Homepage | Bartolomeo Stellato

Bartolomeo Stellato

Assistant Professor

Operations Research and Financial Engineering

I am an Assistant Professor at the Department of Operations Research and Financial Engineering at Princeton University. I am an associate faculty with Department of Electrical and Computer Engineering, 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.

News

Nov 2023	Proud of my student Irina Wang for winning the Princeton SEAS Award for Excellence! 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	Our work RSQP: Problem-Specific Architectural Customization for Accelerated Convex Quadratic Optimization has been accepted to the International Symposium on Computer Architecture
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! Our paper End-to-End Learning to Warm-Start for Real-Time Quadratic Optimization has been accepted to L4DC New preprint Learning Rationality in Potential Games with my student Stefan Clarke, postdoc Gabriele Dragotto, and in collaboration with Jaime Fernandez Fisac. I gave a talk on Learning for Optimization under Uncertainty at the IPAM Workshop on Artificial Intelligence and Discrete Optimization. Video is available here. Thanks to the organizers, Xavier Bresson, Bistra Dilkina, Andrea Lodi, and Pascal Van Hentenryck for the invitation!
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	New preprint End-to-End Learning to Warm-Start for Real-Time Quadratic Optimization, joint with collaborators Georgina Hall, Brandon Amos, and my student Rajiv Sambharya.
Nov 2022	I gave a talk on Learning for Fast and Robust Real-Time Optimization at UC Berkeley Mechanical Engineering Seminar. Thanks Francesco Borrelli for the invitation! Honored to receive the 2022 Franco Strazzabosco Young Investigator Award for Research in Engineering from the Italian Scientists and Research Scholars in North America Foundation (ISSNAF)!
Oct 2022	I gave a talk on Real-Time Decision-Making for Clean and Resilient Energy Systems at the ISSNAF Franco Strazzabosco Award Symposium. Video here. 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. Irina Wang won the INFORMS Computing Society Student Paper Award for our paper Mean Robust Optimization!
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! Cole Becker won the Princeton SEAS Mueller Prize and the John Ogden Bigelow Jr. Prize in Electrical Engineering. 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 4^th 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. I gave a talk on Accelerating quadratic optimization with reinforcement learning at the INFORMS Annual Meeting.
Sep 2021	Irina Wang and Vinit Ranjan joined our group as graduate students. Our papers Accelerating Quadratic Optimization with Reinforcement Learning and Is Bang-Bang Control All You Need? Solving Continuous Control with Bernoulli Policies were accepted at NeurIPS 2021.
Jul 2021	I received internal funding from Princeton CSML to support Vineet Bansal, a Research Software Engineer, to work on OSQP.
May 2021	I received the 250^th Anniversary Fund for Innovation for Undergraduate Education for redesigning ORF307. I gave a talk on Real-time decision-making via data-driven optimization at the Joint Princeton Robotics and Optimization Seminar (video).
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 3^rd 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!