Profile picture for user VRIZZI S

Stefano Vrizzi



29 rue d'Ulm
75005 Paris France

Human reinforcement learning
Mathematics of Neural Circuits
Short bio


How do your own beliefs shape society?


This is the fundamental question I am after. To answer it, I pursued knowledge in two fields. On one side, I specialised in information processing at individual level; on the other side, I trained at the intersection between economics and sustainability.

My current PhD project links the individual and collective aspects, focusing on how cognitive biases shape the financial market.


In resonance with the Socratic know thyself, my primary effort is to spread awareness about how we, as humans, process information, especially how we think and decide. For this reason, I have also been highly involved in science communication,  open access and conference management.


Further information on my background is available on the following tabs and on my résumé.

Information processing




To begin with, I learnt the biological and physiological bases of information processing by graduating from my BSc in Neuroscience (with industrial placement) at the University of Leeds, UK. In parallel, I started building a theoretical and computational background, which developed through my research experience, my MSc in Neural Information Processing at the International Max Planck Research School in Tübingen, Germany, and previous foundational studies in Physics at the University of Trieste, Italy.



PHD [1]

Currently, I am extending a reinforcement learning Agent-Based Model (ABM) called SYMBA (SYstème Multiagent Boursier Artificiel), which simulates stock market trading. This model is novel in two key features: i) agents can learn trading strategies by reinforcement learning and ii) their orders are collected and managed by a centralised order book. My role is to embed cognitive biases in the learning algorithms of the agents. This extension would enable us to study the impact of the distribution of cognitive biases in market dynamics, both though computational simulations and human interaction with the platform in experimental trials.




Broadly speaking, I am interested in understanding how we understand, how differently and the implications of this heterogeneity in society.


As humans understand the surroundings through their brain, my first goal was to learn how to model neural activity. My research experience in computational neuroscience started since my first year of BSc [2]. The focus on spinal motor pathways allowed me to to begin my work from a simpler, yet fascinating, circuitry and from a field where behavioural measurements (e.g. electromyography of motor output) are less subject to interpretation than in cognition. My pivotal training in computational neuroscience came as undergraduate research fellow [3], to extend a previous model to simulate the effects of artificially induced neural plasticity by a Brain Computer Spinal Cord Interface (BCSI).


As a following step, I investigated heterogeneity of human information processing in three main projects:

- my BSc dissertation [4], quantifying distance between muscle activity patterns from different subjects, given the same motor task;

- my MSc lab rotation [5], classifying muscle activity patterns within-subject from high density recordings during daily motor tasks;

- my work as research assistant [6], analysing test-retest inter-individual variability in human reinforcement learning


Let us then think of the nervous system, or more generally an organism, as an information processing system that transforms an input into an output. We can wonder not only about how it can process information, but also how it cannot. There may be physical constraints imposing patterns and rules, which would determine the extent and possible types of heterogeneity of information processing across population, therefore scaling down the dimensionality of our question.


Since the states accessible to a physical system are commonly dictated by energy constraints, I explored the relation between energy and information as physical quantities for my MSc thesis [7], comparing analytical predictions with computational results concerning the work extracted from a generalised Szilard engine. This project introduced me to the field of information thermodynamics and statistical physics.


After learning about the components of my system of interest, my research scaled bottom-up to cognition and behaviour [6], keeping a quantitative approach.


In summary, my primary research experience is based on computational models and machine learning. While in my daily work I employ supervised learning and unsupervised learning to study human reinforcement learning models, my current PhD project is extending my research to multi-agent simulations.


(Given the amount of computation involved, I am also aiming to advance my coding efficiency to minimise the impact of computation on climate change.)




