# Tommaso d'Orsi

__Email__: tommaso (dot) dorsi (at) inf (dot) ethz (dot) ch

__ Office__: CAB H36.2
I am PhD student at ETH Zurich in the Department of Computer Science. I am fortunate to have
David Steurer
as my advisor.

I am broadly interested in theoretical computer science.
Most recently, I have been working on the statistical and computational aspects of average-case problems, the sum-of-squares hierarchy and robust algorithms.

### Publications:

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,
*in submission*.

Higher degree sum-of-squares relaxations robust against oblivious outliers
[arXiv]

with
Rajai Nasser, Gleb Novikov and David Steurer,
SODA 2023.

A Ihara-Bass formula for non-boolean matrices and strong refutations of random CSPs
[arXiv]

with
Luca Trevisan, *in submission*.

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,
SIAM Conference in Discrete Mathematics 2018.

### Teaching:

**2022:**
- Algorithms and Data structures (Head TA)
**2021:**
- Algorithms and Data structures (Head TA)
- Optimization for Data Science (TA)
**2020:**
- Algorithms and Data structures (TA)
- Optimization for Data Science (TA)
- Presenting Theoretical Computer Science (TA)
**2019:**
- Algorithms and Data structures (TA)
- Optimization for Data Science (TA)
**2018:**
- Algorithms and Data structures (TA)