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Addressing data and computation efficiency challenges in real-world deployments of RL Agents. It achieves significant efficiency gains in deep RL benchmarks as well as theoretical milestones.

Game-theoretic decision-making in multi-agent systems. I developed optimistic TS type algorithm that significantly reduce experimental costs in applications such as traffic management and radar communications.

First probabilistic framework for sequential random projection, an approach rooted in the challenges of sequential decision-making under uncertainty; A non-trivial martingale extension of Johnson-Lindenstrauss (JL) to sequentially adaptive data processes.

Stabilizing policy optimization when off-policy data are reused, addressing the data efficiency issue in RL for real-world problems.