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 Large-scale Computer Systems Group.

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

Computer Systems
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

MegBA is accepted to ECCV 2022

Our Paper “MegBA: A GPU-Based Distributed Library for Large-Scale Bundle Adjustment” is accepted by ECCV 2022.

Jul 10, 2022

Ekko is accepted to 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.

Mar 18, 2022

Quiver is open-sourced

Quiver is a distributed graph learning library for PyTorch Geometric (PyG). Its excellent performance and scalability has made it quickly become the recommended distributed library for PyG.

Oct 31, 2021

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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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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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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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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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