 |
Xiao WangPostdoc@University of Washington |
About Me
|
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
|
|
|
|
|
|
|
|
|
|
|
|
Recent News
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Selected Publications
Computational Biology
|
DiffModeler: Large Macromolecular Structure Modeling for Cryo-EM Maps Using Diffusion Model Xiao Wang, Han Zhu, Genki Terash, Manav Taluja, Daisuke Kihara Nature Methods, 2024 Paper Code Server |
|
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 Server Selected for research briefing CryoREAD provides fully automated DNA–RNA structure modeling for cryo-EM maps in Nature Methods. |
|
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 Server Selected as the cover of Volume 21 issue 1 of Nature Methods |
|
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 Database Server |
|
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 Server |
|
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 Server |
 |
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 Server |
 |
Protein Docking Model Evaluation by Graph Neural Networks Xiao Wang, Sean T Flannery, Daisuke Kihara Frontiers in Molecular Biosciences, 2021 Paper code |
 |
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 Server |
Self-Supervised Learning
|
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 |
|
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 |
|
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 |
 |
Contrastive Learning with Stronger Augmentation Xiao Wang, Guo-Jun Qi IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE T-PAMI), 2022 Paper Code |
 |
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 |
 |
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
|
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. |
|
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
|
|
|
|
|
|
|
|
|
|
|
|
|
|
 
|
|