AI Systems | Reinforcement Learning | Simulation

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I build large-scale decision systems at the intersection of reinforcement learning, simulation, and LLM evaluation.

How do we design AI systems that make reliable decisions under uncertainty?

At Johns Hopkins Applied Physics Laboratory, I design and deploy simulation and machine learning infrastructure for autonomy programs supporting the US Navy and Coast Guard. My core focus includes:

• Distributed Simulation: Architecting and scaling Monte Carlo pipelines to 1,000,000+ episodes on HPC clusters.
• Reinforcement Learning: Constructing environments that integrate real-time system state into closed-loop training for autonomous agents.
• LLM Evaluation: Developing domain-specific evaluation frameworks and schema compliance protocols to measure hallucination and logical robustness.

Rather than focusing solely on model training, I specialize in the systems around the model — the environments, reward structures, and architectural constraints that determine whether an AI system survives contact with reality.

My approach is grounded in a triple major in EECS, Applied Mathematics, and Economics from University of California, Berkeley, followed by an M.S. in Artificial Intelligence from Johns Hopkins University. This blend of systems engineering, statistical rigor, and economic reasoning informs how I build practical, high-agency AI systems.

I’m interested in joining high-autonomy teams building AI systems that reason, plan, and optimize within complex physical and economic environments.