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Bartolomeo Stellato

I am an Assistant Professor at the Department of Operations Research and Financial Engineering at Princeton University. I am an associated faculty with the Department of Electrical and Computer Engineering, the Department of Computer Science, the Center for Statistics and Machine Learning, and the Robotics at Princeton initiative. I am also a fellow at Princeton Whitman college.

My research lies at the interface of mathematical optimization, machine learning, and optimal control. It focuses on data-driven computational tools to make decisions in highly dynamic and uncertain environments.

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News

Feb 2024
Jan 2024
Join us for the Princeton Workshop on Optimization, Learning, and Control (OLC) on June 27-28 2024! We have a great lineup of speakers and a poster session for junior researchers.
I have been granted associated faculty status at the Princeton Department of Computer Science.
Dec 2023
It was great to attend the IEEE Conference on Decision and Control in Singapore. My student Stefan Clarke gave a talk on Learning Rationality in Potential Games.
Nov 2023
Proud of my student Irina Wang for winning the Princeton SEAS Award for Excellence!
I will be the Vice-Chair of Computational Optimization and Software of the INFORMS Optimization Society until 2025. Excited to work on boosting visibility of computational optimization in and beyond INFORMS!
New preprint Differentiable Cutting-plane Layers for Mixed-integer Linear Optimization led by Gabriele Dragotto and Stefan Clarke, and in collaboration with Jaime Fernandez Fisac! See thread on X! Differentiable optimization 🤝 cutting plane algorithms!
Oct 2023
I have organized one invited session at the INFORMS Annual Meeting where Irina Wang, Rajiv Sambharya, Vinit Ranjan, and I gave a talk. Also, Stefan Clarke and Gabriele Dragotto gave a talk in a separate session.
New preprint GeNIOS: an (almost) second-order operator-splitting solver for large-scale convex optimization led by Theo Diamandis and in collaboration with with Zach Frangella, Shipu Zhao, and Madeleine Udell. Check out the Julia code!
Sep 2023
New preprint Learning to Warm-Start Fixed-Point Optimization Algorithms! We introduce a new learning framework to warm-start fixed-point optimizers, with great computation savings and generalization guarantees. Check out the code! Joint work with collaborators Georgina Hall, Brandon Amos, and led by my student Rajiv Sambharya.
It was great to attend the visit the Zuse Institute Berlin for the final Conference on the Thematic Einstein Semester. I gave a talk on Mean Robust Optimization.
Aug 2023
Gabriele Dragotto won the DataX Postdoctoral Research Fellowship from Princeton CSML!
Jul 2023
It was great to attend the International Conference on Stochastic Programming in UC Davis. Irina Wang gave a talk on Mean Robust Optimization and I gave one on Learning for Robust Optimization.
Amit Solomon just joined our group as a Research Software Engineer (RSE)! He will help us build great software packages to disseminate our work on numerical optimization. Amit is part of a new initiative by Princeton Research Computing to greatly expand the number of RSEs.
Our work Learning Rationality in Potential Games has been accepted to IEEE CDC 2023! It is led by my student Stefan Clarke in collaboration with Gabriele Dragotto and Jaime Fernandez Fisac. See you all in Singapore!
Jun 2023
It was great to attend L4DC! My student Rajiv Sambharya presented a poster on End-to-End Learning to Warm-Start for Real-Time Quadratic Optimization.
I have organized two minisymposia on Data-Driven Decision-Making Under Uncertainty at SIAM Conference on Optimization together with Dick den Hertog and Bart Van Parys. My student Irina Wang gave a talk on Mean Robust Optimization and I presented our work Learning for Robust Optimization.
May 2023
I gave a talk on Learning for Optimization under Uncertainty at the Mixed-Integer Programming Workshop at the University of Southern California. I really enjoyed it! Thanks to the brilliant organizers for the invitation!
Apr 2023
Mar 2023
I gave a talk on Learning for Optimization under Uncertainty at the Conference on Information Sciences and Systems at Johns Hopkins University. Thanks to Enrique Mallada and Mahyar Fazlyab for the invitation!
Thrilled to receive the NSF CAREER Award for the project entitled Learning for Real-Time Embedded Optimization!
