Hong-Yu Chen | Northwestern University

Department of Computer Science.
Address: Mudd Hall 3205, 2233 Tech Drive, Third Floor, Evanston, IL 60208.
Email: charlie.chen@u.northwestern.edu.

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Hi, I’m a second-year PhD student at Northwestern University in MAGICS lab advised by Prof. Han Liu. I focus on advancing the understanding and application of foundation models. My research explores their theoretical foundations, including their universal approximation capabilities, potential to perform complex algorithms, and their links to associative memory such as Hopfield Model. I also work on extending their applications beyond language and vision, particularly in time series analysis and other scientific domain such as astrophysics. I received my B.S. degree in Physics from National Taiwan University, advised by Prof. Hsi-Sheng Goan.

news

Sep 29, 2026 One paper accepted to NeurIPS 2026 on precision-aware Hopfield retrieval.
Jul 11, 2026 StarEmbed received the Outstanding Paper Award at the AI4Physics Workshop at ICML 2026.
Jul 03, 2026 StarEmbed selected for an oral presentation at the AI4Physics Workshop at ICML 2026.
Apr 30, 2026 Four papers accepted to ICML 2026: universal approximation of softmax attention, universality and function composition of in-context learning, theoretical advantage of chain-of-thought, and a benchmark for astronomical time series.
Mar 20, 2026 Honored to receive the Lambda Research Grant.

selected publications

* denotes equal contribution.

  1. In-Context Universal Approximation, Compositional Generalization, and Algorithm Emulation
    Jerry Yao-Chieh Hu*, Hong-Yu Chen*, Po-Chiao Lin*, Maojiang Su, and Han Liu
  2. Universal Approximation with Softmax Attention
    Jerry Yao-Chieh Hu*, Hude Liu*, Hong-Yu Chen*, Weimin Wu, and Han Liu
  3. Chain-of-Thought Gradient Descent
    Hong-Yu Chen*, Venkat Sripad Ganti*, Jerry Yao-Chieh Hu, Hude Liu, and Han Liu
  4. StarEmbed: Benchmarking Time Series Foundation Models on Astronomical Observations of Variable Stars
    Weijian Li*, Hong-Yu Chen*, Nabeel Rehemtulla*, Ved G. Shah, Dennis Wu, Dongho Kim, Qinjie Lin, Adam A. Miller, and Han Liu
  5. Outlier-Efficient Hopfield Layers for Large Transformer-Based Models
    Jerry Yao-Chieh Hu, Pei-Hsuan Chang, Haozheng Luo, Hong-Yu Chen, Weijian Li, Wei-Po Wang, and Han Liu