Bartolomeo Stellato
Assistant Professor
I am an Assistant Professor at the Department of Operations Research and Financial Engineering at Princeton University. I am an associated faculty in the Department of Electrical and Computer Engineering, the Department of Computer Science, and the Program in Applied and Computational Mathematics. I am also an affiliated faculty in the AI at Princeton initiative, the Princeton Institute for Computational Science and Engineering, Data and Intelligent Systems, and the Robotics at Princeton initiative. I am a fellow at the Princeton Whitman and Yeh colleges.
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.
Upcoming Talks
Nov 2026INFORMS Annual Meeting · San Francisco, CA
Dec 2026CDC Workshop on Distributionally Robust Optimization · Honolulu, HI
Jul 2027International Symposium on Mathematical Programming (ISMP) · Amsterdam, NL
View all talks →
News
Oct 2026
Always nice to be back in Lucca! I gave a talk on Learning, Verification, and AI-Assisted Code Generation for Real-Time Optimization at the DYSCO Seminar of the IMT School for Advanced Studies Lucca. Thank you to Alberto Bemporad and Stephen Boyd for the kind invitation!
I have been granted associated faculty status at the Princeton Program in Applied and Computational Mathematics (PACM).
New preprint The Exact Worst-Case Asymptotic Rate of Nesterov Acceleration for Strongly Convex Optimization with Sebastian Pokutta. We characterize the exact worst-case asymptotic rate of Nesterov's method with standard constant parameters. It is attained by a single function in finite dimension and behaves like for large ! Check out the interactive companion website to explore the worst-case spirals.
Honored that our work with Rajiv Sambharya, Georgina Hall, and Brandon Amos received an honorable mention for the 2026 INFORMS Computing Society Prize! The prize recognizes three papers on learning to optimize: Learning to Warm-Start Fixed-Point Optimization Algorithms (JMLR), Learning Algorithm Hyperparameters for Fast Parametric Convex Optimization (SIMODS), and Data-Driven Performance Guarantees for Classical and Learned Optimizers (JMLR). Congratulations to Rajiv, who led all three!
Our ICML 2026 paper Batched First-Order Methods for Parallel LP Solving in MIP with Nicolas Blin, Stefano Gualandi, Christopher Maes, and Andrea Lodi received an honorable mention for the 2026 INFORMS Harvey J. Greenberg Research Award!
Sep 2026
So proud of my fourth PhD student Stefan Clarke for successfully defending his thesis Machine Learning and Conformal Prediction for Accelerating Mixed Integer Optimization 🎉 Stefan will join Tower Research Capital!
Pranav Reddy has joined our group as a PhD student.
Alec Zhu has joined our group as a PhD student.
Aug 2026
Always nice to be back at Lehigh! I gave a talk on Distributionally-Robust Learning to Optimize at the MOPTA Conference in Bethlehem, PA.
Jul 2026
It was a pleasure to present Data-Driven Perspectives on First-Order Methods via Distributionally Robust Optimization at the International Conference on Optimization and Machine Learning in Taipei. Thank you to the organizers, and to Sebastian Pokutta and Antoine Deza for the kind invitation!
Major update to our preprint Data-driven Analysis of First-Order Methods via Distributionally Robust Optimization with Jisun Park and Vinit Ranjan. New in this version: probabilistic convergence rates for gradient descent and Nesterov's fast gradient method beyond the worst-case , and a theorem showing how our DRO-PEP bounds interpolate between empirical and worst-case performance!
Jun 2026
It was great to give a talk on Exact Verification of First-order Methods via Mixed-Integer Linear Programming at the SIAM Conference on Optimization in Edinburgh.
So proud of my former PhD student Rajiv Sambharya, who will join Texas A&M Industrial and Systems Engineering as a tenure-track Assistant Professor after his postdoc at UPenn! 🎉
Our paper Exact Verification of First-Order Methods via Mixed-Integer Linear Programming has been accepted to the SIAM Journal on Optimization! This is great work with Vinit Ranjan, Jisun Park, Stefano Gualandi, and Andrea Lodi.
