I have been researching away at Microsoft Research New England (Cambridge, MA) since it’s founding in 2008. Previously, I served as an Assistant Professor in Computer Science at Georgia Tech and TTI-Chicago. I was extremely fortunate to receive a PhD at CMU from the ingenious Avrim Blum (where I was also supervised by Randy Pausch for a time), followed by an NSF postdoc at MIT under the wise guidance of Santosh Vempala.
I co-chaired COLT 2010, a conference on machine learning theory, with Mehryar Mohri. Together with Josh Tenenbaum and Sham Kakade, we started the New England Machine Learning Day in 2012. I’ll be chairing NEML 2017 once again. Lastly, I’ll be co-chairing HCOMP 2017, a conference on crowdsourcing and human computation, October 24-26 in Québec City. Hope to see you there!
Adam Tauman Kalai received his BA (1996) from Harvard, and MA (1998) and PhD (2001) under the supervision of Avrim Blum from CMU. After an NSF postdoctoral fellowship at M.I.T. with Santosh Vempala, he served as an Assistant Professor at the Toyota Technological Institute at Chicago and then at Georgia Tech. He is now a Principal Researcher at Microsoft Research New England. His honors include an NSF CAREER award and an Alfred P. Sloan fellowship. His research focuses on machine learning, human computation, and algorithms.
Students & Teaching
Previous students/interns: Jake Abernethy (Berkeley→Georgia Tech), Ken Arnold (Harvard), Pranjal Awasthi (CMU→Rutgers), Nina Balcan (CMU→CMU), Harry Bovik (CMU→CMU), Danielle Bragg (UW), Konstantina Christakopoulou (UMN), Abie Flaxman (CMU→UW), Varun Kanade (Georgia Tech→Oxford), Katrina Ligett (CMU→Caltech), Azarakhsh Malekian (UMD→Toronto), Brendan McMahan (CMU→Google), Aditya Menon (UCSD→NICTA), Ankur Moitra (MIT→MIT), Claire Monteleoni (MIT→GWU), Peter Organisciak (UIUC→HathiTrust), Aaron Roth (CMU→UPenn), Omer Tamuz (Weizmann→Caltech), Jason Tsay (CMU), Shubham Tulsiani (IIT→Berkeley), Duru Turkoglu (U. Chicago→DePaul), Greg Valiant (Berkeley→Stanford), Elad Verbin (Tel Aviv University→Aarhus), Miaomiao Wen (CMU→Coursera), and Kuat Yessenov (MIT→Google).
Spring 2008: Game Theory and Computer Science, (Georgia Tech)
Fall 2006: Machine Learning Theory (Weizmann Institute)
Autumn 2004: Online Algorithms (University of Chicago)
Compression without a Common Prior: an Information-Theoretic Justification for Ambiguity in Natural LanguageBrendan Juba, Adam Kalai, Sanjeev Khanna, Madhu Sudan, in Innovations in Computer Science (ICS), January 1, 2011,
Improved Sentence Completions in Email Using Context
Many people find text autocompletion helpful, but current systems are limited to predicting only one or two words at a time. We consider the “Megacomplete” text prediction problem of suggesting substantial chunks of text (e.g., sentences or longer), in situations where the context makes a certain completion highly likely. We design a system that learns to identify good completions based on an individual’s corpus of thousands of sent email messages, based on features of the context (e.g., subject) and the text typed so far. We evaluate the system based both on exact text matches and on human evaluation of completion acceptability. A companion paper studies usability factors of the system.