Yaoyao Liu

• Faculty Affiliate, Siebel School of Computing and Data Science 

Computer vision, machine learning, artificial intelligence, and medical image analysis.

• Top 100 Most Cited CVPR Papers over the Last Five Years (Ranked by Google Scholar Metrics)
• ECVA PhD Award
• CVPR 2023 Doctoral Consortium
• WACV 2024 Doctoral Consortium

Yaoyao Liu is an assistant professor in the School of Information Sciences at the University of Illinois Urbana-Champaign. Previously, he completed his PhD in computer science at Max Planck Institute for Informatics and his BS in electronic information engineering at Tianjin University. He was also a postdoctoral fellow at Johns Hopkins University. His research lies at the intersection of computer vision and machine learning—with a special focus on building intelligent visual systems that are continual and data-efficient. His research interests include continual learning, few-shot learning, semi-supervised learning, generative models, 3D geometry models, and medical imaging.

• Spring 2025 - Concepts of Machine Learning - IS327C

Wufei Ma, Qihao Liu, Jiahao Wang, Xiaoding Yuan, Angtian Wang, Yi Zhang, Zihao Xiao, Guofeng Zhang, Beijia Lu, Ruxiao Duan, Yongrui Qi, Adam Kortylewski, Yaoyao Liu, Alan Yuille. "Generating Images with 3D Annotations Using Diffusion Models," International Conference on Learning Representations (ICLR), 2024.

Yixiao Zhang, Xinyi Li, Huimiao Chen, Alan Yuille, Yaoyao Liu, Zongwei Zhou. "Continual Learning for Abdominal Multi-Organ and Tumor Segmentation,” International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2023.

Yaoyao Liu, Bernt Schiele, Andrea Vedaldi, Christian Rupprecht. "Continual Detection Transformer for Incremental Object Detection," IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023.

Yaoyao Liu, Yingying Li, Bernt Schiele, Qianru Sun. "Online Hyperparameter Optimization for Class-Incremental Learning," Thirty-seventh AAAI Conference on Artificial Intelligence (AAAI), 2023.

Qianru Sun*, Yaoyao Liu* (equal contribution), Zhaozheng Chen, Tat-Seng Chua, Bernt Schiele. "Meta-Transfer Learning through Hard Tasks," IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), Volume 44, Number 3, 2022.

Yaoyao Liu, Bernt Schiele, Qianru Sun. "RMM: Reinforced Memory Management for Class-Incremental Learning," Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS), 2021.

Yaoyao Liu, Bernt Schiele, Qianru Sun. "Adaptive Aggregation Networks for Class-Incremental Learning," IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021.

Yaoyao Liu, Bernt Schiele, Qianru Sun. "An Ensemble of Epoch-wise Empirical Bayes for Few-shot Learning," European Conference on Computer Vision (ECCV), 2020.

Yaoyao Liu, An-An Liu, Yuting Su, Bernt Schiele, Qianru Sun. "Mnemonics Training: Multi-Class Incremental Learning without Forgetting," IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020.

Qianru Sun*, Yaoyao Liu* (equal contribution), Tat-Seng Chua, Bernt Schiele. "Meta-Transfer Learning for Few-Shot Learning," IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019.

"Generating Images with 3D Annotations Using Diffusion Models." CVPR 2024 Area Chair Workshop, June 2024.

"Learning from Imperfect Data: Incremental Learning and Few-shot Learning." Berkeley Artificial Intelligence Research Lab (BAIR), UC Berkeley, October 2023.

"Learning from Imperfect Data: Incremental Learning and Few-shot Learning." Computational Imaging Seminar, Purdue University, August 2023.

"Learning from Imperfect Data: Incremental Learning and Few-shot Learning." Vision and Graphics Seminar, Massachusetts Institute of Technology, April 2023.

"Learning from Imperfect Data: Incremental Learning and Few-shot Learning." External Speaker Series, University of Illinois Urbana-Champaign, April 2023.

"Learning from Imperfect Data: Incremental Learning and Few-shot Learning." VIGR Seminar, Columbia University, March 2023.

"Meta-transfer Learning for Few-shot Learning." École Polytechnique Fédérale de Lausanne (EPFL), March 2023.

"Learning from Imperfect Data: Incremental Learning and Few-shot Learning." Computational Cognition, Vision, and Learning Lab, Johns Hopkins University, January 2023.

"Learning from Imperfect Data: Incremental Learning and Few-shot Learning." Visual Geometry Group (VGG), University of Oxford, November 2022.

"Learning from Imperfect Data: Incremental Learning and Few-shot Learning." Fudan Vision and Learning Laboratory, Fudan University, September 2022.