|Academic Profile |
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Asst Prof Wen Bihan
Nanyang Assistant Professor, School of Electrical & Electronic Engineering
|Dr. Bihan Wen received the B.Eng. degree in Electrical and Electronic Engineering (EEE) from Nanyang Technological University (NTU), Singapore, in 2012, the MS and PhD degrees in Electrical and Computer Engineering from University of Illinois at Urbana-Champaign (UIUC), USA, in 2015 and 2018, respectively. He joined Nanyang Technological University in March 2019 and now is a Nanyang Assistant Professor. His research interests span areas of machine learning, computational imaging, computer vision, image and video processing, and big data applications. |
He is an elected member of the IEEE Computational Imaging (CI) Technical Committee. He regularly serves as the Area Chair for ICIP and ICASSP, and and serves on the program committees or reviewers for top AI conferences (e.g., NIPS, ICML, CVPR, ICCV, ECCV, IJCAI, AAAI). He also co-organized the LCI workshop at ICCV 2019, and the MIPR 2019 as the Session Chairs. He was the recipient of the 2016 Yee Fellowship, and the 2012 Professional Engineers Board (PEB) Gold Medal.
|1. Machine Learning|
- Deep learning, Transform learning, Dictionary learning, Tensor modeling, etc.
2. Image and Video Processing
- Denoising, Super-Resolution, Inpainting, Restoration, etc.
3. Computer Vision
- Robust classification / segmentation, Object detection, Crowd counting, Image retrieval, etc.
4. Computational Imaging
- Magnetic resonance imaging (MRI), Computed tomography (CT), Synthetic-aperture radar (SAR), etc.
5. Inverse Problems
- Blind compressed sensing, Ill-posed inference, Data reconstruction and modeling, etc.
We are ALWAYS looking for GOOD and Highly Motivated
a) PhD students
b) Post-doc Research Fellows
c) Research Associates
If you are interested of working with me, please send your resume / CV.
- Robust Machine Learning with Rigorous Formulations
- Theoreticall-Grounded and Robust Artificial Intelligence for Real-World Challenges
- Weakly Supervised Learning Based Anomaly Detection in Microscopic Images for Automatic Detection of Noises
- Z Zha, X Yuan, B Wen, J Zhou, J Zhang, C Zhu. (2019). From Rank Estimation to Rank Approximation: Rank Residual Constraint for Image Restoration. IEEE Transactions on Image Processing, .
- B. Wen, S. Ravishankar, and Y. Bresler. (2019). VIDOSAT - High-dimensional Sparsifying Transform Learning for Online Video Restoration. IEEE Transactions on Image Processing, 28(4), 1691--1704.
- D. Liu, B. Wen, Y. Fan, C. C. Loy, T. S. Huang. (2018). Non-Local Recurrent Network for Image Restoration. Neural Information Processing Systems (NeurIPS).
- D. Liu, B. Wen, X. Liu, Z. Wang, and T. Huang. (2018). International Joint Conference on Artificial Intelligence (IJCAI): When Image Denoising Meets High-Level Vision Tasks: A Deep Learning Approach. International Joint Conference on Artificial Intelligence (IJCAI) (pp. 842–848).
- B. Wen, Y. Li, L. Pfister, and Y. Bresler. (2017). Joint Adaptive Sparsity and Low-rankness on the Fly: An Online Tensor Reconstruction Method for Video Denoising. IEEE International Conference on Computer Vision (ICCV).