Research Bio

TCS phase (2013-2018)

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).

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

Neuroscience-ML phase (2018-2022)

Over the last three years, I've been interested in theoretical neuroscience. I've been a fellow of the Brain and Computation Program of the Simons Institute for the Theory of Computing, and since then I've been working on algorithmic problems such as network alignment (BrainAlign), in order to investigate, through a comparative approach, how computation is organized in the brain (AC). I've also been working on pruning algorithms for artificial neural networks (LTH), with some hope to get insights on synaptic pruning and related phenomena in the brain.

Since 2021, I've become interested in the sub-area of scientific computing known as Scientific Machine Learning, in particular within the Julia programming language ecosystem.


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: November 19, 2022. Website built with Franklin.jl and the Julia programming language.