Weina Wang

Weina Wang is an Assistant Professor in the Computer Science Department at Carnegie Mellon University, joining in Fall 2018. Her research lies at the intersection of applied probability, stochastic systems, and reinforcement learning, focusing on decision-making in large-scale systems with applications to computing resource orchestration, data privacy, and graph statistics. She has received prestigious awards including the NSF CAREER Award (2022), ACM MobiHoc Best Paper (2022), and ACM SIGMETRICS Rising Star Research Award (2023).

• PhD in Electrical Engineering, Arizona State University (2016)
• Bachelor’s in Electronic Engineering, Tsinghua University (2009)

Her recent publications span restless bandits, queueing theory, and attributed graph alignment, reflecting her dual focus on fundamental limits and algorithmic solutions. Notable collaborations include work on privacy-preserving data routing, phase-aware scheduling, and erasure-coded servers for heterogeneous traffic. She has advised PhD students Jalani Williams, Tuhinangshu Choudhury, and Yige Hong.

Her research has been recognized with best paper awards and grants like the NSF CAREER. She also contributes to professional societies, recently joining the INFORMS Applied Probability Society council. Her teaching includes courses like Probability and Computing and Fundamentals of MDPs and Reinforcement Learning.