I am happy to share a new paper on optimization for combinatorial testing with Changkun Guan, Hunter Gehman, Sumit Dhar, Mikey Ferguson, John Hooker, and Marcel Schoppers. Link in the comments. This is one of the most fun optimization problems that I have worked on!

Optimal Combinatorial Testing with Constraints: The Balancing Act

YouTube video by Thiago Serra

youtu.be

I wrote a piece for ORMS Tomorrow called "PhD: My mistakes, my lucky breaks, and what I tell my students now". This article draws from my own experience as a student & from what I have learned by advising PhD students so far. Thank you Dan Ni Lin for the invitation! www.informs.org/content/down...

MIP Europe 2026 will feature a poster session. If you wish to participate, make sure to register by July 10th. www.mixedinteger.org/EUROMIP/2026... (Poster submission deadline itself is August 31, July 10 is deadline to register)

But if I were to stay here for longer than 4 hours, would Google Scholar notice it?

Wrapping up #cors2026, Justin Dumouchelle talked about our joint work (along with Shunyu Yao and Beste Basciftci) on decoupling optimization from feasibility when solving chance-constrained optimization models. 1/N

Carlos Zetina talked at #cors2026 about the benefits of GPU acceleration in solving linear programs using the FICO Xpress solver. Their solver has the two most popular methods implemented: - PDHG is faster in the problems that it can solve - cuPDLP+ solves more problems 1/2

One use case of machine learning in optimization is circumventing the cost of optimization algorithms. Jerry Sun presented such a case at #cors2026 by predicting Farkas multipliers to prove that a solution is infeasible with respect to the linear relaxation without solving the linear relaxation.

Seyedeh Parisa Moosavi talked at #cors2026 about relaxations for weakly-coupled Markov decision processes. She considered an example of managing fatigue in crew scheduling: teams need a break to recharge, but their breaks need to be coordinated with the breaks of other teams. 1/2

This Tuesday I am giving a tutorial on constraint learning at #cors2026 When: Tuesday, June 9, 10:45 AM Where: Walter Light Hall 205

Kimia Ghobadi talked at #cors2026 about inverse optimization challenges: - solution of the problem (the closest cost vector making a solution optimal) may not be unique - solving it becomes expensive with more observations - it is difficult to establish confidence on the retrieved parameters 1/N

Danielle Ripsman talked at #cors2026 about how to combine inverse optimization and generative models in healthcare applications, proposing a generative inverse optimization model.

Rana Shiravi talks at #cors2026 about using inverse optimization to understand dosage tradeoffs in radiotherapy treatments. 1/2

Mahya Hemmati talks at #cors2026 about solving multistage stochastic optimization problems by approaching them as a two-stage rolling horizon stochastic problem and using decision-focused learning with a diffusion model to learn the distribution of scenarios.

Utsav Sadana talks at #cors2026 about how to avoid underestimating entropic risk by using parametric bootstrapping.

Gabriel Fortin-Leblanc talked at #cors2026 about a more tractable approach for solving optimization problems with decision-dependent uncertainty.

El Mehdi Er Raqabi talked at #cors2026 about how to use an optimization proxy approach to solver Benders subproblems: - Predict the dual multipliers (this is where ML replaces optimization) - Project the solution (to ensure feasibility) - Complete (a closed-form step to strengthen the cut)

Mahsa Hajamini talks at #cors2026 about how to retrieve a tree of scenarios for a multistage stochastic program to obtain the true optimal first-stage decision. 1/2

Ahmed Mohammed talks at #cors2026 about how to circumvent the weakness of Benders cuts in the classical Benders decomposition. 1/2

Timo Berthold strikes again at #cpaior2026 by talking about how to beat DeepMind’s AlphaEvolve geometric packing algorithms with off-the-shelf MINLP solvers. 1/N

Beste Basciftci gives the best paper award talk at #cpaior2026 on designing transit networks. This work expands her #cpaior2020 paper by distinguishing between core riders (who will use the system regardless) and latent riders (who will evaluate the new system and may decide to use it). 1/N

Yingqian Zhang gives the last keynote of #cpaior2026 on end-to-end deep reinforcement learning for solving combinatorial optimization problems. 1/N

Congratulations to my colleague Beste Basciftci and coauthors on winning the best paper award at #cpaior2026. Go Hawks!

Anna Münz talked at #cpaior2026 about a bilevel optimization model to verify the quality of the solutions obtained with predict-and-optimize models.

Timo Berthold talked at #cpaior2026 about how to use machine learning in branching decisions to solve mixed-integer nonlinear programs. 1/N

Yudong Will Xu talked at #cpaior2026 about how to use iterative neural heuristics to perform large neighborhood search, bridging the methodological gap between OR and ML approaches.

Bryan Alvarado-Ulloa talks at #cpaior2026 about a new approach to solve pseudo-Boolean optimization problems (aka, binary linear programs) using predict and search. The novelty of their approach is learning from the assignments shared by all optimal solutions instead of those of a single one. 1/2

Axel Parmentier is giving today’s keynote at #cpaior2026 on decision-focused learning, stressing the importance of not treating prediction and optimization as strictly separate tasks: slightly inaccurate predictions may trickle down to infeasible or suboptimal solutions. 1/N

Rahul Patel talked at #cpaior2026 about solving multi objective optimization problems using relaxed decision diagrams. This is motivated by the fact that the number of nodes associated with solutions at the Pareto frontier can be considerably small. 1/N

Laurent Michel talked at #cpaior2026 about how to use GPUs to accelerate the construction of exact decision diagrams by expanding the nodes in each layer in parallel.

Wenjie Xiao talked at #cpaior2026 about how mixed-integer convex optimization is advantageous to solve the graph isomorphism problem on many classes of graphs.