Lucas Pesenti

I am a 4th-year Ph.D. student in Statistics and Computer Science at Bocconi University. Until last year, I was advised by Luca Trevisan, and I am now advised by Laura Sanità and co-advised by Pravesh Kothari. Before that, I studied at École Normale Supérieure de Paris and got my M.Sc. from the Parisian Master of Research in Computer Science.

My research interests lie in theoretical computer science, with a focus on approximation algorithms, their connections with statistical physics and random matrix theory, and applications of continuous methods to discrete mathematics.

Email: lucas dot pesenti at phd dot unibocconi dot it

We introduce a new approach for analyzing a broad class of nonlinear iterative algorithms on random matrices using Fourier analysis. As the dimension of the input matrix goes to infinity, the Fourier basis simplifies, allowing us to implement heuristic cavity-based reasoning into rigorous arguments.

We present rounding schemes for semidefinite programming relaxations of the problem of maximizing cubic polynomials over the hypercube or the sphere, complementing the well-developed theory for quadratic polynomials. This answers a question posed by Trevisan on the existence of certificates for the optimum value of cubic polynomials matching the guarantees of a search algorithm of Khot and Naor.

We analyze a new algorithmic framework for discrepancy minimization based on regularization. We demonstrate how varying the regularizer allows us to re-interpret several breakthrough results in algorithmic discrepancy theory, ranging from Spencer's theorem to Banaszczyk's bounds.