- Reinforcement learning
- Dynamical systems
- Stochastic systems and control
- Multi-agent systems
- Biological system analysis
- Large data sets
Much of this group’s current work is centered on novel aspects of decision and control, stochastic dynamical systems, system identification and data science. Topics of current interest include Markov decision processes, reinforcement learning, path and trajectory planning, analysis of inherent control in biological systems, multi-agent systems – in particular adaptation, dynamics, and distributed resource allocation, game theoretic analysis of queueing and communication networks, scaling limits of stochastic models and probabilistic model uncertainty. Research on large data sets includes topological properties of systems and their applications, spectral and geometric analysis of state space of systems, operator-theoretic approach to dynamical systems, and their modeling from observations. Research on control in biological systems uses methodologies that range from analytic and numerical through toy model simulation and experiments, all the way to biological networks and human action-perception cycles.
