Edinburgh Research Explorer

Dr Luo Mai

Lecturer in Data Centric Systems

Research Interests

My primary research interests lie at the intersection between distributed systems, machine learning and big data processing. Much of my work has focused on building scalable and self-tuning systems for large-scale machine learning and big data processing. Recently, I have been focusing on designing distributed systems for training big vision and language models, large graph neural networks and deep reinforcement learning models. This has led to publications at prominent conferences including OSDI, NSDI, USENIX ATC, VLDB and CoNEXT, as well as popular open-source machine learning software including KungFu, TensorLayer and HyperPose.

Qualifications

BSc in Software Engineering, Xidian University, China, 2011.
MRes in Advanced Computing, Imperial College London, UK, 2012.
PhD in Computer Science, Imperial College London, UK, 2018.

Biography

Luo Mai is a Lecturer in Data-Centric Systems in the School of Informatics at the University of Edinburgh. He also maintains an honorary affiliation with the department of computing at Imperial College London. Prior, he is a research associate at Imperial College London and a visiting researcher at Microsoft. Luo received his PhD from Imperial College London in 2018. He is the recipient of a prestigious Google PhD fellowship.

Luo’s research interests are at the intersection between distributed systems, machine learning and data management. He has received the Alibaba Innovative Research Award in 2020, Microsoft Azure Research Award in 2018, and Best Open-Source Software Award from ACM Multimedia in 2017

Research outputs

  1. KungFu: Making Training in Distributed Machine Learning Adaptive

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  2. Spotnik: Designing Distributed Machine Learning for Transient Cloud Resources

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  3. Move Fast and Meet Deadlines: Fine-grained Real-time Stream Processing with Cameo

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

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