Anirudh Subramanyan is talking at #informs2026 about our ongoing work on surveying and systematizing formulations and algorithms for optimization over trained neural networks.

When should you embed a neural network in your optimization model? How should that network look like? And who is doing that sort of thing anyway? Jiaxiao Fang and I try to address those and other questions in "Constraint Learning by Optimizing over Trained Neural Networks: A Pragmatic Tutorial"

The preprint of "Optimization for One, Feasibility for Most: Decoupling Optimization from Feasibility in Chance-Constrained Programs", coauthored with Justin Dumouchelle, Shunyu Yao, and Beste Basciftci, is now on arXiv: arxiv.org/abs/2610.19027

Optimization for One, Feasibility for Most: Decoupling Optimization from Feasibility in Chance-Constrained Programs

Chance-constrained stochastic programs (CCSPs) require solutions that satisfy uncertain constraints with high probability. Standard approaches either embed scenarios directly into optimization by cons...

arxiv.org

Thiago Serra@thserra.bsky.social · 5mo ago

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

Selvaprabu (Selva) Nadarajah is visiting the University of Iowa Tippie College of Business and giving a Business Analytics seminar on solving real options problems for energy options, which concerns trading on energy investments, using Markov Decision Process models.

**Please repost** If you're enjoying Paper Skygest -- our personalized feed of academic content on Bluesky -- we'd appreciate you reposting this! We’ve found that the most effective way for us to reach new users and communities is through users sharing it with their network

I am endorsing this declaration about the misalignment between AI and mathematics: mathandai.org

✊

Terence Tao@teorth.bsky.social · 2mo ago

A group of 25 Fields Medalists, including myself, have made a joint declaration on Math and AI: mathandai.org . We welcome additional signatories. See also this article in the Economist announcing the declaration: www.economist.com/science-and-...

One missing post from last week’s #FutureBAProf workshop: Violet (Xinying) Chen talking about surviving the tenure track. Chen talked first about understanding expectations, building a balanced publication portfolio, networking for visibility, and knowing when to change plans.

Wrapping up #FutureBAProf, Gilbert Laporte and Ahmed Abbasi discussed the future of Business Analytics. They both emphasized the importance of students to read as much as they can to become better scholars.

Today we have a special session at the #FutureBAProf workshop in memory of Warren Boe, a long time member and benefactor of the Business Analytics Department at The University of Iowa Tippie College of Business.

Qi Luo did such an amazing job last year at #FutureBAProf workshop that he is back again to talk about the academic job market from the applicant perspective

Thiago Serra@thserra.bsky.social · last yr.

Qi Luo presented the applicant perspective about the academic job market at the #FutureBAProf workshop with a personal perspective on having switched from a position in industrial engineering to another one in business analytics. 1/2

Wrapping up the first day of talks at #FutureBAProf, Qi Feng talked about the on campus interview: what schools are looking for, what proxies they use to assess the candidates, and how the candidates can demonstrate their potential

Qihang Lin and Violet (Xinying) Chen talked at #FutureBAProf about academic job applications and conference interviews.

Gilbert Laporte talked about how to structure a paper at #FutureBAProf Among many pearls and some jokes, the advice on how to write the introduction was very interesting: it should answer the “what?” and “why?”

Continuing the Research in Business Schools session at #FutureBAProf, Ahmed Abbasi talks about the fast-pace of change in scholarship on human-centered computational design with the emergence of inferential, generative, and agentic models.

Starting the Research in Business Schools session at #FutureBAProf, Annabelle Feng talks about the importance of thinking of your long-term research agenda: trends come and go, along with editorial priorities of journals, but the important questions remain, and they center on how we make decisions.

We are kicking off the 5th edition of the #FutureBAProf workshop with Ann Melissa Campbell’s opening and Sam Burer’s introduction to business analytics.

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.