Academic Profile

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 Chairs for ICIP and ICASSP, and 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.

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Asst Prof Wen Bihan
Nanyang Assistant Professor, School of Electrical & Electronic Engineering

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), X-Ray, 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 Self-Motivated 

a) PhD students 

b) Post-doc Research Fellows 

c) Research Associates / Project Officer 

If you are interested of working with me, please send your resume / CV. Only qualified candidates will be contacted.

  • AI-Enabled Airplane Recognition Using Radar Cross-Section Signals (AIR REUS)

  • Autonomous Inspection for Maritime and Urban Industries

  • Detection of Attacks on Artificial Intelligence Systems

  • Development of fundamental AI technologies for medical AI applications

  • Efficient Object Detection for X-Ray Machines

  • Learning-based Control of an Innovative UAV Analysed in a Specialized Testbed for Urban Air Mobility

  • P1.3 Big Data for Robot Joints Self-Learning and Transfer Learning Analytics & Controlling Motorized Mobile Platform Using Deep Learning and EEG

  • P1.3 Big Data for Robot Joints Self-learning and Transfer Learning Analytics & Controlling Motorized Platform Using Deep Learning and EEG

  • Physics-Driven Machine Learning for Computational Imaging

  • Radar Based Perception Using Artificial Intelligent

  • Robust Machine Learning with Rigorous Formulations

  • Sense Only What You Need: An End-to-End Deep Sensing Scheme for Task-Driven SAR

  • Theoreticall-Grounded and Robust Artificial Intelligence for Real-World Challenges
  • Ding Liu, Bihan Wen, Jianbo Jiao, Xianming Liu, Zhangyang Wang, Thomas S Huang. (2020). Connecting image denoising and high-level vision tasks via deep learning. IEEE Transactions on Image Processing, 29, 3695-3706.

  • Bihan Wen, Saiprasad Ravishankar, Luke Pfister, Yoram Bresler. (2020). Transform Learning for Magnetic Resonance Image Reconstruction: From Model-Based Learning to Building Neural Networks. IEEE Signal Processing Magazine, 37(1), 41-53.

  • 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, 29, 3254 - 3269.

  • Zhuotao Liu, Yangxi Xiang, Jian Shi, Peng Gao, Haoyu Wang, Xusheng Xiao, Bihan Wen and Yih-Chun Hu. (2019). Proceedings of the ACM SIGSAC Conference on Computer and Communications Security (CCS): Hyperservice: Interoperability and programmability across heterogeneous blockchains. (pp. 549-566).

  • 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.