HIGHLIGHTS
Data, Systems, and Society: Harnessing AI for Societal Good
Harnessing the power of data and AI methods to tackle complex societal challenges requires transdisciplinary collaborations across academia, industry, and government. In this compelling book, Munther A. Dahleh, founder of the MIT Institute for Data, Systems, and Society (IDSS), offers a blueprint for researchers, professionals, and institutions to create approaches to problems of high societal value using innovative, holistic, data-driven methods. More
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The first podcast on control theory.
Episode 27: Munther Dahleh on L1 control, agile robotic maneuvering, abstractions, cascaded failures, markets, data and systems for societal problems
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IDSS Data Nation
We face many overwhelming challenges in America today: systemic racism, data privacy, and political misinformation. Scholars and industry experts often disagree on how to find solutions. So, how can we find the right way to move forward? We let the data speak for itself.
Join hosts Liberty Vittert and Munther Dahleh on Data Nation, a production of MIT’s Institute for Data, Systems, and Society. Available on on Apple, Spotify, or other podcast platforms.
LATEST PUBLICATIONS
Towards data auctions with externalities,
Anish Agarwal, Munther Dahleh, Thibaut Horel, Maryann Rui
November 2024
Contingent linear financial networks
Handbook of Financial Integration, 2024
Excess Fatal Overdoses in the United States During the COVID-19 Pandemic by Geography and Substance Type: March 2020–August 2021
Jay Chandra , Marie-Laure Charpignon , Anushka Bhaskar, Andrew Therriault, Yea-Hung Chen, Alyssa Mooney ,Munther A. Dahleh , Mathew V. Kiang , and Francesca Dominici
American Journal of Public Health, June 2024.
Data-driven control of COVID-19 in buildings: a reinforcement-learning approach
Ashkan Haji Hosseinloo, Saleh Nabi, Anette Hosoi, and Munther A. Dahleh
IEEE Transactions on Automation Science and Engineering, 2023.
Selling Information in Competitive Environments
Alessandro Bonatti, Munther Dahleh, Thibaut Horel, Amir Nouripour
To appear in the Journal of Economic Theory.
Causal Matrix Completion
A. Agarwal, M.A. Dahleh, D. Shah, and D. Shen
COLT 2023
Incentive Compatibility in Two-Stage Repeated Stochastic Games
Bharadwaj Satchidanandan, Munther A. Dahleh
IEEE Transactions on Control of Network Systems, 2023.
Learning Good State and Action Representations for Markov Decision Process via Tensor Decomposition
C. Ni, Y. Duan, M. A. Dahleh, M. Wang, A. R. Zhang
Published in the Journal of Machine Learning Research, February 2023
Coordination via Selling Information
A. Bonatti, M. A. Dahleh, T. Horel, A. Nouripour
February 2023
See All Highlighted Research
ABOUT THE GROUP
The Munther Dahleh Research Group works in the general area of “Decisions Under Uncertainty”.
The group addresses foundational issues in terms of fundamental limits and capabilities of real-time decision-making under uncertainty, societal challenges in specific domains such as finance, transportation, power grid and digital platforms, and translation challenges by working directly with stakeholders.