Xupeng Miao is currently a Post Doctoral Fellow working with Prof. Zhihao Jia and Prof. Tianqi Chen in Catalyst Group and Parallel Data Lab at Computer Science Department of Carnegie Mellon University. He is broadly interested in machine learning systems, data management and distributed computing. He is the creator of Hetu, a highly efficient distributed deep learning system, and continuously leading the team development, welcome to join us!
Before that, he received his Ph.D. degree in computer science from Peking University in June 2022, supervised by Prof. Bin Cui. He was a research intern in System Research Group of Microsoft Research Asia (MSRA), supervised by Dr. Jilong Xue. Besides, he has accumulated for more than 5 years industrial internship experience at the Machine Learning & Data Platform Department of Tencent.
I’m on the academic job market for 2024. Please feel free to reach out if you have openings.
|Nov 7, 2023||One paper about LLM serving over preemptive instances was accepted by ASPLOS 2024.|
|May 16, 2023||We announce a new LLM inference engine called SpecInfer.|
|May 13, 2023||Three papers were accepted by VLDB 2023.|
|Mar 23, 2023||One paper was accepted by OSDI 2023.|
|Jan 30, 2023||I was grateful to be awared 2022 ACM China Doctoral Dissertation Award.|
- ASPLOSSpotServe: Serving Generative Large Language Models on Preemptible InstancesProceedings of ASPLOS Conference 2024
- arXivSpecInfer: Accelerating Generative Large Language Model Serving with Speculative Inference and Token Tree VerificationarXiv preprint arXiv:2305.09781 2023
- VLDBSDPipe: A Semi-Decentralized Framework for Heterogeneity-aware Pipeline-parallel TrainingProc. VLDB Endow. 2023
- VLDBGalvatron: Efficient Transformer Training over Multiple GPUs Using Automatic ParallelismProc. VLDB Endow. 2023
- SCISHetu: A highly efficient automatic parallel distributed deep learning systemSci. China Inf. Sci. 2022
- VLDBHET: Scaling out Huge Embedding Model Training via Cache-enabled Distributed Framework (Best Scalable Data Science Paper)Proc. VLDB Endow. 2022
- SIGMODHET-GMP: A Graph-based System Approach to Scaling Large Embedding Model TrainingIn Proceedings of SIGMOD Conference 2022
- SIGMODHeterogeneity-Aware Distributed Machine Learning Training via Partial ReduceIn Proceedings of SIGMOD Conference 2021