Dongliang Guo
I am currently a PhD student at the University of Virginia,
advised by Dr. Sheng Li. Prior to that, I received my bachelor's degree in Software Engineering from University of Eletronic Science and Technology of China (UESTC).
Email: dongliang.guo at virginia.edu
Google Scholar
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LinkedIn
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Github
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Research
My research interests are including multi-modal learning, computer vision and Trustworthy learning (explainable AI, fairness).
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Education
Ph.D., 2021 - now, University of Virginia
B.Eng., 2016 - 2020, University of Electronic Science and Technology of China
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Working Experience
Amazon Applied Scientist Intern
Seattle, WA May, 2024 - Aug, 2024
Amazon, Multi-modal fraud detection Applied Scientist Intern
Seattle, WA May, 2023 - Aug, 2023
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Fair attribute completion on graph with missing attributes
Dongliang Guo, Zhixuan Chu, Sheng Li
International Conference on Learning Representations (ICLR) 2023
code
It aims to generate fair node embeddings for some realistic networks with some nodes with missing attributes,
by completing the node embeddings and jointly mitigating the feature and topological unfairness.
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Ada-VAD: Domain Adaptable Video Anomaly Detection
Dongliang Guo, Yun Fu, Sheng Li
SIAM International Conference on Data Mining (SDM) 2024
code
It adapts the proposed multi-modal VAD model from scene to scene by data augmentation and mutual information optimization.
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Explainable Anomaly Detection in Images and Videos: A Survey
Yizhou Wang*, Dongliang Guo*, Sheng Li, Octavia Camps, Yun Fu
code
We collect and conclude the existing explainable anomaly detection methods, introduce useful datasets and metrics, and propose some promising future directions.
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A Survey of Trustworthy Representation Learning Across Domains
Ronghang Zhu, Dongliang Guo, Daiqing Qi, Zhixuan Chu, Xiang Yu, Sheng Li
ACM Transactions on Knowledge Discovery from Data (TKDD) 2024
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Services
Program Committee Member / Reviewer: ICLR 2024, NeurIPS 2023-24, ICML 2024, CVPR 2024, IEEE CIM, IEEE Big Data, IEEE TKDD, IEEE TNNLS, IEEE TPAMI.
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Teaching
CS 5012: Foundations of Computer Science (Spring 2023)
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