We are inviting the OR/MS community to contribute problems to a new, community-driven benchmark for evaluating large language models (LLMs) on optimization modeling tasks. LLMs are beginning to lower the barrier to entry for optimization modeling, but realizing that potential requires rigorous benchmarks that reflect what actually makes a modeling task hard — and those… Read more »
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Mind the Gap: Mixtures of Gaussians in Approximate Differential Privacy
Our paper Mind the Gap: Mixtures of Gaussians in Approximate Differential Privacy, authored by Huikang, Aras and Wolfram, has been accepted at the Proceedings of the 43rd International Conference on Machine Learning (ICML).
An MILP-Based Solution Scheme for Factored Markov Decision Processes
Our paper An MILP-Based Solution Scheme for Factored Markov Decision Processes, authored by Huikang, Man-Chung and Wolfram, has been accepted at Operations Research.
From Data to Donations: Optimal Fundraising Campaigns for Non-Profit Organizations
Wolfram gave a seminar at CMU Tepper on optimal fundraising for non-profit organisation on Apr 24, 2026.
Odysseus 2027 Workshop
Wolfram joined the Scientific Committee of Odysseus 2027 — the 10th International Workshop on Freight Transportation and Logistics. The workshop will be held in Argostoli, Kefalonia, Greece, May 24–28, 2027, and is organized by the Athens University of Economics and Business.
2026 ICS Prize Committee
Wolfram will serve on the 2026 INFORMS Computing Society Prize Committee.
Factored MDPs
Wolfram was giving a talk on Factored MDPs at the Banff Workshop on Contextual Stochastic Optimization on Wed, Feb 25th, 2026.
Data-Driven and Robust MDPs
Wolfram gave a keynote talk on Data-Driven and Robust Markov Decision Processes at the 10th AIROYoung Workshop and Ph.D. School organized in Padova (Italy) on 9–13 February 2026.
It’s All in the Mix: Wasserstein Classification and Regression with Mixed Features
Our paper It’s All in the Mix: Wasserstein Classification and Regression with Mixed Features, authored by Reza, Aras and Wolfram, has been accepted at Manufacturing & Service Operations Management.
A Note on Piecewise Affine Decision Rules for Robust, Stochastic, and Data-Driven Optimization
Our paper A Note on Piecewise Affine Decision Rules for Robust, Stochastic, and Data-Driven Optimization, co-authored with Simon Thomä (RWTH Aachen) and Max Schiffer (TU Munich), has been accepted at Operations Research.
