Andrea Lodi

About

Andrea Lodi is an Andrew H. and Ann R. Tisch Professor at the Jacobs Technion-Cornell Institute at Cornell Tech and the Technion. He is a member of the Operations Research and Information Engineering field at Cornell University. He received his Ph.D. in system engineering from the University of Bologna in 2000 and was a Herman Goldstine Fellow at the IBM Mathematical Sciences Department in New York during 2005–2006. He served as a full professor of operations research at DEI, the University of Bologna, between 2007 and 2015. Since 2015, he has held the position of Canada Excellence Research Chair in Data Science for Real-time Decision Making at Polytechnique Montréal. His main research interests include mixed-integer linear and nonlinear programming, as well as data science. His work has received several recognitions, including IBM and Google faculty awards. He has authored more than 100 publications in top journals of mathematical optimization and data science and serves as an editor for several prestigious journals in the area. Additionally, he has been the network coordinator and principal investigator of two large EU projects/networks and has been a consultant for the IBM CPLEX research and development team since 2006. He is also the co-principal investigator of the project 'Data Serving Canadians: Deep Learning and Optimization for the Knowledge Revolution,' funded by the Canadian Federal Government, and the scientific co-director of IVADO, the Montréal Institute for…

Research topics

• Computer Science
• Computer Security
• Management science
• Operations research
• Data science
• Operations management
• Engineering

Selected publications

• Machine learning for combinatorial optimization: A methodological tour d'horizon

  Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) · 2021-01-01 · 1330 citations

  articleOpen accessCorresponding

  This paper surveys the recent attempts, both from the machine learning and operations research communities, at leveraging machine learning to solve combinatorial optimization problems. Given the hard nature of these problems, state-of-the-art algorithms rely on handcrafted heuristics for making decisions that are otherwise too expensive to compute or mathematically not well defined. Thus, machine learning looks like a natural candidate to make such decisions in a more principled and optimized wa…

  PublisherDOI

• Combinatorial Optimization and Reasoning with Graph Neural Networks

  Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) · 2021-01-01 · 172 citations

  article

  Combinatorial optimization is a well-established area in operations research and computer science. Until recently, its methods have mostly focused on solving problem instances in isolation, ignoring the fact that they often stem from related data distributions in practice. However, recent years have seen a surge of interest in using machine learning, especially graph neural networks, as a key building block for combinatorial tasks, either directly as solvers or by enhancing the former. This pape…

  PublisherDOI

• Operational Research: methods and applications

  Journal of the Operational Research Society · 2023 · 92 citations

  Throughout its history, Operational Research has evolved to include methods, models and algorithms that have been applied to a wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first summarises the up-to-date knowledge and provides an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The articl…

  PublisherDOI

• Structured pruning of neural networks for constraints learning

  Operations Research Letters · 2024-10-16 · 6 citations

  articleOpen accessSenior author

  In recent years, the integration of Machine Learning (ML) models with Operation Research (OR) tools has gained popularity in applications such as cancer treatment, algorithmic configuration, and chemical process optimization. This integration often uses Mixed Integer Programming (MIP) formulations to represent the chosen ML model, that is often an Artificial Neural Networks (ANNs) due to their widespread use. However, ANNs frequently contain a large number of parameters, resulting in MIP formula…

  PublisherDOI

• The Cut-and-Play Algorithm: Computing Nash Equilibria via Outer Approximations

  Operations Research · 2025-07-08 · 3 citations

  preprintOpen access

  When players in a game face messy decisions—like yes/no choices, rules layered within rules, or conflicting objectives—traditional algorithms often fail to find stable outcomes. Carvalho, Dragotto, Lodi, and Sankaranarayanan introduce a new algorithm, Cut-and-Play, that breaks this barrier. Unlike previous methods, Cut-and-Play handles nonconvex and unbounded decision spaces—the kind often found in real-world markets, public policy, and artificial intelligence systems. It works by iteratively so…

  PublisherOA PDFDOI

Frequent coauthors

• Andrea Tramontani

  66 shared

• Karen Aardal

  53 shared

• Laurence A. Wolsey

  UCLouvain

  51 shared

• Frederik von Heymann

  Polytechnique Montréal

  49 shared

• Guy Desaulniers

  Group for Research in Decision Analysis

  44 shared

• Margarida Carvalho

  Université de Montréal

  41 shared

• C. Crosti

  39 shared

• Silvano Martello

  32 shared

Education

• PhD, DEI

  Università degli Studi di Bologna

  2000

Awards & honors

• IBM and Google faculty awards

Similar researchers at Cornell University

• David Rupperth-73
• Robert A. Jarrowh-68
• Adrian Lewish-54