​Please join RBC Borealis for a Research Talk hosted in-person at MIT CSAIL.

All MIT PhDs and postdocs are invited to attend.

​We will also share more information and answer questions on our current research, career opportunities, early talent programs and more.

​Machine Learning for Combinatorial Optimization

​Many important problems—like planning routes, assigning resources, or selecting the most useful subset of items—require making discrete choices under constraints.

Although modern machine learning has achieved remarkable success, it still struggles with this type of decision-making problems. This limitation is not accidental: most ML models rely on smooth, differentiable functions, while combinatorial problems live in inherently discrete, constrained spaces.

In this talk, Akbar Rafiey will present a framework for bridging this gap by transforming hard combinatorial problems into continuous formulations that are compatible with gradient-based optimization, while still guaranteeing high-quality discrete solutions. I will describe two recent results: one for problems involving permutations, such as routing and matching, and another for problems involving constrained set selection.

​About the Speaker

​Akbar Rafiey is a Visiting Assistant Professor of CSE at NYU and an ML Researcher at RBC Borealis. Previously, he was a Postdoctoral Researcher at the Halıcıoğlu Data Science Institute at the University of California San Diego. He holds a PhD in Computer Science and an MSc in Mathematics from Simon Fraser University.

His research lies at the intersection of mathematics, theoretical computer science, and machine learning, with a focus on combinatorial optimization, responsible optimization, and the use of machine learning for scalable combinatorial optimization.