About Me

I’m Jiamian Wang (王加冕). I’m a Ph.D. student at Golisano College of Computing and Information Sciences, Rochester Institute of Technology, advised by Dr. Zhiqiang Tao. Prior to joining RIT, I spent one year in the Department of Computer Science and Engineering at Santa Clara University (San Jose, USA). I received my M.S. degree from University of Southern California (Los Angeles, USA) in 2020 and B.E. degree from Tianjin University (Tianjin, China) in 2018, respectively.

My research interest includes Compressive Sensing (SCI), Uncertainty Quantification (UQ), and Image Super-Resolution (SR). I have a high passion for exploiting/measuring the trustworthiness/reliability of the novel deep learning models and expediting the deployment of cutting-edge cameras/sensors with deep learning-based solutions.

🔥🔥 I’m actively looking for internship opportunities (including but not limited to above topics) with a flexible starting date (Spring/Summer 2024). Please drop me an email (jw4905 [at] rit [dot] edu) if you are interested!


2023.08: [ICCV’23] Code, including training, testing scripts, and pretrained models have been released🎖🔥🔥. Check out the SR_pruning_official for more details! Looking forward to the discussions🌝.

2023.07: [ICCV’23] One paper on efficient image super-resolution (Arxiv) is accepted by ICCV’23🎊🎊🎉🎉. Looking forward to sharing our work in Paris!

2023.06: [Preprint] Check out our Federated learning method on snapshot compressive imaging hardware cooperation, Federated Hardware-Prompt Learning (FedHP). This is the first attempt of discussing the power of FL in the field of SCI🚀🚀.

2023.03: [Preprint] Check out our new pruning method that flexibly handles diverse off-the-shelf SR network architectures without pre-training the dense model: Arxiv. Thanks to my co-authors’ great support💪💪!

Selected Publications and Preprints

Cooperative Hardware-Prompt Learning for Snapshot Compressive Imaging.
Jiamian Wang, Zongliang Wu, Yulun Zhang, Xin Yuan, Tao Lin, Zhiqiang Tao.
Arxiv, 2023.
Iterative Soft Shrinkage Learning for Efficient Image Super-Resolution.
Jiamian Wang, Huan Wang, Yulun Zhang, Yun Fu, Zhiqiang Tao.
ICCV, 2023.
Modeling Mask Uncertainty in Hyperspectral Image Reconstruction.
Jiamian Wang, Yulun Zhang, Xin Yuan, Ziyi Meng, Zhiqiang Tao.
ECCV Oral (2.7%), 2022.
Calibrate Automated Graph Neural Network via Hyperparameter Uncertainty.
Xueying Yang, Jiamian Wang, Sheng Li, Zhiqiang Tao.
CIKM, 2022.
S2-Transformer for Mask-Aware Hyperspectral Image Reconstruction.
Jiamian Wang, Kunpeng Li, Yulun Zhang, Xin Yuan, Zhiqiang Tao.
Arxiv, 2022.
A new backbone for hyperspectral image reconstruction.
Jiamian Wang, Yulun Zhang, Xin Yuan, Yun Fu, Zhiqiang Tao.
Arxiv, 2022.

Professional Services

  • Journal Reviewer: Pattern Recognition, JSTSP, IJCV, TIP, TNNLS, TETCI, Neurocomputing, etc.
  • Conference Reviewer: CIKM’21 CIKM’22 ACM SIGKDD’22, etc.

Skills & Languages

  • Programming: Python, MATLAB, Linux Bash, C/C++
  • Cloud Service: Slurm, AWS, Colab
  • Languages: English, Chinese (Mandarin)