Bio
I currently work on building, understanding, and integrating machine learning/artificial intelligent systems.
I’m interested in the intersection of machine learning, intelligent agents, digital health, wearables, signal processing, and understanding brain and body connections.
I completed my PhD work in Neural and Biomedical Engineering with fellowships awarded from the NIH and Grove School of Engineering.
Experience
Select Publications
A Scalable Framework for Closed-Loop Neuromodulation with Deep Learning. Nigel Gebodh, Vladimir Miskovic, Sarah Laszlo, Abhishek Datta, Marom Bikson. bioRxiv 2023.01.18.524615; doi: https://doi.org/10.1101/2023.01.18.524615
Dataset of concurrent EEG, ECG, and behavior with multiple doses of transcranial electrical stimulation. Nigel Gebodh, Zeinab Esmaeilpour, Abhishek Datta, Marom Bikson. Nature Sci Data 8, 274 (2021); doi: https://doi.org/10.1038/s41597-021-01046-y
Inherent physiological artifacts in EEG during tDCS. Nigel Gebodh, Zeinab Esmaeilpour, Devin Adair, Kenneth Chelette, Jacek Dmochowski, Adam J Woods, Emily S Kappenman, Lucas C Parra, Marom Bikson. Neuroimage 185, (2019/1/15); doi: https://doi.org/10.1016/j.neuroimage.2018.10.025
See all publications here.
Education
The Grove School of Engineering,
The City College of New York, CUNY
NIH-GRISE and Grove School of Engineering Fellow
PhD & MPhil
Building improved wearables, and assessing neural (sleep/attention/vigilance) and physiological function (EEG, ECG, EMG etc.) under brain stimulation with computational modeling, and machine learning.
MSci
Understanding the human visual system by mapping the early visual cortex. Applying machine learning, signal detection, and signal processing techniques.
BE
Medical device design, device manufacturing, and rapid prototyping.