Zengchang Qin, PhD
College of Engineering and Computer Science
Professor in Artificial Intelligence/Machine Learning
Biography
Dr. Zengchang Qin received the B.E. degree in Automation from Heilongjiang University, Harbin, China, in 2001 and the M.Sc. and Ph.D. degrees in Machine Learning and Artificial Intelligence from University of Bristol, UK, in 2003 and 2006, respectively. He was a Postdoctoral Research Fellow with the EECS department, University of California Berkeley (2006-2008), a Visiting Fellow of the Department of Statistics, University of Oxford (2008-2009), a Visiting Scholar with Robotics Institute, Carnegie Mellon University (2009-2010). Since 2010, he has been a faulty member of Beihang University, Beijing, China, before joining the VinUniversity. His current research activity is focused on Artificial Intelligence, Uncertainty Reasoning, Machine Learning, Natural Language Processing, Image Processing and Computational Game Theory. He published 2 books and over 130 technical papers in journal and conferences. He has been cited nearly 5000 times and his h-index is 33 and i10-index is 75. He also served as PC members of international conferences including NeuroIPS, ACM-MM, AAAI, IJCAI, ICLR, ICML and etc.
• Artificial Intelligence
• Machine Learning and Data Mining
• Deep Learning
• LLMs and Applications
• Agentic AI
• Medical Image Processing
• Computer Vision
• Natural Language Processing
• Vision and Language Problem
• Uncertainty Reasoning
• Fuzzy Reasoning
• Computational Game Theory
• Computational Social Science
• Machine Learning and Data Mining
• Principals of AI
• Deep Learning and Applications
• Natural Language Processing
1. Zengchang Qin and Yongchuan Tang (2014), Uncertainty Modeling for Data Mining: A Label Semantics Approach, Springer. ISBN 978-3-642-41250-9
2. Jing Yu, Xiaoze Jiang, Zengchang Qin, Weifeng Zhang, Yue Hu and Qi Wu (2021), Learning dual encoding model for adaptive visual understanding in visual dialogue, IEEE Transactions on Image Processing，Vol. 30: pp. 220-233.
3. Tao Wan, Chunxue Wu, Ming Meng, Tao Liu, Chuzhong Li, Jun Ma, Zengchang Qin (2021), Radiomic features on multiparametric MRI for preoperative evaluation of pituitary macroadenomas consistency: preliminary findings, Journal of Magnetic Resonance Imaging (JMRI), Pub Date: 22 September 2021.
4. Tao Wan, Jianhui Chen, Zhonghua Zhang, Deyu Li and Zengchang Qin (2021), Automatic vessel segmentation in X-ray angiogram using spatio-temporal fully-convolutional neural network, Biomedical Signal Processing and Control, Vol. 68: 102646.
5. Jing Yu, Weifeng Zhang, Yuhang Lu, Zengchang Qin, Yue Hu, Jianlong Tan, Qi Wu (2020), Reasoning on the relation: enhancing visual representation for visual question answering and cross-modal retrieval, IEEE Transactions on Multimedia, Vol. 22(12): pp. 3196-3209.
6. Weifeng Zhang, Jing Yu, Hua Hu, Haiyang Hu, Zengchang Qin (2020), Multimodal feature fusion by relational reasoning and attention for visual question answering, Information Fusion, Vol. 55: pp. 116-126.
7. Tao Wan, Xiaoqing Shang, Weilin Yang, Jianhui Chen, Deyu Li and Zengchang Qin (2018), Automated coronary artery tree segmentation in X-ray angiography using improved Hessian based enhancement and statistical region merging, Computer Methods and Programs in Biomedicine, Vol. 157: pp. 179-190.
8. Zengchang Qin, Farhan Kahwar and Tao Wan (2016), Collective game behavior learning with probabilistic graphical models, Neurocomputing, Vol. 194: pp. 74-86.
9. Yuhe Liu, Chuanjian Liu, Kai Han, Quan Tang and Zengchang Qin (2023), Boost semantic segmentation from the perspective of explicit class embeddings, IEEE/CVF International Conference on Computer Vision (ICCV-2023), pp. 821-831.
10. Zheng He, Zeke Xie, Quanzhi Zhu, Zengchang Qin (2022), Sparse double descent: where network pruning aggravates overfitting, International Conference on Machine Learning (ICML-2022), pp. 8635-8659.
11. Shunyu Zhang, Xiaoze Jiang, Zequn Yang, Tao Wan, Zengchang Qin (2022), Reasoning with multi-structure commonsense knowledge in visual dialog, Conference on Computer Vision and Pattern Recognition (CVPR-2022) Workshop.
12. Xiaoze Jiang, Siyi Du, Zengchang Qin, Yajing Sun and Jing Yu (2020), KBGN: Knowledge-bridge graph network for adaptive vision-text reasoning in visual dialogue, Proceedings of ACM International Conference on Multimedia (Oral, ACM-MM 2020), pp. 1265-1273.
13. Xiaoze Jiang, Jing Yu, Yajing Sun, Zengchang Qin, Zihao Zhu, Yue Hu and Qi Wu (2020), DAM: deliberation, abandon and memory networks for generating detailed and non-repetitive responses in visual dialogue, IJCAI-2020, pp. 687-693.
14. Xiaoze Jiang, Jing Yu, Zengchang Qin, Yingying Zhuang, Xingxing Zhang, Yue Hu and Qi Wu (2020), DualVD: An adaptive dual encoding model for deep visual understanding in visual dialogue, AAAI-2020, pp. 11125-11132, AAAI Press.
15. Yifan Liu, Ke Chen, Chris Liu, Zengchang Qin, Zhenbo Luo, Jingdong Wang (2019), Structured knowledge distillation for semantic segmentation, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (Oral, CVPR-2019), pp. 2604-2613, IEEE Press.
• 2006: Ph.D. in Artificial Intelligence, Department of Engineering Mathematics, University of Bristol, Bristol, UK
• 2003: MSc in Machine Learning and Data Mining, Department of Computer Science, University of Bristol, Bristol, UK
• 2001: B.E. in Automation, School of Electrical Engineering, Heilongjiang University, Harbin, China
• 2010 Best Paper Nomination, ACM-ICMR
• 2021 Best Student Paper, IFSA World Conference, International Fuzzy System Association
