Feng is an Assistant Professor (tenure track) at the Computer Science & Engineering Department at University of Nevada, Reno. Feng earned both M.S. and Ph.D. degrees in Computer Science from College of William and Mary. His M.S. and Ph.D. advisor is Professor Evgenia Smirni. He got his B.S. degree in Computer Science from Northeastern University.
Feng Yan’s main research projects have been focused on improving the performance and efficiency of big data applications and underlying infrastructures using various modeling and system techniques. He closely collaborates with industry partners (e.g., Microsoft Research, HP Labs, IBM Research, EMC, NetApp) to solve big and important problems, ranging from data center infrastructures to cluster computing frameworks (e.g., Hadoop, Spark) to large-scale data-intensive computing systems (e.g., distributed deep learning systems). His research outcomes have been published in premiere venues (more than 20 publications in 5 years), turned into patents and software prototypes.
Feng Yan is also actively serving the performance, system, and data science research communities. He has served as TPC member for several data science conferences, such as IEEE BigData, ALLDATA, DATA ANALYTICS, and as reviewers for about 20 different journals and conferences, including IEEE TCC, ACM TOS, IEEE TII, ACM SIGMETRICS, IFIP Performance, IEEE ICDCS, IEEE/IFIP DSN, USENIX ICAC, ACM/SPEC ICPE, IEEE/ACM CCGrid, IEEE IC2E.
