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Xiao WangPostdoc@University of Washington |
About Me
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I am now a postdoc of Noble Research Lab and Sheng Wang's Lab, under the supervision of Prof. William Stafford Noble and Prof. Sheng Wang. Prior to that, I obtained a Computer Science Ph.D. degree from Department of Computer Science, Purdue University, advised by Prof. Daisuke Kihara. My research interests lie in computational biology, self-supervised learning, as well as all other intelligent systems. Starting from 2018, I mainly worked with Prof. Daisuke Kihara on the macromolecular structure modeling, prediction and evaluation. In summer 2019, I did an internship in Futurewei AI Lab supervised by Dr. Lin Chen, Prof. Guo-Jun Qi and Prof. Jiebo Luo. In summer 2020, I interned in JD AI Research supervised by Dr. Jingen Liu and Prof. Jiebo Luo. In summer 2021, I did internship in Facebook AI Research supervised by Dr. Xinlei Chen, Dr. Yuandong Tian and Haoqi Fan. During internships, my research focus is self-supervised learning(SSL). Before that, I graduated with a bachelor's degree in computer science from Xi'an Jiaotong University, Xi'an, China. During my undergraduate, I mainly worked on intelligent transportation systems under the supervision of Prof. Li Li from Tsinghua University and Prof.Fei-Yue Wang from State Key Laboratory of Management and Control for Complex Systems of Chinese Academy of Sciences. Also, I did a summer intern at Purdue in 2017, working on protein model evaluation supervised by Prof. Daisuke Kihara. |
Fellowship&Awards
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Recent News
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Selected Publications
Computational Biology
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De Novo Structure Modeling for Nucleic Acids in cryo-EM Maps Using Deep Learning Xiao Wang, Genki Terash, Daisuke Kihara Nature Methods, 2023 Paper Code Colab Selected for research briefing CryoREAD provides fully automated DNA–RNA structure modeling for cryo-EM maps in Nature Methods. |
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DeepMainmast: integrated protocol of protein structure modeling for cryo-EM with deep learning and structure prediction Genki Terash, Xiao Wang, Devashish Prasad, Tsukasa Nakamura, Daisuke Kihara Nature Methods, 2023 Paper Code Colab Selected as the cover of Volume 21 issue 1 of Nature Methods |
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DAQ-Score Database: Assessment of Map-Model Compatibility for Protein Structure Models from Cryo-EM Maps Tsukasa Nakamura, Xiao Wang, Genki Terash, Daisuke Kihara Nature Methods, 2023 Paper Code |
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Residue-Wise Local Quality Estimation for Protein Models from Cryo-EM Maps Genki Terash*, Xiao Wang*, Sai Raghavendra Maddhuri Venkata Subramaniya, John J. G. Tesmer, Daisuke Kihara Nature Methods, 2022 Paper Code Colab |
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Protein Model Refinement for Cryo-EM Maps Using DAQ score Genki Terashi, Xiao Wang, Daisuke Kihara Acta Crystallographica Section D: Structural Biology, 2022 Paper Code Colab |
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Detecting Protein and DNA/RNA Structures in Cryo-EM Maps of Intermediate Resolution Using Deep Learning Xiao Wang, Eman Alnabati, Tunde W Aderinwale, Sai Raghavendra Maddhuri, Genki Terashi, Daisuke Kihara Nature Communications, 2021 Paper Code Code_Ocean Colab |
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Protein Docking Model Evaluation by Graph Neural Networks Xiao Wang, Sean T Flannery, Daisuke Kihara Frontiers in Molecular Biosciences, 2021 Paper code |
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Protein Docking Model Evaluation by 3D Deep Convolutional Neural Network Xiao Wang, Genki Terashi, Charles W Christoffer, Mengmeng Zhu, Daisuke Kihara Bioinformatics , 2019 Paper code |
Self-Supervised Learning
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On the Importance of Asymmetry for Siamese Representation Learning Xiao Wang*, Haoqi Fan*, Yuandong Tian, Daisuke Kihara, Xinlei Chen Conference on Computer Vision and Pattern Recognition (CVPR), 2022 Paper Code |
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CaCo: Both Positive and Negative Samples are Directly Learnable via Cooperative-adversarial Contrastive Learning Xiao Wang, Yuhang Huang, Dan Zeng, Guo-Jun Qi IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE T-PAMI), 2023 Paper Code |
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Adco: Adversarial contrast for efficient learning of unsupervised representations from self-trained negative adversaries Xiao Wang*, Qianjiang Hu*, Wei Hu, Guo-Jun Qi Conference on Computer Vision and Pattern Recognition (CVPR), 2021 Paper Code |
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Contrastive Learning with Stronger Augmentation Xiao Wang, Guo-Jun Qi IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE T-PAMI), 2022 Paper Code |
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EnAET: Self-Trained Ensemble AutoEncoding Transformations for Semi-Supervised Learning Xiao Wang, Daisuke Kihara, Jiebo Luo, Guo-Jun Qi IEEE Transactions on Image Processing (IEEE TIP) , 2020 Paper code Learning generalized transformation equivariant representations via autoencoding transformations Guo-Jun Qi, Liheng Zhang, Feng Lin, Xiao Wang IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE T-PAMI), 2020 Paper code |
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CoSeg: Cognitively Inspired Unsupervised Generic Event Segmentation Xiao Wang, Jingen Liu, Tao Mei, Jiebo Luo IEEE Transactions on Neural Networks AND Learning Systems (IEEE TNNLS), 2022 Paper code |
Intelligent Transportation
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Capturing Car-Following Behaviors by Deep Learning Xiao Wang, Rui Jiang, Li-Li, Yilun Lin, Xinhu Zheng, Fei-Yue Wang IEEE Transactions on Intelligent Transportation Systems (IEEE-T-ITS), 2017 Paper Nominated for George N. Saridis Best Transactions Paper Award. |
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Long memory is important: A test study on deep-learning based car-following model Xiao Wang, Rui Jiang,Li-Li, Yilun Lin, Fei-Yue Wang Physica A: Statistical Mechanics and its Applications, 2019 Paper |
Open Source Projects
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