Software & Code
About My Work
I aim to build inference tools that are automatic, flexible, and scalable. Technically, I have developed:
(1) Adaptive Markov Chain Monte Carlo algorithms for unbiased hypothesis comparison,
(2) Amortized simulation-based inference methods leveraging probabilistic deep learning.
These tools address challenges in Bayesian inference from brain recordings across multiple spatiotemporal scales and are integrated into the
EBRAINS platform.
1) Virtual Brain Inference (VBI) at whole-brain level
2) Bayesian Virtual Epileptic Patient (BVEP)
Using Stan/PyMc3/Numpyro/SBI
3) Dynamic Causal Modeling (DCM-PPLs)
4) Inference on Macroscopic Dynamics (Inf-MFM)
5) 2D-Epileptor (Slow-fast dynamical system)
8) PkPd Models using Stan
9) Hidden Markov Model (HMM) using PyMC
10) Vector Autoregressive (VAR) Model
14) Delay Differential Equations (DDEs)