Design, build, and evaluate agentic AI systems for scientific discovery. Bring in domain-specific tools, simulators, datasets, and models (e.g., genomics pipelines, structure prediction, physics-based simulation) so agents can use them reliably at scale. Work with scientists inside and outside Google to choose high-impact problems, check agent-generated hypotheses, and close the loop between prediction and discovery. Set and lead a research agenda.
Identify new directions at the intersection of agents and science, publish in top Machine Learning (ML) venues, and help shape our long-term strategy for AI-accelerated science. Own the robust, scalable infrastructure for agent execution, orchestration, and evaluation, and help research ideas become reliable systems that others can build on.
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