Email : firstname.lastname@example.org
I am an assistant professor in the operations research and statistics group at the MIT Sloan School of Management. My research is located on the interface between optimization and machine learning. In particular, I develop novel mathematical methodologies and algorithms with which we can turn data into better decisions. Although most of my research is methodological, I do enjoy applications related to problems in renewable energy.
Liu, Z., B.P.G. Van Parys, and H. Lam (2023). “Smoothing $f$-divergence distributionally robust optimization: Rate optimality and complexity independence”. In: SIAM Journal on Optimization. Submitted. Link
Bennouna, M.A. and B.P.G. Van Parys (2022). “Holistic robust data-driven decisions”. In: Mathematical Programming. Submitted. Link
Bennouna, M.A., R. Lucas, and B.P.G. Van Parys (2023). “Certified robust neural networks: Generalization and corruption resistance”. In: International Conference on Machine Learning (ICML). Honolulu, Hawaii, USA, pp. 2092–2112. Won the Best Student Paper Award at 2023 INFORMS Data Science Workshop, Runner up Best Data Science Paper Award at the 2023 INFORMS Annual Meeting. Link
Wang, I., C. Becker, B.P.G. Van Parys, and B. Stellato (2022). “Mean robust Optimization”. In: Mathematical Programming. Major Revision. Won the 2022 INFORMS Computing Society Student Paper Prize. Link
Das Gupta, S., B.P.G. Van Parys, and E.K. Ryu (2023). “Branch-and-bound performance estimation programming: A unified methodology for construction optimal optimization methods”. In: Mathematical Programming. Appeared also in Quanta Magazine. Link
I am always interested in students or collaborators interested in working on