May 2026
So proud of my senior thesis students for their 2026 ORFE awards: Zach Yaninek received the Kenneth H. Condit '13 Prize, Allen Shen the Ahmet S. Cakmak Prize, and Elliot Lee the Procter and Gamble Prize, shared with Liubomir Baicev! Elliot also received the Beth N. Rom-Rymer '73 Senior Thesis Prize in Global Health and Health Policy. 🎉
Enjoyed giving a talk on Data-Driven Perspectives on First-Order Methods for Convex Optimization at the UCSB CCDC Seminar. Thanks Tobia Marcucci for the invitation!
New preprint Disjunctive Sum of Squares with Amir Ali Ahmadi, Sanjeeb Dash, and Yixuan Hua. We certify polynomial nonnegativity with multiple low-degree algebraic identities found in parallel, yielding SDP hierarchies fixed semidefinite constraint size!
Three papers accepted to ICML 2026! Batched First-Order Methods for Parallel LP Solving in MIP with Nicolas Blin, Stefano Gualandi, Christopher Maes, and Andrea Lodi; Conformal Prediction for Early Stopping in Mixed Integer Optimization with Stefan Clarke; and Lower Bounds for Frank-Wolfe on Strongly Convex Sets with Jannis Halbey, Daniel Deza, Max Zimmer, Christophe Roux, and Sebastian Pokutta.
New preprint Learning Distributionally Robust First-Order Methods for Convex Optimization with Vinit Ranjan and Jisun Park. We propose a distributionally robust approach to learn hyperparameters of first-order methods, unifying learning to optimize and worst-case optimal algorithm design, while remaining robust to distribution shifts!
So proud of my graduate student Irina Wang for successfully defending her thesis Data-Driven Optimization for Fast and Reliable Decision-Making Under Uncertainty 🎉 Irina will spend a year as a postdoc at Georgia Tech ISyE with Yao Xie, then join MIT Sloan OR Stats as Assistant Professor!
Apr 2026
Had a lot of fun giving a talk on Data-Driven Perspectives on First-Order Methods for Convex Optimization at the MIT LIDS Seminar. Thanks Gioele Zardini for the invitation! Check out the video!
It was great to give a talk on Data-Driven Perspectives on First-Order Methods for Convex Optimization at the Princeton PACM Colloquium. Thanks Amit Singer for the invitation!
Had a lot of fun giving a talk on Data-Driven Perspectives on First-Order Methods for Convex Optimization at the Yale FDS Colloquium. Thanks Manolis Zampetakis for the invitation! Check out the video!
Mar 2026
It was great to give a talk on Exact Verification of First-order Methods via Mixed-Integer Linear Programming at the INFORMS Optimization Society Conference in Atlanta.
Feb 2026
It was great to give a talk on Data-Driven Analysis of First-Order Methods via Distributionally Robust Optimization at the BIRS Workshop on Contextual Stochastic Optimization. Check out the video!
Honored to receive a 2026 Sloan Research Fellowship in Mathematics from the Alfred P. Sloan Foundation. This wouldn't have been possible without my entire research group at Princeton!
New preprint Lower Bounds for Frank-Wolfe on Strongly Convex Sets with Jannis Halbey, Daniel Deza, Max Zimmer, Christophe Roux, and Sebastian Pokutta. We show that the known convergence rate for Frank-Wolfe on smooth and strongly convex sets is tight!
New preprint Conformal Prediction for Early Stopping in Mixed Integer Optimization with Stefan Clarke. We use conformal prediction to terminate MIP solvers early with guaranteed high-quality solutions!
Congratulations to Yixuan Hua for receiving the IBM PhD Fellowship Award!
