Research in Psychology, Neurophysiology, Data Science
I am a researcher at the French National Institute of Computer Science (INRIA) and a certified psychologist, working and living in Paris.
My goal is to tackle brain disorders by combining cognitive neuroscience and machine learning. I have specialized in large-scale data analysis and predictive modeling with electrophysiology (EEG, MEG) in neurology. A central part of my work is about orchestrating the study of neuronal dynamics with development of domain-sensitive biomarkers, statistical methods and computational algorithms for effective learning from brain data.
What brought me here is inpsiration drawn from social cognition research which I have been studying since my undergraduate days and which has profoundly shaped my view of science and society. The research of tomorrow will be increasingly interdisciplinary and cooperation-driven. I therefore keep investing in the social infrastructure of science by openly developing and disseminating academic software for data analysis, building on top of the freely available open source programming languages Python and R.
PhD in Psychology, 2015
University of Cologne, Germany
Diploma in Psychology, 2010
University of Gießen, Germany
Tools for large-scale analysis
Coding, mentoring and dissemination for the MNE project.
ML/AI controled drug-dosage during anesthesia.
Supplementing diagnosis in disorders of consciousnes with EEG and ML
For a plain English summary, consider the eLife digest by Helena Perez Valle.
To get an overview on the paper, consider this Twitter thread:
I am very excited to share our latest work published in @eLife! We combined #MEG, #fMRI and #MRI for #BrainAge prediction & #biomarker development. Each modality added unique information & enhanced brain-behavior mapping! https://t.co/iCLYKsuUWY Thread👇 pic.twitter.com/dNEMlGnqXe
— Denis A. Engemann (@dngman) May 19, 2020
For a high-level presentation of the main findings check-out our OHBM 2020 Poster.
Or visit this Twitter thread:
Excited to share our paper @NeuroImage_EiC by @DavSabbagh with @PierreAblin @GaelVaroquaux @agramfort https://t.co/ke0wO8aqwk Nonlinear subject-level regression on M/EEG using linear models without source localization: theory + empirical benchmarks Thread👇 pic.twitter.com/0vevsO3UmG
— Denis A. Engemann (@dngman) May 18, 2020
For a discussion of the neurophysiologicla implications, please consider the companion article by Rodika Sokoliuk and Damian Cruse
Should you have no time to read the paper, this Twitter thread gives you the gist:
1/ New paper out! @Brain1878 https://t.co/l3VIfg4s9F Robust #MachineLearning of diagnosis from #EEG in disorders of #consciousness powered by @CEAParisSaclay @cdf1530 @Coma_Science @dc_uba @ERC_Research @Inria_Saclay @Inserm @mne_news @NeuroSpin_91 @icm_institute @scikit_learn pic.twitter.com/JsDMUYWw6C
— Denis A. Engemann (@dngman) October 4, 2018
