Dr Renyu Yang

Profile

I am currently a lead Research Fellow funded by UK EPSRC, with the School of Computing and Leeds Institute for Data Analytics (LIDA), University of Leeds, UK. Prior to this, I was a research scientist at Edgetic Ltd., a UK-based high-tech spin-out company that employs distributed scheduling, machine learning, hardware-software modeling, etc. to reshape the future of data center efficiency and intelligence. During 2014 to 2016, I was also with Alibaba Group, participating in the development and research on intelligent resource scheduling and performance optimization at Internet scale. My research interests include large-scale dependable distributed systems, trustworthy data-centric engineering, deep learning systems and applications such as graph representation learning, anomaly detection, etc.

I have published 50+ refereed journal and conference papers, including top ACM/IEEE Transactions and conference proceedings such as TPDS, TC, TKDE, TNNLS, TSC, TOIS, TKDD, ACM Computing Surveys (CSUR), VLDB, SC, WWW, ICDCS, DSN, SoCC, SECON, etc. I won the Best Paper Award in IEEE ISADS 2013 for energy-efficient computing and Alan Turing Institute Post-Doctoral Enrichment Award 2022 for resilient deep reinforcement learning, and was awarded the Grand Class of Scientific and Technological Progress Award of Chinese Institute of Electronics of year 2017 for the key contribution to the reliable resource management at massive scale and its industrial and societal impact.

I co-authored/co-led several national and international projects, including UK EPSRC, Innovate UK, EU Horizon 2020, UK Alan Turing Institute grants, China 973/863/Key R&D Programme, etc., worth more than £8 million over the last five years. I co-founded the highly successful and internationally leading research consortium COLAB between Leeds, Alibaba and Beihang University. I served as program co-chairs of IEEE International Conference on Joint Cloud Computing (JCC) 2019 and 2020, and area chair of IEEE International Conference on Cloud Computing (CLOUD) 2021, guest editor of Big Data and Cognitive Computing Journal, review editor in Editorial Board of Frontiers in Big Data and Frontiers in HPC, and program committee member of top-tier conferences including IJCAI, ECAI, AAAI, CLOUD, HPCC, etc.

Research interests

Broadly, I am interested in designing system modules and/or mechanisms for tackling trade-offs in efficiency, performance, reliability and cost for distributed systems of big data, cloud computing and IoT at scale. My research mainly focuses on:

1) resource efficiency of large-scale datacenters through data-driven design, QoS-aware scheduling and resource management, and multi-objective optimization, etc.;

2) system dependability by leveraging fault tolerance and failover, long-tail task mitigation, and quantitative reliability modeling etc.;

3) machine/deep learning systems and applications including data-centric engineering, GPU scheduling and parallelism, graph representation learning, anomaly detection, etc.

Recent publications and research outcomes can be found in my personal website.  

Qualifications

• PhD
• BSc

Professional memberships

• IEEE
• ACM

Student education

I have a teaching role in the school for undergraduate degree courses, undergraduate final year projects, and MSc projects.  I also co-supervise PhD students in the areas of distributed computing systems, cloud computing, and applied machine learning. 

Research groups and institutes

• Distributed Systems and Services