# francesco coghi

I am a **PostDoctoral Research Fellow** at the Nordic Institute for Theoretical Physics (NORDITA). My research focuses on the development and application of methods to study fluctuations and rare events in nonequilibrium systems.

Hannes Alfvéns väg 12, SE-106 91 Stockholm francesco[dot]coghi[at]gmail[dot]com

I am a **PostDoctoral Research Fellow** at the Nordic Institute for Theoretical Physics (NORDITA). My research focuses on the development and application of methods to study fluctuations and rare events in nonequilibrium systems.

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

October 2021 - Present

*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).

October 2017 - October 2021

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

September 2016 - July 2017

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

September 2015 - July 2017

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

September 2012 - July 2015

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

September 2009 - July 2012

I am attracted by comprehending how **fluctuations** arise in **out-of-equilibrium systems.** In particular, I am interested in (i) developing **methods** to understand both the **likelihood** of these fluctuations and the **physical mechanisms** that generate them and (ii) in applying these methods to understand **rare events** of interesting physical models (from complex networks, biophysics, etc...).

I list here topics and project I like and work on.

A general theory of fluctuations for systems driven by a memory kernel 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.

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 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

with David Stuhrmann

September 2021

Stanford University, Palo Alto, (CA, US)

Mar 13 - 22 2023

MPI PKS Dresden (DE)

Jan 11 - 15 2023

ENS Lyon (FR)

working with dr. freddy bouchet on rare event algorithms.

Sep 11 - Oct 12 2022

Applied Math, Stellenbosch (RSA)

07 Oct - 10 Dec 2021

ENS Lyon (FR)

Jun 13 - 15 2021

LJAD, Nice (FR)

Jun 5 - 30 2021

LJAD, Nice (FR)

Sep 01 - 24 2020

LJAD, Nice (FR)

Ago 21 - Sep 26 2019

Universita di Padova (IT)

Mar 13 - 15 2019

SISSA, Trieste (IT)

May - Jul 2017

NITheP, Stellenbosch (RSA)

Jan - Apr 2017

LJAD, Nice (FR)

Jun 06-08 2022

ICTP, Trieste (IT)

May 14-25 2018

Imperial College London (UK)

Dec 09-10 2019

Beg Rohu, Quiberon (FR)

Jun 24 - Jul 06 2019

King's College London (UK)

Apr 09 2019

King's College London (UK)

Apr 17 2018

ICTP, Trieste (IT)

Apr 10 - May 5 2017

- (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.*

Master student in Computational Physics at Stockholm University.

Working on adaptive algorithms to sample rare events.