Jan 2026
Our paper Learning Algorithm Hyperparameters for Fast Parametric Convex Optimization has been accepted to the SIAM Journal on Mathematics of Data Science! This is great work with Rajiv Sambharya.
New preprint Batched First-Order Methods for Parallel LP Solving in MIP with Nicolas Blin, Stefano Gualandi, Christopher Maes, and Andrea Lodi. We extend the primal-dual hybrid gradient algorithm to solve batches of linear programs on GPUs for mixed-integer programming!
Our paper On the (linear) convergence of Generalized Newton Inexact ADMM has been accepted to Transactions on Machine Learning Research! This is great work with Zach Frangella, Theo Diamandis, and Madeleine Udell.
Vinit Ranjan has joined our group as a Postdoctoral Research Associate!
It was great to give a talk on Data-driven Analysis of First-Order Methods via Distributionally Robust Optimization at EPFL. Thanks Colin Jones and Daniel Kuhn for the invitation and the kind hospitality!
Dec 2025
It was great to attend the ScaleOPT Workshop on GPU-Accelerated Scalable Optimization at NeurIPS. I gave a talk on Accelerating Parametric Convex Optimization: Design and Verification of First-Order Methods. Thanks to the organizers, especially Parth Nobel, for such a great event!
New preprint Learning-Based Hierarchical Approach for Fast Mixed-Integer Optimization with my student Stefan Clarke. We use decision-focused learning to accelerate MIP algorithms.
Proud of my graduate student Vinit Ranjan for successfully defending his thesis entitled Beyond the Worst Case: Verification of First-Order Methods for Parametric Optimization Problems 🎉
Our paper GeNIOS: an (almost) second-order operator-splitting solver for large-scale convex optimization has been accepted to Mathematical Programming Computation. This is great work led by Theo Diamandis and in collaboration with with Zach Frangella, Shipu Zhao, and Madeleine Udell. Check out the Julia code!
Nov 2025
New preprint Data-driven Analysis of First-Order Methods via Distributionally Robust Optimization with my postdoc Jisun Park and student Vinit Ranjan. We study the probabilistic performance of first-order methods, directly from past algorithm trajectories! 🔬
Oct 2025
I had fun at INFORMS this year. I gave a talk on the Exact Performance Verification of First‑Order Methods in Parametric Convex Optimization and my postdoc Jisun Park gave a talk on Data-driven Analysis of First-Order Methods via Distributionally Robust Optimization in our session with Ben Grimmer and Tetiana Parshakova. My student Irina Wang also gave a talk on Data-Driven Optimization for Fast and Reliable Decision-Making Under Uncertainty
It was great to visit Rutgers to give an ISE Seminar Data-Driven Algorithm Design and Verification for Parametric Convex Optimization. Thank Robert Mieth and David Coit for hosting me.
Daniel Deza has joined our group as a PhD student.
Sep 2025
It was great to be back at Oxford to give a talk on Data-Driven Algorithm Design and Verification for Parametric Convex Optimization at a joint seminar between the Mathematical Institute and the Control Group. Thanks a lot Coralia Cartis and Paul Goulart for hosting me.
It was a great pleasure to be the opponent of Manu Upadhyaya for his PhD defense at Lund University, entitled Lyapunov analyses for first-order methods: Theory, automation, and algorithm design. Thanks Pontus Giselson for hosting me.
Claudio Vestini has joined our group as a Senior Thesis student in exchange from the University of Oxford.
Our paper AlgoTune: Can Language Models Speed Up General-Purpose Numerical Programs? has been accepted to NeurIPS 2025 Benchmark Track. We built algotune.io, a new LLM benchmark library for numerical programs.
Our paper entitled Data-Driven Performance Guarantees for Classical and Learned Optimizers with Rajiv Sambharya has been accepted to the Journal of Machine Learning Research!
