- Large Data Sets for Scientific Applications
- Query Processing for Data-Intensive Science Applications
- Redundancy Coding and Auditing of Outsourced Storage Services
- Adaptive Performance Management for Network Data Protocols
- Scalable Systems that Support Neuroscience Imaging Analysis and Annotation
Randal Burns research interests lie in building the high-performance, scalable data systems that allow scientists to make discoveries through the exploration, mining, and statistical analysis of big data. Recently, he has focused primarily on high-throughput neuroscience, but retains a vigorous interest in high-performance computing numerical simulations.
Randal is both a member of and on the steering committee of the Kavli Neuroscience Discovery Institute. He is a member of the Institute for Data-Intensive Science and Engineering. He is on the Steering Committee of the Science of Learning Institute. He was a research staff member in storage systems at IBM’s Almaden Research Center from 1996-2002. He earned his bachelor’s in geophysics at Stanford University (1993), and his master’s (1997) and doctorate (2000) in computer science at the University of California, Santa Cruz.