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Les Quais de Saône, Lyon
Welcome!
I am a postdoctoral researcher in the group of Florent Krzakala at EPFL. My research lies at the intersection of probability theory (especially random matrix theory), high-dimensional statistics, and the mathematical foundations of machine learning. My postdoctoral research is supported by the Simons Collaboration on the Physics of Learning and Neural Computation.
I completed my Ph.D. in mathematics at ENS Lyon, where I was fortunate to be co-advised by Alice Guionnet (CNRS and ENS Lyon) and Gérard Ben Arous (NYU Courant). My doctoral thesis is available [here]. Before that, I studied mathematics at ETH Zurich, writing my master's thesis under the supervision of Dominik Schröder. Further details can be found in my CV [here].
I am a member of the European Women in Mathematics (EWM). I have also recently signed the Slow Science Manifesto in support of sustainable and thoughtful scientific practices.
Feel free to contact me at vanessa [dot] piccolo [at] epfl [dot] ch!
News
News
October 2026: New paper Symmetry-Aware Feature Learning: A Polynomial Separation for Multi-Index Models, with Jivan Waber, Yatin Dandi, and Florent Krzakala. We study how symmetry changes feature learning in high-dimensional Gaussian multi-index models.
Fall 2026: I am teaching Statistical Physics of Computation at EPFL. The course is organized into three main modules: inference and information-theoretic bounds, taught by Vittorio Erba; random matrix theory; and learning from data and neural networks.
September 2026: I joined the Simons Collaboration on the Physics of Learning and Neural Computation as a postdoctoral researcher.
August 2026: New paper Spectral Phase Transitions in Gaussian Multi-Index Models, with Florent Krzakala and Pierre Mergny. We characterize spectral phase transitions for matrix-valued spectral estimators and show that an optimal spectral method reaches the weak-recovery threshold.
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