ML-NYC Speaker Series and Happy Hour: Andrea Lodi

The ML in NYC Speaker Series + Happy Hour is excited to host Professor Andrea Lodi as our October speaker! His talk will take place on Monday, October 6th, at 4pm at the Flatiron Institute. As always, there will be a reception afterward for all attendees.

Title: Machine Learning Augmented Combinatorial Optimization

Abstract: Mixed Integer Linear Programming (MILP) is a pillar of mathematical optimization that offers a powerful modeling language for a wide range of applications. During the past decades, enormous algorithmic progress has been made in solving MILPs, and many commercial and academic software packages exist. Nevertheless, the availability of data, both from problem instances and from solvers, and the desire to solve new problems and larger (real-life) instances, trigger the need for continuing algorithmic development. MILP solvers use branch and bound as their main component. In recent years, there has been an explosive development in the use of machine learning algorithms for enhancing all main tasks involved in the branch-and-bound algorithm, such as primal heuristics, branching, cutting planes, node selection and solver configuration decisions. This talk presents a survey of such approaches, addressing the vision of integration of machine learning and mathematical optimization as complementary technologies, and how this integration can benefit MILP solving. In particular, we give detailed attention to machine learning algorithms that automatically optimize some metric of branch-and-bound efficiency. We also address how to represent MILPs in the context of applying learning algorithms, MILP benchmarks and software.

Bio: Andrea Lodi is the Andrew H. and Ann R. Tisch Professor of operations research and information engineering at Cornell Tech, the Jacobs Technion-Cornell Institute, and Cornell Engineering. Before joining Cornell, Lodi was a Herman Goldstine Fellow at the IBM Mathematical Sciences Department of New York and a full professor of operations research at the Department of Electrical, Electronic, and Information Engineering at the University of Bologna. He was also the Canada Excellence Research Chair in “Data Science for Real-time Decision Making” at Polytechnique Montréal. His main research interests are in mixed-integer linear and nonlinear programming and data science. His work has received several recognitions, including the IBM and Google faculty awards. Andrea is the recipient of the INFORMS Optimization Society 2021 Farkas Prize and was elected an INFORMS Fellow in 2023. Andrea has been the principal investigator of scientific projects, often involving industrial partners, for Italy, the European Union, Canada, and the United States. In the period from 2006 to 2021, he was a consultant for the IBM CPLEX research and development team, developing CPLEX, one of the leading software programs for mixed-integer optimization. Andrea Lodi has co-founded IVADO Labs, a Montreal-based company that focuses on AI projects mainly in the supply chain domain.