I am a PhD candidate in the Computer Science Department at Stanford University where I study databases and computer architecture. I have received masters degrees in Computer Science (September 2016) and Electrical Engineering (June 2015) from Stanford University as well as bachelors degrees in Computer Engineering and Computer Science from the University of Wisconsin-Madison (May 2013).
My research focuses on the intersection of cutting edge database theory and modern hardware trends---in particular high performance join processing. I conduct research under Professors Christopher Ré and Kunle Olukotun. I am a member of Hazy Research, the InfoLab, and the Pervasive Parallelism Laboratory.
Research summary: Did you know that the join operation, often the bottleneck operation in traditional databases, can run with a suboptimal asymptotic bound? We do! Using recent theoretical guarantees and modern hardware advances we have developed a new style of relational data processing engine that provides tight theoretical guarantees. We have applied our engine in the graph and RDF domains, but are quickly expanding out to other workloads. We demonstrate high performance when compared to traditional data processing engines and competitive performance when compared to specialized graph and RDF engines. My previous research has focused on designing a graph analytics DSL in an advanced compiler framework for parallel and heterogeneous architectures.
Relevant technical course work includes: databases, algorithms, computer architecture, operating systems, machine learning, programming languages, computer graphics, digital system design and synthesis, and logic design.