Munther A. Dahleh received his Ph.D. degree from Rice University, Houston, TX, in 1987 in Electrical and Computer Engineering. Since then, he has been with the Department of Electrical Engineering and Computer Science (EECS), MIT, Cambridge, MA, where he is now the William A. Coolidge Professor of EECS. He is also a faculty affiliate of the Sloan School of Management. He is currently the Director of the newly formed MIT Institute for Data, Systems, and Society (IDSS). Previously, he held the positions of Associate Department Head of EECS, Acting Director of the Engineering Systems Division, and Acting Director of the Laboratory for Information and Decision Systems.
Dahleh is well-known for his seminal contributions to the field of networked systems and robust control. His research has impacted several application domains including transportation and autonomous systems, power grid, financial systems, and social networks. Dahleh’s work has appeared in economics and operations research venues, as well as 8 different IEEE Transactions, from Automatic Control to Biomedical Engineering to Network Science.
He is four-time recipient of the George Axelby outstanding paper award for best paper in IEEE Transactions on Automatic Control. He is also the recipient of the Donald P. Eckman award from the American Control Council in 1993 for the best control engineer under 35. He has given many keynote lectures at major conferences.
In particular, his research interests include:
- Networked Systems: Foundational theory for the interaction between physical and information networks, Information propagation, distributed decisions, learning network structure from data.
- Social Networks: Information cascades in stochastic networks, opinion dynamics, global games in modeling outcomes of crises.
- Systemic Risk: The development of a foundational theory for the early detection and control of systemic risk resulting from idiosyncratic disturbance affecting components of a networked system.
- Transportation Systems: Dynamic models of congestion under disruptions, dependence of fragility on network topology, cascaded failures, value of side information on performance.
