marc santolini
Long term fellow
I am a research fellow at LPI and a visiting researcher at the Georgia Tech School of Public Policy. My focus is in network and data science to study collaborative phenomena and the evolution of science. I am also the co-founder of Just One Giant Lab, a nonprofit initiative aimed at developing decentralized open science using smart digital tools.
Marc's Bio

I majored in theoretical Physics and minored in philosophy of science at ENS, Paris. I followed up with a PhD in the Statistical Physics Department of ENS investigating gene regulatory networks using tools from physics and machine learning. During my postdoc at the BarabasiLab of Northeastern University and the Division of Network Medicine at Harvard Medical School, I have investigated the networks underlying biological systems at all scales, from network medicine (protein interactome analysis) and personalized medicine to hospital network analysis, to the making of biology by studying the iGEM competition, an international student competition of synthetic biology. Since my arrival at LPI Paris, I have been studying collaborative learning and solving using network approaches on large empirical datasets, with the end goal to develop tools fostering collective intelligence for social impact. 

Apart from the lab, I enjoy playing music (all sorts of guitars and world percussions) and exploring the bottomless pit of weirdness that is the internet (Ben Levin, Bill Wurtz…). Passionate about spontaneous jamming, I also explore how group rituals and facilitation mechanisms help achieve collective flow states, with a focus on multi-modal, dialogical embodied practices to experience philosophical and contemplative concepts such as emergence, inter-being, or at-onement. Since 2023 I am a core resident at the Life Itself Bergerac Praxis Hub where I further explore these practices during long-term residencies.

Collaborative learning from student forum and phone call data
Reconstruct student interaction network from online and phone call data to predict grades.
Quantifying the rise and fall of Scientific Research Fields
Mapping scientific trajectories to uncover the universal patterns behind paradigms
Big data for collective emotion analysis
We leverage large scale data from Youtube and Twitter to analyse the dynamics of collective emotions
The Paleome: a network analysis of dinosaur ecosystems
Reconstructing dinosaur networks from site collection open data to better understand ancestral ecosystems.
Mobility analysis
Analysis of mobility data for social good. Analysis of individual trajectories and global open data of human travels.
The AI network scientist
Classification of graphs using Neural Networks
Social bias in collective knowledge production
Study the decade long discovery of the protein interaction network to measure social bias in knowledge production
Network seminars at CRI
This event brings together network scientists to discuss open network problems
iGEM Team Interaction Study: collaboration and performance of iGEM scientific teams
In this project, we study the collaboration patterns of iGEM teams underlying their performance and learning using data-driven social network analyses
Radicalization of digital communities
Use large-scale datasets from Twitter to understand the dynamics of radicalization of digital communities
Interdisciplinarity and team performance in the iGEM competition
Quantifying how background and skills diversity affect team performance in iGEM teams
ArchaeoEcology: a large-scale investigation of human ecosystems in deep time
This collaboration with Stefani Crabtree aims at investigating the network impact of humans in their ecosystems from archeological sites
Dynamics and structure of collaborations in open-source communities
We study the 27,000 most popular repositories from GitHub to quantify the universal organizational principles behind large-scale self-organized communities.
The gene networks driving ageing
Infer the gene regulatory network changes occuring during ageing and the Smurf transition.
COVID-19 Symptoms Twitter Analysis
Understanding how local tweets about symptoms correlate with case numbers
Recommender systems to enhance collective intelligence for patient-led research
We are investigating how to make it easier for patients to do research together on the questions that matter most to them