Kevin M. Passino

Professor of Electrical and Computer Engineering

OSU Distinguished Scholar

Mathematical Biosciences Institute

Humanitarian Engineering Center

Dept. Evolution, Ecology, and Organismal Biology

Chronic Brain Injury, DT

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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 treatments for individuals (e.g., brains) and groups, with an emphasis on STEM to help with mental health. Currently, for projects and directions, our group is focusing on stress reduction, mood disorders (depression, bipolar illness), and attention (ADHD). EEG, ECG, and heart rate/heart rate variability (HRV) signals are measured. Bio-signal driven auto-generation of music and video for personalized virtual reality treatments (e.g., therapeutic virtual environments and psychoeducation), "adaptive ambienc"e (3d multimodal sensory modulation via feedback of biosignals). Also, work on apps, web sites, and integration into social support networks (e.g., peer-to-peer approaches).

  • 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.

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