I currently work on big data and distributed systems. Specifically, to accelerate large-scale machine learning using accelerators such as GPUs, and in distributed systems such as Spark. I also worked on NoSQL and services computing.
My work and code have been incorporated into IBM patent portfolio and software such as Spark, BigInsights and Cognos. I am visiting professor at Tsinghua University and Tianjin University, China, and associate editor of IEEE Transactions on Automation Science and Engineering. See my GitHub and publications.
CuMF_SGD: Parallelized Stochastic Gradient Descent for Matrix Factorization on GPUs accepted by HPDC 2017! It is an SGD version of cuMF and complements the previously released ALS one. Outperform all previous approaches with a single GPU! [arXiv] [GitHub]
With only one machine with four Nvidia GPU cards, cuMF can be 6-10 times as fast, and 33-100 times as cost-efficient, compared with the state-of-art distributed CPU solutions. Moreover, cuMF can solve the largest matrix factorization problem ever reported yet in current literature.
From 2008 to 2010 I worked at Computation Institute, University of Chicago and Argonne National Laboratory, on caGrid Workflow Toolkit, a web-service-based scientific workflow platform for cancer Biomedical Informatics Grid (caBIG). It was funded by US National Cancer Institute and adopted by many major US bioinformatics projects.