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Mardavij Roozbehani

Principal Investigator, Institute for Data, Systems, and Society

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Machine Learning Optimization

Mardavij Roozbehani is a principal investigator at the MIT Institute for Data, Systems, and Society and its Laboratory for Information and Decision Systems. His research interests are in engineering and economic applications of control theory and optimization with an emphasis on understanding robustness, efficiency, and risk in networked systems. Recent application areas include power systems, transportation networks, financial networks, and data-driven risk-sharing. Roozbehani developed methods of generating automated proofs for safety and performance guarantees for software systems, finite-state systems, and embedded systems, and bridged a gap between computer science and control theory. His earlier work also included finite-state approximation of dynamical systems, quantized control, and Delta-Sigma modulation.

Roozbehani has received an American Institute of Aeronautics and Astronautics graduate award for safety verification of real-time software systems, and is a co-recipient of a SIAM/SICON best paper award. He earned a PhD in aeronautic and astronautics from MIT.

 

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