Sun-Yuan Kung

Sun-Yuan Kung is a Professor of Electrical and Computer Engineering at Princeton University. His research focuses on Biological & Biomedical, Computing & Networking, Data & Information Science, and Security & Privacy. His work centers on developing high-performing learning networks, with an emphasis on deep learning processors and their applications in speech and image processing. Kung's research explores methods for improving network structure through joint parameter/structural gradient-type approaches, aiming to enhance model robustness and enable explainable neural networks. His contributions include the development of internal learning paradigms and metrics for evaluating hidden layers and nodes, as well as advancements in deep compression and explainable AI. He has published extensively in top-tier journals and conferences, including IEEE Signal Processing Magazine and ACM Transactions on Sensor Networks.

Sun-Yuan Kung is a Professor of Electrical Engineering at Princeton University, specializing in Machine Learning and Artificial Intelligence. His research spans a wide range of topics, including neural networks, signal processing, and wireless communication systems. Kung's work explores the theoretical foundations and practical applications of machine learning, such as principal component neural networks, face recognition, and adaptive filtering. He has also contributed to the development of VLSI array processors and network coding techniques for wireless networks. His research integrates mathematical modeling with engineering applications, emphasizing the intersection of artificial intelligence and signal processing.