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| [ AI Week NYC ] |
| Ml-NYC Meetup
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| With Andrea Lodi (Prof., Cornell Tech). |
| Flatiron Institute, 162 5th Ave, Ingrid Daubechies Auditorium |
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Oct 06 (Mon) , 2025 @ 04:00 PM
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FREE |
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/-73.99071503,40.74046326,13,0,0/550x200?access_token=pk.eyJ1IjoiZ2FyeXNndWlkZSIsImEiOiJjamtobmRiYmgwb3gwM3F1a2o2Mmd0YWw3In0.DinA62CR3-OKtf3axa5O7Q)
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| DETAILS |
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Title: Machine Learning Augmented Combinatorial Optimization
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, & many commercial & academic software packages exist. Nevertheless, the availability of data, both from problem instances & from solvers, & the desire to solve new problems & larger (real-life) instances, trigger the need for continuing algorithmic development. MILP solvers use branch & 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 & solver configuration decisions. This talk presents a survey of such approaches, addressing the vision of integration of machine learning & mathematical optimization as complementary technologies, & 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 & software.
Bio: Andrea Lodi is the Andrew H. & Ann R. Tisch Professor of operations research & information engineering at Cornell Tech, the Jacobs Technion-Cornell Institute, & Cornell Engineering. Before joining Cornell, Lodi was a Herman Goldstine Fellow at the IBM Mathematical Sciences Department of New York & a full professor of operations research at the Department of Electrical, Electronic, & 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 Montral. His main research interests are in mixed-integer linear & nonlinear programming & data science. His work has received several recognitions, including the IBM & Google faculty awards. Andrea is the recipient of the INFORMS Optimization Society 2021 Farkas Prize & 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, & the United States. In the period from 2006 to 2021, he was a consultant for the IBM CPLEX research & 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.
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