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Kai Zhao, Ph.D.


University of California, Los Angeles

Kai Zhao is currently a postdoctoral researcher at the University of California, Los Angeles working with professor Kyung Hyun Sung on medical image analysis. Before joining UCLA, he was a senior research scientist in Tencent Youtu lab. He received his Ph.D from Nankai University in 2020, under the supervision of professor Ming-ming Cheng. Before that, he spent 7 wonderful years in Shanghai University where he got BS and MS degrees in 2014 and 2017, respectively, under the supervision of Wei Shen (now professor at Shanghai Jiaotong University).

Dr. Zhao's research interests broadly lie at the intersection of Machine Learning , Computer Vision , and Medical Image Analysis . He co-authored over 20 research papers on top-tier venues in the field of AI and computer vision, with over 4,000 citations on Google Scholar. His research on palmprint recognition was highlighted by MIT technology review, and applied to WeChat palm payment. He was an active contributor to open-source projects such as PyTorch and mmdetection.

Professional Experience:

  • Mar 2022~: Postdoctoral researcher, UCLA, Los Angeles, USA.
  • Oct 2020~Feb 2022: Senior research scientist, Tencent Youtu lab, Shanghai, PR China.
  • Sep 2018~Jan 2019: Research intern, Panasonic Research Development Center, Singapore.
  • Sep 2017~Jun 2020: Ph.D. in Computer Science, Nankai Univeristy, Tianjin, PR China.
  • Jul 2016~Nov 2016: Research intern, Tencent Youtu lab, Shanghai, PR China.
  • Sep 2014~Jun 2017: MS in electronic engineering, Shanghai University, Shanghai, PR China.
  • Sep 2010~Jun 2014: BS in electronic engineering, Shanghai University, Shanghai, PR China.

Publications:

