Luo Mai

Assistant Professor

University of Edinburgh

About Me

I am an Assistant Professor in the School of Informatics at the University of Edinburgh, where I lead the Large-Scale Machine Learning Systems Group and co-lead the UK EPSRC Centre for Doctoral Training in Machine Learning Systems.

My research aims to design and implement scalable, efficient, and reliable computer systems. My work has been recognized at major conferences such as OSDI, SOSP, NSDI, USENIX ATC, CoNEXT, JMLR, ICML, NeurIPS, ECCV, and VLDB, earning awards from Google, Microsoft, Alibaba, and Tencent. I am the author of the open-source textbook Machine Learning Systems: Design and Implementation and co-founder of several popular open-source projects, including TensorLayer, TorchOpt, and ServerlessLLM.

Before joining Edinburgh, I was a research associate at Imperial College London, working with Peter Pietzuch and a visiting researcher at Microsoft Research. My PhD, supervised by Paolo Costa and Alexander L. Wolf, was supported by a Google Fellowship in Cloud Computing.

If you’re interested in pursuing a PhD or postdoctoral research with me, please email your CV along with a brief description of your proposed research.

Interests

Machine Learning Systems
Operating Systems
Distributed Systems
Data Management

Education

PhD in Computer Science, 2018

Imperial College London, UK
MRes in Advanced Computing, 2012

Imperial College London, UK

News

Tenplex in SOSP 2024.

Trilled to receive Microsoft Research Startrack Scholar Award.

ServerlessLLM in OSDI 2024.

TorchOpt becomes a PyTorch Ecosystem Project, and its paper gets in JMLR 2023.

Trilled to be nominated for the Chancellor's Rising Star in Research.

Gear library in ICML 2023.

Ekko system in OSDI 2022

MegBA library in ECCV 2022

Cameo system in NSDI 2021

KungFu system in OSDI 2020

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Publications

Marcel Wagenländer, Guo Li, Bo Zhao, Luo Mai, Peter Pietzuch (2024). Tenplex: Dynamic Parallelism for Deep Learning using Parallelizable Tensor Collections. In SOSP.

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Yao Fu, Leyang Xue, Yeqi Huang, Andrei-Octavian Brabete, Dmitrii Ustiugov, Yuvraj Patel, Luo Mai (2024). ServerlessLLM: Locality-Enhanced Serverless Inference for Large Language Models. In OSDI.

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Jie Ren*, Xidong Feng*, Bo Liu*, Xuehai Pan*, Yao Fu, Luo Mai, Yaodong Yang (2023). TorchOpt: An Efficient Library for Differentiable Optimization. In JMLR.

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Hanjing Wang*, Man-Kit Sit*, Congjie He, Ying Wen, Weinan Zhang, Jun Wang, Yaodong Yang, Luo Mai (2023). GEAR: A GPU-Centric Experience Replay System for Large Reinforcement Learning Models. In ICML.

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Chijun Sima*, Yao Fu*, Man-Kit Sit, Liyi Guo, Xuri Gong, Feng Lin, Junyu Wu, Yongsheng Li, Haidong Rong, Pierre-Louis Aublin, Luo Mai (2022). Ekko: A Large-Scale Deep Learning Recommender System with Low-Latency Model Update. In USENIX OSDI.

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Bo Liu, Xidong Feng, Jie Ren, Luo Mai, Rui Zhu, Haifeng Zhang, Jun Wang, Yaodong Yang (2022). A Theoretical Understanding of Gradient Bias in Meta-Reinforcement Learning. In NeurIPS.

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Jie Ren*, Wenteng Liang*, Ran Yan, Luo Mai, Shiwen Liu, Xiao Liu (2022). MegBA: A GPU-Based Distributed Library for Large-Scale Bundle Adjustment. In ECCV.

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Le Xu, Shivaram Venkataraman, Indranil Gupta, Luo Mai, Rahul Potharaju (2021). Move Fast and Meet Deadlines: Fine-grained Real-time Stream Processing with Cameo. In USENIX NSDI.

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Zihan Ding, Tianyang Yu, Yanhua Huang, Hongming Zhang, Guo Li, Quancheng Guo, Luo Mai, Hao Dong (2021). Efficient Reinforcement Learning Development with RLzoo. In ACM Multimedia (Open-source Software Competition).

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Yixiao Guo, Jiawei Liu, Guo Li, Luo Mai, Hao Dong (2021). Fast and Flexible Human Pose Estimation with HyperPose. In ACM Multimedia (Open-source Software Competition).

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