Physiologically-informed Artificial Intelligence (PI-AI) for Improving Human Machine Interaction

2nd December 2020

Timing : 1 pm EST

For zoom link to the talks, please email with your institute email and mention affiliation

For a list of all talks at the NanoBio seminar Series 2020, see here

The intertwining of humans and machines is ever increasing, with integrated human/machine ‘teams’ under development and even being deployed. Challenging is how to replicate interactions between humans and machines, given how we now humans interact with one another. Humans constantly value intermediate decisions with respect to context through internal models of their confidence, expected reward, risk etc, before they generate a behavior. Such information about human decision-making is expressed not just through behavior, such as speech or action, but more subtlety through physiological changes, small changes in facial expression, posture etc. These are cues that human beings utilize to infer the current disposition of one another. Socially and emotionally intelligent people are excellent at picking up on this information and using it to guide their decisions and interactions with team members. The ability to pick up on these cues is important for developing trust bonds between team members, enabling just-in-time prediction of team member states and needs. In this talk I will present some of the work we are doing, using non-invasive physiological sensing, to infer cognitive/physiologically states of humans while they interact with machines/agents. We show examples of how this state information can inform autonomy so as to improve human-machine interaction.

Paul Sajda
Director of the Laboratory for Intelligent Imaging and Neural Computing (LIINC)
Professor of Biomedical Engineering
Professor of Electrical Engineering
Professor of Radiology (Physics)
Columbia University

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).