Optimization × AI Forum
Progress in AI for mathematics and science is now so rapid that workshops, conferences, and arXiv postings can no longer keep pace. The Forum offers a timely, interactive venue for researchers to share recent progress, practical experience, limitations, and open questions. Talks will center on the following main themes: AI for optimization modeling, AI for algorithm design, and AI for optimization theory and research. Beyond presenting new and exciting results, we hope to capture how the community is thinking about the questions this progress raises, such as what AI is actually contributing, what human guidance is still needed, how results are validated, and where current approaches fall short.
Praktische info:
Leertraject
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- Prijs: Gratis
Every other Friday on Zoom, starting November 6, 2026
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This forum aims to provide a timely, interactive platform for exploring how AI, as a tool, can advance optimization research. Through our online seminar series, we bring together researchers working on modeling, algorithms, and theory to discuss recent progress, share practical experience, examine limitations, and identify open questions.
The seminar series covers three main themes:
- AI for Optimization Modeling: Using AI to identify problem structure and appropriate assumptions; formulate optimization models; implement, debug, and validate the models; and refine them iteratively.
- AI for Algorithm Design: Using AI to design and improve algorithms; learn, select, and combine algorithmic components; and configure and accelerate solvers. This also includes integrating AI predictions into optimization methods and generating candidate solutions through prompting.
- AI for Optimization Theory: Using AI to develop, simplify, and verify proofs; formulate conjectures and construct counterexamples; examine assumptions and clarify theoretical insights; and propose new research directions. A particular focus is combining AI with formal verification methods and tools for optimization theory.
We emphasize AI as a tool for advancing optimization research. Specifically, we focus on generalizable, principled approaches to using AI, as well as practical experience in applying it to challenging optimization problems. Across all themes, the key research questions are
- How was AI used and what did it contribute?
- What human expertise and guidance were needed?
- How were the results validated?
- Why was AI suited to the task, and what are the limitations of the approach?
Speakers
Here is a list of confirmed speakers, and we are actively inviting more researchers to join the discussion.
- Bin Dong (Peking University)
- Jianhao Ma (Tsinghua University)
- Sebastian Pokutta (TU Berlin / Zuse Institute Berlin)
- Suvrit Sra (TU Munich)
- Weijie Su (University of Pennsylvania)
- Madeleine Udell (Stanford University)
- Zaiwen Wen (Peking University)
- Stephen Wright (University of Wisconsin–Madison)
- Wotao Yin (Alibaba DAMO Academy)