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

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! |
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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
- preprintZenFlow: Enabling Stall-Free Offloading Training via Asynchronous Updates2025
- NSDI’26ZipLLM: Efficient LLM Storage via Model-Aware Synergistic Data Deduplication and Compression2025
- VLDB’25mLoRA: Fine-Tuning LoRA Adapters via Highly-Efficient Pipeline Parallelism in Multiple GPUs2024
- VLDB’24DLRover-RM: Resource Optimization for Deep Recommendation Models Training in the CloudProceedings of the VLDB Endowment, 2024