Welcome to my page!
Currently, I am a Senior Research Fellow at INS (UMR1106).
My work aims to infer the causal mechanisms using Virtual Brain Twins, leveraged by probabilistic inference to enhance diagnostics, targeted interventions, therapies, and clinical decision-making.
What is inference and how to make inference?
My research integrates mathematical modelling (spiking, mean-field, and whole-brain level), multimodal imaging data, and Bayesian inference to study brain (dys)function.
Bridging with clinical translation toward precision medicine, I have developed Virtual Brain Twins to identify the causal mechanisms underlying different brain disorders and recently in cognitive tasks, leveraging principles from statistical physics, dynamical systems, and probabilistic machine learning (MCMC, SBI, Generative AI).
I perform probabilistic inference for:
- Cohort studies Focusing on longitudinal trajectories, which do not necessarily require a control group.
- Case-Control studies Disregarding heterogeneity and instead focusing on the average patient. This approach is often noninformative for constructing models in neurodegenerative and psychiatric disorders.
- Normative studies An emerging approach for quantifying and describing how individuals deviate from expected patterns learned from a healthy population (e.g., using GAMLSS models).
- Personalized Causal studies in precision medicine Focusing on individualized anatomical data and mechanistic models, inverted by state-of-the-art probabilistic inference methods (e.g., Adaptive Hamiltonian Monte Carlo and Normalizing Flows).
Background
During my master’s degree, I investigated how variations in the autapses through gap junction, and duration of synaptic activity influence the spike rate of a Hodgkin–Huxley neuron under delayed feedback, an early step that grounded my interest in neuronal dynamics. During my PhD, my research goal was to better understand how anesthetic actions at the molecular level translate into changes in coarse-grained EEG measurements, using mathematical mean-field models and bio-inspired optimization techniques.During my postdoc, my first project focused on developing Bayesian inference at the whole-brain level to estimate the epileptogenicity of each brain region and better guide surgical strategies. This approach has reached clinical translation in epilepsy, where it integrates clinical knowledge to enhance personalized predictions and allows for reliable hypothesis testing (RHU-EPINOV clinical trial).
Subsequently, I developed probabilistic machine learning to address the computational cost of Monte Carlo methods. This led to several international collaborations for studying brain (dys)function, such as healthy aging, Parkinson’s, Alzheimer’s, multiple sclerosis, and social facilitation.
Recently, I have designed probabilistic tools for automatic Dynamic Causal Modeling and inference across brain scales and neuroimaging modalities (MEG/EEG, SEEG, fMRI). These studies led to the development of Virtual Brain Twins—digital replicas of individual brains designed to assist clinical decision-making in diagnostics, prognosis, and therapy.