  • Kaifeng Pang and Kai Zhao* and Alex Ling Yu Hung and Haoxin Zheng and Ran Yan and Kyunghyun Sung, "NExpR: Neural Explicit Representation for fast arbitrary-scale medical image super-resolution", Computers in Biology and Medicine, 2025, DOI: 10.1016/j.compbiomed.2024.109354 [PDF] (* corresponding author)
  • Zhao, Kai and Hung, Alex Ling Yu and Pang, Kaifeng and Zheng, Haoxin and Sung, Kyunghyun, "MRI Super-Resolution with Partial Diffusion Models", IEEE Transactions on Medical Imaging, 2024, DOI: 10.1109/TMI.2024.3483109 [PDF]
  • Zhao, Kai and He, Zuojie and Hung, Alex and Zeng, Dan, "Dominant Shuffle: A Simple Yet Powerful Data Augmentation for Time-series Prediction", arXiv:2405.16456, 2024
  • Zhao, Kai and Wang, Tao and Zhang, Ruixin and Shen, Wei, "Adaptive feature alignment for adversarial training", Pattern Recognition Letters, 2024, DOI: j.patrec.2024.10.004 [PDF]
  • Hung, Alex Ling Yu and Zhao, Kai and Zheng, Haoxin and Yan, Ran and Raman, Steven S and Terzopoulos, Demetri and Sung, Kyunghyun, "Med-cDiff: Conditional medical image generation with diffusion models", Bioengineering, 2023
  • Zhao, Kai and Hung, Alex Ling Yu and Pang, Kaifeng and Wu, Holden and Raman, Steve and Sung, Kyunghyun, "PCa-Mamba: Spatiotemporal State Space Models for Prostate Cancer Detection in Multi-Parametric MRI", IEEE Transactions on Medical Imaging (under review) [PDF]
  • Zhao, Kai and Pang, Kaifeng and Hung, Alex LingYu and Zheng, Haoxin and Yan, Ran and Sung, Kyunghyun, "A Deep Learning-Based Framework for Highly Accelerated Prostate MR Dispersion Imaging", Cancers, 2024, DOI: 10.3390/cancers16172983 [PDF]
  • Zhao, Kai and Han, Qi and Zhang, Chang-Bin and Xu, Jun and Cheng, Ming-Ming, "Deep hough transform for semantic line detection", IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, DOI: 10.1109/TPAMI.2021.3077129 [PDF]
  • Zhao, Kai and Shen, Lei and Zhang, Yingyi and Zhou, Chuhan and Wang, Tao and Zhang, Ruixin and Ding, Shouhong and Jia, Wei and Shen, Wei, "BezierPalm3D: Synthetical Pretraining for Palmprint Authentication", IEEE Transactions on Image Processing, (under review) [PDF]
  • Hung, Alex Ling Yu and Zheng, Haoxin and Zhao, Kai and Pang, Kaifeng and Terzopoulos, Demetri and Sung, Kyunghyun, "Cross-Slice Attention and Evidential Critical Loss for Uncertainty-Aware Prostate Cancer Detection", International Conference on Medical Image Computing and Computer-Assisted Intervention, 2024
  • Xu, Yating and Zhao, Kai and Zhang, Liangang and Zhu, Mengyao and Zeng, Dan, "Hyperspectral anomaly detection with vision transformer and adversarial refinement", International Journal of Remote Sensing, 2023
  • Zhao, Kai and Shen, Lei and Zhang, Yingyi and Zhou, Chuhan and Wang, Tao and Zhang, Ruixin and Ding, Shouhong and Jia, Wei and Shen, Wei, "B'ezierpalm: A free lunch for palmprint recognition", European Conference on Computer Vision, 2022, DOI: 10.1007/978-3-031-19778-9_2 [PDF]
  • Wang, Xuehui and Zhao, Kai and Zhang, Ruixin and Ding, Shouhong and Wang, Yan and Shen, Wei, "Contrastmask: Contrastive learning to segment every thing", Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022
  • Shen, Lei and Zhang, Yingyi and Zhao, Kai* and Zhang, Ruixin and Shen, Wei, "Distribution alignment for cross-device palmprint recognition", Pattern Recognition, 2022, DOI: 10.1016/j.patcog.2022.108942 [PDF] (* corresponding author)
  • Li, Jia and Zhang, Junjie and Chen, Fansheng and Zhao, Kai and Zeng, Dan, "Adaptive material matching for hyperspectral imagery destriping", IEEE Transactions on Geoscience and Remote Sensing, 2022
  • Zhao, Kai and Wang, Xuehui and Chen, Xingyu and Zhang, Ruixin and Shen, Wei, "Rethinking mask heads for partially supervised instance segmentation", Neurocomputing, 2022, DOI: 10.1016/j.neucom.2022.10.003 [PDF]
  • Gao, Shang-Hua and Cheng, Ming-Ming and Zhao, Kai and Zhang, Xin-Yu and Yang, Ming-Hsuan and Torr, Philip, "Res2net: A new multi-scale backbone architecture", IEEE transactions on pattern analysis and machine intelligence, 2019
  • Zhao, Kai and Gao, Shanghua and Wang, Wenguan and Cheng, Ming-Ming, "Optimizing the F-measure for threshold-free salient object detection", Proceedings of the IEEE/CVF international conference on computer vision, 2019 [PDF]
  • Zhao, Kai and Xu, Jingyi and Cheng, Ming-Ming, "Regularface: Deep face recognition via exclusive regularization", Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019 [PDF]
  • Shen, Wei and Guo, Yilu and Wang, Yan and Zhao, Kai and Wang, Bo and Yuille, Alan, "Deep differentiable random forests for age estimation", IEEE transactions on pattern analysis and machine intelligence, 2019
  • Kai Zhao and Wei Shen and Shanghua Gao and Dandan Li and Ming-Ming Cheng, "Hi-Fi: Hierarchical Feature Integration for Skeleton Detection", Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018, DOI: 10.24963/ijcai.2018/166 [PDF]
  • Shen, Wei and Guo, Yilu and Wang, Yan and Zhao, Kai and Wang, Bo and Yuille, Alan L, "Deep regression forests for age estimation", Proceedings of the IEEE conference on computer vision and pattern recognition, 2018
  • Shen, Wei and Zhao, Kai and Jiang, Yuan and Wang, Yan and Bai, Xiang and Yuille, Alan, "Deepskeleton: Learning multi-task scale-associated deep side outputs for object skeleton extraction in natural images", IEEE Transactions on Image Processing, 2017
  • Shen, Wei and Zhao, Kai and Guo, Yilu and Yuille, Alan L, "Label distribution learning forests", Advances in neural information processing systems, 2017
  • Shen, Wei and Zhao, Kai and Jiang, Yuan and Wang, Yan and Zhang, Zhijiang and Bai, Xiang, "Object skeleton extraction in natural images by fusing scale-associated deep side outputs", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016

The list is generated from publications.bib. Please visit my Google Scholar for full publication list.

Academic services:

  • Conference reviewer for: CVPR, ICCV, ECCV, ICML, ICLR, NeurIPS, AISTAT, ACCV.
  • Journal reviewer for: IEEE Transactions on Image Processing, Medical Imaging, Pattern Analysis and Machine Intelligence, Multimedia, as well as Pattern Recognition.
  • Guest Editor: MDPI Diagnostics.
School of Athens
The School of Athens.

The logical, deterministic, and predictable nature of mathematics gives me some sense of security. I feel unsure about a conclusion until it is mathematically proven.