New preprint Learning Rationality in Potential Games with my student Stefan Clarke, postdoc Gabriele Dragotto, and in collaboration with Jaime Fernandez Fisac.
Feb 2023
New preprint The Benefit of Uncertainty Coupling in Robust and Adaptive Robust Optimization in collaboration with Liangyuan Na and Dimitris Bertsimas.
New preprint On the (linear) convergence of Generalized Newton Inexact ADMM. Great collaboration with Zach Frangella, Shipu Zhao, Theo Diamandis, and Madeleine Udell.
Jan 2023
New preprint Equitable Data-Driven Resource Allocation to Fight the Opioid Epidemic: A Mixed-Integer Optimization Approach, in collaboration with my senior thesis student Joyce Luo (now at MIT ORC).
Dec 2022
Nov 2022
I have been granted associated faculty status at the Princeton Department of Electrical and Computer Engineering.
Oct 2022
I gave a talk on Mean Robust Optimization at the Future of Analytics and Operations Research workshop at the INFORMS Annual Meeting.
I have organized one invited session at the INFORMS Annual Meeting where Irina Wang, Rajiv Sambharya, and Vinit Ranjan gave a talk.
Sep 2022
Gabriele Dragotto joined our group at ORFE and Jaime Fernandez Fisac group at ECE as a postdoc to work on multi-agent networks.
Stefan Clarke (PhD) and Cole Becker (MSE) joined our group as graduate students.
Jul 2022
I have organized two invited sessions at ICCOPT where Irina Wang, Cole Becker, Rajiv Sambharya, and Vinit Ranjan gave a talk.
New preprint Mean Robust Optimization in collaboration with Bart Van Parys and my students Irina Wang and Cole Becker.
Vineet Bansal was featured on the CSML news for his brilliant work on OSQP!
Jun 2022
I gave a talk on Real-time decision-making via data-driven optimization at Politecnico di Milano at the DEIB Department Seminar. First time I was there after 10 years! Thanks Maria Prandini for the invitation!
Our proposal for an ORFE Research Software Engineer was selected for funding!
May 2022
Rajiv Sambharya won the Excellence in Teaching Award from the Princeton Graduate School for his work on ORF307!
Joyce Luo won the Sigma Xi Book Award (video at this link).
Mar 2022
I gave a talk on Real-time decision-making via data-driven optimization at the Cornell ORIE Seminar. Thanks Andrea Lodi for the invitation!
I gave a talk on Real-time decision-making via data-driven optimization at the NASA JPL Multi-Agent Tech Talks. Thanks Federico Rossi for the invitation!
Our paper Embedded Code Generation with CVXPY was accepted for publication in IEEE Control Systems Letters. It will be presented at IEEE CDC 2022.
Feb 2022
I received the Princeton SEAS Innovation Award for the proposal "Learning Task-specific Optimizers for Real-time Autonomous Systems”!
Jaime and I won the Metropolis Project research award for the proposal “Towards Resilient Urban Networks with Differentiable Agent Decision Models”!
Our paper Online mixed-integer optimization in milliseconds was accepted for publication in INFORMS Journal on Computing.
Jan 2022
I served in the program committee of the 4th L4DC conference.
Our paper CoCo: Online Mixed-Integer Control via Supervised Learning was accepted for publication in Robotics and Automation Letters!
Oct 2021
I became one of the project maintainers of CVXPY, which is part of NumFOCUS.
Sep 2021
Irina Wang and Vinit Ranjan joined our group as graduate students.
Jul 2021
I received internal funding from Princeton CSML to support Vineet Bansal, a Research Software Engineer, to work on OSQP.
May 2021
Mar 2021
Our paper Machine Learning for Real-Time Heart Disease Prediction was accepted for publication in IEEE Journal of Biomedical and Health Informatics.
Feb 2021
I started teaching the undergraduate optimization class ORF307.
Rajiv Sambharya joined our group as a graduate student.
Jan 2021
Our paper on the OSQP solver won the Best Paper Award for the journal Mathematical Programming Computation!
I served in the program committee of the 3rd L4DC conference.
I gave a talk on Real-time decision-making via data-driven optimization at the Raytheon Technologies Research Center. Thanks Alessandro Pinto for the invitation.
Nov 2020
I received the Pierskalla Best Paper Award from the INFORMS Health Applications Society together with the amazing folks of the COVIDAnalytics project!
Sep 2020
I started teaching the graduate optimization class ORF522.
I started organizing the Princeton Optimization Seminar.
Jul 2020
I joined the ORFE Department at Princeton University!