Seminar Details
2026-09-29 (14:00) : Hybrid artificial intelligence for solving computationally hard problems
At Euler building (room A.002)
Duration: 60 minutes
Organized by Mathematical Engineering
Speaker :
Quentin Cappart (UCLouvain)
Quentin Cappart
Quentin Cappart is professor at UCLouvain (Belgium) and Polytechnique Montréal (Canada), both in the department of computer engineering and software engineering. He is the recipient of 2024 ACP Early Career Researcher Award, a regular member of CIRRELT, and affiliate member of MILA. His research interests lie in artificial intelligence, particularly in the areas of constraint programming, machine learning, and combinatorial optimization. His goal is to develop decision-making tools that can leverage different aspects of artificial intelligence to enable better decisions. He applies these methods to real-world problems arising in various industrial fields, including transportation, logistics, and healthcare.
Abstract :
Combinatorial optimization provides methods to make the
best possible decisions in complex scenarios, with practical
applications in areas such as transportation, logistics, and healthcare.
Traditionally, solving methods for combinatorial problems (such as
integer programming, constraint programming, or local search) have
focused on solving isolated problem instances, often overlooking the
fact that these instances frequently originate from related data
distributions. In recent years, there has been a growing interest in
leveraging machine learning, particularly neural networks, to enhance
combinatorial solvers by utilizing historical data. Despite this
interest, it remains unclear how to effectively integrate learning into
such engines to boost overall performance. In this presentation, I will
share my journey in tackling this challenge.