معرفی
Xiaorui Liu is an Assistant Professor in the Department of Computer Science at North Carolina State University's College of Engineering, where he joined the faculty in August 2022. He also holds a courtesy appointment in the Department of Electrical and Computer Engineering. His research focuses on developing scalable and trustworthy machine learning systems with applications in data science, AI, and intelligent computing.
Dr. Liu earned his Ph.D. in Computer Science from Michigan State University in 2022 under the supervision of Dr. Jiliang Tang. He completed both his Master's and Bachelor's degrees at South China University of Technology. His academic journey reflects a strong foundation in computer science with specialized expertise in machine learning and graph-based systems.
Liu's research spans trustworthy artificial intelligence, large-scale machine learning, deep learning on graphs/language/vision, and generative AI. His work addresses fundamental challenges in graph neural networks, adversarial robustness, and scalable AI systems. He has published extensively in top-tier conferences including NeurIPS, ICML, ICLR, KDD, AISTATS, and SIGIR, demonstrating both theoretical depth and practical applications across multiple domains.
His publications reveal a strong focus on making AI systems more robust, efficient, and applicable to real-world problems. Recent work explores the intersection of large language models with graph structures, adversarial robustness in deep learning systems, and scalable approaches to graph neural networks that can handle industrial-scale data.
- NSF Career Award (2025)
- AAAI-2025 New Faculty Highlights
- National AI Research Resource Pilot Award (2024)
- ACM SIGKDD Outstanding Dissertation Award (Runner-up, 2023)
- Amazon Research Award (2023)
- NCSU Data Science Academy Award (2023)
Dr. Liu actively mentors multiple PhD and Master's students including Weizhi Gao, Zhichao Hou, Xingyue Shi, and Daniel Buchanan. His group has secured research funding from Amazon, Snap Research, and the National Science Foundation. He regularly serves as an organizer, senior program committee member, and reviewer for major venues including ICML, NeurIPS, ICLR, KDD, AAAI, and IJCAI. He has co-organized tutorials at SDM 2024, AAAI 2024, KDD 2023, and other leading conferences, helping to disseminate knowledge about large-scale graph neural networks and trustworthy AI.
His research group, the Network and Data Science Lab, focuses on developing techniques for large-scale machine learning on graphs with applications in cybersecurity, manufacturing, biology, and healthcare. The group has established collaborations with Oak Ridge National Laboratory, Amazon, and other industry partners to address real-world challenges in AI systems.
