Bin Li

Bin Li is an Associate Professor at the School of Electrical Engineering and Computer Science. His research focuses on wireless networks, network scheduling, sufficient dimension reduction, and statistical inference.

• NSF-funded projects: EAGER: TaskDCL, CAREER: Wireless Collaborative Mixed Reality Networking, CNS Core: Scalable Algorithms for Virtual Reality Over Wireless Networks.
• Grants include foundational work in AI-driven task training, geospatial digital twins, and joint communication-computation-learning systems.

His research spans wireless scheduling algorithms, data freshness optimization, and nonlinear sufficient dimension reduction. Recent work explores Fréchet regression, functional graphical models, and kernel-based hypothesis testing. Articles highlight interdisciplinary applications in computer science, statistics, and mathematics.

Statistical methods dominate his contributions, including Bayesian credible sets, copula models, and additive independence frameworks. Collaborations extend to multi-source genomic data analysis and immersive educational platforms via augmented reality.

With an h-index of 16 and 74 research outputs, Bin Li’s expertise intersects wireless network optimization and statistical learning. His work addresses challenges in edge computing, cloud offloading, and cyber-physical systems through algorithmic innovation and theoretical rigor.