My research spans the areas of machine learning, computer vision, and AI for healthcare, intending to make people have more convenient and healthier lives. Towards this vision, I develop novel machine learning models and apply them to (1) enable human-centered AI, such as understanding human actions and people’s daily activities, and (2) advance contactless health monitoring and develop new digital biomarkers for diseases like Parkinson’s, Alzheimer’s, epilepsy, etc.
I am a WISDM speaker in MIT Office of Innovation. I am passionate about fostering STEM community diversity, equity, and inclusion through speaking, mentoring, teaching, academic collaborations, and outreach.
[05/2023] I was honored to be invited to give a guest lecture for the ROB-UY 3203 Robot Vision course at New York University in the Spring of 2023.
[01/2023] AI-based biomarker for Parkinson's disease has been selected to Notable Advances 2022 by Nature Medicine, as one of the ten global breakthroughs and critical developments that moved medicine forward in 2022. Notable Advances 2022 are selected from all the papers of Nature, Science, Lancet, New England Journal of Medicine and their subsidiary journals, and all the reports of the World Health Organization (WHO).
Contactless Oxygen Monitoring with Radio Waves and Gated Transformer Hao He*, Yuan Yuan*, Ying-Cong Chen*, Peng Cao and Dina Katabi
(* indicates equal contribution, order determined via a random coin flip) NeurIPS Workshop on Learning from Time Series for Health, Neural Information Processing Systems (NeurIPS), 2022 Machine Learning for Healthcare (MLHC), 2023 [MLHC Version][NeurIPSw Version]
Unsupervised Learning for Human Sensing Using Radio Signals Tianhong Li*, Lijie Fan*, Yuan Yuan* and Dina Katabi
(* indicates equal contribution) Winter Conference on Applications of Computer Vision (WACV), 2022 [PDF][Poster][BibTeX]
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), 2019 [PDF][Code][BibTeX]Star
Efficient Batch Black-box Optimization with Deterministic Regret Bounds Yueming Lyu, Yuan Yuan and Ivor W. Tsang Under Review (JMLR), 2019 [arXiv][BibTeX]
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), 2017 [PDF][Dataset][BibTeX]
Adaptive Block Coding Order for Intra Prediction in HEVC
Amin Zheng, Yuan Yuan, Jiantao Zhou, Yuanfang Guo, Haitao Yang and Oscar C. Au IEEE Transaction on Circuits and Systems for Video Technology, vol.26, no.11, pp.2152-2158, Nov. (TCSVT), 2016 [PDF][BibTeX]
Image Compression Via Dense Descriptors Assisted Synthesis Yuan Yuan, Amin Zheng, Haitao Yang and Oscar C. Au International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2015 [PDF][BibTeX] Oral Presentation
Motion Vector Fields Based Video Coding
Amin Zheng, Yuan Yuan, Hong Zhang, Haitao Yang, Pengfei Wan and Oscar C. Au International Conference on Image Processing (ICIP), 2015 [PDF][BibTeX]
Motion Estimation Via Hierarchical Block Matching and Graph Cut
Amin Zheng, Yuan Yuan, Sunil Prasad Jaiswal and Oscar C. Au International Conference on Image Processing (ICIP), 2015 [PDF][BibTeX]
Image Colorization Via Color Propagation and Rank Minimization
Yonggen Ling, Oscar C. Au, Jiahao Pang, Jin Zeng, Yuan Yuan and Amin Zheng International Conference on Image Processing (ICIP), 2015 [PDF][BibTeX]
Image Compression Via Sparse Reconstruction Yuan Yuan, Oscar C. Au, Amin Zheng and Haitao Yang International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014 [PDF][Poster][BibTeX]
Natural Image Matting via Adaptive Local and Nonlocal Sample Clustering
Haiyan Yang, Oscar C. Au, Yuan Yuan and Wenxiu Sun International Conference on Image Processing (ICIP), 2014 [PDF][BibTeX] Top 10% Paper Award
Organizing committee of ICLR workshop, Machine Learning for IoT: Datasets, Perception, and Understanding, 2023 [Workshop][Website]
PC Member of workshop on Learning with Limited Labelled Data for Image and Video Understanding, CVPR, 2022
PC Member of the International Conference on Learning Representations (ICLR) workshop, PAIR^2Struct, 2022
PC Member of the IEEE International Conference on Computer Vision (ICCV) workshop, the first workshop on statistic deep learning in computer vision, 2019
Conference Program Committee Member/Reviewer:
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, 2018, 2019, 2020, 2021, 2022, 2023
IEEE International Conference on Computer Vision (ICCV), 2017, 2019, 2021, 2023
International Conference on Machine Learning (ICML), 2021, 2022, 2023
Neural Information Processing Systems (NeurIPS), 2020, 2021, 2022, 2023
International Conference on Learning Representations (ICLR), 2022, 2023, 2024
European Conference on Computer Vision (ECCV), 2020, 2022
Annual Meeting of the Association for Computational Linguistics (ACL), 2021
AAAI Conference on Artificial Intelligence (AAAI), 2019, 2020, 2021
Winter Conference on Applications of Computer Vision (WACV), 2022, 2023
International Joint Conferences on Artificial Intelligence (IJCAI), 2017
Journal Reviewer
IEEE Transactions on Image Processing (TIP)
IEEE Transactions on Circuits and Systems for Video Technology (TCSVT)
IEEE Transactions on Multimedia (TMM)
IEEE Transactions on Neural Networks and Learning Systems (TNNLS)
Teaching
Duties at various times have included: weekly tutorial classes, weekly computer lab exercises, office hours, mark homework and exam papers.
Teaching Seminar, Nanyang Technology University (NTU) -- Deep Generative Modeling, May 2023
