Tianyang Wang
Assistant ProfessorUniversity Hall 4157
Research and teaching interests: AI, machine learning, computer vision, broad data science, and biomedical informatics.
Office hours: By appointment
Education:
- Ph.D., Southern Illinois University, Computer Science
- M.S., Jilin University, Computer Science
- B.S., Jilin University, Software Engineering
Dr. Wang is an assistant professor of computer science at the University of Alabama at Birmingham (UAB). Prior to joining UAB, he was an assistant professor at the Austin Peay State University. He was also a visiting researcher at Baidu Inc in 2019. Since 2020, he has been serving as a remote researcher in the XuLab at the Carnegie Mellon University. Dr. Wang published more than 30 papers in well-known journals and conferences, including the IEEE Transactions on Neural Networks and Learning Systems (TNNLS), the IEEE International Conference on Computer Vision (ICCV), the Association for the Advancement of Artificial Intelligence (AAAI) conference, and the European Conference on Machine Learning (ECML). He also regularly serves several academic conferences and journals as a reviewer, such as the Elsevier Journal of Pattern Recognition, the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), and the IEEE Winter Conference on Applications of Computer Vision (WACV).
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Research Interests
Dr. Wang’s research interests include artificial intelligence, machine (deep) learning, computer vision, broad data science and biomedical informatics. He is also quite interested in applying AI and machine learning on interdisciplinary research, such as AI-assisted cellular analysis and health care. His recent effort focuses on boosting large models for downstream tasks. For details, please refer to Dr. Wang’s personal webpage.
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Select Publications
- A. Deng, X. Li, D. Hu, T. Wang, H. Xiong, and C. Xu. Towards Inadequately Pre-trained Models in Transfer Learning, the IEEE International Conference on Computer Vision (ICCV), 2023.
- Z. Zhang, X. Li, T. Wang, T. Hong, J. Ma, H. Xiong, and C. Xu. Overcoming Catastrophic Forgetting for Fine-tuning Pre-trained GANs, the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), 2023.
- S. Huang, T. Wang, H. Xiong, B. Wen, J. Huan, and D. Dou. Temporal Output Discrepancy for Loss Estimation-based Active Learning, the IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2022.
- T. Wang, X. Li, P. Yang, G. Hu, X. Zeng, S. Huang, C. Xu, and M. Xu. Boosting Active Learning via Improving Test Performance, the Association for the Advancement of Artificial Intelligence Conference (AAAI), 2022.
- S. Huang, T. Wang, H. Xiong, J. Huan, and D. Dou. Semi-Supervised Active learning with temporal Output Discrepancy, the IEEE International Conference on Computer Vision (ICCV), 2021.
- T. Wang, J. Huan, M. Zhu. Instance-based Deep Transfer Learning, the IEEE Winter Conference on Applications of Computer Vision (WACV), 2019.