Two IDSS Faculty Appointed as New Chairs
IDSS is pleased to announce two new chair appointments for 2021.
Fotini Christia has been selected as the holder of the Ford International Professor in recognition of her distinguished record of scholarly, professional, and pedagogical contributions to the study of ethnic conflict, political and economic development.
Fotini is a Professor of Political Science and Director of the MIT Sociotechnical Systems Research Center (SSRC). She received her PhD in Public Policy from Harvard University in 2008 and has been awarded an inaugural Andrew Carnegie fellowship and a Harvard Academy fellowship among others. Her research interests deal with issues of conflict and cooperation in the Muslim world, and she has worked out of Afghanistan, Bosnia-Herzegovina, Iraq, Yemen and the Palestinian Territories.
Her book, Alliance Formation in Civil Wars, published by Cambridge University Press in 2012, was awarded the Luebbert Award for Best Book in Comparative Politics, the Lepgold Prize for Best Book in International Relations and the Distinguished Book Award of the Ethnicity, Nationalism, and Migration Section of the International Studies Association. Her research has also appeared in Science, Review of Economic Studies, Journal of Development Economics, American Political Science Review, and Journal of Comparative Politics, among other journals.
She has written opinion pieces for Foreign Affairs, The New York Times, The Washington Post and The Boston Globe. She graduated magna cum laude with a joint BA in Economics-Operations Research and a Masters in International Affairs from Columbia University in 2001.
–
Devavrat Shah has been selected as the holder of the Andrew (1956) and Erna Viterbi Professor of Electrical Engineering and Computer Science
Devavrat Shah, a faculty member of Electrical Engineering and Computer Science at MIT, is a member of the Laboratory for Information and Decision Sciences (LIDS) and the Institute for Data, Systems and Society (IDSS). He was the founding director of the Statistics and Data Science Center (SDSC) at MIT from 2016 to 2021.
Devavrat’s research focuses on statistical inference and stochastic networks. His contributions span a variety of areas, including resource allocation in communications networks; inference and learning on graphical models; algorithms for social data processing including ranking, recommendations, and crowdsourcing; and more recently causal inference. His work spans a range of areas across electrical engineering, computer science, and operations research.
Devavrat received a bachelor’s degree in computer science and engineering from the Indian Institute of Technology in Bombay, where he received the Presidents of India Gold Medal, which is awarded to the best graduating student across all engineering disciplines. He received a PhD in computer science from Stanford University with the George B. Dantzig Dissertation Award from Institute for Operations Research and the Management Sciences (INFORMS).
His work has received broad recognition including the Rising Star Award from the Association for Computing Machinery (ACM) Special Interest Group for the computer systems performance evaluation community (SIGMETRICS) and the Erlang Prize from the Applied Probability Society of INFORMS, in addition to paper prize awards, including the Best Publication Award from the Applied Probability Society of INFORMS, Best Paper Award from Manufacturing and Service Operations Management Society of INFORMS, INFOCOM Best Paper Award, N(eur)IPS Best Paper Award, and the ACM SIGMETRICS Best Paper Award. He has received the 2019 and 2020 ACM SIGMETRICS Test-of-Time Paper award. He is a distinguished alumni of his alma mater IIT Bombay.
In 2013, he founded the machine learning start-up Celect, Inc., which helps retailers with optimizing inventory by accurate demand forecasting. In August 2019, Celect, Inc. was acquired by Nike, Inc. In 2019, Shah founded Ikigai Labs, with the mission of building self-driving organizations by empowering data business operators to make data-driven decisions with ease of spreadsheets.
