I am a Research Scientist at Google Research in NYC, where I work on sparsity, information retrieval, foundation models, and their intersections.
Before Google, I was a postdoc at UC Berkeley working with Prof. Yi Ma. I received my PhD in ECE at Johns Hopkins University in 2018, advised by Prof. René Vidal. Prior to Hopkins, I got my B.S. and M.S. degrees at Peking University.
My research areas broadly include machine learning, computer vision, optimization and signal processing. I am interested in the development of mathematical principles and practical numerical algorithms for analyzing and interpreting modern data.
My CV can be found here.
🔥 News
- [2025.07] Gemma3n, available on Hugging Face, comes with activation sparsity from using our Statistical Top-K introduced in the Spark Transformer paper.
- [2025.06] Paper Release: Spark Transformer, which follows up on our earlier works — Lazy Neuron, 2022 and HiRE, 2024 — to introduce a strong activation sparsity (8% nonzeros in FFN and top-256 in Attention) to modern LLMs.
- [2025.03] Co-organized Conference on Parsimony and Learning (CPAL) at Stanford, CA
📝 Recent Publications
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Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, LongContext, and Next Generation Agentic Capabilities
Gemini Team, Google
[Tech Report] -
Spark Transformer: Reactivating Sparsity in FFN and Attention
Chong You*, Kan Wu*, Zhipeng Jia*, Lin Chen*, Srinadh Bhojanapalli, Jiaxian Guo, Utku Evci, Jan Wassenberg, Praneeth Netrapalli, Jeremiah J. Willcock, Suvinay Subramanian, Felix Chern, Alek Andreev, Shreya Pathak, Felix Yu, Prateek Jain, David E. Culler, Henry M. Levy, Sanjiv Kumar
[Arxiv] -
Efficient and Asymptotically Unbiased Constrained Decoding for Large Language Models
Haotian Ye, Himanshu Jain, Chong You, Ananda Theertha Suresh, Haowei Lin, James Zou, Felix Yu
International Conference on Artificial Intelligence and Statistics (AISTATS), 2025
[Arxiv] -
Generalized Neural Collapse for a Large Number of Classes
Jiachen Jiang*, Jinxin Zhou*, Peng Wang, Qing Qu, Dustin Mixon, Chong You, Zhihui Zhu
International Conference on Machine Learning (ICML), 2024
[Arxiv] -
It’s an Alignment, Not a Trade-off: Revisiting Bias and Variance in Deep Models
Lin Chen, Michal Lukasik, Wittawat Jitkrittum, Chong You, Sanjiv Kumar
International Conference on Learning Representations (ICLR), 2024
[Arxiv] -
Functional Interpolation for Relative Positions Improves Long Context Transformers
Shanda Li, Chong You, Guru Guruganesh, Joshua Ainslie, Santiago Ontanon, Manzil Zaheer, Sumit Sanghai, Yiming Yang, Sanjiv Kumar, Srinadh Bhojanapalli
International Conference on Learning Representations (ICLR), 2024
[Arxiv] -
Deep Self-expressive Learning
Chen Zhao, Chun-Guang Li, Wei He, Chong You
Conference on Parsimony and Learning (CPAL), 2024
[Arxiv] -
HiRE: High Recall Approximate Top-$k$ Estimation for Efficient LLM Inference
Yashas Samaga B L, Varun Yerram, Chong You, Srinadh Bhojanapalli, Sanjiv Kumar, Prateek Jain, Praneeth Netrapalli
[Arxiv]