Agentic Memory
Continual learning of AI agents — in-context learning, continual fine-tuning, unlearning, and in-context world models that adapt to evolving environments.

CS PhD @ University of Southern California · zizhaoh@usc.edu
advised by Jesse Thomason & Mohammad Rostami @ Amazon
I'm a researcher and engineer on Agentic AI. I work on agentic memory (continual learning, unlearning), AI alignment, and self-improving AI.
I earned my BS in Physics at Georgia Tech and am now a CS PhD at USC, in the GLAMOR Lab.
I'm conducting LLM unlearning research for the US Government's IARPA. Previously I was an ML domain lead at Handshake AI and a data engineer at Scale AI, where I collaborated with teams from OpenAI, Meta, and Anthropic to improve their unreleased black-box models.
Outside research I do K-pop dance covers, played in my high school soccer league, and spend downtime on competitive strategy games, anime, and movies.
Continual learning of AI agents — in-context learning, continual fine-tuning, unlearning, and in-context world models that adapt to evolving environments.
Keeping capable models safe and under human control — post-training guardrails, multi-agent interaction risks, and AI behavioral study.
Models that get better from their own output — through synthetic data, test-time training, on-policy distillation, and in-context RL.