About

ML engineer at Google, developing learned compiler optimizations for TPUs using graph neural networks and reinforcement learning. Previously, I worked on model post-training and inference optimization at VMware, including contributions to vLLM.

My personal research focuses on embodied AI and robotics. Currently exploring robotics foundation models, reproducing and fine-tuning the latest VLA and RL research in simulation (MuJoCo/MJX), and running small-scale real robot experiments.

Across these projects, I’m interested in the relationship between learning algorithms, the experience they learn from, and the computational constraints under which they operate.