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


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.


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.


I am grateful for research support over the years from the NSF (including a CAREER award), BSF, and IARPA.

Professional Bio

Sanmay Das is an associate professor in the Computer Science and Engineering Department at Washington University in St. Louis. Prior to joining Washington University, he was on the faculty at Virginia Tech and at Rensselaer Polytechnic Institute. Das received Ph.D. and S.M. degrees from the Massachusetts Institute of Technology, and an A.B. from Harvard University, all in Computer Science. Das' research is in artificial intelligence and machine learning, and especially their intersection with finance, economics, and the social sciences. He has received an NSF CAREER Award and the Department Chair Award for Outstanding Teaching at Washington University. Das has served as program co-chair of AAMAS and AMMA, and regularly serves on the senior program committees of major AI conferences like AAAI and IJCAI. 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.


  • February 2018: I will be at AAAI. Among other things I am giving a What's Hot Talk 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. In 2018, I am an Area Chair for AAAI, and am on the SPC for AAMAS, IJCAI, and EC.

  • April 2017: Mithun just defended his terrific dissertation!

  • Assorted Older Links

  • 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

  • Zhuoshu Li
  • Hao Yan

    Former Ph.D. Students

  • 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


    ACM SIGAI Vice-Chair (2013--)
    IJCAI Sister Track Co-Chair 2015; Senior PC 2016, 2013, 2011.
    AAMAS Program Co-Chair 2017; Sponsorships Co-Chair 2013; Senior PC 2012; PC 2013-2015.
    AMMA PC Co-Chair 2009; General Co-Chair 2011; PC 2015.
    ACM EC Workshops Chair 2011; PC 2012-2014, 2016.
    AAAI Senior PC 2012-2014, 2016, 2018; PC 2015
    NetEcon PC 2017.
    ICML PC 2012, 2016.
    NIPS Reviewer 2012.
    ICDM PC 2008-10, 2012.
    KDD PC 2009.
    SDM PC 2008


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