Dr. Xin Liang is an assistant professor with the Department of Computer Science at Missouri University of Science and Technology. He received his Ph.D. in Computer Science from University of California, Riverside in 2019. Prior to that, he received his B.S. in Computer Science from Peking University in 2014, with a minor in Math and Applied Math. During his Ph.D. studies, he has worked as student interns at the Extreme Scale Resilience Group and the Parallel Extreme-Scale Data Analytics Team at Argonne National Laboratory, the Scalable Machine Learning Group at Pacific Northwest National Laboratory, and the Data Science at Scale Team at Los Alamos National Laboratory. Prior to joining MS&T, he was a Computer/Data Scientist in the Workflow Systems Group at Oak Ridge National Laboratory, where he led the ESAMR project funded by the Director's Research and Development (DRD) program.
Dr. Liang's research interests lie broadly in the areas of high-performance computing, parallel and distributed systems, scientific data management, large-scale data analytics, and distributed machine learning. He has published in many highly competitive conferences and journals such as IEEE/ACM SC, ACM HPDC, ACM PPoPP, ACM ICS, IEEE IPDPS, ACM PACT, IEEE BigData, IEEE Cluster, IEEE TPDS etc. He has received Dissertation Year Fellowship (DYP) from UCR and Best Paper Award from IEEE Cluster 2018. He is one of the key developers of SZ and major contributors of MGARD, which are two widely used data reduction software in the scientific computing community.
High-performance computing, parallel and distributed systems, scientific data management, large-scale data analytics, and distributed machine learning