Carnegie Mellon University

 Min Xu

Associate Professor, Computational Biology Department, Co-Director, MS in Computational Biology Program


Gates Hillman Center 7709
Computational Biology Department, SCS
Carnegie Mellon University
5000 Forbes Avenue
Pittsburgh, PA 15213



Administrative Assistant: Ally Ricarte


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.
 Ali Dabouei

 Mostofa Rafid Uddin

Ph.D. Student

 Yizhou Zhao

Research Assistant (ECE)

Highlighted Publications

Zeng X, Kahng A, Xue L, Mahamid J, Chang Y, Xu M. High-throughput cryo-ET structural pattern mining by deep unsupervised clustering. Proceedings of the National Academy of Sciences

Uddin M, Howe G, Zeng X, Xu M. Harmony: A Generic Unsupervised Approach for Disentangling Semantic Content from Parameterized Transformations. IEEE conference on computer vision and pattern recognition (CVPR 2022)

Zeng X, Xu M. Gum-Net: Unsupervised geometric matching for fast and accurate 3D subtomogram image alignment and averaging. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR 2020)

Wang T, Li X, Yang P, Hu G, Zeng X, Huang S, Xu C, Xu M. Boosting Active Learning via Improving Test Performance. AAAI Conference on Artificial Intelligence. (AAAI 2022) 

Zeng X, Howe G, Xu M. End-to-end robust joint unsupervised image alignment and clustering. International Conference on Computer Vision (ICCV 2021)

Zhu X, Chen J, Zeng X, Liang J, Li C, Liu S, Behpour S, Xu M. Weakly Supervised 3D Semantic Segmentation Using Cross-Image Consensus and Inter-Voxel Affinity Relations. International Conference on Computer Vision (ICCV 2021)

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