Paul Sajda


1010 Northwest Corner Building
Mail Code 8904

Tel(212) 854-5279

Paul Sajda is interested in what happens in our brains when we make a rapid decision and, conversely, what processes and representations in our brains drive our underlying preferences and choices, particularly when we are under time pressure. His work in understanding the basic principles of rapid decision-making in the human brain relies on measuring human subject behavior simultaneously with cognitive and physiological state. 

Research Interests

Neural Engineering: Using principles of reverse neuroengineering to characterize the cortical networks underlying perceptual and cognitive processes, such as rapid decision-making in the human brain. , My laboratory pursues both basic and applied neuroscience research projects, with emphasis on non-invasive multi-modal neuroimaging, visual perception, , brain-computer interfaces, application of machine learning to analysis of neural data and computational modeling of large neural systems

Important in his approach is his use of machine learning and data analytics to fuse these measurements for predicting behavior and infer brain responses to stimuli.  Sajda applies the basic principles he uncovers to construct real-time brain-computer interfaces that are aimed at improving interactions between humans and machines. He is also applying his methodology to understand how deficits in rapid decision-making may underlie and be diagnostic of many types of psychiatric diseases and mental illnesses.

Of particular interest to Sajda is how different areas in the human brain interact to change our arousal state and modulate our decision-making. Specifically he is using simultaneous EEG and fMRI together with pupillometry to identify and track spatiotemporal interactions between the anterior cingulate cortex, dorsolateral prefrontal cortex, and subcortical nuclei such as the locus coeruleus. He has found that the dynamics of these interactions are altered under stress, particularly when dealing with high-pressure decisions with critical performance boundaries.  These findings are being transitioned to applications ranging from tracking the cognitive state of pilots while operating fighter aircraft to identifying biomarkers of healthy thought patterns in patients being treated for  major depressive disorders and/or  complicated grief. Sajda has co-founded several neurotechnology companies and works closely with a range of scientists and engineers, including neuroscientists, psychologists, computer scientists, and clinicians. 

Sajda received a BS in electrical engineering from Massachusetts Institute of Technology (MIT) in 1989 and an MSE and PhD in bioengineering from the University of Pennsylvania, in 1992 and 1994, respectively. He is a fellow of the IEEE, AMBIE and AAAS.


  • Visiting professor, University of Glasgow, 2015-
  • Professor of biomedical engineering, Electrical Engineering and Radiology, Columbia University, 2012-
  • Member, Graduate Group, Neurobiology and Behavior, Columbia University, 2008-
  • Visiting scientist, RIKEN Brain Sciences Institute, JAPAN, 2008
  • Director, Laboratory for Intelligent Imaging and Neural Engineering (LIINC), Columbia University, 2000-
  • Associate professor of biomedical engineering and radiology, Columbia University, 2000-2012
  • Head, Adaptive Image & Signal Processing Group, Sarnoff Research Center, 1997-2000
  • Technology leader, Adaptive Image & Signal Processing, Sarnoff Research Center, 1996-1997
  • Member of technical staff, Sarnoff Research Center, 1994-1996


  • Fellow, American Association for the Advancement of Science
  • Fellow, American Institute for Medical and Biological Engineering
  • Fellow, Institute of Electrical and Electronic Engineers
  • Member, Engineering in Medicine and Biology Society
  • Member, Association for Research in Vision and Ophthalmology
  • Member, Association for Computing Machinery
  • Member, Society for Neuroscience,


  • Elected Fellow of the AAAS, 2016
  • Elected Fellow of the IEEE, 2012
  • Elected editor-in-chief of IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2011
  • Elected Fellow of the American Institute for Medical and Biological Engineering  (AIMBE), 2009
  • Awarded Japan Society for the Promotion of Science (JSPS) Fellowship, 2008
  • National Science Foundation CAREER Award, 2002
  • Sarnoff Technical Achievement Award, 1996


