University of Michigan · CSE

Hi, I’m Runyu Lu.

I am a second-year PhD student in Computer Science and Engineering at the University of Michigan, co-advised by Professors Mosharaf Chowdhury and Ang Chen. I am also doing research in NVIDIA GEAR Lab. I am interested in Robotics(Embodied AI) and ML systems.

Email Me
Runyu Lu standing at a harbor.

Research Overview

  • Robotics(Embodied AI)

    I am currently working on VLA post-training and physical agents. While today's LLMs, VLMs, and AI agents excel at reasoning and diverse virtual tasks, I'm excited to learn and contribute to extending the intelligence into the physical world.

  • ML System

    I am interested in many aspects of ML systems. I worked on DiT-based Image/Video Generation, Multimodal Model Training, GPU Sharing for LLM Serving, and LLM-enabled Compiler Fuzzing.

Papers

  1. DSA: Efficient Inference For Video Generation Models via Distributed Sparse Attention

    S. Li, R. Lu, Q. Chen, H. Yin, Y. Lyu, Y. Wen, I. Tsang, T. Zhang

    The Fourteenth International Conference on Learning Representations (ICLR), 2026

  2. TetriServe: Efficient DiT Serving for Heterogeneous Image Generation

    R. Lu*, S. He*, W. Tan, S. Li, R. Wu, J. J. Ma, A. Chen, M. Chowdhury

    The 31st ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS'26), Volume 2

  3. Cornstarch: Distributed Multimodal Training Must Be Multimodality-Aware

    I. Jang, R. Lu, N. Bansal, A. Chen, M. Chowdhury

    arXiv preprint arXiv:2503.11367, 2025

  4. Whitefox: White-box compiler fuzzing empowered by large language models

    C. Yang, Y. Deng, R. Lu, J. Yao, J. Liu, R. Jabbarvand, L. Zhang

    Proceedings of the ACM on Programming Languages 8 (OOPSLA2), 709-735, 2024

  5. MuxServe: Flexible Spatial-Temporal Multiplexing for Multiple LLM Serving

    J. Duan, R. Lu, H. Duanmu, X. Li, X. Zhang, D. Lin, I. Stoica, H. Zhang

    Forty-first International Conference on Machine Learning (ICML), 2024