David Holcman is an applied mathematician, biophysicist and computational biologist at École Normale Supérieure in Paris.
He is known for his work on the narrow escape problem, based on analysis of the Laplace equation,[1][2] the redundancy principle in biology based on extreme statistics,[3][4][5] the modeling of molecular trafficking in neurobiology, of diffusion and electrodiffusion in nanodomains such as dendritic spines, the modeling of neuronal networks dynamics such as Up and Down states in electrophysiology.
He developed multiscale methods and simulations to analyse large amount of molecular super-resolution trajectories, and polymer physics modeling and analysis to study cell nucleus organization.
[10] His research interests include mathematical biology, stochastic processes, data modeling, computational methods, stochastic simulations, theory of cellular microworld, neuronal networks, computational biology and neuroscience, asymptotic approaches in partial differential equations, predictive medicine, electroencephalography (EEG) analysis, and modeling organelles in cells.
[11] His recent works concern predicting the brain state transition during general anesthesia based on real-time operative multi-dimensional dynamics including time-frequency patterns and signal suppressions.