iiinova — where your digital self keeps evolving

Bartolomeo Stellato @stella.to · 23/09/2026

📢 So proud of my fourth PhD student Stefan Clarke for successfully defending his thesis "Machine Learning and Conformal Prediction for Accelerating Mixed Integer Optimization" 🎉 Next stop: Tower Research Capital. Congrats Dr. Clarke!

Stefan Clarke and Bartolomeo Stellato in front of the title slide of Stefan's PhD defense

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Bartolomeo Stellato @stella.to · 14/07/2026

📢 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

Log-log plot of function-value gap versus iterations for gradient descent and the fast gradient method on random quadratics; DRO-PEP expectation and CVaR bounds decay faster than the worst-case O(1/K) rate, matching the new theoretical rates.

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Bartolomeo Stellato @stella.to · 07/05/2026

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!

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Bartolomeo Stellato @stella.to · 31/12/2025

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!

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Bartolomeo Stellato @stella.to · 24/12/2025

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 🍕

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Bartolomeo Stellato @stella.to · 18/12/2025

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

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Bartolomeo Stellato @stella.to · 08/09/2025

📢 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/...

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Bartolomeo Stellato @stella.to · 08/08/2025

📢 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/...

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Bartolomeo Stellato @stella.to · 26/02/2025

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

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Bartolomeo Stellato @stella.to · 29/11/2024

Clustering is a powerful tool for decision-making under uncertainty! Work w/ my students Irina Wang (lead) and Cole Becker, in collab. w/ Bart Van Parys 🧵 (7/7)

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Bartolomeo Stellato @stella.to · 29/11/2024

We have several examples in the paper. Here is a sparse portfolio optimization one. Clustering barely affects the solution objective. Speedups are more than 3 orders of magnitude. 🧵 (6/7)

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Bartolomeo Stellato @stella.to · 29/11/2024

By varying the number of clusters K, our method bridges Robust and Distributionally Robust optimization! We also derive theoretical bounds on 1) how to adjust the Wasserstein ball radius to compensate for clustering, and 2) how to exactly quantify the effect of clustering 🧵 (5/7)

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Bartolomeo Stellato @stella.to · 29/11/2024

In Mean Robust Optimization, we define an uncertainty set around the cluster centroids with weights defined by the amount of samples in each cluster. 🧵 (4/7)

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Bartolomeo Stellato @stella.to · 29/11/2024

Our procedure: we first cluster N data points into K clusters. Then, we solve the Mean Robust Optimization problem. 🧵 (3/7)

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Bartolomeo Stellato @stella.to · 29/11/2024

Robust optimization is tractable but, often, very conservative. Wasserstein Distributionally Robust Optimization is less conservative but, often, computationally expensive. How can we bridge the two? 🧵 (2/7)

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Bartolomeo Stellato @stella.to · 29/11/2024

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

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Bartolomeo Stellato @stella.to · 07/09/2024

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!

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Bartolomeo Stellato @stella.to · 08/08/2024

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

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Bartolomeo Stellato @stella.to · 31/07/2024

In particular, none of this would have been possible without Goran Banjac with whom and I shared countless hours developing OSQP. Here is a picture of us in 2016 having Korean BBQ in the Bay Area (where it all began!)

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Bartolomeo Stellato @stella.to · 31/07/2024

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

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