Paul Sajda, Ph.D.

Chief Scientist, Co-founder


I am an East Coast guy with a West Coast persona, having grown up and lived on the I-95 corridor, from Philly to Boston. Though an academic at Columbia University, I love the start-up culture and have experience in the neurotech and human-machine interaction space. In addition to neurotech, brain imaging, and machine learning, I am an exercise nut and love to spin (or cycle if I am on the West Coast). I am also a big baseball fan — go Mets! My favorite thing to do is to travel the world with my wife and daughter.


Ph.D. Bioengineering, University of Pennsylvania
M.S. Bioengineering, University of Pennsylvania
B.S. Electrical Engineering, M.I.T.

Paul is a Professor of Biomedical Engineering, Electrical Engineering, and Radiology at Columbia University in New York City. As the Director of the Laboratory for Intelligent Imaging and Neural Computing (LIINC) and Co‐Director of Columbia’s Center for Neural Engineering and Computation (CNEC), he runs a federally-funded research group that produces cutting-edge innovation in neuro-engineering. He performs additional duties as the Editor‐in‐Chief for the IEEE Transactions in Neural Systems and Rehabilitation Engineering and as the Chair of the IEEE Brain Initiative. Before Columbia, he was the Head of the Adaptive Image and Signal Processing Group at the David Sarnoff Research Center in Princeton, NJ.

Paul is a recipient of the NSF CAREER Award and the Sarnoff Technical Achievement Award. Other honors include being named a Fellow of the IEEE, the American Institute of Medical and Biological Engineering (AIMBE), and the American Association for the Advancement of Science (AAAS).

Notable Publications

  1. “Quality of evidence for perceptual decision making is indexed by trial-to-trial variability of the EEG.” Proceedings of the National Academy of Sciences 2009
  2. “Cortically-coupled computer vision for rapid image search.” IEEE Transactions on neural systems and rehabilitation engineering 2006.
  3. “Response error correction – a demonstration of improved human-machine performance using real-time EEG monitoring.” IEEE transactions on neural systems and rehabilitation engineering 2003.

Google Scholar Profile