Stefano Vrizzi

What is the impact of individual decisions on a collective system?

This is the fundamental question driving my career. 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, the key marker of collective resource management.

My PhD project aims to better understand finance bottom-up. More specifically, I am assessing which combination of cognitive traits, in a Multi-Agent Reinforcement Learning model, capture more closely the statistics of London Stock Exchange data.

Along scientific research, my primary effort is to encourage learning through conversations. For this reason, I have been highly involved in conference management, science communication and open access.

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

EDUCATIONAL BACKGROUND

My fundamental question grabbed me at high school, in Italy, where a science-focused programme combined philosophy classes and the active participation in international projects on human rights, the Model of United Nations.
I decided to start my journey by learning the biological and physiological bases of information processing, graduating from my BSc in Neuroscience (with industrial placement) at the University of Leeds, UK. In order to be able to model my question, I started building a theoretical and computational background through my BSc 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 Multi-Agent Reinforcement Learning (MARL) model 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. After embedding cognitive traits in the learning algorithms of the agents, I am assessing the impact of the distribution of cognitive traits in market dynamics through computational simulations.

PREVIOUS SCIENTIFIC RESEARCH

At individual level, I am interested in understanding how differently humans process information and make decisions; at a collective level, I am interested in the effect of this heterogeneity on society.

As we, as humans, interpret the surroundings through our nervous system, my first goal was to learn how to model neural activity. My research experience in computational neuroscience developed since my first year of BSc [2]. I started from a simpler, yet fascinating, circuitry: the spinal motor pathways. Here, behavioural measurements (e.g. electromyography of motor output) are less subject to interpretation than in cognition. A key milestone in my training in computational neuroscience came as undergraduate research fellow [3], where I extended a computational model to simulate the effects of artificially induced neural plasticity by a Brain Computer Spinal Cord Interface (BCSI).

I then wondered not only about how we can process information, but also how we cannot. There may be physical constraints imposing patterns and rules, which would determine the extent and possible types of heterogeneity of information processing across a population of information processing systems. 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 physiological components and physical constraints of information processing, my research scaled up to motor and behavioural tasks, while keeping a quantitative approach. I investigated heterogeneity in human information processing in three main projects:

- my BSc dissertation [4], quantifying distance between muscle activity patterns among human 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

In summary, my primary research experience has been based on computational models and machine learning, especially supervised learning and unsupervised learning, while my current PhD project focuses on Multi-Agent Reinforcement Learning simulations.

SUPERVISORS

[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.

There are two main fields dealing with the eco (from ancient Greek, oikos, 'house') we inhabit: eco-logy and eco-nomics. The former describes the eco, 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. 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 econometrics, transformative economic policies and climate change, I also collaborated with Rethinking Economics at Sciences Po to popularise the debate on carbon tax.

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

• Currently, my work has been possible thanks to the AI for the Sciences doctoral programme, as part of the Horizon 2020-Marie Skłodowska-Curie Actions-COFUND funding programme. My further training is kindly supported by my doctoral school « Frontières de l'innovation en recherche et éducation » (FIRE).

Previous funding

• Erasmus+
• Deutschlandstipendium, Tübingen, Germany
• Washington Research Foundation Undergraduate Fellowship in Neuroengineering, USA
• Faculty of Biological Sciences Excellence Scholarship I and II, Leeds, UK