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, where I lead the Large-scale AI Systems group. My research lies at the intersection of distributed systems, machine learning and data management.

Before coming to Edinburgh, I was a research associate at Imperial College London, where I am currently affiliated as an Honorary Research Fellow. I received my PhD from Imperial College London in 2018 under the supervision of Paolo Costa. My PhD study was supported by a Google PhD Fellowship. During my study, I visited Microsoft Research as a research intern (2015, 2016) and a visiting researcher (2017).

Prospective Students: If you are interested in working with me as a PhD/MRes student, please get in touch ( how to make contact). I have full PhD studentships available! Studentships are also available at the CDT in NLP and CDT in Biomedical AI. Check here for how to apply.


  • Systems and networking
  • Machine learning systems
  • Data management and analytics


  • PhD in Computer Science, 2018

    Imperial College London, UK

  • MRes in Advanced Computing, 2012

    Imperial College London, UK

  • BSc in Computer Science, 2011

    Xidian University, China

Recent News

KungFu to appear at OSDI 2020

Our Paper “KungFu: Making Training in Distributed Machine Learning Adaptive” is accepted by USENIX Symposium on Operating Systems Design and Implementation (OSDI) 2020.

Cameo to appear at NSDI 2021

Our Paper “Move Fast and Meet Deadlines: Fine-grained Real-time Stream Processing with Cameo” is accepted by USENIX Symposium on Networked Systems Design and Implementation (NSDI) 2021.

Invited talk in the AI systems workshop at SOSP 2019

I am invited to give a talk: “Adaptive Distributed Training of Deep Learning Models” in the Workshop on AI Systems at ACM Symposium on Operating Systems Principles (SOSP) 2019.

Recent Publications

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


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

PDF Code

(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.

PDF Code

(2019). Taming Hyper-parameters in Deep Learning Systems. In ACM SIGOPS Operating Systems Review (Invited Paper).


Open-source Projects


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




Marcel Wagenlander

Visiting Student


Ioan Budea

MEng Student


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