Yu Sun Source Confirmed

OverviewAI-generated summary

Yu Sun's research focuses on the application of advanced computational techniques, particularly machine learning and deep learning, to complex scientific and engineering problems. Sun has investigated the development of novel algorithms for tomographic imaging, including coordinate-based internal learning (CoIL) and the recovery of refractive index maps from intensity-only measurements using neural fields. Other areas of work include the development of scalable plug-and-play algorithms for signal processing, such as ADMM with convergence guarantees, and efficient model-based deep learning frameworks like SGD-Net with theoretical assurances.

Further research extends to optimization problems, including video coding optimization within the AVS2 standard and the application of improved RRT* algorithms combined with BP neural networks for robot arm path planning. Sun has also explored predictive modeling, such as forecasting stock prices using LSTM-XGBoost models optimized with Bayesian methods. Additionally, research includes efforts in object detection, specifically for aircraft in remote sensing imagery using progressive class-aware instance enhancement.

With a career citation count of 3,641 and an h-index of 21, Sun's work has been recognized as highly cited. Sun maintains an active laboratory website and has been recently active in research, with the most recent publication dating to 2025.

Metrics

• h-index: 21
• Publications: 116
• Citations: 3,641