**BioSwarm Project - DGA***(November 2023 – 2027)*Principal investigator together with Olivier Simonin of a 4-year national research project on algorithms for coordination tasks in drone swarms, funded by the DGA (the French Government Defence procurement and technology agency).**BraInside Project - DGA***(November 2019 – 2023)*Principal investigator together with Laurent Viennot of a 4-year national research project on artificial neural networks, funded by the DGA (the French Government Defence procurement and technology agency).

Damien Rivet, Post-doc (BraInside project), COATI Team, 2022-07 — 2023-06.

Paulo Bruno Serafim, Research Engineer (BraInside project), COATI Team, 2022-04 — 2022-09.

Emilio Cruciani, I3S Post-doc fellowship, 2019-11 — 2020-10.

Arthur Carvalho Walraven Da Cunha, INRIA UCA, 2020-10 — 2023-09.

Francesco D'Amore, INRIA UCA, co-supervised with Nicolas Nisse, 2019-10 — 2022-10.

Emilio Cruciani, Gran Sasso Science Institute, co-supervised with G. D'Angelo and L. Becchetti, until 2019-12.

Starting in 2019, I've been working on the theory of sparsification of artificial neural networks, in particular in connection to the Lottery Ticket Hypothesis (LTH, SLTH, RSS).

Starting in 2021, I've also been contributing integrated assessment modeling software in Julia, with the goal of applying **scientific machine learning** to develop models related to sustainable development goals.

Since 2017, I've been interested in theoretical and computational neuroscience. I've been a fellow of the Brain and Computation Program of the Simons Institute for the Theory of Computing, and subsequently, I've been working on providing algorithmic and mathematical tools to investigate how the central nervous system is organized. In that respect, I worked on the problem of network alignment applied to brain atlases (BrainAlign) and on a temporal version of the Hyperbolic Random Graph as a null model for fMRI data (Hyper). I've also worked on the Assembly Calculus, a theoretical framework that explains the emergence of high-level cognition from the low-level behavior of neurons and synapses through an algorithmic formalization of Hebbian learning (AC).

Originally motivated by an interest in the theory of complex systems, my research has focused on computational dynamics (CompDyn, SurvDyn), i.e., simple distributed probabilistic algorithms which allow multi-agent systems to solve global coordination tasks. This class of algorithms has been studied extensively from the perspective of computability theory. However, due to the lack of mathematical tools to rigorously model the behavior of these systems in the short term, efforts to explore these dynamics algorithmically succeeded only recently. My main contributions in this area have been on the fundamental distributed-computing problems of Consensus (StabCons, NoisyUnd), Majority Consensus (SimpleDyn, UndDyn, PhaseTrans), and Distributed Clustering (DistCom, MetaStab, PPComDet, FYPComDet), where I have contributed to proving rigorous results on unexpected aspects of the evolution of computational dynamics (see also (IgnComp, ConsBroad)). Another important part of my research has been to strive to use the aforementioned mathematical tools to problems in theoretical biology, in particular the study of the collective behaviors of biological systems (InfoFlow). In this respect, I have worked on the algorithmic analysis of the behavior of organisms such as ant species (Levy) and Physarum polycephalum (DistFlow).

Besides all that, I investigated some other distributed-computing problems (RepBins, MinMsg, NoisCons, ParLoad), enjoyed working on some algorithm engineering projects (Kadabra), and studied the complexity of certain combinatorial puzzles and games (Candy, PegS, CoG).

You can find some of my code on my Github page.

Here's my Mathematics Genealogy Project page. My Erdős number is 3, thanks to Giorgio Gambosi.

CC BY-SA 4.0 Emanuele Natale. Last modified: January 18, 2024.
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