5/9/24 | 4:15pm | E51-376

Bartolomeo Stellato

Assistant Professor
Princeton

Abstract: In this fast-paced world, intelligent systems need to make reliable, real-time decisions in response to changing conditions and unexpected disruptions. Mathematical optimization is instrumental in this process, but it must offer reliable computations and robust solutions, especially in the presence of uncertainty. In this talk, we will explore how we can harness data to develop optimization tools that can handle uncertainty and operate in real-time. We will investigate learning techniques such as clustering and differentiable optimization to design tractable, robust problem formulations. We will also discuss how to use machine learning to design efficient optimization algorithms for continuous and mixed-integer optimization, with a focus on guaranteeing convergence in real-time operations. By customizing problem formulations and solution algorithms to specific tasks, we will show how we can significantly enhance the computational performance and reliability of decision-making pipelines.

Bio: Bartolomeo Stellato is an Assistant Professor in the Department of Operations Research and Financial Engineering at Princeton University. Previously, he was a Postdoctoral Associate at the MIT Sloan School of Management and Operations Research Center. He received a DPhil (PhD) in Engineering Science from the University of Oxford, a MSc in Robotics, Systems and Control from ETH Zürich, and a BSc in Automation Engineering from Politecnico di Milano. He is the developer of OSQP, a widely used solver in mathematical optimization. Bartolomeo Stellato's awards include the NSF CAREER Award, the Franco Strazzabosco Young Investigator Award from ISSNAF, the Princeton SEAS Innovation Award in Data Science, the Best Paper Award in Mathematical Programming Computation, and the First Place Prize Paper Award in IEEE Transactions on Power Electronics. His research focuses on data-driven computational tools for mathematical optimization, machine learning, and optimal control.