I'm a research scientist at Intel Parallel Computing Lab focusing on hardware/software co-design for large-scale machine learning and data analytics. Most of my research is released part of SPLATT, an open source software toolkit for sparse tensor factorization. Another recent project is FROSTT, a collection of open sparse tensor datasets.
I completed my PhD in computer science at the University of Minnesota in April 2019. I was advised by George Karypis and focused on developing parallel algorithms for large-scale tensor factorization.
I also completed four internships, most recently at the Intel Parallel Computing Lab where I developed high performance algorithms for graph analytics and was also involved in hardware-software codesign. Before Intel, I spent the summer of 2013 at Lawrence Livermore National Laboratory (LLNL) porting LULESH to OpenACC. The two summers before LLNL (2011 and 2012), I interned at Lexmark as the developer of a particle flow modeling engine in CUDA.