Hanzhe Liang

Shenzhen, China

Incoming Ph.D. Student, MBZUAI

lianghanzhe2023@email.szu.edu.cn

I am Hanzhe Liang, a Ph.D. student in Computational Biology at Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), supervised by Jun Wen. My doctoral research focuses on precision medicine. Prior to joining MBZUAI, I received dual bachelor’s degrees in Management and Science from Shenzhen University in June 2026, with my undergraduate studies primarily focusing on financial technology. During my undergraduate years, I also received research training in real-world computer vision under the guidance of Can Gao, Jinbao Wang, and Linlin Shen.

Experience

2026 – present — Ph.D. Student, Department of Computational Biology, MBZUAI
2026 – 2026 — Visiting Student, Department of Computational Biology, MBZUAI
2023 – 2025 — Research Assistantship, Computer Vision Institute and School of Artificial Intelligence, Shenzhen University
2023 – 2026 — Bachelor of Management and Bachelor of Science, Shenzhen University and Audencia

news

Aug 07, 2026	IEC3D-AD was accepted by IEEE Transactions on Circuits and Systems for Video Technology.
Jul 30, 2026	MVFM-3DAD was accepted by the International Conference on Image and Graphics 2026.
Jul 22, 2026	IRM3D-AD was accepted by IEEE Transactions on Artificial Intelligence.
Jul 03, 2026	I left Shenzhen University and joined MBZUAI.
Jun 14, 2026	BinaryAD was accepted by Pattern Recognition.

Precision Medicine

CONTEXTOR: Contextualized High-order Contrastive Learning

Ze Cai^*, Hanzhe Liang^*, Sihang Zeng, Binbin Zhou^†, and Jun Wen^†

The Forty-Third International Conference on Machine Learning, 2026

Real-World Computer Vision

IEC3D-AD: A 3D Dataset of Industrial Equipment Components for Unsupervised Point Cloud Anomaly Detection

Bingyang Guo, Hongjie Li, Ruiyun Yu, Hanzhe Liang, and Jinbao Wang

IEEE Transactions on Circuits and Systems for Video Technology, 2026
MVFM-3DAD: Multi-view Flow Matching for 3D Anomaly Detection via Density Proxy Estimation

Liangwei Li, Lin Liu, Jing Zhang, Xiaohui Du, Ruqian Hao, Juanxiu Liu, and Hanzhe Liang^†

The 14th International Conference on Image and Graphics, 2026
An Information-aware Reconstruction Model for Multi-category 3D Anomaly Detection

Hanzhe Liang, Can Gao^†, Zehai Niu, Jie Zhang, Jinbao Wang, and Linlin Shen

IEEE Transactions on Artificial Intelligence, 2026
BinaryAD: Efficient Image Anomaly Detection via Binarized Representations

Junjie Chen, Wenjing Zhang, Pengfei Wang, Bingyang Guo, Hanzhe Liang, Linlin Shen, Jinbao Wang, and Zhichao Lu

Pattern Recognition, 2026
A Unified Reconstruction Method with Multi-scale Feature Fusion for Multi-category 3D Anomaly Detection

Hanzhe Liang, Chenxi Hu, Yejin Tang, Linlin Shen, Jinbao Wang, and Can Gao

Neural Networks, 2026
A Lightweight 3D Anomaly Detection Method with Rotationally Invariant Features

Hanzhe Liang, Jie Zhou, Can Gao, Bingyang Guo, Jinbao Wang, and Linlin Shen

Pattern Recognition, 2025
Taming Anomalies with Down-up Sampling Networks: Group Center Preserving Reconstruction for 3D Anomaly Detection

Hanzhe Liang, Jie Zhang, Tao Dai, Linlin Shen, Jinbao Wang, and Can Gao

The 33rd ACM International Conference on Multimedia, 2025
Look Inside for More: Internal Spatial Modality Perception for 3D Anomaly Detection

Hanzhe Liang, Guoyang Xie, Chengbin Hou, Bingshu Wang, Can Gao^†, and Jinbao Wang^†

The Thirty-Ninth AAAI Conference on Artificial Intelligence, 2025

Open-Set Supervised 3D Anomaly Detection: An Industrial Dataset and a Generalisable Framework for Unknown Defects

Hanzhe Liang^*, Luocheng Zhang^*, Junyang Xia, HanLiang Zhou, Bingyang Guo, Yingxi Xie, Can Gao, Ruiyun Yu, Jinbao Wang, and Pan Li

arXiv preprint arXiv:2604.01171, 2026
C3D-AD: Toward Continual 3D Anomaly Detection via Kernel Attention with Learnable Advisor

Haoquan Lu, Hanzhe Liang, Jie Zhang, Chenxi Hu, Jinbao Wang, and Can Gao

arXiv preprint arXiv:2508.01311, 2025