Emma Frejinger on Machine Learning and Optimization for Decision-Making Under Uncertainty

About the talk:
Decision makers across various domains often face problems that are subject to uncertainty. Consider planning transport services, operating power systems, determining infrastructure locations, and setting pricing strategies. The integration of machine learning and optimization methods has gained significant attention, both for accelerating solution methods and for enhancing models by training machine learning algorithms on task-specific losses rather than conventional prediction losses. Based on a recent survey on contextual stochastic optimization, we provide an overview of this literature and highlight key challenges, notably decision-dependent uncertainty which remains particularly difficult to address.

About the speaker
Emma Frejinger xxxxx. is a professor in the Department of Computer Science and Operations Research at Université de Montréal where she holds a Canada Research Chair and an industrial chair funded by the Canadian National Railway Company. Her research is application-driven and focuses on innovative combinations of methodologies from machine learning and operations research to solve large-scale decision-making problems. Emma has extensive experience leading collaborative research projects and working with industry, predominantly within the transportation sector. She serves as a scientific advisor for IVADO Labs, an AI solution provider; as an academic affiliate with Analysis Group; and as an associate member of the machine learning institute Mila. Before joining Université de Montréal in 2013, Emma was a faculty member at KTH Royal Institute of Technology in Sweden. She holds a Ph.D. in mathematics from EPFL.

Contact: Rune M. Jensen, rmj@itu.dk