Student

Colleen Que

Colleen Que is a graduate student at Carnegie Mellon University pursuing her master’s degree in computer vision. She received a bachelor’s degree from Stevens Institute of Technology in Computer Science. Previously, she participated in a summer research program at the University of Pennsylvania, where she worked on robot path planning for the Internet of Things for Precision Agriculture (IoT4Ag) Research Center.

Collaborator

Qifeng Wu

Qifeng is a research scholar at CompBio dept, Carnegie Mellon University. He completed his Master’s degree in Electrical and Computer Engineering at Northeastern University, during which he worked with Prof. Yun Raymond Fu on talking head synthesis. He has a broad research interest on the application of multimodal modeling and generative AI. He has previously interned at Liberty Mutual Insurance, and bitHuman Inc.

Postdoctoral Mentor

Xingjian Li

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.

Faculty Advisor

Min Xu

Dr. Xu is currently developing computer vision and machine learning methods for the automatic structural analysis of cell systems at molecular resolution and in close-to-native states. In particular, his research focuses on information extraction and modelling of the structures and spatial organizations of macromolecules and their interactions with organelles in single cells captured by cryo electron-tomography 3D images. This emerging research field aims to address fundamental biological questions using a wide range of state-of-the-art computational and mathematical techniques.