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 is currently serving as an associate editor for IEEE Transactions on Circuits and Systems for Video Technology (T-CSVT), and an associate editor for Micromachines. He is the TPC Chair of the upcoming IEEE MIPR 2022. He co-organized the LCI workshop at ICCV 2019 and 2021, and the IEEE SPACE webinar. He regularly serves as the Area Chairs for IEEE ICIP, ICASSP and ICME, and the program committees for many AI conferences (e.g., NeurIPS, ICML, CVPR, ICCV, ECCV, IJCAI, AAAI). He was the recipient of the 2016 Yee Fellowship and the 2012 Professional Engineers Board (PEB) Gold Medal. One paper he co-authored won the Best Paper Runner-Up from IEEE ICME 2020.

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

1. Machine Learning - Deep learning, Transform learning, Tensor modeling, Reinforcement Learning, 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. NP-Hard Problems - Blind compressed sensing, Ill-posed inference, Sparse Coding, Combinatorial problems, etc. 

We have multiple openings and 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.


  • 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.


  • R. Zhang, L. Guo, S. Huang and B. Wen. (2021). ReLLIE: Deep Reinforcement Learning for Customized Low-Light Image Enhancement. ACM International Conference on Multimedia (ACM MM), 2021.


  • T. Bai, J. Luo, J. Zhao, B. Wen and Q. Wang. (2021). Recent Advances in Adversarial Training for Adversarial Robustness. International Joint Conference on Artificial Intelligence (IJCAI), 2021.