Jun Guo, Ph.D.

Postdoctoral Researcher

JguoArtificial Intelligence in Biomedical Imaging Lab (AIBIL)
Center for Biomedical Image Computing & Analytics (CBICA)
Department of Radiology
Perelman School of Medicine
University of Pennsylvania

 

 

Richards Labs, Suite 700D
3700 Hamilton Walk
Philadelphia, PA 19104
Email

Google Scholar

Educational Qualifications

Ph.D. in Computer Science and Technology, Tsinghua University

Research Summary

Jun Guo obtained his Ph.D. in computer science and technology from the Tsinghua-Berkeley Shenzhen Institute (TBSI), Tsinghua University in 2020. His research interests span pattern recognition and machine learning with a special focus on sparse learning, matrix factorization, and their applications to multimedia and data processing. Recently, his research topics include: matrix factorization and efficient optimization algorithms; large-scale feature selection, clustering, and data analytics; learning to hash, information retrieval, and heterogeneous data fusion; sparse and low-rank modeling, subspace learning, and regression analysis; mechanism of human collective learning and its application to machine learning.

Conferences

[1] Jun Guo, Heng Chang, and Wenwu Zhu, “Preserving Ordinal Consensus: Towards Feature Selection for Unlabeled Data,” in AAAI Conference on Artificial Intelligence (AAAI), Spotlight & Poster Presentation, pages 75~82, New York City, New York, USA, February 2020. 
[2] Jun Guo and Jiahui Ye*, “Anchors Bring Ease: An Embarrassingly Simple Approach to Partial Multi-view Clustering,” in AAAI Conference on Artificial Intelligence (AAAI), Spotlight & Poster Presentation, pages 118~125, Honolulu, Hawaii, USA, January 2019. (*: co-first author) 
[3] Jun Guo and Wenwu Zhu, “Partial Multi-view Outlier Detection Based on Collective Learning,” in AAAI Conference on Artificial Intelligence (AAAI), Oral Presentation, pages 298~305, New Orleans, Louisiana, USA, February 2018. 
[4] Jun Guo and Wenwu Zhu, “Dependence Guided Unsupervised Feature Selection,” in AAAI Conference on Artificial Intelligence (AAAI), Oral Presentation, pages 2232~2239, New Orleans, Louisiana, USA, February 2018. 
[5] Jun Guo, Yanqing Guo, Xiangwei Kong, and Ran He, “Unsupervised Feature Selection with Ordinal Locality,” in IEEE International Conference on Multimedia and Expo (ICME), Oral Presentation, pages 1213~1218, Hong Kong, China, July 2017. 
[6] Jun Guo, Yanqing Guo, Bo Wang, Xiangwei Kong, and Ran He, “Topology Preserving Dictionary Learning for Pattern Classification,” in IEEE International Joint Conference on Neural Networks (IJCNN), Oral Presentation, pages 1709~1715, Vancouver, Canada, July 2016. 
[7] Jun Guo, Yanqing Guo, Xiangwei Kong, Man Zhang, and Ran He, “Discriminative Analysis Dictionary Learning,” in AAAI Conference on Artificial Intelligence (AAAI), Oral Presentation, pages 1617~1623, Phoenix, Arizona, USA, February 2016. 
[8] Jun Guo, Yanqing Guo, Yi Li, Bo Wang, and Ming Li, “Locality Sensitive Discriminative Dictionary Learning,” in IEEE International Conference on Image Processing (ICIP), Oral Presentation, pages 1558~1562, Quebec City, Canada, September 2015. 

Journal

[1]  Jun Guo and Wenwu Zhu, “Collective Affinity Learning for Partial Cross-Modal Hashing,” IEEE Transactions on Image Processing, volume 29, pages 1344~1355, DOI: 10.1109/TIP.2019.2941858.