Theodoros (Theo) Zanos is an Assistant Professor at the Feinstein Institute for Medical Research and the head of the Neural Encoding and Data Analytics lab at the Center of Bioelectronic Medicine. He lives in New York, NY.
Before joining Feinstein Institute, he was a research associate and a postdoctoral fellow at the laboratory of Dr. Christopher Pack, in the department of Neurology and Neurosurgery, at the Montreal Neurological Institute at McGill University. He acquired his MSc and PhD in Biomedical Engineering in 2006 and 2009 respectively, at the Viterbi School of Engineering at University of Southern California, under the supervision of Dr. Vasilis Marmarelis. In 2004, he graduated with an Electrical and Computer Engineering diploma from the Aristotle University of Thessaloniki.
Theo has a proven track record in developing quantitative models of neuronal circuits and neuroprosthetic algorithms predicting neuronal activity in the visual cortex and the hippocampus, performing single and multi-electrode recordings and stimulation (intracortical and transcranial) in behaving primates and rats, relating research results to visual perception, eye movement behavior, learning and memory.
He has been awarded the Center of Excellence in Commercialization and Research Grant ($80k – 2010), the Jean Timmins Fellowship Grant ($40k – 2012) and the Research Assistant Fellowship Grant ($120k - 2004).
He has published his research results in 15 peer-reviewed papers, in journals like Neuron, Journal of Neuroscience, Journal of Neurophysiology, IEEE Transactions in Neural Systems, Annals of Biomedical Engineering and others, with over 220 citations (h-index=7), presented work in 16 international conferences, awards for best Senior Thesis (2004) and best oral presentation (2006)
His expertise includes the following areas:
Computational Neuroscience: nonlinear neural systems modeling; functional connectivity algorithms; cortical wave detection algorithms; neural signal processing; FEM-based EEG source localization
Signal Processing - Systems Identification: predictive Volterra models; time-frequency analysis of signals; statistical signal processing; nonlinear dynamic systems modeling; machine learning
Neuroengineering: neuroprosthetic multi-input multi-output computational algorithms predicting neuronal ensemble activity; invasive (intracortical microstimulation - ICMS) and non-invasive (transcranial current stimulation - tCS) neurostimulation;
Programming: Matlab; GUI Development; Toolbox Development; Parallel Processing Programming; Python
Neurophysiology: chronic electrode arrays; visual cortex and hippocampal recordings from behaving primates; hippocampal recordings from behaving rats and slice preparations; temporal lobe recordings from humans; eye tracking signal analysis & saccade dynamics analysis; single and multi-electrode recordings; EEG; tCS; deep brain stimulation recordings; closed-loop visual stimulation; monkey behavior and training
He is a reviewer for the Cerebral Cortex journal, Frontiers in Neural Circuits journal, Annals of Biomedical Engineering journal, IEEE Signal Processing Letters journal, IEEE Engineering in Medicine and Biology Conferences and IEEE Neural Engineering Conferences. For four years, he was a guest lecturer for the Computational Neuroscience Course (Neur 603) of the Integrated Program in Neuroscience (IPN) at McGill University. He served as a chair or scientific committee member in several scientific symposia, as a consultant for NIH grants and more recently as the lead research scientist for a wearables project developing Closed-Loop Mobile EMG-based Arm Rehabilitation App (CLM-ARP). He has trained and supervised 6 graduate and undergraduate students.