Computer Science Faculty
Yuan Yuan
Assistant Professor
Yuan Yuan
Ph.D.
Contact
245 Beacon Street 528E
Telephone: 617-552-6157
Email: yuanyua@bc.edu
Education
- Ph.D., Hong Kong University of Science and Technology
- B.S., Huazhong University of Science and Technology
Research Interests
- Machine Learning
- Deep Learning
- Computer Vision
- AI for Healthcare
Department
Biography
Yuan Yuan is an Assistant Professor of Computer Science at Boston College. Previously, she was a postdoctoral research associate at MIT CSAIL. Before her time at MIT, she obtained her Ph.D. degree from the Hong Kong University of Science and Technology and was a visiting research scholar at the Robotics Institute of Carnegie Mellon University.
Her research primarily focuses on deep learning, computer vision, and the application of AI in healthcare and medicine. Her work has attracted widespread media attention, featuring in outlets such as Forbes, The Washington Post, BBC, TechCrunch, and Engadget, among others. Remarkably, her work on an AI-powered digital biomarker for the diagnosis and progression tracking of Parkinson's disease was recognized as one of the top ten notable advances in medicine of 2022.
SELECTED PUBLICATIONS
Continuous Invariance Learning. Yong Lin, Fan Zhou, Lu Tan, Lintao Ma, Jianmeng Liu, Yansu He, Yuan Yuan, Yu Liu, James Y. Zhang, Yujiu Yang, Hao Wang. International Conference on Learning Representations (ICLR), Vienna, Austria, 2024.
TokenCut: Segmenting Objects in Images and Videos with Self-supervised Transformer and Normalized Cut. Yangtao Wang, Xi Shen, Yuan Yuan, Yuming Du, Maomao Li, Xu Hu, James Crowley and Dominique Vaufreydaz. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2023.
Artificial Intelligence Detects Parkinson’s Disease and Estimates Disease Severity and Progression from Nocturnal Breathing. Yuzhe Yang*, Yuan Yuan*, Guo Zhang, Hao Wang, Ying-Cong Chen, Yingcheng Liu, Christopher G. Tarolli, Daniel Crepeau, Jan Bukartyk, Mithri R. Junna, Aleksandar Videnovic, Terry D. Ellis, PT, Melissa C. Lipford, Ray Dorsey and Dina Katabi. (* corresponding authors) Nature Medicine, 2022.
Contactless Oxygen Monitoring with Radio Waves and Gated Transformer. Yuan Yuan*, Hao He*, Ying-Cong Chen*, Peng Cao and Dina Katabi. (* equal contribution) Machine Learning for Healthcare (MLHC), New York, USA, 2023.
Addressing Feature Suppression in Unsupervised Visual Representations. Tianhong Li*, Lijie Fan*, Yuan Yuan, Hao He, Yonglong Tian, Rogerio Feris, Piotr Indyk and Dina Katabi. (* equal contribution) Winter Conference on Applications of Computer Vision (WACV), Hawaii, USA, 2023
Targeted Supervised Contrastive Learning for Long-Tailed Recognition. Tianhong Li*, Peng Cao*, Yuan Yuan, Lijie Fan, Yuzhe Yang, Rogerio Feris, Piotr Indyk and Dina Katabi. Computer Vision and Pattern Recognition (CVPR), New Orleans, USA, 2022.
Self-Supervised Transformers for Unsupervised Object Discovery using Normalized Cut. Yangtao Wang, Xi Shen, Xu Hu, Yuan Yuan, James Crowley and Dominique Vaufreydaz. Computer Vision and Pattern Recognition (CVPR), New Orleans, USA, 2022.
Unsupervised Learning for Human Sensing Using Radio Signals. Tianhong Li*, Lijie Fan*, Yuan Yuan*, and Dina Katabi. (* equal contribution) Winter Conference on Applications of Computer Vision (WACV), Hawaii, USA, 2022.
Subgroup-based Rank-1 Lattice Quasi-Monte Carlo. Yueming Lyu, Yuan Yuan and Ivor W. Tsang. Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS), virtual, 2020.
In-Home Daily-Life Captioning Using Radio Signals. Lijie Fan*, Tianhong Li*, Yuan Yuan, and Dina Katabi. European Conference on Computer Vision (ECCV), virtual, 2020. (Oral)
Learning Longterm Representations for Person Re-Identification Using Radio Signals. Lijie Fan*, Tianhong Li*, Rongyao Fang*, Rumen Hristov, Yuan Yuan and Dina Katabi. Computer Vision and Pattern Recognition (CVPR), virtual, 2020.
Marginalized Average Attentional Network For Weakly-Supervised Learning. Yuan Yuan, Yueming Lyu, Xi Shen, Ivor W. Tsang and Dit-Yan Yeung. International Conference on Learning Representations (ICLR), New Orleans, USA, 2019.
Temporal Dynamic Graph LSTM for Action-driven Video Object Detection. Yuan Yuan, Xiaodan Liang, Xiaolong Wang, Dit-Yan Yeung and Abhinav Gupta. International Conference on Computer Vision (ICCV), Venice, Italy, 2017.