About
Dr. Xiaoyong (Brian) Yuan is an Assistant Professor in the Department of Electrical and Computer Engineering at Clemson University. Dr. Yuan earned his Ph.D. in Computer Science from the University of Florida in 2020, an M.E. in Software Engineering from Peking University in 2015, and a B.S. in Mathematics from Fudan University in 2012. Prior to joining Clemson, he was an Assistant Professor at Michigan Tech University from 2020 to 2023. His research expertise spans the fields of artificial intelligence (AI), cybersecurity, and edge computing, with a particular focus on the development of trustworthy, efficient, and secure AI driven systems. He has published over 40 papers in prestigious IEEE and ACM journals and conferences. Dr. Yuan’s research has been supported by multiple grants from the National Science Foundation (NSF) and industry award. His contributions have been recognized through several awards, including the ORAU Ralph E. Powe Junior Faculty Enhancement Award (2022), the Michigan Tech ICC Achievement Award (2022), and the IEEE CIS TETCI Outstanding Paper Award (2025). Dr. Yuan has been serving as an associate editor for IEEE Transactions on Neural Networks and Learning Systems (TNNLS) since 2022 and a reviewer for top-tier AI conferences, e.g., NeurIPS, ICML, ICLR, and AAAI.
Visit Dr. Yuan's Faculty Profile.
How their research is transforming health care
Dr. Yuan’s recent research focuses on two key areas: efficient AI and trustworthy AI. He specializes in designing and optimizing AI algorithms tailored for deployment on resource constrained platforms, such as smartphones, IoT devices, and embedded systems. Additionally, he addresses critical security and privacy challenges inherent to AI algorithms and systems, significantly improving their privacy and robustness in safety-sensitive healthcare environments.Recently, Dr. Yuan has expanded his research into phenotypic drug discovery (PDD) by integrating morphological profiling with generative and explainable AI techniques. His ongoing project aims to create a morphology-guided, scalable, and explainable molecule design platform using diffusion-based generative models. This platform aims to synthesize morphological data at scale, substantially reducing the high costs associated with traditional high-throughput microscopy methods. The proposed approach also aims to enhance explainability and verifiability by incorporating retrieval-augmented generation (RAG), linking molecular designs directly to scientifically verified knowledge bases. Through these innovative efforts, Dr. Yuan’s work holds the potential to revolutionize healthcare technology, offering advanced and trustworthy solutions that accelerate drug discovery and enhance patient care.
News and related media
Brian Yuan PI of $409.4K NSF Collaborative Research Grant - https://blogs.mtu.edu/computing/2022/06/23/brian-yuan-pi-of-409-4k-nsf-collaborative-research-grant/
Health Research Expertise Keywords
Artificial Intelligence in Healthcare, AI-driven Drug Discovery, Privacy-Preserving Healthcare Systems, Edge AI for Healthcare, Explainable AI for Biomedicine
