Zhengyang Wang
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Zhengyang Wang, Ph.D. |
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Short Biography
I am currently an Applied Scientist in the Amazon Product Graph group. My manager is Dr. Xin Luna Dong, who leads the efforts to build Amazon Product Knowledge Graph. Prior to joining Amazon, I received my Ph.D. degree in Computer Science at Texas A&M University under the supervision of Dr. Shuiwang Ji, who leads the Data Integration, Visualization, and Exploration (DIVE) Laboratory. I obtained my master degree in Mathematics & Computer Science at New York University. My research interests include machine learning, data mining and deep learning.
News
Our survey paper “Self-Supervised Learning of Graph Neural Networks: A Unified Review” has been made publicly available! Paper Link
Our paper “Global Voxel Transformer Networks for Augmented Microscopy” has been published in Nature Machine Intelligence! TAMU News
Education
Ph.D., Computer Science, Texas A&M University, Aug 2018 - Dec 2020
Ph.D. Student, Computer Science, Washington State University, May 2016 - July 2018
M.S., Mathematics and Computer Science, New York Univeristy, August 2013 - May 2015
B.S., Information and Computational Mathematics, Nanjing University, September 2009 - June 2013
Non-degree Exchange program, Applied Mathematics and Statistics, Stony Brook University, August 2012 - December 2012
Publications
Conference Papers
Yaochen Xie, Zhengyang Wang, and Shuiwang Ji
Noise2Same: Optimizing A Self-Supervised Bound for Image Denoising
Advances in Neural Information Processing Systems (NeurIPS), 20320-20330, 2020
[paper]Zhengyang Wang, Bunyamin Sisman, Hao Wei, Xin Luna Dong, and Shuiwang Ji
CorDEL: A Contrastive Deep Learning Approach for Entity Linkage
Proceedings of the 20th IEEE International Conference on Data Mining (ICDM), 1322-1327, 2020
[paper]Hongyang Gao, Zhengyang Wang, and Shuiwang Ji
Kronecker Attention Networks
Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD), 229-237, 2020
[paper]Zhengyang Wang, Na Zou, Dinggang Shen, and Shuiwang Ji
Non-Local U-Net for Biomedical Image Segmentation
Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI), 6315-6322, 2020
[paper][code]Zhengyang Wang, Hao Yuan, and Shuiwang Ji
Spatial Variational Auto-Encoding via Matrix-Variate Normal Distributions
Proceedings of the 2019 SIAM International Conference on Data Mining (SDM), 648-656, 2019
[paper][code]Hongyang Gao, Zhengyang Wang, and Shuiwang Ji
ChannelNets: Compact and Efficient Convolutional Neural Networks via Channel-Wise Convolutions
Advances in Neural Information Processing Systems (NeurIPS), 5203-5211, 2018
[paper][code]Zhengyang Wang, and Shuiwang Ji
Smoothed Dilated Convolutions for Improved Dense Prediction
Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD), 2486-2495, 2018
Best Paper Award Nomination
[paper][code]Hongyang Gao, Zhengyang Wang, and Shuiwang Ji
Large-Scale Learnable Graph Convolutional Networks
Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD), 1416-1424, 2018
[paper][code]Lei Cai, Zhengyang Wang, Hongyang Gao, Dinggang Shen, and Shuiwang Ji
Deep Adversarial Learning for Multi-Modality Missing Data Completion
Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD), 1158-1166, 2018
[paper][code]Zhengyang Wang, and Shuiwang Ji
Learning Convolutional Text Representations for Visual Question Answering
Proceedings of the 2018 SIAM International Conference on Data Mining (SDM), 594-602, 2018
[paper][code]
Journal Articles
Zhengyang Wang, Yaochen Xie, and Shuiwang Ji
Global Voxel Transformer Networks for Augmented Microscopy
Nature Machine Intelligence (NMI), 3, 161–171, 2021
[paper][code]Zhengyang Wang, and Shuiwang Ji
Second-Order Pooling for Graph Neural Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2020