Aug 2025
It was great to attend the workshop Graph Learning Meets Theoretical Computer Science at the Simons institute at Berkeley! I gave a talk on Exact Performance Verification of First‑Order Methods in Parametric Convex Optimization. Thanks, Christopher Morris and Andrea Lodi, for the invitation! You can find a recording 📹 here
Our paper Verification of First-Order Methods for Parametric Quadratic Optimization with Vinit Ranjan accepted in Mathematical Programming! We introduce an optimization-based framework to verify finite-step convergence of first-order methods, directly capturing the structures of parametric linear and quadratic problems.
Paul Häusner joined our group as a Visiting Research Student Collaborator (VSRC) for the Fall semester.
Jul 2025
Anushka Acharya joined our group as a Research Software Engineer working on first-order methods.
It was great to attend ICSP in Paris this year (back to Paris again!). I gave a semi-plenary entitled Learning Data-Driven Uncertainty sets with Mean Robust Optimization at the minisymposium entitled Robust Optimization and Machine Learning, organized by Aras Selvi and Mengmeng Li. My student Irina Wang gave a great talk entitled Learning Decision-Focused Uncertainty Sets in Robust Optimization in the same minisymposium.
It was great to attend ICCOPT in LA this year. I chaired a session entitled Analysis and Design of First Order Methods for Convex Optimization, where Ernest Ryu, my student Vinit Ranjan, and my postdoc Jisun Park gave fantastic talks. I also gave a talk on Data-Driven Performance Guarantees for Classical and Learned Optimizers in the minisymposium AI Meets Optimization organized by Wotao Yin and Jialin Liu. Thanks for the invitation!
New preprint AlgoTune: Can Language Models Speed Up General-Purpose Numerical Programs?. We build algotune.io, a new benchmark library for numerical programs, including lots of exciting optimization and control examples.
Jun 2025
I gave a talk on Less Pessimistic Analysis of First-Order Methods: Exploiting Structure and Data at the SIERRA Team Seminar hosted by Adrien Taylor and Francis Bach. It was great to spend June in Paris visiting Adrien Taylor. Thanks a lot for the hospitality!
Gave a talk on Exact Verification of First-order Methods
via Mixed-Integer Linear Programming at the Portuguese American Optimization Workshop (PAOW) in Horta, Azores. Thanks Tommaso Giovannelli, and Luis Nunes Vicente for organizing such a great event!
I gave a talk on Data-Driven Algorithm Design and Verification for Parametric Convex Optimization at the École Polytechnique. Thanks Leo Liberti for hosting me.
I gave a talk on Data-Driven Algorithm Design and Verification for Parametric Convex Optimization at the OPTIMA Seminar. Thanks Peter Stuckey for hosting me.
May 2025
Gave a talk on Exact Verification of First-order Methods
via Mixed-Integer Linear Programming at the Converence on Discrete Optimization and Machine Learning at the Research Institute for Mathematical Sciences (RIMS), Kyoto, Japan. It was a great event! Thanks Antoine Deza and Sebastian Pokutta for the invitation
Great to give a talk on Exact Verification of First-order Methods
via Mixed-Integer Linear Programming at the CRM Montreal workshop on Combinatorial Optimization and Data Science. Thanks Margarida, Quentin, and Utsav for the kind invitation!
Apr 2025
New preprint Data Compression for Fast Online Stochastic Optimization with my student Irina Wang and Marta Fochesato (ETH Zürich). We present a fast online clustering method to efficiently solve stochastic optimization problems in real-time! This extends our previous work Mean Robust Optimization.
Our paper Exact Verification of First-Order Methods via Mixed-Integer Linear Programming has been updated on arXiv with many new proximal algorithm steps, more scalable formulations, and an exciting optimal control example!
Mar 2025
I gave a talk on Data-Driven Algorithm Design and Verification for Parametric Convex Optimization at the UPenn Optimization Seminar. Thanks Jason Altschuler for the kind invitation!
Feb 2025
I gave a talk on Data-Driven Algorithm Design and Verification for Parametric Convex Optimization at the EURO Seminar Series on Operational Research and Machine Learning. Thanks Dolores Romero Morales for the kind invitation! 📹 A recording is available here.
