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

“Context is the new weight.”
I'm a researcher and engineer on Agentic AI. I work on agentic memory and AI alignment.
I earned my BS in Physics at Georgia Tech and am now a CS PhD at USC.
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.
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.
photonics & metasurface design · dynamic systems
bio-inspired flight, sensing, and locomotion
policy learning for physical control and agent behavior
regularization design for variational autoencoders
diffusion models · vision-language model architecture
in-context learning, continual fine-tuning, unlearning, and memory scaffolds; multi-agent coordination and predictive world models — adapting agents at the context, model, and population level