Yongjun Zhang
· Assistant ProfessorStony Brook University · Mathematics
Active 1992–2024
About
Yongjun Zhang joined Stony Brook University in 2020 as an Assistant Professor in the Institute for Advanced Computational Science and the Department of Sociology. He received his Ph.D. in sociology from the University of Arizona in 2020, where he also earned a Computational Social Science Certificate. His research focuses on computational and quantitative sociology, studying topics such as politics, organizations, networks, and inequality. His current work combines statistical, network, and computational methods with big data sources, including large-scale GPS data, administrative records such as FEC records, voter files, and consumer profiles, as well as social media data like billions of tweets. His research aims to understand mobility, segregation, and polarization across different settings in the U.S. and globally. Yongjun Zhang has published in top journals such as the American Journal of Sociology and Demography.
Research topics
- Artificial Intelligence
- Computer Science
- Data Mining
- Machine Learning
- Information Retrieval
- Data science
- Computer vision
- Remote sensing
Selected publications
Remote Sensing of Environment · 2020 · 188 citations
- Computer Science
- Artificial Intelligence
- Computer Science
IEEE Transactions on Geoscience and Remote Sensing · 2020 · 397 citations
- Computer Science
- Computer Science
- Artificial Intelligence
Change detection (CD) is one of the main applications of remote sensing. With the increasing popularity of deep learning, most recent developments of CD methods have introduced the use of deep learning techniques to increase the accuracy and automation level over traditional methods. However, when using supervised CD methods, a large amount of labeled data is needed to train deep convolutional networks with millions of parameters. These labeled data are difficult to acquire for CD tasks. To address this limitation, a novel semisupervised convolutional network for CD (SemiCDNet) is proposed based on a generative adversarial network (GAN). First, both the labeled data and unlabeled data are input into the segmentation network to produce initial predictions and entropy maps. Then, to exploit the potential of unlabeled data, two discriminators are adopted to enforce the feature distribution consistency of segmentation maps and entropy maps between the labeled and unlabeled data. During the competitive training, the generator is continuously regularized by utilizing the unlabeled information, thus improving its generalization capability. The effectiveness and reliability of our proposed method are verified on two high-resolution remote sensing data sets. Extensive experimental results demonstrate the superiority of the proposed method against other state-of-the-art approaches.
Image retrieval from remote sensing big data: A survey
Information Fusion · 2020 · 226 citations
Senior authorCorresponding- Computer Science
- Computer Science
- Data science
Frequent coauthors
- 54 shared
Yansheng Li
Wuhan University
- 50 shared
Jocelyn Chanussot
Laboratoire Jean Kuntzmann
- 50 shared
Giuseppe Scarpa
Parthenope University of Naples
- 50 shared
Liang-Jian Deng
- 50 shared
Gemine Vivone
- 50 shared
Mercedes E. Paoletti
Universidad de Extremadura
- 50 shared
Antonio Plaza
Universidad de Extremadura
- 49 shared
Xavier Briottet
Université de Toulouse
Labs
Institute for Advanced Computational SciencePI
Education
- 2000
Ph.D., Computer Science
University of California, Los Angeles
- 1996
M.S., Computer Science
University of California, Los Angeles
- 1993
B.S., Computer Science
University of Science and Technology of China
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