Xingchen Wan
Senior Research Scientist at Google DeepMind
San Francisco, California, United States
Summary
👤
Senior🎓
Top SchoolXingchen Wan is a Senior Research Scientist at Google DeepMind with a decade of experience pushing large language models toward practical, secure, and agentic applications. His work spans coding agents, post-training techniques for Gemini, and enterprise-focused LLM improvements developed at Google Cloud—projects that include Vertex AI Prompt Optimizer and multiple NeurIPS/ACL/ICLR papers. He combines rigorous academic training (MEng and PhD from Oxford) with hands-on production impact, earning a Google Cloud Tech Impact Award for prompt optimization. Notably, he has led research on automating multi-agent LLM systems and agentic RAG, bridging cutting-edge research with deployable cloud products. Based in San Francisco, he brings research depth and applied engineering to scale LLMs for real-world and security-sensitive use cases.
10 years of coding experience
2 years of employment as a software developer
PhD Machine Learning, PhD Machine Learning at University of OxfordSingapore-Cambridge GCE A-Levels, Singapore-Cambridge GCE A-Levels at River Valley High School
Github Skills (15)
nas9
neural-architecture-search9
bayesian9
kernel8
bayesian-inference5
python5
classification5
machine-learning4
deep-learning4
probabilistic-programming4
adversarial-attacks4
variational-inference4
graph4
pytorch4
jekyll2
Programming languages (3)
Github contributions (5)
xingchenwan/Casmopolitan
Jun 2021 - Aug 2022
[ICML'21] Think Global and Act Local: Bayesian Optimisation for Categorical and Mixed Search Spaces
Contributions:13 commits, 10 pushes, 2 branches in 1 year 2 months
bayesian
xingchenwan/nasbowl
Jun 2020 - Dec 2021
[ICLR '21] Interpretable Neural Architecture Search using Bayesian Optimisation with Weisfiler-Lehman Kernel (NAS-BOWL)
Contributions:22 commits, 15 pushes, 5 comments in 1 year 6 months
bayesiankernelnasneural-architecture-search
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