Luo Mai

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 Software System 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.


  • Distributed Systems
  • Machine Learning
  • Data Management
  • Quantum Computing


  • 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


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.

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.

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.


(2022). Ekko: A Large-Scale Deep Learning Recommender System with Low-Latency Model Update. In USENIX OSDI.


(2022). A Theoretical Understanding of Gradient Bias in Meta-Reinforcement Learning. In NeurIPS.


(2022). TorchOpt: An Efficient library for Differentiable Optimization. In NeurIPS OPT Workshop.

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(2022). MegBA: A GPU-Based Distributed Library for Large-Scale Bundle Adjustment. In ECCV.

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(2021). Move Fast and Meet Deadlines: Fine-grained Real-time Stream Processing with Cameo. In USENIX NSDI.


(2021). Efficient Reinforcement Learning Development with RLzoo. In ACM Multimedia (Open-source Software Competition).

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(2021). Fast and Flexible Human Pose Estimation with HyperPose. In ACM Multimedia (Open-source Software Competition).

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(2020). KungFu: Making Training in Distributed Machine Learning Adaptive. In USENIX OSDI.

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(2020). Spotnik: Designing Distributed Machine Learning for Transient Cloud Resources. In USENIX HotCloud.


(2019). CrossBow: Scaling Deep Learning with Small Batch Sizes on Multi-GPU Servers. In VLDB.

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Fast and Easy Distributed Graph Learning for PyTorch GitHub stars


Adaptive Large-scale Deep Learning GitHub stars


Easy-to-use Deep Learning Library GitHub stars


Real-time Visual Computing Library GitHub stars


Reinforcement Learning Model Zoo GitHub stars


Deep Learning on Multi-GPU Servers GitHub stars


Grad Students


Yao Fu

PhD Student


Man-Kit Sit

PhD Student


Leyang Xue

PhD Student (Co-supervised with Mahesh)


Congjie He

PhD Student


Service and Awards

Research Awards

  • Tencent Research Award, 2022
  • Alibaba Innovative Research Award, 2020
  • Microsoft Azure Research Award, 2018
  • ACM Multimedia Best Open-Source Software Award, 2017
  • Google PhD Fellowship in Cloud Computing, 2012 - 2016
  • ACM CoNEXT Conference Best Paper Finalist, 2014
  • IEEE MASS Conference Best Paper Finalist, 2012

Conference Committee Member

  • APSys 2023, Middleware 2023, ICDE 2023, MICRO 2022 (ERC), ANCS 2022, ICDE 2022, ICDE 2021


  • IF-2.03, Informatics Forum, University of Edinburgh, Edinburgh, EH8 9AB