Yue Cheng, PhD

Assistant Professor

Volgenau School of Engineering

Other Positions:


Research Theme:

Digital Technologies - Inventing new algorithms, digital techniques, and technologies

Key Interests:

Cloud Computing, Serverless Computing, Distributed Systems, Storage Systems, High-performance Computing, Operating Systems, Machine Learning Systems


PhD, Computer Science, Virginia Tech

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

B. Carver et al., Wukong: A scalable and locality-enhanced framework for serverless parallel computing. ACM SoCC (2020).

Z. Chai, et al., TiFL: A Tier-based Federated Learning System. ACM HPDC (2020).

A. Wang, et al., InfiniCache: Exploiting ephemeral serverless functions to build a cost-effective memory cache. USENIX FAST (2020).



Volgenau School of Engineering

Contact Yue :

Email: yuecheng@gmu.edu

LinkedIn: N/A