I’m a research scientist at Google DeepMind, where I co-founded and co-lead the Machine Learning for Computer Architecture team. I build AI agents that write, optimize, and verify performance-critical software and hardware.

My recent work focuses on agentic code optimization. ECO, our LLM-driven optimizer deployed across Google’s production fleet, has landed more than 6,400 commits and saved the equivalent of several hundred thousand CPU cores. I also use AlphaEvolve to discover compiler heuristics, warehouse-scale code layouts, and FHE kernels for TPUs. With multi-agent systems, I helped build a cycle-accurate RISC-V CPU simulator from scratch that boots an operating system.

Earlier, I co-authored Self-Refine, one of the first works to show that LLMs can improve their own outputs through self-feedback. It was an early step toward recursive self-improvement and has been cited more than 7,000 times.

My work has earned multiple IEEE Micro Top Picks honors, and I’m an inductee of the ISCA Hall of Fame and a Senior Member of IEEE and ACM. I received my Ph.D. from Georgia Tech, where my research was supported by the Microsoft Research PhD Fellowship and the Qualcomm Innovation Fellowship.