Monthly Archives: November 2018

Misha Pavel: Digital Phenotyping Using Computational Models of Neuropsychological Processes Underlying Behavioral States and their Dynamics

Misha Pavel holds a joint faculty appointment in the College of Computer & Information Science and Bouvé College of Health Sciences. His background comprises electrical engineering, computer science and experimental psychology, and his research is focused on multiscale computational modeling of behaviors and their control, with applications ranging from elder care to augmentation of human performance. Professor Pavel is using these model-based approaches to develop algorithms transforming unobtrusive monitoring from smart homes and mobile devices to useful and actionable knowledge for diagnosis and intervention. Under the auspices of the Northeastern-based Consortium on Technology for Proactive Care, Professor Pavel and his colleagues are targeting technological innovations to support the development of economically feasible, proactive, distributed, and individual-centered healthcare. In addition, Professor Pavel is investigating approaches to inferring and augmenting human intelligence using computer games, EEG and transcranial electrical stimulation. Previously, Professor Pavel was the director of the Smart and Connected Health Program at the National Science Foundation, a program co-sponsored by the National Institutes of Health. Earlier, he served as the chair of the Department of Biomedical Engineering at Oregon Health & Science University, a Technology Leader at AT&T Laboratories, a member of the technical staff at Bell Laboratories, and faculty member at Stanford University and New York University. He is a Senior Life Member of IEEE.

Digital Phenotyping Using Computational Models of Neuropsychological Processes Underlying Behavioral States and their Dynamics

Human behaviors are both key determinants of health and effective indicators of individuals’ health and mental states. Recent advances in sensing, communication technology and computational modeling are supporting unprecedented opportunity to monitor individuals in the wild – in their daily lives. Continuous monitoring, thereby, enables Digital Phenotyping – characterization of health states, inferences of subtle changes in health states and thereby facilitating theoretical insights into human neuropsychology and neurophysiology. Moreover, temporally dense measurements may provide opportunities for optimal just-in-time interventions helping individuals to improve their health behaviors. Harvesting the potential benefits of digital phenotyping is, however, limited by the variability of behaviors as well as contextual and environmental effects that may significantly distort measured data. To mitigate these adverse effects, we have been developing computational models of a variety of physiological, neuropsychological and behavioral phenomena. In this talk, I will briefly discuss a continuum of models ranging from completely data-driven to principle-based, causal and mechanistic. I will then describe a few examples of approaches in several domains including cognition, sensory-motor behaviors and affective states. I will also describe a framework that can use such approaches as components of future proactive and distributed care, tailored to individuals.

His talk takes place on Monday, December 3, 2018 at 13:00 in room A113.