008-10, 2012.
    KDD PC 2009.
    SDM PC 2008

    (Co)-organizer:

    SIGAI Career Network Conference 2015, 2016
    AMMA 2009 and 2011 (plus Steering Committee)
    2008 RPI CS Day on Machine Learning and Data Mining