Xiao Wang Postdoc@University of Washington CS Ph.D.@Purdue University E-mail: wang3702 [at] uw [dot] edu

About Me

Xiao Wang is currently a postdoc at University of Washington, advised by Prof. William Stafford Noble and Prof. Sheng Wang. Prior to that, he obtained a CS Ph.D. degree from Purdue University, advised by Prof. Daisuke Kihara. He graduated with a bachelor's degree in CS from Xi'an Jiaotong University, Xi'an, China. He used interned in Futurewei AI lab, Jingdong AI research, Meta AI (FAIR Labs). His research interests include AI for biology, representation learning and generative modeling.

Fellowship&Awards

Jan 2024 - Dec 2024: UW Data Science Postdoctoral Fellow

July 2022 - June 2023: NSF MolSSI Fellowship with $80,000 for stipend, tuition and fees.

Aug 2018 - July 2019: Chiang Chen Overseas Fellowship with $50,000 for tuition and fees.

Sep 2017 - July 2018: HIWIN Outstanding Student Scholarship with ￥10,000 for tuition.

Dec 2017: Top 10 Undergraduate of Xi'an Jiaotong University.

Sep 2016 - July 2017: National Scholarship with ￥8,000 for tuition.

Selected publications

2025

A generalizable foundation model for chromatin architecture, single-cell and multi-omics analysis across species Xiao Wang*, Yuanyuan Zhang*, Suhita Ray, Anupama Jha, Tangqi Fang, Shengqi Hang, Sergei Doulatov, William Stafford Noble, Sheng Wang BioRxiv, 2025 Paper Code Colab [AI for biology], [Generative modeling], [Representation learning]

CryoNeRF: generalizable automated cryo-EM reconstruction using neural radiance field Huaizhi Qu*, Xiao Wang*, Yuanyuan Zhang, Sheng Wang, William Stafford Noble, Tianlong Chen BioRxiv, 2025 Paper Code [AI for biology], [Representation learning]

FlowTS: Time Series Generation via Rectified Flow Yang Hu*, Xiao Wang*, Zezhen Ding, Lirong Wu, Huatian Zhang, Stan Z. Li, Sheng Wang, Jiheng Zhang, Ziyun Li, Tianlong Chen Arxiv, 2025 Paper Code [Generative modeling]

2024

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 [AI for biology], [Generative modeling]

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, 2024 Paper Code Colab Server Selected as the cover of Volume 21 issue 1 of Nature Methods [AI for biology]

2023

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. [AI for biology]

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 [AI for biology]

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 [Representation learning]

2022

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 [AI for biology]

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 [Representation learning]

Contrastive Learning with Stronger Augmentation Xiao Wang, Guo-Jun Qi IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE T-PAMI), 2022 Paper Code [Representation learning]

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 [Representation learning]

2021

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 [AI for biology]

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 [Representation learning]

2020

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 [Representation learning]

Open Source Projects

HiCFoundation:HiCFoundation is a generalizable Hi-C foundation model for chromatin architecture, single-cell and multi-omics analysis across species.

CryoNeRF: CryoNeRF is a computational tool for homogeneous and heterogeneous (conformational and compositional) cryo-EM reconstruction in Euclidean 3D space.

FlowTS: FlowTS is a rectified flow-based time series generation algorithm.

DiffModeler:DiffModeler is a computational tool using diffusion model to automatically build full protein complex structure by taking native/AF2 single-chain structure as input.

DeepMainMast: DeepMainMast is a computational tool using deep learning to automatically build full protein complex structure from cryo-EM map.

AdPE: AdPE is a MIM based self-supervised learning method with adversarial embeddings.

CryoREAD: Cryo_READ is a computational tool using deep learning to automatically build full DNA/RNA atomic structure from cryo-EM map.

DAQ_Refine: DAQ_Refine is a protein structure refinement tool by DAQ-score and ColabFold.

Asym Siam: Asym-Siam experimentally verified the importance of asymmetry for Siamese Representation Learning with obvious improvement.

CaCo: CaCo is a state-of-the-art cooperative-adversarial contrastive learning method where both positive and negative samples are directly learnable.

DAQ: DAQ is a software accesses the quality of protein models built from cryo-Electron Microscopy (EM) maps.

OC_Finder: OC_Finder is a computational tool using deep learning for fully automated osteoclast segmentation, classification, and counting.

CoSeg: CoSeg is a self-supervised learning-based event boundary detection method.

CLSA: CLSA is a general contrastive learning framework by introducing the information from stronger augmentation.

AdCo: AdCo is an algorithm for effective self-supervised learning through adversarial training of negative examples.

GNN DOVE: GNN_DOVE is a software can evaluate the quality of protein-docking models using graph neural networks by reformulating protein structures as graphs.

Attention_AD: Attention_AD is a software that can distinguish active and inactive peptides for gene expression using Long Short Term Memory (LSTM).

Emap2sec+: Emap2sec+ is a software detects local structure information of proteins and DNA/RNA in cryo-EM maps using deep learning.

EnAET: EnAET is a software that benefits semi-supervised learning via self-trained ensemble auto-encoding transformations.

DOVE: DOVE is a software can evaluate the quality of protein-docking models using 3D neural networks.