Summary
Zhe Feng is a Staff Research Scientist at Google DeepMind with 11 years of research experience at the intersection of machine learning, economics, and large language model reasoning. He leads work on improving LLM post-training efficiency via multi-agent thinking and agentic reinforcement learning, building on a strong track record of applied research in search ads optimization and auction-based mechanisms. His publications appear in top venues including JACM, Communications of the ACM, NeurIPS, ICML and ICLR, reflecting both theoretical depth and practical impact. Trained at Shanghai Jiao Tong and Harvard with visiting research at UC Berkeley, he blends rigorous mathematical foundations with production-focused experimentation at scale. An early contributor to auction and market-design problems in industry internships, he brings a rare combination of mechanism-design expertise and agentic ML innovation to deployable AI systems.
11 years of coding experience
9 years of employment as a software developer
Visiting Student Research visiting student, Visiting Student Research visiting student at University of California, Berkeley
Ph.D Student Computer Science, Ph.D Student Computer Science at Harvard University
Summer Exchange Student Business, Summer Exchange Student Business at University of California, Berkeley, Haas School of Business
Bachelor of Science (B.S.) Mathematics & Applied Mathematics, Bachelor of Science (B.S.) Mathematics & Applied Mathematics at Shanghai Jiao Tong University
Chinese, English