Dr Renyu Yang
I am currently a research fellow, funded by a UK EPSRC grant, with the School of Computing, Faculty of Engineering and Physical Sciences, University of Leeds, UK. I am also a visiting research scientist with the Big Data and Brain Computing Research Center (BDBC), Beijing, China. I was a research scientist at Edgetic Ltd., a UK-based startup high-tech company that employs distributed scheduling, machine learning, hardware-software modeling, etc. to reshape the future of data center efficiency. During 2014 to 2016, I was also with Fuxi, the Distributed Resource Scheduling Team in Alibaba Group, participating in the development and research on resource scheduling and performance optimization at Internet scale. I have been leading several China national and international projects in terms of distributed resource scheduling, cloud storage and geo-distributed data processing for intelligent decision making and massive-scale data analysis. We are building large-scale resource management infrastructures and system profiling framework to support those functionalities. I has published more than 40 peer-reviewed papers, in the field of distributed systems, cloud computing and big data analytics. They appear in top venues such as IEEE Transactions on Parallel and Distributed Systems (TPDS), IEEE Transactions on Computers (TC), IEEE Transactions on Knowledge and Data Engineering (TKDE), IEEE Transactions on Services Computing (TSC), ACM Transactions on Knowledge Discovery from Data (TKDD), IEEE Internet Computing, VLDB, IEEE ICDCS, IEEE DSN, USENIX LISA, ACM SoCC, etc. One of my work won the Best Paper Award in IEEE ISADS 2013. I was awarded the Grand Class of Scientific and Technological Progress Award of Chinese Institute of Electronics of year 2017 (the only grand class award since the award is established) for the key participation and contribution to the reliable resource management and scheduling at massive scale.
My research mainly focuses on: 1) scalable and intelligent resource scheduling for datacentres, IoT, edges at Internet scale; 2) system dependability by leveraging effective system failover, long-tail task mitigation, and quantitative reliability modeling etc.; 3) improved system utility for resource management through resource over-subscription mechanism and multi-objective optimization, etc.; and 4) applied machine learning/deep learning, e.g., graph embedding, representation learning. Recent publications and research can be found in my personal website.
I have a teaching role in the school for undergraduate degree courses and MSc projects. I also co-supervise PhD students in the areas of distributed computing systems, cloud computing, and applied machine learning.