Tommaso d'Orsi

Email: tommaso (dot) dorsi (at) unibocconi (dot) it
Office: 4-h-02

I am a Research Fellow at Bocconi hosted by Luca Trevisan. I am also a visiting Researcher at Google.

I recently received my PhD from ETH Zurich where I was fortunate to have David Steurer as my advisor. During my PhD, I spent several months at Google research, Vincent Cohen-Addad was my host.

I am broadly interested in computer science, from both a theoretical and applied perspective. The bulk of my research lies somewhere in the intersection of algorithm design, learning theory, privacy and computational complexity.

Dissertation: Information-computation gaps in robust statistics [pdf]
Recepient of the ETH Medal, 2023.


Max-Cut with ε-Accurate Predictions [arXiv]
with Vincent Cohen-Addad, Anupam Gupta, Euiwoong Lee and Debmalya Panigrahi, in submission.

A Near-Linear Time Approximation Algorithm for Beyond-Worst-Case Graph Clustering
with Vincent Cohen-Addad, and Aida Mousavifar, ICML 2024.

Multi-View Stochastic Block Models
with Vincent Cohen-Addad, Silvio Lattanzi, and Rajai Nasser, ICML 2024.

Perturb-and-Project: Differentially Private Similarities and Marginals
with Vincent Cohen-Addad, Alessandro Epasto, Vahab Mirrokni and Peilin Zhong, ICML 2024.

Private graphon estimation via sum-of-squares [arXiv]
with Hongjie Chen, Jingqiu Ding, Yiding Hua, Chih-Hung Liu and David Steurer, STOC 2024.

Private estimation algorithms for stochastic block models and mixture models [arXiv]
with Hongjie Chen, Vincent Cohen-Addad, Alessandro Epasto, Jacob Imola, David Steurer, and Stefan Tiegel, NeurIPS 2023 (spotlight).

Reaching the Kesten-Stigum Threshold in the Stochastic Block Model under Node Corruptions [arXiv]
with Jingqiu Ding, Yiding Hua, David Steurer, COLT 2023.

A Ihara-Bass formula for non-boolean matrices and strong refutations of random CSPs [arXiv]
with Luca Trevisan, CCC 2023.

Higher degree sum-of-squares relaxations robust against oblivious outliers [arXiv]
with Rajai Nasser, Gleb Novikov and David Steurer, SODA 2023.

On the well-spread property and its relation to linear regression [arXiv]
with Hongjie Chen, COLT 2022.

Fast algorithm for overcomplete order-3 tensor decomposition [arXiv]
with Jingqiu Ding, Chih-Hung Liu, David Steurer and Stefan Tiegel, COLT 2022.

Robust Recovery for Stochastic Block Models [arXiv]
with Jingqiu Ding, Rajai Nasser and David Steurer, FOCS 2021.

Consistent Estimation for PCA and Sparse Regression with Oblivious Outliers [arXiv]
with Chih-Hung Liu, Rajai Nasser, Gleb Novikov, David Steurer and Stefan Tiegel, NeurIPS 2021.

The Complexity of Sparse Tensor PCA [arXiv]
with Davin Choo, NeurIPS 2021.

Consistent regression when oblivious outliers overwhelm [arXiv]
with Gleb Novikov and David Steurer, ICML 2021.

Sparse PCA: Algorithms, Adversarial Perturbations and Certificates [arXiv]
with Pravesh Kothari, Gleb Novikov and David Steurer, FOCS 2020.

Coloring graphs with no clique immersion
with Paul Wollan, DM18.


Algorithms and Data structures (Head TA)
Algorithms and Data structures (Head TA)
Optimization for Data Science (TA)
Algorithms and Data structures (TA)
Optimization for Data Science (TA)
Presenting Theoretical Computer Science (TA)
Algorithms and Data structures (TA)
Optimization for Data Science (TA)
Algorithms and Data structures (TA)