Research Focus
My research covers a range of topics including distributed systems, storage systems, serverless/ cloud computing, operating systems, and high-performance computing. I conduct research to enable efficient and flexible (i.e., ease-of-use, ease-of-programming, and ease-of-deployment) systems for the growing data demands of modern applications running on existing as well as emerging computing platforms such as serverless computing. Specifically, my research is driven by the complexities of modern computing and data-intensive systems, and the need for more efficient and flexible approaches to manage such complexities.
Current Projects
■ NSF: MRI: Acquisition of an Adaptive Computing Infrastructure to Support Compute- and Data-Intensive Multidisciplinary Research, $750,000.
■ NSF: OAC Core: SMALL: DeepJIMU: Model-Parallelism Infrastructure for Large-scale Deep Learning by Gradient-Free Optimization, $498,609.
■ NSF: SPX: Collaborative Research: Cross-stack Memory Optimizations for Boosting I/O Performance of Deep Learning HPC Applications. $320,603.
Select Publications
■ Z. Chai, et al., TiFL: A Tier-based Federated Learning System. ACM HPDC (2020).
