Min Xu

Prior to joining MBZUAI, Professor Xu was a postdoctoral researcher at USC and a recipient of USA NIH and NSF awards. His career has centered on developing AI methods for the analysis of biomedical images and other biomedical data, in particular, Cellular Cryo-Electron Tomography (Cryo-ET) 3D image data. He is currently also an Assistant Professor at the Computational Biology Department within the School of Computer Science at Carnegie Mellon University, USA.

• Ph.D. in computational biology and bioinformatics from University of Southern California (USC), USA.
• Master’s of Science from the School of Computing at the National University of Singapore, Singapore.
• Master’s of Arts in applied mathematics from the University of Southern California (USC), USA.
• Bachelor of Engineering in computer science from the Beihang University, China.

• Recipient of USA NIH and NSF awards.

Xu has published more than 70 research papers in prestigious peer-reviewed conferences and journals, such as CVPR, ICCV, AAAI, ISMB, MICCAI, PNAS, Bioinformatics, PLOS Computational Biology, Structure, and JSB.

• 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).
• 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) arXiv:2112.05683
• 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).
• Du X, Wang H, Zhu Z, Zeng X, Chang Y, Zhang J, Xu M. Active learning to classify macromolecular structures in situ for less supervision in cryo-electron tomography. Bioinformatics. doi:10.1093/bioinformatics/btab123 arXiv:2102.12040
• Zeng X, Xu M. Gum-Net: Unsupervised geometric matching for fast and accurate 3D subtomogram image alignment and averaging. IEEE conference on computer vision and pattern recognition (CVPR 2020).