Assistant Professor, Graduate Student Advisor

David Jangraw studied electrical engineering as an undergraduate at Princeton University. He then spent a year as a research assistant in Columbia University’s Neuroscience department, studying attention and reward using single-cell recordings in rhesus monkeys. He earned his PhD from Columbia’s Biomedical Engineering Department, where his work with Paul Sajda developed novel BCIs and characterized the EEG and ocular responses to “naturalistic” scenarios that mimic real life. David joined the National Institutes of Health (NIH) as a post-doctoral fellow with Peter Bandettini, where he used fMRI data collected during reading to predict future recall. In 2018, he joined Daniel Pine’s Emotion and Development Branch at the NIH, which studies and treats mood and anxiety disorders in adolescents. In his spare time, David has consulted for startups, judged a BCI-focused architecture seminar, developed a bioinstrumentation course, and served as an adjunct professor at American University. He once scanned an opera singer’s brain as part of the NIH’s Sound Health initiative, and his results may be the only neural data ever projected onto the jumbotron at Nationals Park.


Jangraw, David C., et al. "A functional connectivity-based neuromarker of
sustained attention generalizes to predict recall in a reading task."
Neuroimage 166 (2018): 99-109.

Jangraw, David C., et al. "Neurally and ocularly informed graph-based models
for searching 3D environments." Journal of neural engineering 11.4 (2014):

Jangraw, David C., et al. "NEDE: An open-source scripting suite for
developing experiments in 3D virtual environments." Journal of neuroscience
methods 235 (2014): 245-251.

Peck, Christopher J., Jangraw, David C., et al. "Reward modulates attention
independently of action value in posterior parietal cortex." Journal of
Neuroscience 29.36 (2009): 11182-11191.

Chang, Shih-Fu, et al. "Rapid image annotation via brain state decoding and
visual pattern mining." U.S. Patent No. 8,671,069. 11 Mar. 2014.

Areas of Expertise and/or Research

Human Neuroimaging, Machine Learning, Brain-Computer Interfaces, EEG, fMRI, Eye Tracking


  • M.S. and PhD in Biomedical Engineering, Columbia University
  • B.S.E. in Electrical Engineering, Princeton University


Office Location:

Votey 309A

  1. The Glass Brain Lab

Courses Taught

  • BME111: BME Core 3: Signals and Systems
  • BME229/EE229: Biosignal Decoding
  • BME296: Brain-Computer Interfaces