I am a fifth-year Ph.D. student and currently pursuing the degree in the School of Computing at the University of Nebraska–Lincoln. My major is Computer Science and my research interests include Wireless Communication, Digital Twin, Machine Learning, and Edge Computing.

Currently, I hold the position of Graduate Research Assistant at the Intelligent Network sysTem (INT) Laboratory under the guidance of Dr. Qiang Liu. I received the M.E. degree in transportation information engineering and control from Xidian University, Xi’an, China, in 2022, and received the Outstanding Graduate Student Award.

Education

• Ph.D. student in Computer Science, University of Nebraska–Lincoln, Aug. 2022 – Present
• M.E. in Traffic Information Engineering and Control, Xidian University, Sept. 2019 – June 2022
• B.E. in Communication Engineering, Lanzhou Jiaotong University, Lanzhou, China, Sept. 2015 – June 2019

Experience

• Network System Research Intern, Nokia Bell Labs, Murray Hill, NJ, Jun. 2026 – Aug. 2026
  • Designed a real-time wireless digital twin for dynamic environments, combining 3D Gaussian Splatting with a Transformer-based radiance model to generalize across object layouts without per-scene retraining, plus an online adaptation pipeline that keeps the twin synchronized with live measurements, reducing reconstruction error by over 2 dB and cutting update latency from tens of minutes to milliseconds versus prior methods. (Paper submitted for publication)
• Graduate Research Assistant, University of Nebraska–Lincoln, Aug. 2022 – Present
  • Designed a generalizable wireless digital twin that conditions on point-cloud-based scene geometry and uses a physics-informed sparse attention mechanism with a Transformer-based decoder to predict wireless channel propagation across arbitrary indoor layouts without per-scene retraining, achieving 31.6% higher SSIM and 92.0% lower LPIPS than the leading neural baseline, validated on NVIDIA Sionna RT across 30 indoor scenes. (ICCCN 2026 Best Paper Award)
  • Built the first online digital network twin, combining a Bayesian-optimization-based material-tuning algorithm with a continually-updated neural radio radiance field to keep simulator and neural predictions synchronized in real time, validated against live measurements from an operational cellular network and achieving 57.5% and 36.4% lower prediction error than simulator-based and neural baselines respectively at 0.98s update latency. (INFOCOM 2026)
  • Built an end-to-end 5G testbed (OpenAirInterface RAN and Core with Ettus USRP B210) and designed a cost-aware Bayesian-optimization pipeline that reduces the simulation-to-reality discrepancy of digital network twins by over 92%, along with an adversarial learn-to-attack and defense framework that recovers neural network-configuration policies to within 1% of attack-free optimal performance. (ICC 2023; INFOCOM 2023 Poster; INFOCOM Workshop 2024)
• Graduate Teaching Assistant, University of Nebraska–Lincoln, Jan. – May 2024; Jan. – May 2025
  • CSCE 464/864 Internet Systems and Programming