Yi Fang
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Ph.D.
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Research Associate Professor
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Global Network Associate Professor
Yi Fang is an Associate Professor of Electrical and Computer Engineering at the NYU Abu Dhabi and NYU Tandon. He directs the NYU Multimedia and Visual Computing Lab. He received his Ph.D. degree from Purdue University, West Lafayette in 2011. Upon one year of industry experience in Siemens in Princeton, New Jersey and in Riverain Technologies in Dayton, Ohio, and a half-year academic experience as a senior staff scientist at Department of Electrical Engineering and Computer science, Vanderbilt University, Nashville, he joined New York University Abu Dhabi as an Assistant Professor of Electrical and Computer Engineering. Professor Fang's research focuses on 3D Computer Vision and Machine Learning with applications to robotics and autonomous driving. He is currently working on the development of 3D deep learning technologies in large-scale visual computing, cross-domain and cross-modality models, and their various industrial applications. Professor Fang has published more than 50 papers in international journals and conferences including IEEE TPAMI, TIP, TCybernetics, TNNLS, CVPR, ICCV, ECCV, NeurIPS, AAAI, IJCAI, ACMMM, IROS, etc. Professor Fang has served as the publicity chair for the 27th ACM International Conference on Multimedia (ACMMM) in Nice, France. He has served as co-chair for the IROS 2019 session "Human-centered Robotics" and will serve as the program chair for the Asian Conference on Machine Learning (ACML 2021) and the area chair of the Conference on Computer Vision and Pattern Recognition (CVPR 2021, CVPR 2022) and International Conference on Computer Vision (ICCV 2021).
Journal Articles
Fan Zhu and Yi Fang*, “Heat Diffusion Long-Short Term Memory Learning for 3D Shape Analysis”, 14th European Conference on Computer Vision (ECCV), 2016
Meng Wang and Yi Fang*, “Global Consistent Shape Correpondence for Efficient and Effective Active Shape Models”, ACM Multimedia 2016 (ACMMM), 2016
Meng Wang and Yi Fang*, “Local diffusion map signature for symmetry-aware non-rigid shape correspondence”, ACM Multimedia 2016 (ACMMM), 2016
Jin Xie, Guoxian Dai, Fan Zhu, Edward Wong and Yi Fang*, “DeepShape: Deep-Learned Shape Descriptor for 3D Shape Retrieval”, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2016
Fan Zhu, Ling Shao and Yi Fang*, “From Handcrafted to Learned-Based Representations for Human Action: A Survey”, Image and Vision Computing (IMAVIS), 2016
Meng Wang, Jin Xie, Fan Zhu and Yi Fang*, “Linear Discrimination Dictionary Learning for Shape Descriptors”, Pattern Recognition Letters (PRL), 2016
Guoxian Dai, Jin Xie, Fan Zhu and Yi Fang*, “Learning a discriminative deformation-invariant 3D shape descriptor via many-to-one encoder”, Pattern Recognition Letters (PRL), 2016
Jin Xie and Yi Fang*, “Dynamic Texture Recognition With Video Set Based Collaborative Representation”, Image and Vision Computing (IMAVIS), 2016
Jin Xie and Yi Fang*, “Learned Binary Spectral Shape Descriptor for 3D Shape Correspondence “, IEEE Computer Vision and Pattern Recognition (CVPR), 2016
Fan Zhu, Ling Shao and Yi Fang*, “Boosted Cross-Domain Dictionary Learning for Visual Categorization”, IEEE Intelligent Systems, 2016
Tiantian Xu, Fan Zhu, Edward Wong and Yi Fang*, “Dual Many-to-One-Encoder-Based Transfer Learning for Cross-Dataset Human Action Recognition”,Image and Vision Computing (IMAVIS), 2016
Fan Zhu, Jin Xie and Yi Fang*, “Pyramid Cross-Domain Neural Networks for Sketch-Based 3D Shape Retrieval”,The Thirtieth AAAI Conference on Artificial Intelligence (oral), AAAI 2016
Yi Fang*, Jin Xie, Guoxian Dai, Meng Wang, Fan Zhu, Tiantian Xu and Edward Wong, “3D Deep Shape Descriptor”, IEEE Computer Vision and Pattern Recognition (CVPR), 2015
Jin Xie, Yi Fang*, Fan Zhu and Edward Wong, “DeepShape: Deep Learned Shape Descriptor for 3D Shape Matching and Retrieval”, IEEE Computer Vision and Pattern Recognition (CVPR), 2015
Jin Xie, L. Zhang, J. You and S. Shiu, “Effective Texture Classification by Texton Encoding Induced Statistical Features,” Pattern Recognition (PR), vol. 48, issue 2, pp. 447-457, February 2015
Zhengjian Kang, Edward K. Wong, “Parts-based Multi-task Learning for Visual Tracking,” IEEE International Conference on Image Processing (ICIP), 2015.
Yi Fang*, Mengtian Sun, Guoxian Dai, and Karthik Ramani, “The intrinsic geometric structure of protein-protein interaction networks for protein interaction prediction”, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2015
Jin Xie, Fan Zhu, Guoxian Dai, Yi Fang*, Deep Progressive Shape-Distribution-Encoder for 3D Shape Retrieval, ACM Multimedia 2015 (ACMMM), 2015
Jing Zhu, Fan Zhu, Edward Wong, Yi Fang*, Learning Pairwise Neural Network Encoder for Depth Image-based 3D Model Retrieval, ACM Multimedia 2015 (ACMMM) , 2015
Other Publications
Kaimo Hu and Yi Fang*, 3D Laplacian pyramid signature, Lecture Notes in Computer Science, Volume 9010, Part III, 2014
Yi Fang*, Biharmonic Shape Signature for Robust Partial Shape Matching, PlosOne (Major Revision), 2014
Yi Fang*, Karthik Ramani, Heat-passing framework for robust interpretation of data in networks, PlosOne, 2014
Yi Fang*, Mengtian Sun, Guoxian Dai, and Karthik Ramani, The intrinsic geometric structure of protein-protein interaction networks for protein interaction prediction, Lecture Notes in Computer Science, Volume 8590, pp 487-493, 2014
Yi Fang*, Mengtian Sun, Guoxian Dai and Karthik Ramani, Global voting model for protein function prediction from protein-protein interaction networks, Lecture Notes in Computer Science, Volume 8590, pp 466-477, 2014
Zhengjian Kang, Edward K. Wong, “Learning Multi-Scale Sparse Representation for Robust Visual Tracking,” (oral), IEEE International Conference on Image Processing (ICIP), pp. 4897-4901, 2014.
