Bart Van Parys

Email : bart@vanparys.xyz
Email : bart.van.parys@cwi.nl

I am an associate professor in the stochastics group at the national research institute for mathematics and computer science (CWI) in Amsterdam, the Netherlands. 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.

Spotlight Publications

• Van Parys, B.P.G. (2024). “Efficient data-driven optimization with noisy data “. In: Operations Research Letters. Link

• 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

• 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

Interests

I am always interested in students or collaborators interested in working on

• Optimization Algorithms
• Robust Analytics
• Statistical Efficiency