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My main research interests are in machine learning theory, approximation
algorithms, on-line algorithms, algorithmic game theory /
mechanism design, algorithmic fairness, and non-worst-case
analysis of algorithms. Many years ago I also did work in AI Planning. I am a member of the Simons Collaboration on the Theory of Algorithmic Fairness.
Before joining TTIC, I spent 25 wonderful years on the CS faculty at Carnegie Mellon University.
I am on the Steering Committees for FOCS, ITCS, and FORC, and on the editorial board for JACM. I am also on the Advisory Board of a new open access journal TheoretiCS. I was recently Program Chair for the 2019 Innovations in Theoretical Computer Science (ITCS) Conference, on the Organizing Committee for the STOC 2018 and STOC 2017 Theory Fest, and a member of the SafeToC committee. For more information on my research, see the publications and research interests links below.
Publications | Research Interests |
Survey Talks | Courses Taught |
Blum, Hopcroft, & Kannan, Foundations of Data Science. (This pre-publication version is free to view and download for personal use only. Not for re-distribution, re-sale or use in derivative works. Please do not re-post or mirror.) |
Current PhD advisees: Kevin Stangl, Han Shao, Naren Manoj (jointly advised with Yury Makarychev), Keziah Naggita (jointly advised with Matt Walter), Kavya Ravichandran (jointly advised with Nati Srebro), Melissa Dutz.
Former PhD advisees: Prasad Chalasani, Santosh Vempala, Carl Burch, Adam Kalai, John Langford, Nikhil Bansal, Martin Zinkevich, Shuchi Chawla, Brendan McMahan, Maria-Florina (Nina) Balcan, Shobha Venkataraman, Mugizi Robert Rwebangira, Katrina Ligett, Aaron Roth, Or Sheffet, Pranjal Awasthi, Liu Yang, Ankit Sharma, Jamie Morgenstern, Nika Haghtalab.
Though I am no longer at CMU, I endorse the CMU SCS Reasonable Person Principle which basically asks to be reasonable and assume everyone else is doing likewise.