Hi, I'm Xingfeng
ML leader and researcher. From first-principles materials discovery to billion-scale recommendation systems — building ML systems that work at scale across scientific research, consumer hardware, and ads/recommendation.
About
I build ML systems that work at scale. My path has been non-linear: physics at Tsinghua, a PhD in computational physics at Maryland, display ML at Apple, and now ads retrieval, recommendation, and AI automation at Meta.
Along the way, my research on solid-state battery electrolytes has been cited 10,000+ times (21 publications, 20 patents), and I've shipped production ML systems serving billions of users. I'm broadly interested in using AI to understand and transform how complex systems operate.
Blog
Experience & Education
Research Scientist, Tech Lead
Meta
2022 – Present
ML systems at production scale across ads retrieval, recommendation, and AI-powered automation.
- AI Agent Automation: Built autonomous ML agents that automate research workflows and organizational knowledge retrieval; championed AI-native engineering practices across the org (published technical guide)
- Recommendation Systems: Core member of the team integrating LLMs into Threads recommendation (highlighted in Q1'25 Earnings Call), pioneering LLM-powered content understanding and ranking for Meta’s text-first social platform
- Ads Retrieval & Ranking: Co-authored HSNN (EDBT 2026), the core ML architecture behind Meta Andromeda — next-gen ads retrieval engine enabling 10,000x model complexity, +6% recall, +8% ads quality across Facebook & Instagram. Highlighted in three consecutive earnings calls (Q4'24, Q2'25, Q4'25)
Display Machine Learning Engineer
Apple
2018 – 2022
Applied ML to display physics, production optimization, and user experience across all Apple products.
- Applied ML (CNN, clustering, anomaly detection) to manufacturing data and built reporting pipelines, improving display quality across all Apple products
- 2 US patents: foldable display compensation (US11817065) and OLED leakage reduction (US12369474)
Ph.D., Physics
University of Maryland, College Park
2013 – 2018
Computational materials science under Prof. Yifei Mo. Research on solid-state battery electrolyte design via first-principles methods and machine learning.
- Designed vector representation of crystal structures enabling ML in materials science; discovered 13 new materials via unsupervised learning
- Pioneered computational studies of fast ion diffusion and solid-solid interface stability
- 21 publications in Nature Materials, Nature Communications, Joule, etc. with 10,000+ total citations
- 20 issued patents; Co-PI of NSF award 1550423
B.S., Physics (Highest Honors)
Tsinghua University
2009 – 2013
Awards & Leadership
- Advisory board member, Paperclip — largest Chinese popular science video channel, 10M+ subscribers globally (2020–2021)
- Vice President, Kedao — community promoting green energy and public policy, 100+ members across disciplines (2016–2019)
- Vice President, Tsinghua Alumni Association at Greater DC Area (2015–2019)
- Co-PI, NSF Award 1550423 (2017)
- Fellowship and Scholarship, UMD and Tsinghua University (2009–2018)
Research Highlights
HSNN: Hierarchical Retrieval at Meta Scale
Co-authored HSNN, the core ML architecture behind Meta Andromeda — Meta's next-generation personalized ads retrieval engine. Advances beyond the Two Tower model with sublinear O(log N) cost, enabling a 10,000x increase in model complexity, +6% recall improvement, and +8% ads quality on selected segments.
Solid-State Battery Research Pipeline
A computational pipeline from stability prediction to ML-accelerated materials discovery for solid-state battery electrolytes, spanning my PhD and post-doctoral research. See overall impact analysis →