Applied Probability

Reinforcement learning for power systems control

Adaptive topology control in power systems using reinforcement learning and spectral clustering.

MCMC methods for rare events and non-convex supports

Developing novel sampling methods, with applications to rare event sampling and stochastic optimization.

Rare events in stochastic networks

Unveiling how correlated noise and coupled network dynamics can increase the likelihood of rare events, such as blackouts in power systems with a large renewable penetration

Random-access networks

Quantifying throughput and delay performance of wireless networks protocols using Markov chains and heavy-traffic limits.