AI systems under uncertainty.
Technical writing on AI systems that operate in noisy, large-scale environments: separating signal from noise, modeling behavior, adapting to feedback, and making reliable decisions.
My work spans experimental physics and AI, from searching for possible dark matter interactions in a particle detector to building AI systems for detecting fake accounts, hacked accounts, bots, automation, scraping, spam, and other abusive behavior in large-scale online environments.
Latest
Writing
Jan 9, 2026
Reinforcement Learning for Orbital Transfers at the 2026 AI Winter School (Brown University)
Hands-on workshop using reinforcement learning (PPO) to solve orbital transfer problems. Environment design, reward shaping, and training RL agents for space mechanics.
Aug 7, 2025
From Demos to Deployment: Insights from Berkeley’s Agentic AI Summit 2025
Agentic AI is graduating from cool demos to real‑world deployment. Hardware gains, open standards, rigorous evals, and secure governance are paving the way for billions of reliable, low‑cost digital assistants.
Feb 10, 2025
Exploring LLMs and RAG at the 2025 AI Winter School (Brown University)
Hands-on workshop on Large Language Models and Retrieval-Augmented Generation (RAG) for physics researchers. Using OpenAI API and LLaMA for domain-specific applications.