My background is in complex systems physics and computational neuroscience. Currently, I am at MIT focusing on computational neuroscience, foundations of physical computing and biological computation, and bio-inspired intelligence.
In the past, I have been in various research institutes:
- New England Complex Systems Institute
- Harvard's Wyss Institute for Biologically Inspired Engineering
- Unite de Neurosciences, Information et Complexite (UNIC) of Centre National de la Recherche Scientifique (CNRS) ; Now "Departmente Neuroscience Integratives et Computationnelles" at "Institute de Neurosciences Paris-Saclay"
- Sorbonne (Universite Pierre-et-Marie-Curie)
- Harvard MGH Cortical Physiology Lab
Here is a more deatailed bio:
My curiosity about the brain led me to a career in pure research, beginning with stints at Harvard’s Martinos Center for Biomedical Imaging, UCSD’s Multimodal Imaging Lab, and the MGH Cortical Neurophysiology Lab. During these formative years, I focused on the multimodal investigation and electromagnetic source localization of spatiotemporal patterns of sleep rhythms and thalamocortical oscillations, deepening my interest in neural dynamics and biophysics.
Driven by a desire to delve deeper into the theoretical underpinnings of neural computation, I pursued a Ph.D. in physics of complex systems and computational neuroscience at the Laboratory of Computational Neuroscience in Unite de Neurosciences, Information et Complexite (UNIC-CNRS). My doctoral thesis, titled “Electromagnetic Signature of Human Cortical Dynamics during Wakefulness and Sleep,” explored spectral dynamics and frequency scaling of electromagnetic measurements (MEG/EEG) of brain activity, assessed self-organized criticality in invasive ensemble recordings across different functional states (wake-sleep cycle), and analyzed the network properties of excitation and inhibition in cortical microcircuits during wakefulness and sleep.
Following my Ph.D., I served as an independent institute technology fellow at Harvard's Wyss Institute for Biologically-Inspired Engineering and the New England Complex Systems Institute. There, I extended my research on neural ensembles to investigate state-dependent rhythmic activity and the multiscale balance of excitation and inhibition in sleep-wake cycles, as well as network disorders of excitation/inhibition (such as seizures).
Later, I joined MIT Physics and the Center for Brains, Minds and Machines, where I continued exploring the intersection of neuroscience, computation, and physics. Utilizing information theory, I studied emergent phenomena and causally linked dynamics at both macroscopic and microscopic scales in excitable media. My work included investigating pairwise and higher-order interactions of inhibitory and excitatory neurons during wakefulness and sleep, examining the role of diverse cortical compute nodes and projections, and contributing to our understanding of how cognitive computing is shaped by a dynamic framework.
A brief tenure at the Allen Discovery Center saw me developing a mathematical framework using category theory to explore dynamic complex interactions and regulatory mechanisms in biological computation. This included developing a neural network model of cellular homeostasis optimized for enhanced predictive and reconfiguration capacities in changing environments.
Currently, I'm back at MIT’s McGovern Institute for Brain Research, focusing on leveraging complex systems physics-based approaches to understand collective neural computation. I spearhead projects examining the complexity and regularity of neural dynamics, creating measures inspired by statistical physics to examine higher-order motifs and correlations across multiple spatiotemporal scales, developing generative models of neural activity to probe micro/mesoscopic links in spatiotemporal dynamics, and utilizing data-driven modeling to understand computation-control through the lens of excitatory and inhibitory neuron interactions.