Education Background
Ph.D. Computer Science and Engineering, University of Notre Dame, 2017-2022
M.S. Computer Software and Theory, Xidian University, 2014-2017
B.S. Software Engineering, Xidian University, 2010-2014
Research Field
Biography
Professor Han is an Assistant Professor at the School of Data Science, The Chinese University of Hong Kong, Shenzhen. He obtained his Ph.D. degree from University of Notre Dame at the Department of Computer Science and Engineering, 2022. Before that, he received a B.S. degree in software engineering and a M.S. degree in computer software and theory in 2014 and 2017, respectively. Both degrees are from Xidian University. His research focuses on visualization, user interface and interaction, human-computer interaction, and deep learning. Currently, his interests lie in how to apply deep learning techniques to enhance the visualization process and develop smart interfaces for users to better interact with computers.
Prof. Han has published more than 20 papers in the field of visualization, including 6 papers in the top journal IEEE TVCG and 6 long articles in top visualization conferences (i.e., IEEE VIS, EuroVIS, and PacificVis) . He was invited to serve as a program committee member at IEEE VIS Short Paper 2021 and 2022.
A. Book chapters
1. Sebastian Weiss, Jun Han, Chaoli Wang, and Rüdiger Westermann. Deep Learning-Based Upscaling for In Situ Volume Visualization. In Hank Childs, Janine Bennett, and Christoph Garth (Eds.) In Situ Visualization for Computational Science, Springer.
B. Journal articles
1. Chaoli Wang and Jun Han. DL4SciVis: A State-of-the-Art Survey on Deep Learning for Scientific Visualization. IEEE Transactions on Visualization and Computer Graphics, Accepted.
2. Jun Han and Chaoli Wang. SSR-TVD: Spatial Super-Resolution for Time-Varying Data Analysis and Visualization. IEEE Transactions on Visualization and Computer Graphics, Accepted.
3. Jun Han and Chaoli Wang. SurfNet: Learning Surface Representations via Graph Convolutional Network. Computer Graphics Forum (EuroVis 2022), 41(3), Jun 2022.
4. Jun Han, Hao Zheng, Danny Z. Chen, and Chaoli Wang. STNet: An End-to-End Generative Framework for Synthesizing Spatiotemporal Super-Resolution Volumes. IEEE Transactions on Visualization and Computer Graphics (IEEE VIS 2021), 28(1):270-280, Jan 2022.
5. Jun Han, Hao Zheng, Yunhao Xing, Danny Z. Chen, and Chaoli Wang. V2V: A Deep Learning Approach to Variable-to-Variable Selection and Translation for Multivariate Time-Varying Data. IEEE Transactions on Visualization and Computer Graphics (IEEE SciVis 2020), 27(2):1290-1300, Feb 2021.
6. Jun Han, Jun Tao, and Chaoli Wang. FlowNet: A Deep Learning Framework for Clustering and Selection of Streamlines and Stream Surfaces. IEEE Transactions on Visualization and Computer Graphics, 26(4):1732-1744, Apr 2020.
7. Jun Han, Jun Tao, Hao Zheng, Hanqi Guo, Danny Z. Chen, and Chaoli Wang. Flow Field Reduction via Reconstructing Vector Data from 3D Streamlines Using Deep Learning. IEEE Computer Graphics and Applications (Special Issue on Deep Learning in Visualization and Image Processing), 39(4):54-67, Jul/Aug 2019.
8. Sicong Liu, Zimu Zhou, Junzhao Du, Longfei Shangguan, Jun Han, and Xin Wang. UbiEar: Bringing Location-independent Sound Awareness to the Hard-of-hearing People with Smartphones. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 1(2):17:1-17:24, Sep 2017.
C. Conference articles
1. Reshika P. Velumani, Meng Xia, Jun Han, Chaoli Wang, Alexis K.-H. Lau, and Huamin Qu. AQX: Explaining Air Quality Forecast for Verifying Domain Knowledge Using Feature Importance Visualization. In Proceedings of ACM International Conference on Intelligent User Interfaces, Virtual, pages 720-733, Mar 2022.
2. Hao Zheng, Jun Han, Hongxiao Wang, Lin Yang, Zhuo Zhao, Chaoli Wang, and Danny Z. Chen. Hierarchical Self-Supervised Learning for Medical Image Segmentation Based on Multi-Domain Data Aggregation. In Proceedings of International Conference on Medical Image Computing and Computer Assisted Interventions, Virtual, pages 622-632, Sep 2021.
3. Li Guo, Shaojie Ye, Jun Han, Hao Zheng, Han Gao, Danny Z. Chen, Jian-Xun Wang, and Chaoli Wang. SSR-VFD: Spatial Super-Resolution for Vector Field Data Analysis and Visualization. In Proceedings of IEEE Pacific Visualization Symposium, Virtual, pages 71-80, Jun 2020.
4. Hao Zheng, Lin Yang, Jianxu Chen, Jun Han, Yizhe Zhang, Peixian Liang, Zhuo Zhao, Chaoli Wang, and Danny Z. Chen. Biomedical Image Segmentation via Representative Annotation. In Proceedings of AAAI Conference on Artificial Intelligence, Honolulu, HI, pages 5901-5908, Jan 2019.
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