I am a research staff member (RSM) at IBM T.J. Watson Research Center, working on big data solutions for massive graphs and graphcial models using high performance computing, including highly efficient large scale graph runtime libraries, graph data storage and management, and graph-based analytics on various parallel computing platforms. Our system achieves orders of magnitudes higher throughput compared to several existing solutions, and is also highly amenable for leveraging various hardware advances. Our work has been partially incorporated into IBM System G, a comprehensive graph analytic platform.
I was a computing innovation postdoctoral researcher at IBM in 2010~1012. Prior to that, I received a Ph.D. degree in the Computer Science Department at the University of Southern California (USC), Los Angeles, in 2010, advised by Professor Viktor K. Prasanna. My dissertation was the Exploration of Parallelism for Probabilistic Graphical Models at Multiple Granularities. I received a Masters degree of Engineering on Statistical Machine Learning from the Department of Automation, Tsinghua University, China, in 2006, and my Bachelor's degree in Computing Engineering from the University of Electronic Science and Technology of China (UESTC), China, in 2003.
I publish extensively with 40+ referreed papers and I am also active in professional communities, serving as a steering/general/program/publicity chair, a guest editor, or a TPC member in several international conferences, workshops, or international journals, such as IEEE IPDPS’14, HiPC’14, ICPADS’14, ICME’14, IEEE BigData’14, etc. I was awarded the IBM Research Division Eminence & Excellence. I am a director in the board of the Linked Data Benchmark Council supported by EU since 2014. I was a NSF/CRA Computing Innovative Fellow (CIFellow) in 2010~2012
See my personal webpage at: https://sites.google.com/site/yinglongxia/