Jan 2025
Ryne Beeson and I just received a Princeton AI Seed Grant entitled Accelerating Space Exploration with AI. We will explore how to use diffusion models and reinforcement learning to accelerate the computation of optimal spacecraft trajectories.
I gave a talk on Data-Driven Algorithm Design and Verification for Parametric Convex Optimization at the exciting Grid Science Winter School and Conference organized by Los Alamos National Laboratory!
Dec 2024
It is always great to be back at IEEE CDC, which this year in Milan (very close to home!). I gave a talk on Learning Data-Driven Uncertainty sets with Mean Robust Optimization at the Workshop on Control and Optimization in the Probability Space.
New preprint Exact Verification of First-Order Methods via Mixed-Integer Linear Programming led by my student Vinit Ranjan and in collaboration with Stefano Gualandi and Andrea lodi.
New preprint Learning Algorithm Hyperparameters for Fast Parametric Convex Optimization with Rajiv Sambharya. What happens to the hyperparameters of learned optimizers? Turns out, we learn long steps! 🚀
Our paper entitled A neural network-based approach to hybrid systems identification for control has been accepted to Automatica. Joint work with Filippo Fabiani, Daniele Masti and Paul Goulart.
Nov 2024
It was great to visit the University of Minnesota and give a talk at the ISyE Seminar on Data-Driven Algorithm Design and Verification for Parametric Convex Optimization. Thanks Saumya Sinha for the invitation!
I am now an affiliated faculty to the AI at Princeton initiative.
Excited to welcome Yanjun Liu as a graduate student in our research group
Our paper The Benefit of Uncertainty Coupling in Robust and Adaptive Robust Optimization has been accepted to INFORMS Journal on Optimization!
Oct 2024
Honored to receive the 2025 ONR Young Investigator Award for our project entitled “Data-Driven Analysis and Design of Mathematical Optimization Algorithms”!
I had the pleasure of giving a talk at Northwestern IEMS Seminars entitled Data-Driven Algorithm Design and Verification for Parametric Convex Optimization. Thanks a lot Simge Küçükyavuz for the invitation.
Sep 2024
I just had a great visit to Johns Hopkins University. I gave a talk at the MINDS / CIS Seminar Series entitled Data-Driven Algorithm Design and Verification for Parametric Convex Optimization. Thanks a lot Nicolas Loizou for the invitation.
So proud of my first graduate student Rajiv Sambharya for successfully defending his thesis entitled Learning to Accelerate Optimization Algorithms with Guarantees 🎉 Rajiv will soon start a postdoc at UPenn Engineering.
Excited to welcome Jisun Park as a postdoc in our research group!
Jul 2024
It was great to organize Princeton Workshop on Optimization, Learning, and Control last June! Thanks to everyone who attended and made it a success! Missed the live sessions? Catch up on all the talks with the video recordings 📹 here.
Our work on the OSQP solver has received the prestigious Beale — Orchard-Hays Prize for Excellence in Computational Mathematical Programming! 🎉 Here is a 🧵 on X
🔈 Excited to be interviewed by Ashley Kilgore with my previous senior thesis student, Joyce Luo, now at MIT, on the INFORMS Resoundingly Human podcast! 🎙️. We discussed our paper Equitable Data-Driven Facility Location and Resource Allocation to Fight the Opioid Epidemic that just appeared on Manufacturing & Service Operations Management. Tune in to discover how data and optimization combat the opioid epidemic 🌎 💊
Jun 2024
Our paper Learning to Warm-Start Fixed-Point Optimization Algorithms has been accepted in the Journal of Machine Learning Research! Joint work with Georgina Hall, Brandon Amos, and led by my student Rajiv Sambharya. See Rajiv's 🧵 on X!
