Human-Computer Interaction
Alignment, post-training personalization, safety, and interpretability — making capable models legible and controllable for the people who use them.

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), human-computer interaction (alignment, personalization, safety, interpretability), 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.
Alignment, post-training personalization, safety, and interpretability — making capable models legible and controllable for the people who use them.
Models that get better from their own output — through synthetic data, test-time training, on-policy distillation, and in-context RL.