Ningfang Mi

Ningfang Mi is an Associate Professor in the Department of Electrical and Computer Engineering at Northeastern University. She holds a Ph.D. from the College of William and Mary (2009), an M.S. from the University of Texas at Dallas (2004), and a B.S. from Nanjing University (2000). Her research focuses on capacity planning, resource management, storage systems (particularly NVMe flash devices), distributed data processing, and cloud computing. She directs the Northeastern University Computer Systems Research Laboratory (NUCSRL), which specializes in performance evaluation and system modeling for large-scale computing systems.

Education: Ph.D. Computer Science (2009, College of William and Mary), M.S. Computer Science (2004, University of Texas at Dallas), B.S. Computer Science (2000, Nanjing University).

Research Interests: Her work bridges theoretical performance modeling and practical system optimization. Key areas include improving data center efficiency, managing heterogeneous architectures, and developing scheduling algorithms for Hadoop/YARN clusters. She has pioneered techniques for flash storage optimization and containerized application scheduling on NVMe SSDs.

Publications: Over 50 peer-reviewed articles in top venues like IEEE Transactions on Computers, ACM Transactions on Modeling and Computer Simulation, and conferences such as IEEE Cloud, HPEC, and IPCCC. Recent work addresses serverless computing scalability, GPU idleness reduction in distributed deep learning, and federated learning client selection policies.

Awards: NSF CAREER Award (2015), AFOSR YIP Award (2014), IBM Faculty Award (2010), and multiple best paper awards (2017-2020).

Grants: Led NSF, AFOSR, and Samsung-funded projects totaling over $1M. Current focus includes AI-enabled educational data processing frameworks and NVMeOF zoning configurations for storage systems.

Labs/Teams: NUCSRL collaborators include PhD students (e.g., Janki Bhimani, Danlin Jia) and industry partners like Samsung Semiconductor. Current initiatives explore disaggregated storage systems and elastic resource management in cloud infrastructures.