Weining Kang

Weining Kang is an Associate Professor in the Department of Mathematics and Statistics at the University of Maryland, Baltimore County (UMBC). Her research focuses on probability theory, stochastic processes, stochastic networks, and queueing systems. She holds a Ph.D. in Mathematics from the University of California, San Diego (2005).

Her work emphasizes fluid models for many-server queues, stochastic networks with abandonment, and reflected diffusions. Notable contributions include analyzing nonlinear Volterra equations in queueing systems, equivalence of fluid models for Gt/GI/N+GI queues, and stationary distribution characterizations for reflected diffusions. She collaborates frequently with experts like K. Ramanan and G. Pang on stochastic network dynamics and performance analysis.

Recent publications (2023-2007) explore long-time limits of measure-valued equations, submartingale problems for diffusions, and diffusion approximations for input-queued switches. Her work bridges theoretical stochastic analysis with practical applications in operations research and network engineering.

While no formal awards are listed, her extensive peer-reviewed publications and collaborative research highlight her contributions to stochastic systems analysis. She advises on fluid model methodologies and has contributed to ACM Sigmetrics and SIAM journals.