Kevin M. Passino
Professor of Electrical and Computer Engineering
Mathematical Biosciences Institute
Humanitarian Engineering Center
Dept. Evolution, Ecology, and Organismal Biology
Chronic Brain Injury, DT
Contact and visitor information
Research: Technology for Mental Health
Technology 4 Mental Health research group uses interdisciplinary STEM-health/social sciences foundations, and feedback control theory/engineering to develop technology-based treatments for individuals (e.g., brains) and groups:
- Challenges: The theme here is to determine how to assist (help) humans via novel technologies: Currently, for projects and directions, our group is focusing on:
Virtual Reality, Music, and Networks for Mental Health: Stress reduction, emotion regulation, attention (ADHD), mood disorders (depression, bipolar illness). EEG, ECG, and heart rate variability (HRV) signals. Bio-signal driven auto-generation of music and video for personalized virtual reality treatments (e.g., therapeutic virtual environments and psychoeducation), apps, web sites, integration into social support networks.
Humanitarian/Development Engineering, On social networks: cooperative management of community technology and cooperative sustainable community development.
- Stable Optimal Social/Cooperation Networks: The theme for how we approach problems is via social/cooperative principles: Coordinated motion for animal and robot swarms over networks (agents), optimal social foraging (honeybees, group search), social/group choice (speed-accuracy tradeoff in honeybees; swarm cognition; and performance in choice discrimination, distractor avoidance, and individual-level irrationality filtering to achieve better swarm-level performance), cooperative allocation over networks (social foraging with optimal distributions, fast/accurate redistributions, and open-monitoring vs. focus/exploit trade-offs; computer networks; group/agent distributions for tasks; and sensing, search, patrol, and surveilance; distributed temperature and smart lights regulation), distributed synchronization (fire flies, sychronized swarms), cooperative task scheduling and sharing, coordinated attention (stable focus/re-focus), decentralized learning (inter-agent space regulation for autonomous agent groups), cooperative point pursuit in an electromechanical arcade, distributed assignment/concensus over a network (coordinated decisions on who does what and when), social dilemmas on task completion (participate or not on a community development project), collective optimization (bacterial foraging) and its use in learning, immunity-based learning, distributed evolution of decision-makers (robust controller design over the internet of things), network topology evolution from optimal allocations, and intelligent/autonomous control functional architectures. Principles for these, such as (i) kin (family), mutualism, direct reciprocity, indirect reciprocity, spatial, multilevel, etc.; and (ii) social cognition incorporating interaction rules, planning, attention, learning, evolution, and foraging (see biomimicry book). Courses taught include those listed at the above link, and ECE 7858 Intelligent Control. Fieldwork has been done in biology (e.g., the honey bee social choice problem), via collaborators, and in engineering in our Distributed Dynamical Systems Laboratory. See publications, books, and directions.
- Methods: Themes of our methods: (i) Mathematical modeling/analysis approaches including feedback control theory, stability analysis, optimization, game theory, and networks; (ii) computational analysis (simulations/statistical) and visualization; and (iii) technology interventions/solutions via distributed, mobile/stationary, networked computing. See publications and books.