Bartolomeo Stellato
@stella.to
Assistant Professor @Princeton ORFE l Real-time optimizer I http://osqp.org developer | From 🇮🇹 in 🇺🇲 | https://stella.to
📢 Updated preprint "Data-driven Analysis of First-Order Methods via Distributionally Robust Optimization" with Jisun Park and Vinit Ranjan. ✨ New: probabilistic convergence rates beyond worst-case O(1/K) 📈 and how our bounds interpolate between data and worst case. 📄 arxiv.org/abs/2511.17834
So proud of my graduate student Irina Wang for successfully defending her PhD thesis "Data-Driven Optimization for Fast and Reliable Decision-Making Under Uncertainty" 🎉 Next: a year as postdoc with Yao Xie at Georgia Tech ISyE, then Assistant Professor at MIT Sloan OR Stats. Congrats Irina!
Honored to receive a 2026 Sloan Research Fellowship in Mathematics. This wouldn't be possible without my entire research group at Princeton, and I'm grateful to the colleagues who supported my research. #SloanFellow
Alfred P. Sloan Foundation@sloanfoundation.bsky.social · 6mo agoCongrats to the 126 early-career scholars awarded a 2026 Sloan Research Fellowship, whose creativity and innovation set them apart as the next generation of scientific leaders! Our #SloanFellows represent 7 fields and 44 institutions across the US and Canada. Full list: sloan.org/fellowships/...
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...
AI helps Princeton scientists plot the best paths for space exploration
When a spacecraft or probe is sent to explore Mars or do flybys of one of Saturn’s moons, it stays in constant contact with mission control back on Earth, where scientists recalculate and adjust as ne...
ai.princeton.edu
📚 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!
Learning Algorithm Hyperparameters for Fast Parametric Convex Optimization
We introduce a machine-learning framework to learn the hyperparameter sequence of first-order methods (e.g., the step sizes in gradient descent) to quickly solve parametric convex optimization problem...
arxiv.org
Rajiv Sambharya@rajivsambharya.bsky.social · 2y agoWe learned the hyperparameters to accelerate algorithms over a family of problems. Turns out that we only need 10 training instances in each example and learn long steps for (prox) gd! Check out this work with @stellato.io paper: arxiv.org/pdf/2411.15717 code: github.com/stellatogrp/...
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...
Anytime Acceleration of Gradient Descent
This work investigates stepsize-based acceleration of gradient descent with {\em anytime} convergence guarantees. For smooth (non-strongly) convex optimization, we propose a stepsize schedule that all...
arxiv.org
📚 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.
2025 ICS Conference
The 18th INFORMS Computing Society (ICS) Conference welcomes you to Toronto, Canada. We invite researchers, practitioners, and innovators to come together and share insights at the cutting edge where...
sites.google.com
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
2025 Young Investigator Award Recipients | Office of Naval Research
See a list of the 2025 recipients of the U.S. Department of the Navy's Young Investigator Program.
buff.ly
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! 🎉
