Research

My research insterest lies broadly in machine learning, optimization, statistics. Previously, I also conducted research in robotics.

• Geometric Machine Learning 4/25-current

• Distributionally Robust Optimization 4/25-current

• Robot Calibration Algorithms 10/24-2/25
  • S. Yao, Y. Wu, R.H. Taylor, E.M. Boctor, Boost Calibration for Dual-Arm Co-Robotic Ultrasound System. 2025 IEEE International Ultrasonics Symposium (IUS) [paper] [poster]

• Physics-Informed Neural Networks (PINN) for Multi-Physics Coupling (Undergraduate’s thesis) 10/23-7/24
  • S. Yao, W. Huang, Y. Hu, Q. He, Boundary Region Reinforcement Physics-Informed Neural Networks for PDEs solving. Engineering Applications of Artificial Intelligence (EAAI), Under reviewed [paper]

• Data-Driven Urban Traffic Risk Analysis 9/22-8/23
  • S. Yao, H. Li, X. Hu, K. Hermann, K. Zhang, Y. Li, M. Li, Identifying Traffic Risk Hotspots Using Spatial-temporal Network Kernel Density Estimation: A Novel Optimal Parameter Selection Method with Dual Dataset Validation. Transportation Research Board (TRB) 103rd Annual Meeting, Poster Presented [paper]