Résumé

Problem uncertainty typically limits how well decisions can be tailored to the problem at hand but often can not be avoided. The availability of large quantities of data in modern applications however presents an exciting opportunity to nevertheless make better informed decisions. Capitalizing on this opportunity requires developing novel tools on the intersection between operations research, stochastics as well as data science. In a modern setting the primitive describing uncertainty is often messy data rather than classical distributions. Simply quantifying the probability of an undesirable outcome becomes a challenging uncertainty quantification problem which I approach with a distributional optimization lens. Distributional robust optimization (DRO) has recently gained prominence as a paradigm for making data-driven decisions which are protected against adverse overfitting effects. We justify the effectiveness of this paradigm by pointing out that certain DRO formulations indeed enjoy optimal statistical properties. Furthermore, DRO formulations can also be tailored to efficiently protect decisions against overfitting even when working with messy corrupted data. Finally, as such formulations are often computationally tractable they provide a practical road to the development of tomorrow’s trustworthy decision systems.

Biographie

Dr. Bart Van Parys is an associate professor in the stochastics group at the National research institute for mathematics and computer science (CWI) in Amsterdam, the Netherlands. Before joining CWI, he was an Assistant Professor in Operations Research and Statistics at the MIT Sloan School of Management. His research is located on the interface between optimization and machine learning, focusing on the development of novel mathematical methodologies and algorithms for better decision making based on data. Bart received a PhD degree with Manfred Morari from ETHZ in 2015.