Luo Mai

Assistant Professor

University of Edinburgh

About Me

I am an Assistant Professor (UK Lecturer) in the School of Informatics at the University of Edinburgh. I am a member of the Institute of Computing Systems Architecture where I am leading the Edinburgh System-X Group. Our research group designs scalable, adaptive and efficient system software to support emerging data-centric applications and utilise novel computing platforms.

Before coming to Edinburgh, I was a research associate (2018 - 2020) at the Imperial College London working with Peter Pietzuch. I earned my PhD degree from Imperial College London under the supervision of Paolo Costa and Alexander L. Wolf. My PhD was supported by a Google Fellowship in Cloud Computing. During my PhD study, I was a research intern (2015, 2016) and a visiting researcher (2017) at Microsoft Research.

I am always looking for self-motivated PhD, MScR and Post-doc. Drop me an email with your CV if you are interested. I am a member of the supervisor teams in the CDT in NLP and the CDT in Biomedical AI. Check here for how to apply.

Interests

Distributed Systems
Machine Learning
Data Management
Quantum Computing

Education

PhD in Computer Science, 2018

Imperial College London, UK
MRes in Advanced Computing, 2012

Imperial College London, UK
BSc in Software Engineering, 2011

Xidian University, China

News

TorchOpt and papers are accepted to NeurIPS 2022

Our paper “A Theoretical Understanding of Gradient Bias in Meta-Reinforcement Learning” is accepted by NeurIPS 2022. This paper is based on our recently released system for differential optimisation: TorchOpt. TorchOpt is accepted as a paper by the 14th International Workshop for Optimization for Machine Learning (OPT) co-located with NeurIPS 2022.

Sep 10, 2022

MegBA in ECCV 2022

Our Paper “MegBA: A GPU-Based Distributed Library for Large-Scale Bundle Adjustment” is accepted by ECCV 2022. ECCV is considered to be one of the top conferences in computer vision, alongside CVPR and ICCV.

Jul 10, 2022

Ekko in OSDI 2022

Our Paper “Ekko: A Large-Scale Deep Learning Recommender System with Low-Latency Model Update” is accepted by USENIX Symposium on Operating Systems Design and Implementation (OSDI) 2022. OSDI is a highly selective flagship conference in computer science, especially on the topic of computer systems.

Mar 18, 2022

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Publications

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.

Jie Ren, Xidong Feng, Bo Liu, Xuehai Pan, Luo Mai, Yaodong Yang (2022). TorchOpt: An Efficient library for Differentiable Optimization. In NeurIPS OPT Workshop.

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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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Luo Mai, Guo Li, Marcel Wagenlander, Konstantinos Fertakis, Andrei-Octavian Brabete, Peter Pietzuch (2020). KungFu: Making Training in Distributed Machine Learning Adaptive. In USENIX OSDI.

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Marcel Wagenlander, Luo Mai, Guo Li, Peter Pietzuch (2020). Spotnik: Designing Distributed Machine Learning for Transient Cloud Resources. In USENIX HotCloud.

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Alexandros Koliousis, Pijika Watcharapichat, Matthias Weidlich, Luo Mai, Paolo Costa, Peter Pietzuch (2019). CrossBow: Scaling Deep Learning with Small Batch Sizes on Multi-GPU Servers. In VLDB.

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