[1] Boris Gutkin, Group for Neural Theory, Département d'études cognitives, École normale supérieure, Paris, France
Stefano Palminteri, Human Reinforcement Learning Team, Laboratoire de Neurosciences Cognitives & Computationnelles, Département d'études cognitives, École normale supérieure, Paris, France
Damien Challet, Laboratoire de Mathématiques et Informatique pour la Complexité et les Systèmes, CentraleSupélec, Gif-sur-Yvette, France


[2] Samit Chakrabarty, School of Biomedical Sciences, Faculty of Biological Sciences, University of Leeds, Leeds, UK
Marc de Kamps, School of Computing, Faculty of Engineering and Physical Sciences, University of Leeds, Leeds, UK


[3] Guillaume Lajoie & Adrienne Fairhall, Fairhall Lab, University of Washington Institute for Neuroengineering, Seattle, USA


[4] Samit Chakrabarty, School of Biomedical Sciences, Faculty of Biological Sciences, University of Leeds, Leeds, UK


[5] Martin Giese, Section for Computational Sensomotorics, Hertie Institute for Clinical Brain Research, Centre for Integrative Neuroscience, Tübingen, Germany
Leonardo Gizzi, Neuromechanics Laboratory, University of Stuttgart, Stuttgart, Germany


[6] Stefano Palminteri, Human Reinforcement Learning Team, Laboratoire de Neurosciences Cognitives & Computationnelles, Département d'études cognitives, École normale supérieure, Paris, France
Maël Lebreton, Paris School of Economics, Paris, France


[7] Matteo Marsili & Edgar Roldan, Quantitative Life Science, International Centre for Theoretical Physics, Trieste, Italy
Anna Levina, Max Planck Institute for Biological Cybernetics, Tübingen, Germany


In addition to the outstanding supervision I had the honour to receive during all these research projects, a crucial building block of my critical thinking development came from periodical meetings with my BSc tutor Dr Ian Wood.

Economics and sustainability

There are two main fields dealing with the eco (from ancient Greek, oikos, house) we inhabit: eco-logy and eco-nomics. The former describes its, the latter norms its management. Society and its wellness therefore depend on the alignment between these two fields.


My experience on this intersection developed especially through my secondary research on eco-economic decoupling at the Global Trends Unit, within the European Parliamentary Research Service at the EU Parliament in Brussels, Belgium. Here I learnt about the socio-economic transitions that several governments envisage to implement to aim for a more sustainable society. The success or failure of these transitions fundamentally rely on the ability to achieve an eco-economic decoupling that is absolute, global, permanent, fast enough and valid across environmental indicators.
To handle the problem, I focused on climate change, namely the economics around the consumption of energy produced from fossil fuels. In addition to participating in summer schools on energy econometricstransformative economic policies and climate change, I also collaborated with Rethinking Economics at Sciences Po to popularise the debate on carbon tax.


Currently, I am leading a project in collaboration with young experts in blockchain (unrelated to cryptocurrencies) and due diligence, in order to develop more effective ways to verify compliance of financial bonds with specific sustainability criteria.

Science communication

My experience in organising large events (600+ participants) began at high school, as student council representative. At university, in 2016, I condensed this experience to found Leeds Neural Networks, together with a group of peer students in Leeds, UK. Starting from the concept that meaning arises from connections, this student society, still running today, aims to connect: connect pieces of knowledge and connect people. It organises formal and informal events to discuss neuroscience and related fields, mostly at the University of Leeds and in town. Side activities also concern charity fundraising for brain tumour research and organising the quiz night for the British Neuroscience Association Festival.


My involvement in science communication also concretised as short articles for the British Neuroscience Association, or by promoting neuroscience with the Human Brain Project, or as TEDx speaker. Since knowledge sharing leverages on open access, I also facilitated workshops at OpenCon 2018 and Mozilla Festival 2019.

This active participation in annual international conferences enabled me to be in charge of different aspects of conference management, for instance for the Global Scholars Symposium 2019 at the University of Oxford, or the ESPAS conference 2019 at the European Parliament.


Currently, I am part of La Fresque du Climat, to give educational workshops on climate change, both locally and at COP26.


Statistique: probabilités (Travaux Dirigés), niveau L2, École d’économie de la Sorbonne, Université Paris 1, Paris, France




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