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 Data Valorization.

Research topics

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

Selected publications

• Hardness of some optimization problems over correlation polyhedra

  HAL (Le Centre pour la Communication Scientifique Directe) · 2026-03-20

  preprintOpen accessSenior author

  <div> We prove the NP-hardness, using Karp reductions, of some problems related to the correlation polytope and its corresponding cone, spanned by all of the n × n rank-one matrices over {0, 1}. The problems are: membership, rank of the decomposition, and a "relaxed rank" obtained from relaxing the zero-norm expression for the rank to an ℓ1 norm. While membership and rank are natural problems for any matrix cone, the relaxed rank problem occurs in some signal processing and statistical applications. </div>

  Publisher

• Note from the Editor

  INFORMS journal on computing · 2026-03-01

  article1st authorCorresponding
  PublisherDOI

• Adaptive First- and Second-Order Algorithms for Large-Scale Machine Learning

  Fields Institute communications · 2026-01-01

  preprintOpen accessSenior authorCorresponding
  PublisherOA PDFDOI

• Accelerated windowing for the crew rostering problem with machine learning

  Computational Optimization and Applications · 2026-05-06

  articleOpen access
  PublisherOA PDFDOI

• E+A (Complex and Rare Epilepsies Alliance): Advancing Collaboration Across European and International Patient Communities and Generating Patient Evidence to Inform Research and Drug Repurposing

  2026-04-17

  articleOpen access

  <p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" class="first" dir="auto" id="d139889e161">Purpose: Rare and complex epilepsies, including developmental and epileptic encephalopathies (DEEs), are associated with major challenges in diagnosis, treatment, care coordination, and daily life. Although advances in genetic testing have improved etiologic identification, patient and caregiver perspectives remain underrepresented in structured cross-country real-world evidence. To address this gap, E+A (Complex and Rare Epilepsies Alliance), in collaboration with EpiCARE, launched a multilingual survey to systematically capture lived experiences, care pathways, and community priorities. <p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" dir="auto" id="d139889e163">Methods: We developed an anonymous, online, patient- and caregiver-centred survey hosted in REDCap and available in 15 languages. The survey collects real-world data on demographics, epilepsy history, diagnosis (including genetic findings), treatments (including anti-seizure medications and other therapies), healthcare access, comorbidities, daily life impact, psychosocial burden, support needs, and unmet needs. Open-text questions allow participants to identify research and policy priorities directly. Development included iterative review by patient representatives and multilingual validation. <p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" dir="auto" id="d139889e165">Results: The survey establishes a harmonised framework for collecting cross-country patient-reported evidence in rare and complex epilepsies, providing structured insight into patient-relevant outcomes beyond seizure control. Expected outputs include mapping diagnostic and treatment journeys, identifying barriers to care and regional inequities, documenting quality-of-life impact and family burden, and defining community-informed priorities. <p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" dir="auto" id="d139889e167">Conclusions: The E+A survey provides a scalable, community-driven model to transform lived experience into actionable evidence, supporting patient-centred research prioritisation and therapeutic development, including drug repurposing, in rare and complex epilepsies.

  PublisherOA PDFDOI

• SMiLE: Provably Enforcing Global Relational Properties in Neural Networks

  Proceedings of the AAAI Conference on Artificial Intelligence · 2026-03-14

  articleOpen accessSenior author

  Artificial Intelligence systems are increasingly deployed in settings where ensuring robustness, fairness, or domain-specific properties is essential for regulation compliance and alignment with human values. However, especially on Neural Networks, property enforcement is very challenging, and existing methods are limited to specific constraints or local properties (defined around datapoints), or fail to provide full guarantees. We tackle these limitations by extending SMiLE, a recently proposed enforcement framework for NNs, to support global relational properties (defined over the entire input space). The proposed approach scales well with model complexity, accommodates general properties and backbones, and provides full satisfaction guarantees. We evaluate SMiLE on monotonicity, global robustness, and individual fairness, on synthetic and real data, for regression and classification tasks. Our approach is competitive with property-specific baselines in terms of accuracy and runtime, and strictly superior in terms of generality and level of guarantees. Overall, our results emphasize the potential of the SMiLE framework as a platform for future research and applications.

  PublisherOA PDFDOI

• Assortment optimization with visibility constraints

  Mathematical Programming · 2025-06-07

  article
  PublisherDOI

• Integer Programming Games

  Foundations and Trends® in Optimization · 2025-02-20 · 1 citations

  article

  We provide a comprehensive survey of Integer Programming Games (IPGs), focusing on both simultaneous games and bilevel programs. These games are characterized by integral constraints within the players’ strategy sets. We start from the fundamental definitions of these games and various solution concepts associated with them, and derive the properties of the games and the solution concepts. For each of the two types of games – simultaneous and bilevel – we have one section dedicated to the analysis of the games and another section dedicated to the development and analyses of algorithms to solve them. The analyses sections present results on the computational complexity of the general game as well as various other restricted versions. These sections also discuss the structural properties of the games and the equilibrium concepts associated with them. The algorithm sections, in contrast, present some of the state-of-the-art algorithms developed to solve these games, either exactly, approximately or fast under fixed-parameter assumptions. These sections also contain proofs of the correctness of these algorithms and an assessment of their theoretical run times in the worst-case scenario.

  PublisherDOI

• The Differentiable Feasibility Pump

  Lecture notes in computer science · 2025-01-01

  book-chapterSenior author
  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 solving simpler approximations of a complex game and then refining them with mathematical “cuts” until a solution is reached. Most strikingly, the algorithm finds equilibria up to 10× faster than existing techniques and is the first of its kind to offer a general-purpose solution method for this class of problems. The work is a leap forward for both the theory and application of strategic decision making.

  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