Proud to celebrate the graduation of my PhD student Vinit Ranjan, who defended his thesis this month: "Beyond the Worst Case: Verification of First-Order Methods for Parametric Optimization Problems" 🎉 Congratulations Dr. Ranjan!

Wishing everyone happy holidays! 🎄 Feeling lucky to work with such a fantastic group of students. Here's to good research, great company, and Neapolitan pizza 🍕

New preprint! 📄 Data-driven convergence guarantees for first-order methods via PEP + Wasserstein DRO. Less pessimistic probabilistic rates that reflect how your solver actually behaves 🎯 📎 arxiv.org/abs/2511.17834 💻 github.com/stellatogrp/dro_pep w/ Jisun Park & Vinit Ranjan #optimization #fom

Autonomous spacecraft are still a far off ideal 🚀 But Ryne Beeson and @stella.to are taking the first steps in that direction by finding the optimal trajectories to a given planet or moon with the help of machine learning: ai.princeton.edu/news/2025/ai...

📚 New Arxiv Paper Title: Data-driven Analysis of First-Order Methods via Distributionally Robust Optimization Authors: Jisun Park, Vinit Ranjan, Bartolomeo Stellato Read more: https://arxiv.org/abs/2511.17834

📢 New in JMLR (w @rajivsambharya.bsky.social)! 🎉 Data-driven guarantees for classical & learned optimizers via sample bounds + PAC-Bayes theory. 📄 jmlr.org/papers/v26/2... 💻 github.com/stellatogrp/...

📢 Our paper "Verification of First-Order Methods for Parametric Quadratic Optimization" with my student Vinit Ranjan (vinitranjan1.github.io/) is accepted in Mathematical Programming! 🎉 🔗 DOI: doi.org/10.1007/s10107-025-02261-w 📄 arXiv: arxiv.org/pdf/2403.033... 💻 Code: github.com/stellatogrp/...

I’m happy to share that I’ll be spending the fall semester at Princeton as a visiting student in the Department of Operations Research and Financial Engineering (ORFE), working with @stellato.io funded through the WASP program. If you’re in the area and would like to connect, feel free to reach out.

🔄 Updated Arxiv Paper Title: Exact Verification of First-Order Methods via Mixed-Integer Linear Programming Authors: Vinit Ranjan, Jisun Park, Stefano Gualandi, Andrea Lodi, Bartolomeo Stellato Read more: https://arxiv.org/abs/2412.11330

📚 New Arxiv Paper Title: Data Compression for Fast Online Stochastic Optimization Authors: Irina Wang, Marta Fochesato, Bartolomeo Stellato Read more: https://arxiv.org/abs/2504.08097

🚀 Gave a talk at the EURO @euroonline.bsky.social Seminar Series on "Data-Driven Algorithm Design and Verification for Parametric Convex Optimization"! 🎥 Recording: https://euroorml.euro-online.org/ Big thanks to Dolores Romero Morales for the invitation! 🙌 #MachineLearning #Optimization #ORMS

The new season of the Robust Optimization Webinar (#ROW) starts this week. Our first presentation will take place this Friday, January 24, at 15:00 (CET). Speaker: Peyman Mohajerin Esfahani (TU Delft) Title: Inverse Optimization: The Role of Convexity in Learning

📚 New Arxiv Paper Title: Exact Verification of First-Order Methods via Mixed-Integer Linear Programming Authors: Vinit Ranjan, Stefano Gualandi, Andrea Lodi, Bartolomeo Stellato Read more: https://arxiv.org/abs/2412.11330

What happens to the hyperparameters of learned optimizers? Turns out, we learn long steps! 🚀 👇 Check out our latest work with @rajivsambharya.bsky.social!

Our paper "Mean robust optimization" has been accepted to Mathematical Programming: https://buff.ly/3B3VpIG 📰 Arxiv (longer version): https://buff.ly/3CT4aWD 👩‍💻 Code: https://buff.ly/3ATqAXh w/ Irina Wang, Cole Becker, and Bart van Parys A thread 🧵 (1/7)👇

arxiv.org/abs/2411.17668 Our postdoc zihan slays another COLT open problem! proceedings.mlr.press/v247/kornows...

📚 New Arxiv Paper Title: Learning Algorithm Hyperparameters for Fast Parametric Convex Optimization Authors: Rajiv Sambharya, Bartolomeo Stellato Read more: http://arxiv.org/abs/2411.15717v1

We are very excited to announce that the 2025 INFORMS Computing Society (ICS) Conference will take place March 14-16, 2025, in Toronto: sites.google.com/view/ics-2025 Submissions for contributed talks are due on December 23. We invite talks that showcase the dynamic interface of CS, AI & #ORMS.

New #arxiv bot for #optimization and #control! 🎉 bsky.app/profile/arxi...

Hey @tmaehara.bsky.social Thanks for the great work on the arxiv bots here! Any chance you could make one for arxiv math.oc (Optimization and Control)? :)

If your favorite book about algorithms is not Algorithms for Toddlers, then you haven’t read this book yet. Today I used it to talk about greedy algorithms in my Applied Optimization #orms class (some pages below). Here is one of the authors reading the whole book: m.youtube.com/watch?v=nnLO...

New starter pack if you're looking for the #orms community on here: go.bsky.app/SvBND16 Also if you have recommendations of people to add, feel free to send them my way.

Honored to receive the 2025 ONR Young Investigator Award for our project entitled “Data-Driven Analysis and Design of Mathematical Optimization Algorithms”! https://buff.ly/4e3cT5f @USNavyResearch #Optimization #MachineLearning

Very proud of my first PhD student Rajiv Sambharya for defending his thesis! 🎉 Rajiv has done excellent work on learning optimization algorithms for large-scale and embedded optimization, with strong convergence and generalization guarantees. He will soon start a postdoc at UPenn Engineering!

It was great to organize Princeton Workshop on #Optimization, #Learning, and #Control last June! Thanks to everyone who attended and made it a success! 🎉 #OLC24 Missed the live sessions? Catch up on all the talks with the video recordings here: https://buff.ly/3YwomX6

Excited to announce that our work on the OSQP solver (https://osqp.org/) has received the Beale — Orchard-Hays Prize (https://buff.ly/3Yqutfx) for Excellence in Computational Mathematical Programming! 🎉