  • S. Saproo, V. Shih, D. C. Jangraw, and P. Sajda, “Neural mechanisms underlying catastrophic failure in human-machine interaction during aerial navigation.,” Journal of neural engineering, vol. 13, no. 6, p. 066005.
  • J. Muraskin, J. Sherwin, G. Lieberman, J.O. Garcia, T. Verstynen, J.M. Vettel  &  P. Sajda (2017) Fusing multiple neuroimaging modalities to assess group differences in perception-action coupling, Proceedings of the IEEE. 105 (1), 83-100.
  • N. Schneck, S. Haufe, T. Tu, and G. A. Bonanno, (2017) “Tracking Deceased-Related Thinking with Neural Pattern Decoding of a Cortical-Basal Ganglia Circuit,” Biological Psychiatry CNNI, (in press).
  • J. Muraskin, S. Dodhia, G. Lieberman, J.O. Garcia, T. Verstynen, J.M.  Vettel, J. Sherwin, and P. Sajda (2016), Brain dynamics of post-task resting state are influenced by expertise: Insights from baseball players. Hum. Brain Mapp., 37: 4454–4471. doi:10.1002/hbm.23321
  • J.S. Sherwin, J. Muraskin and P. Sajda (2015) Pre-stimulus functional networks modulate task performance in time-pressured evidence gathering and decision-making Neuroimage. Volume 111, 1 May 2015, Pages 513-525
  • B. Lou, W-Y Hsu and P. Sajda (2015) Perceptual Salience and Reward Both Influence Feedback-Related Neural Activity Arising from Choice, J. Neurosci, 35 (38) 13064-13075
  • J. Muraskin, J. Sherwin and P. Sajda (2015) Knowing when not to swing: EEG evidence that enhanced perception–action coupling underlies baseball batter expertise, Neuroimage, 123, 1-10.
  • D.C. Jangraw, J. Wang, B. Lance, S-F Chang and P.  Sajda (2014) Neurally and ocularly informed graph-based models for searching 3D environments, Journal of Neural Engineering 11(4):046003.
  • M.G. Philiastides, H. R. Heekeren, and P. Sajda, (2014) Human Scalp Potentials Reflect a Mixture of Decision-Related Signals during Perceptual Choices, J Neurosci, vol. 34, no. 50, pp. 16877–16889.
  • J. M. Walz, R. I. Goldman, M. Carapezza, J. Muraskin, T. R. Brown, and P. Sajda (2013) Simultaneous EEG-fMRI Reveals Temporal Evolution of Coupling between Supramodal Cortical Attention Networks and the Brainstem, J Neurosci. 2013 Dec 4;33(49):19212-22.
  • P. Sajda, L.C. Parra, C. Christoforou, B. Hanna, C. Bahlmann, J. Wang, E. Pohlmeyer, J. Dmochowski, -Fu Chang (2010) In a Blink of an Eye and a Switch of a Transistor: Cortically-coupled Computer Vision", Proceedings of the IEEE vol 98(3): 462-478.
  • R. Ratcliff, M.G. Philiastides , P. Sajda, (2009). Quality of Evidence for Perceptual Decision Making is Indexed by Trial-to-Trial Variability of the EEG. Proceedings of the National Academy of Sciences, 106(16):6539-44.
  • L.C. Parra, C. Christoforou, A. D. Gerson, M. Dyrholm, A. Luo, M. Wagner, M. G. Philiastides, P. Sajda (2008) Spatio-temporal linear decoding of brain state: Application to performance augmentation in high-throughput tasks, IEEE Signal Processing Magazine, vol. 25, no. 1, pp. 95-115.
  • M.G. Philiastides and P. Sajda (2007) EEG-Informed fMRI Reveals Spatiotemporal Characteristics of Perceptual Decision Making, Journal of Neuroscience, Nov 28; 27(48):13082-91.
  • M.G. Philiastides and P. Sajda (2006) Temporal characterization of the neural correlates of perceptual decision making in the human brain, Cerebral Cortex. 16(4): 509-518,  Apr. 2006. (cover article)
  • M.G. Philiastides, R. Ratcliff and P. Sajda (2006) Neural representation of task difficulty and decision making during perceptual categorization: a timing diagram, Journal of Neuroscience, 26(35): 8965-75. (cover article)
  • P. Sajda (2006) Machine learning for detection and diagnosis of disease, Annual Review of Biomedical Engineering, (invited). Vol 8, 537-565.
  • A.D. Gerson, L.C. Parra and P. Sajda (2006) Cortically-coupled computer vision for rapid image search, Neural Systems and Rehabilitation Engineering, IEEE Transactions on. 14(2) 174-179.