Data Science in Domains: Interaction of Physical, Social, and Institutional Systems
As we contemplate the preparation of the future data science learner, I want to highlight a core principle of IDSS and how it affected our decision to set up two of our flagship graduate education programs.

Building a resilient, carbon-neutral electric grid requires energy 'superhighways'
Today’s electric grid infrastructure is just not ready for the scale and speed of energy portfolio transformation being envisioned.

Making Data- Informed Covid-19 Testing Plans
Implementing testing within an organization raises a number of questions. Who should be tested? How often? How do other mitigation efforts impact testing need? How much will it all cost?

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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

An Efficient and Incentive-Compatible Mechanism for Energy Storage Markets
Satchidanandan B., Dahleh M.A. Published in IEEE Transactions on Smart Grid, Vol 13, pp 2245-2258, 2022.

An Online Learning Framework for Targeting Demand Response Customers
Schneider I. Roozbehani M., Dahleh M.A. Published in IEEE Transactions on Smart Grid, 2022.

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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.

Meet the GroupSee Our Research Interests