Meysam Hashemi
Postdoctoral Researcher (machine Learning And Complex Brain Dynamics) at Aix-Marseille University, Faculté de Médecine de la Timon, institut de neurosciences des systèmes
Marseille, Provence-Alpes-Côte d'Azur, France
Summary
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Senior🎓
Top SchoolMeysam Hashemi is a postdoctoral researcher in Marseille specializing in Bayesian inference and machine learning applied to complex brain dynamics, with nine years of experience bridging mathematical modeling, dynamical systems theory, and neuroimaging. He develops probabilistic parameter-estimation methods (MCMC and deep networks) for high-dimensional nonlinear models from single neurons to whole-brain connectomes, targeting clinical problems such as epilepsy prognosis, Alzheimer’s, multiple sclerosis, and anesthesia effects. His PhD work produced a thalamo-cortical neural mass model and optimization pipelines to infer parameters from EEG during propofol anesthesia, giving him rare expertise linking synaptic-level mechanisms to macroscopic signals. Comfortable in probabilistic programming and connectome-based predictive modeling, he focuses on translating computational insights into diagnostic and therapeutic decision support. An often-overlooked strength is his ability to pair mechanistic biophysical models with modern AI to make uncertainty-aware predictions suitable for clinical use.
9 years of coding experience
4 years of employment as a software developer
Doctor of Philosophy (Ph.D.), computational neuroscience, Doctor of Philosophy (Ph.D.), computational neuroscience at Universite de LorraineMaster's degree, Physics, Master's degree, Physics at Institute for Advanced Studies in Basic Sciences, Zanjan
English, French, Persian
Github Skills (8)
bayesian10
stan9
pystan9
statistics8
python7
probabilistic-programming7
machine-learning6
inference3
Programming languages (2)
Github contributions (5)
mhashemi0873/personalwebpage
Jan 2017 - Dec 2025
Contributions:181 pushes, 1 branch in 9 years
ins-amu/BVEP
Jan 2020 - Jul 2022
The Bayesian Virtual Epileptic Patient: a probabilistic framework designed to infer the spatial map of epileptogenicity in a personalized large-scale brain model of epilepsy spread
Contributions:173 commits, 166 pushes, 1 branch in 2 years 6 months
bayesian
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