May 2024
It was great to give a talk at the Robust Optimization Webinar on Learning for Decision-Making under Uncertainty. Check out the video 📹 here! Thanks a lot Jannis Kurtz and Ahmadreza Marandi for the invitation.
It was great to give a talk on Learning for Real-Time Decision Making at the Department of Mathematics of the University of Pavia. Thanks Stefano Gualandi for the kind invitation!
Honored to receive the Howard B. Wentz, Jr. Junior Faculty Award from the Princeton School of Engineering and Applied Science (SEAS)!
It was great to be back at the MIT ORC to give a talk on Learning for Real-Time Decision Making.
Haimin Hu and Zixu Zhang won the Outstanding Presentation Award at the Princeton Research Day for presenting our work Who Plays First? Optimizing the Order of Play in Stackelberg Games with Many Robots. That's work with Gabriele Dragotto Jaime Fernandez Fisac. Check out the 🎥 here!
📣 Registrations open for the Princeton Workshop on Optimization, Learning, and Control (OLC)! Sign up 📝 by June 1 to attend!
Apr 2024
Our paper Exterior-point Optimization for Sparse and Low-rank Optimization has been accepted in the Journal of Optimization Theory and Applications. That's in collaboration with Shuvomoy Das Gupta and Bart Van Parys.
New paper Data-driven performance guarantees for classical and learned optimizers with my student Rajiv Sambharya.
New paper Learning Decision-Focused Uncertainty Sets in Robust Optimization collaboration with Bart Van Parys and my student Irina Wang.
I gave an Autonomy Talk on Learning for Decision-Making under Uncertainty. Thanks Gioele Zardini for the invitation!
Our paper Equitable Data-Driven Facility Location and Resource Allocation to Fight the Opioid Epidemic has been accepted in Manufacturing & Service Operations Management. That's in collaboration with my senior thesis student Joyce Luo (now at MIT ORC).
Proud of my student Irina Wang for receiving the Wallace Memorial Fellowship in Engineering!
Mar 2024
Great to attend the INFORMS Optimization Society Conference. I served as the cluster chair on Emerging Applications of Optimization together with Holly Wiberg. We organized 14 sessions! I also co-chaired two sessions with my students Irina Wang and Rajiv Sambharya.
Received a Seed Grant from the Princeton School of Engineering and Applied Sciences entitled Unsupervised Conditional Generative Machine Learning for Global Nonlinear Optimal Control: Applications in Spaceflight, in collaboration with Ryne Beeson and Adji Bousso Dieng.
New preprint Verification of First-Order Methods for Parametric Quadratic Optimization with my student Vinit Ranjan.
I gave a talk on Learning Decision-Focused Uncertainty Sets for Robust Optimization at the Conference on Information Sciences and Systems at Princeton University. I also organized a session where my students Rajiv Sambharya and Vinit Ranjan, and Jan Drgona and Mahyar Fazlyab gave talks.
The poster submission deadline for the Princeton Workshop on Optimization, Learning, and Control (OLC) has been extended to March 10!
Feb 2024
It was great to attend the AAAI Workshop on Learnable Optimization. I gave a talk on Learning Decision-Focused Uncertainty Sets for Robust Optimization. Thanks Ján Drgoňa for the kind invitation!
I gave a Discrete Optimization Talk on Differentiable Cutting-plane Layers for Mixed-integer Linear Optimization. Thanks Silvia Di Gregorio, Aleksandr Kazachkov, and Elias Khalil for the kind invitation!
New preprint Who Plays First? Optimizing the Order of Play in Stackelberg Games with Many Robots. Great collaboration with Jaime Fernandez Fisac's group, led by Haimin Hu and Gabriele Dragotto.
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
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!
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 have been granted associated faculty status at the Princeton Department of Electrical and Computer Engineering.
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!
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.
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.
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 gave a talk on Accelerating quadratic optimization with reinforcement learning at the INFORMS Annual Meeting.
Sep 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 250th 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 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!