nordita research fellow

See Research to have a look at what I work on.

Lecturer of statistical physics

Lecturer of Statistical Physics (5 ECTS) for the Masters program in Theoretical Physics.

Queen Mary University of London, UK

Project: Large deviation methods for the study of nonequilibrium systems: variational and spectral approaches.
Supervisor:  Dr. Rosemary J. Harris.
Viva: 14 Sep 2021, passed with no corrections. Examiners:  Dr. W. Just (QMUL) and Prof. G. A. Pavliotis (Imperial).

Université Pierre et Marie Curie, Paris, FR

Part of the international master program of Physics of complex systems.

Politecnico di Torino, SISSA & ICTP in Trieste, IT 

Thesis: Large deviations of random walks on random graphs. Supervisors: Prof. Hugo Touchette and Dr. Luca Dall’Asta. Score: 110/110.

Università degli studi di Padova, IT

Thesis: Non-equilibrium statistical mechanics and large deviation theory. Supervisors: Prof. Attilio Stella and Prof. Marco Baiesi. Score: 104/110.

Università degli studi di Padova, IT

Thesis: The diffusion differential equation and applications to the Brownian motion. Supervisor: Prof. Riccardo Colpi. Score: 110/110 cum laude.

methods to study large deviations, rare events, and fluctuation dynamics

A general theory of fluctuations for systems driven by memory does not exist yet. Here, we try to develop a theory of fluctuations and large deviations of self-interacting diffusions, i.e., processes with a drift dependent on their past occupation.

Large deviation theory characterises the leading behaviour of rare events. Going beyond large deviations is a challenging mathematical question, but discrete-time Markov chains seem to offer a good 'first' understanding of subleading behaviour.

Sampling a rare event using Monte Carlo methods is computationally prohibitive. Therefore, one often resorts to sampling from a tilted (on the rare event of interest) distribution. This can be estimated by implementing many-particle approximations of Feynman-Kac formulae. An interesting question that arises here is: what is the error made by these methods?

Many-particle methods are certainly useful, but for large state spaces they may also become computationally expensive when sampling rare events. A possible solution to overcome this problem is to use single particle algorithms that adapt overtime to the rare event to sample. Do we have a way to effectively compare many-particle and adaptive methods?

Often explicit probability distributions of interesting observables may not be easy to calculate. In such cases it is at least useful to have either thermodynamic or probabilistic bounds to guide our understanding of what happens.

applications

Rare events of stochastic processes evolving on graphs may unravel interesting topological features of the graph itself and of the interplay between the inherent randomness of the stochastic process and the randomness of the environment that embeds it.

These are processes that have the property of being re-initialised at random times to a specific initial condition, e.g., the queue at the front-office, or the motion of a protein in a cell. The focus is again on studying their fluctuations by means of large deviation theory.

with Lorenzo Buffoni and Stefano Gherardini (February 2024) Phys. Rev. E 109, 024138

with David Stuhrmann (January 2024) Phys. Rev. Research 6, 013077

with Patrick Pietzonka (May 2023) arXiv:2305.15392

with Leonardo Di Gaetano, Giorgio Carugno, and Federico Battiston (April 2023) arXiv:2303.18169

with Lorenzo Buffoni and Stefano Gherardini (November 2022) J. Stat. Mech. (2023) 063201

with Hugo Touchette (November 2022) Phys. Rev. E 107, 034137

with Giorgio Carugno and Pierpaolo Vivo (June 2022) Phys. Rev. E 107, 024126

with Gabriele Di Bona, Leonardo Di Gaetano and Vito Latora (January 2022) Phys. Rev. Res. 4, L042051

with Giorgio Carugno and Pierpaolo Vivo (January 2022) J. Phys. A: Math. Theor. 55 295001

with Raphael Chetrite and Hugo Touchette (March 2021) Phys. Rev. E 103, 062142

with Rosemary J. Harris (March 2020) J. Stat. Phys 179, 131-154

with Jules Morand and Hugo Touchette (February 2019) Phys. Rev. E 99, 022137

with Filippo Radicchi and Ginestra Bianconi (December 2018) Phys. Rev. E 98, 062317

September 2021

Lecturer of Taylo'r Theorem for first year Calculus One students in engineering.

- (2023) Lecturer of Statistical Physics (6 ECTS) for MSc students in Theoretical Physics

- (2019-20) Teaching assistant of Introduction to probability and Chaos and fractals.
- (2018-19) Teaching assistant of Calculus II and Differential equations.
- (2017-18) Teaching assistant of Calculus II and Introduction to probability.

I have been a private tutor for Combinatorics, Graph theory, Dynamical systems, and Quantum mechanics. 

Then, MSc student in Computational Physics at Stockholm University.
MSc Thesis on adaptive algorithms to sample rare events.