Dr. Jas completed his PhD from Telecom ParisTech. His thesis focused on automating MEG/EEG analysis pipelines. He is a proponent of open and reproducible science. He has been a key contributor to several open source
neuroimaging tools: most notably MNE-Python, MNE-BIDS, and HNN-core. He developed Autoreject, a tool for automatic annotation and repair of artifactual MEG/EEG data.

Currently, he is focusing on the development of next-generation MEG using optically pumped magnetometers(OPMs) and their application to new neuroscience problems.


PhD in Image and Signal Processing, Télécom ParisTech

Select Publications

1. Jas, M., Engemann, D. A., Bekhti, Y., Raimondo, F., & Gramfort, A (2017). Autoreject: Automated artifact rejection for MEG and EEG data. NeuroImage, 159, 417-429.

2. Jas M, Thorpe R, Tolley N, Bailey C, Brandt S, Caldwell B, Cheng H, Daniels D, Pujol CF, Khalil M, Kanekar S, Kohl C, Kolozsvári O, Lankinen K, Loi K, Neymotin S, Partani R, Pelah M, Rockhill A, Sherif M, Hamalainen M, & Jones S (2023). HNN-core: a Python software for cellular and circuit-level interpretation of human MEG/EEG. Journal of Open Source Software, 8(92): 5848.

3. Jas, M., Larson, E., Engemann, D. A., Leppäkangas, J., Taulu, S., Hämäläinen, M., & Gramfort, A. (2018). A reproducible MEG/EEG group study with the MNE software: recommendations, quality assessments, and good
practices. Frontiers in neuroscience, 12, 530.


2018: MILA best poster award (Montreal AI and Neuroscience conference)
2023: Student award at Brain and Human Modeling Conference