Academic Webinar
Reliable AI for Optimization
This talk explores how machine learning can accelerate the repeated solving of large-scale optimization problems in industries such as power systems, supply chains, manufacturing, and transportation.
May 28, 2026
10:00 AM ET | 4PM CET
Academic Webinar
Reliable AI for Optimization
This talk explores how machine learning can accelerate the repeated solving of large-scale optimization problems in industries such as power systems, supply chains, manufacturing, and transportation.
May 28, 2026
10:00 AM ET | 4PM CET
Academic Webinar
Reliable AI for Optimization
This talk explores how machine learning can accelerate the repeated solving of large-scale optimization problems in industries such as power systems, supply chains, manufacturing, and transportation.
May 28, 2026
10:00 AM ET | 4PM CET
Webinar topic
In many industry settings, including the electrical power grid, supply chains, manufacturing, and transportation networks, the same optimization problem is solved repeatedly for instances taken from a distribution that can be learned or forecasted. The scale and complexity of these applications have grown significantly in recent years, challenging traditional optimization approaches.
This talk will showcase:
How to accelerate the solving of these parametric optimization problems to meet real-time constraints present in many applications
The concept of optimization proxies that learn the input/output mappings of parametric optimization problems, computing near-optimal feasible solutions and providing quality guarantees
How to "learn to optimize" highly complex optimization problems, fusing optimization methodologies with supervised learning and reinforcement learning
These methodologies are highlighted on industrial problems in grid optimization, end-to-end supply chains, logistics, and transportation systems. They reveal beautiful connections between machine learning and optimization, leveraging fundamental theoretical results to push the practice of optimization.
Webinar topic
In many industry settings, including the electrical power grid, supply chains, manufacturing, and transportation networks, the same optimization problem is solved repeatedly for instances taken from a distribution that can be learned or forecasted. The scale and complexity of these applications have grown significantly in recent years, challenging traditional optimization approaches.
This talk will showcase:
How to accelerate the solving of these parametric optimization problems to meet real-time constraints present in many applications
The concept of optimization proxies that learn the input/output mappings of parametric optimization problems, computing near-optimal feasible solutions and providing quality guarantees
How to "learn to optimize" highly complex optimization problems, fusing optimization methodologies with supervised learning and reinforcement learning
These methodologies are highlighted on industrial problems in grid optimization, end-to-end supply chains, logistics, and transportation systems. They reveal beautiful connections between machine learning and optimization, leveraging fundamental theoretical results to push the practice of optimization.
Webinar topic
In many industry settings, including the electrical power grid, supply chains, manufacturing, and transportation networks, the same optimization problem is solved repeatedly for instances taken from a distribution that can be learned or forecasted. The scale and complexity of these applications have grown significantly in recent years, challenging traditional optimization approaches.
This talk will showcase:
How to accelerate the solving of these parametric optimization problems to meet real-time constraints present in many applications
The concept of optimization proxies that learn the input/output mappings of parametric optimization problems, computing near-optimal feasible solutions and providing quality guarantees
How to "learn to optimize" highly complex optimization problems, fusing optimization methodologies with supervised learning and reinforcement learning
These methodologies are highlighted on industrial problems in grid optimization, end-to-end supply chains, logistics, and transportation systems. They reveal beautiful connections between machine learning and optimization, leveraging fundamental theoretical results to push the practice of optimization.
Speaker
Meet Your Expert Speaker
Learn from the best in the industry.
Pascal Van Hentenryck
Head of AI Innovation
Dr. Pascal Van Hentenryck works at the intersection of AI and Optimization, with applications in energy, healthcare, logistics and supply chains, manufacturing, and transportation. He developed innovative optimization systems, including CHIP and OPL which have been in commercial use for several decades. Pascal pioneered what is now known as constraint programming, and has made seminal contributions to global, stochastic, and combinatorial optimization. His recent work focuses on AI for Optimization, to bring orders of magnitude speed-ups to solving optimization problems. Pascal is a AAAI and INFORMS fellow, and the recipient of two honorary degrees and numerous research and teaching awards. Pascal used to play soccer competitively, is an avid runner, and likes to travel the world with his family to discover new cultures and their history.
Speaker
Meet Your Expert Speaker
Learn from the best in the industry.
Pascal Van Hentenryck
Head of AI Innovation
Dr. Pascal Van Hentenryck works at the intersection of AI and Optimization, with applications in energy, healthcare, logistics and supply chains, manufacturing, and transportation. He developed innovative optimization systems, including CHIP and OPL which have been in commercial use for several decades. Pascal pioneered what is now known as constraint programming, and has made seminal contributions to global, stochastic, and combinatorial optimization. His recent work focuses on AI for Optimization, to bring orders of magnitude speed-ups to solving optimization problems. Pascal is a AAAI and INFORMS fellow, and the recipient of two honorary degrees and numerous research and teaching awards. Pascal used to play soccer competitively, is an avid runner, and likes to travel the world with his family to discover new cultures and their history.
Speaker
Meet Your Expert Speaker
Learn from the best in the industry.
Head of AI Innovation
Pascal Van Hentenryck
Dr. Pascal Van Hentenryck works at the intersection of AI and Optimization, with applications in energy, healthcare, logistics and supply chains, manufacturing, and transportation. He developed innovative optimization systems, including CHIP and OPL which have been in commercial use for several decades. Pascal pioneered what is now known as constraint programming, and has made seminal contributions to global, stochastic, and combinatorial optimization. His recent work focuses on AI for Optimization, to bring orders of magnitude speed-ups to solving optimization problems. Pascal is a AAAI and INFORMS fellow, and the recipient of two honorary degrees and numerous research and teaching awards. Pascal used to play soccer competitively, is an avid runner, and likes to travel the world with his family to discover new cultures and their history.
