Professor, Department of Computer Science
Contact Information
Building: Nguyen Engineering Building
Room 3619
In the News
Biography
Sanmay Das has broad interests in artificial intelligence, machine learning, and computational social science. Das works on designing effective algorithms for agents in complex, uncertain environments and understanding the social or collective outcomes of individual behavior. His recent work focuses on the algorithmic allocation of scarce societal resources, with an eye toward the distributive justice implications of different policies and mechanisms.
He 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. He has served as the program co-chair of the AAMAS and AMMA conferences and area chair for AAAI and IJCAI, 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 a National Science Foundation 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.
Das’ favorite aspects of academic life are the opportunities to collaborate with and learn from colleagues and students with incredibly diverse interests and experiences, and the joys of mentoring students at all levels, from undergraduate to postdoctoral.
Degrees
- PhD, Computer Science, Massachusetts Institute of Technology
- Master of Science, Electrical engineering and computer science, Massachusetts Institute of Technology
- Bachelor of Arts, Computer Science, Harvard
Research Interests
- Artificial intelligence
- Machine learning
- Computational social science