current research tracks Riemannian Geometry of Policy Learning Studying submanifolds of stabilizing policies for constrained optimal control problems using the Riemannian geometry inherent to the structure of these problems. Learning to Estimate and Identify Estimating latent variables in dynamical systems is a fundamental challenge in data-driven decision-making, particularly when noise characteristics are unknown. Risk-sensitive Decision-making A novel approach to long-term risk-sensitive control in stochastic systems through the so-called ergodic-risk criteria Online Learning and Control A geoemtric approach to fundamental characterization of system-theoretic limitations in online learning and control Large-scale Distributed Policy Learning Large-scale distributed policy learning for networked systems utlizing algebraic structures and game theory