Postdoctoral Fellows
Xingjian Li
Advisor: Min Xu
Xingjian Li is currently a Postdoctoral Associate in Dr. Xu's lab at Carnegie Mellon University. He received his B.S. degree in microelectronics from Tsinghua University in 2008, his M.S. degree in computer science and technology from the Institute of Computing Technology, Chinese Academy of Sciences in 2011, and his Ph.D. degree in computer science from the University of Macau in 2023. In addition, he has extensive experience working as an R&D engineer of NLP in industry. His research interests lie in data-efficient and interpretable machine learning. He is excited to explore practical AI solutions for real-world problems, especially in biological, medical, and other scientific fields.
Renming Liu
Advisor: Jian Ma
Renming Liu obtained his doctoral degree in Computational Mathematics, Science & Engineering from Michigan State University. His Ph.D. research concentrated on creating software and computational methods to predict gene functions and disease associations using graph machine learning and genome-scale biological networks. Currently, in the Ma Lab, Renming is dedicated to developing innovative machine learning methods to model and extract insights from single-cell (epigen)omics data. His work focuses on analyzing genome structures and chromatin accessibility to enhance our understanding of cell biology and gene regulatory mechanisms.
Xueying Zhan
Advisor: Min Xu
Xueying Zhan earned her Bachelor’s degree in Data and Computer Science from Sun Yat-sen University and completed her Ph.D. in Computer Science at City University of Hong Kong. She is currently a postdoctoral researcher in Dr. Xu's lab. Her research primarily involves developing methods to reduce human labeling costs and improve machine learning and deep learning model performance efficiently. Specifically, Xueying aims to create innovative solutions for minimizing human intervention in labeling and addressing label noise challenges in practical applications, such as biomedical image analysis.