Jose M. Carmena
University of California-Berkeley
Jose M. Carmena, PhD is the Chancellor’s Professor in the Department of Electrical Engineering and Computer Sciences, and the Helen Wills Neuroscience Institute, at the University of California-Berkeley, and Co-Director of the Center for Neural Engineering and Prostheses at UC Berkeley and UC San Francisco. His research program in neural engineering and systems neuroscience is aimed at understanding the neural basis of sensorimotor learning and control, and at building the science and engineering base that will allow the creation of reliable neuroprosthetic systems for the severely disabled. Dr. Carmena received the B.S. and M.S. degrees in electrical engineering from the Polytechnic University of Valencia (Spain) in 1995 and the University of Valencia (Spain) in 1997. Following those he received the M.S. degree in artificial intelligence and the Ph.D. degree in robotics both from the University of Edinburgh (Scotland, UK) in 1998 and 2002 respectively. From 2002 to 2005 he was a Postdoctoral Fellow at the Department of Neurobiology and the Center for Neuroengineering at Duke University (Durham, NC). He is Fellow of the Institute of Electrical and Electronics Engineers (IEEE), and member of the Society for Neuroscience, and the Neural Control of Movement Society. Dr. Carmena has been the recipient of the McKnight Technological Innovations in Neuroscience Award (2017), Bakar Fellowship (2012), the IEEE Engineering in Medicine and Biology Society Early Career Achievement Award (2011), the Aspen Brain Forum Prize in Neurotechnology (2010), the National Science Foundation CAREER Award (2010), the Alfred P. Sloan Research Fellowship (2009), the Okawa Foundation Research Grant Award (2007), the UC Berkeley Hellman Faculty Award (2007), and the Christopher Reeve Paralysis Foundation Postdoctoral Fellowship (2003).
Beata Jarosiewicz received her Ph.D. in Neuroscience at the University of Pittsburgh and the Center for the Neural Basis of Cognition in 2003. Her thesis characterized a novel neurophysiological state in the rat, Small Irregular Activity, revealed using then state-of-the-art multi-tetrode electrophysiology. As a postdoc with Dr. Andrew Schwartz at the University of Pittsburgh, she perturbed the decoding of a subset of neurons in a brain-computer interface (BCI) paradigm in NHPs to study how the brain solves the credit assignment problem, introducing a now popular paradigm for studying neural plasticity and dynamics. As a postdoc with Dr. Mriganka Sur at MIT, she used in vivo 2-photon calcium imaging and dual fluorescent retrograde tracing to study how higher-order visual areas obtain their distinct functionality from the tuning properties of spatially interleaved but differentially projecting V1 neurons. In 2010, she joined the BrainGate research team, where she helped enable the first demonstration of brain-controlled robotic limbs by people with paralysis, culminating in a Nature publication that received Clinical Research Forum’s “Herbert Pardes Clinical Research Excellence Award” as the most outstanding clinical research project of 2012. She also devised and implemented BCI self-calibration algorithms that maintained high-quality brain control over a computer cursor across days and weeks without the need for user-in-the-loop calibration tasks. In 2018, she joined the Research team at NeuroPace, where she is helping to optimize the calibration of the RNS System, the first FDA-approved fully implanted brain-responsive neurostimulation device for people with epilepsy.
University of Pittsburgh
Robert Gaunt is an Assistant Professor in Physical Medicine and Rehabilitation at the University of Pittsburgh. Robert earned a B.Eng. degree in Mechanical Engineering from the University of Victoria (Victoria BC, Canada) and a Ph.D. in Biomedical Engineering at the University of Alberta (Edmonton AB, Canada) in 2008. He completed his postdoctoral training with Doug Weber at the University of Pittsburgh. His primary research interests are in the area of sensorimotor control and the development and translation of advanced neuroprosthetic devices. Active research topics include developing novel neural interfaces to regulate bladder function, developing prosthetic control systems for amputees that enable dexterous hand movements through implanted myoelectric interfaces, and developing bidirectional implantable brain machine interfaces to restore movement and sensation to people with upper-limb paralysis. He holds a number of patents and his work has been covered by numerous national and international media outlets.
Paul Sajda is a Professor of Biomedical Engineering, Electrical Engineering and Radiology (Physics) at Columbia University. He is also a Member of Columbia’s Data Science Institute and an Affiliate of the Zuckerman Institute of Mind, Brain and Behavior. He received a BS in electrical engineering from MIT in 1989 and an MSE and PhD in bioengineering from the University of Pennsylvania, in 1992 and 1994, respectively. Professor 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. 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. Professor 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. Professor Sajda is a co-founder of several neurotechnology companies and works closely with a range of scientists and engineers, including neuroscientists, psychologists, computer scientists, and clinicians. He is a fellow of the IEEE, AMBIE and AAAS and Chair of the IEEE Brain Initiative. He is also a recent recipient of the DoD’s Vannevar Bush Faculty Fellowship (VBFF).
Maryam M. Shanechi
University of Southern California
Maryam M. Shanechi is Assistant Professor and Viterbi Early Career Chair in Electrical and Computer Engineering at the Viterbi School of Engineering, University of Southern California (USC). She is also a faculty member at the Neuroscience Graduate Program at USC. She received her B.A.Sc. degree in Engineering Science from the University of Toronto in 2004 and her S.M. and Ph.D. degrees in Electrical Engineering and Computer Science from MIT in 2006 and 2011, respectively. She held postdoctoral positions at Harvard Medical School and at UC Berkeley from 2011-2013. She directs the Neural Systems Engineering Lab at USC. Her research is focused on developing closed-loop neurotechnologies through mathematical decoding and control of brain networks to treat neurological and neuropsychiatric disorders. She is the recipient of various awards including the NSF CAREER Award, the MIT Technology Review’s top 35 innovators under the age of 35 (TR35), the Popular Science Brilliant 10, an ARO multidisciplinary university research initiative (MURI) award, and the ONR Young investigator award.