[paper][code]Hongyang Gao, Zhengyang Wang, and Shuiwang Ji
ChannelNets: Compact and Efficient Convolutional Neural Networks via Channel-Wise Convolutions
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2020
[paper]Yi Liu, Hao Yuan, Zhengyang Wang, and Shuiwang Ji
Global Pixel Transformers for Virtual Staining of Microscopy Images
IEEE Transactions on Medical Imaging (TMI), 39(6): 2256-2266, 2020
[paper]Hongyang Gao, Hao Yuan, Zhengyang Wang, and Shuiwang Ji
Pixel Transposed Convolutional Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 42(5): 1218-1227, 2020
[paper][code]Hao Yuan, Lei Cai, Zhengyang Wang, Xia Hu, Shaoting Zhang, and Shuiwang Ji
Computational Modeling of Cellular Structures using Conditional Deep Generative Networks
Bioinformatics, 35(12): 2141-2149, 2019
[paper][code]
Preprints
Yaochen Xie, Zhao Xu, Zhengyang Wang, and Shuiwang Ji
Self-Supervised Learning of Graph Neural Networks: A Unified Review
[https://arxiv.org/abs/2102.10757]Zhengyang Wang, Meng Liu, Youzhi Luo, Zhao Xu, Yaochen Xie, Limei Wang, Lei Cai, and Shuiwang Ji
MoleculeKit: Machine Learning Methods for Molecular Property Prediction and Drug Discovery
[https://arxiv.org/abs/2012.01981]Xinyi Xu, Zhangyang Wang, Cheng Deng, Hao Yuan, and Shuiwang Ji
Towards Improved and Interpretable Deep Metric Learning via Attentive Grouping
[https://arxiv.org/abs/2011.08877]Meng Liu, Zhengyang Wang, and Shuiwang Ji
Non-Local Graph Neural Networks
[https://arxiv.org/abs/2005.14612]Lei Cai, Zhengyang Wang, Rob Kulathinal, Sudhir Kumar, and Shuiwang Ji
Deep Low-Shot Learning for Biological Image Classification and Visualization from Limited Training Samples
[https://arxiv.org/abs/2010.10050]Zhengyang Wang, Xia Hu, and Shuiwang Ji
iCapsNets: Towards Interpretable Capsule Networks for Text Classification
[https://arxiv.org/abs/2006.00075]
Awards
Texas A&M Institute of Data Science Graduate Travel Grants, 2019
KDD Best Paper Award Nomination, 2018
KDD Student Travel Award, 2018
Alfred Suksdorf Scholarship, Washington State University, 2016
China’s National People’s Scholarship, Nanjing University, 2010, 2011, 2012
Services
Program Committee Member
The 33rd AAAI Conference on Artificial Intelligence (AAAI), 2019
The 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2019
The 28th ACM International Conference on Information and Knowledge Management (CIKM), 2019
The 34th AAAI Conference on Artificial Intelligence (AAAI), 2020
The 8th International Conference on Learning Representations (ICLR), 2020
The 24th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2020
The 37th International Conference on Machine Learning (ICML), 2020
The 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD), 2020
The 34th Conference on Neural Information Processing Systems (NeurIPS), 2020
The 35th AAAI Conference on Artificial Intelligence (AAAI), 2021
The 9th International Conference on Learning Representations (ICLR), 2021
The 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2021
The 38th International Conference on Machine Learning (ICML), 2021
The 27th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD), 2021
Journal Reviewer
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
IEEE Transactions on Neural Networks and Learning Systems (TNNLS)
ACM Transactions on Knowledge Discovery from Data (TKDD)
BMC Bioinformatics
Teaching
Guest lectures at Texas A&M University
Attention Mechanism, Fall 2019: CSCE 636: Neural Networks
Attention Mechanism, Fall 2018: CSCE 636: Neural Networks
Guest lectures at Washington State University
Introduction to Neural Networks, Spring 2018: CPTS 437: Introduction to Machine Learning
Introduction to Neural Networks, Spring 2017: CPTS 483: Introduction to Machine Learning
Support Vector Machine, Spring 2017: CPTS 483: Introduction to Machine Learning