Tingfeng Lan

Computer Science Research Lover at DS2 Lab, University of Virginia.

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I am a PhD student in computer science at \(Ds^2 Lab\) of the University of Virginia under Prof. Yue Cheng.

Generally, I aim to develop high-performance, scalable systems for emerging ML applications. Currently I am working on building better (computing and storage) systems for (distributed) ML applications. For example, rethinking the potential of multi-tier parallel processing within heterogeneous computing environments like CPU-GPU collaborative computing.

Before joining UVA, I earned my bachelor’s degree from Sichuan University, where I had the privilege of being guided by Prof. Mingjie Tang and Prof. Hui Lu from UTA, focusing on optimizing large-scale recommendation model training systems. I was also fortunate to be advised by Prof. Jianguo Wang from Purdue University, with a focus on utilizing machine learning techniques to optimize graph algorithms’ performance.

I am open to other opportunities and new research, so please feel free to reach me at my email erc8gx _AT_ virginia.edu

news

Aug 20, 2025  🎉🎉 Excited to see our work “ZenFlow: Stall-Free Offloading Engine for LLM Training” featured on the PyTorch blog!
Jul 14, 2025  🎉🎉 Our work “ZipLLM: Efficient LLM Storage via Model-Aware Synergistic Data Deduplication and Compression” is accepted by NSDI’26!.
Jan 15, 2025  🎉🎉 Our work “mLoRA: Fine-Tuning LoRA Adapters via Highly-Efficient Pipeline Parallelism in Multiple GPUs” is accepted by VLDB’25!.
Sep 09, 2024  🎉🎉 My new homepage is now live!
Jun 20, 2024  🎉🎉 Our work “DLRover-RM: Resource Optimization for Deep Recommendation Models Training in the Cloud” is accepted by VLDB’24!.

selected publications

  1. preprint
    ZenFlow: Enabling Stall-Free Offloading Training via Asynchronous Updates
    Tingfeng Lan, Yusen Wu, Bin Ma, and 7 more authors
    2025
  2. NSDI’26
    ZipLLM: Efficient LLM Storage via Model-Aware Synergistic Data Deduplication and Compression
    Zirui Wang, Tingfeng Lan, Zhaoyuan Su, and 2 more authors
    2025
  3. VLDB’25
    mLoRA: Fine-Tuning LoRA Adapters via Highly-Efficient Pipeline Parallelism in Multiple GPUs
    Zhengmao Ye, Dengchun Li, Zetao Hu, and 8 more authors
    2024
  4. VLDB’24
    DLRover-RM: Resource Optimization for Deep Recommendation Models Training in the Cloud
    Qinlong Wang*Tingfeng Lan*, Yinghao Tang, and 8 more authors
    Proceedings of the VLDB Endowment, 2024