Me
Xiao Luo

Postdoc @UCLA, xiaoluo[at]cs.ucla.edu

Machine Learning + Statistics + Science

About

I'm currently a postdoctoral researcher in the Department of Computer Science, UCLA, under the supervision of Prof. Yizhou Sun. I completed my Ph.D. at Peking University (PKU) in 2022 under the supervision of Prof. Minghua Deng. I also work closely with Prof. Hongyu Zhao and Prof. Xiting Yan at Yale University. I was a research intern at Microsoft Research Asia (MSRA) and DAMO Academy, Alibaba Group.

Research Interests

My research focuses on the intersection of Machine Learning, Statistical Modeling and Scientific Problems (AI/ML for Science including Biology, Genomics, Health, Mechanics and Information Science). Generally, I work on building computational models for interdisciplinary scientific applications. The ultimate goal of my research is to allow every researcher in any scientific field to benefit from machine learning and data mining. I have a special focus on learning over dynamical systems, data-efficient graph machine learning, and health/biology-related real-world applications.

News

Selected Publications

For full publications, please refer to Google Scholar.

Xiao Luo, Yiyang Gu, Huiyu Jiang, Hang Zhou, Jinsheng Huang, Wei Ju, Zhiping Xiao, Ming Zhang, and Yizhou Sun.
PGODE: Towards High-quality System Dynamics Modeling.
In International Conference on Machine Learning (ICML), 2024.

Xiao Luo, Jingyang Yuan, Zijie Huang, Huiyu Jiang, Yifang Qin, Wei Ju, Ming Zhang, and Yizhou Sun.
HOPE: High-order Graph ODE For Modeling Interacting Dynamics.
In International Conference on Machine Learning (ICML), 2023.

Xiao Luo, Haixin Wang, Zijie Huang, Huiyu Jiang, Abhijeet Sadashiv Gangan, Song Jiang, and Yizhou Sun.
CARE: Modeling Interacting Dynamics Under Temporal Environmental Variation.
In Annual Conference on Neural Information Processing Systems (NeurIPS), 2023. (Accept rate: 26.1%)

Xiao Luo, Yusheng Zhao, Zhengyang Mao, Yifang Qin, Wei Ju, Ming Zhang, and Yizhou Sun.
RIGNN: A Rationale Perspective for Semi-supervised Open-world Graph Classification.
Transactions on Machine Learning Research (TMLR), 2023.

Zijie Huang, Wanjia Zhao, Jingdong Gao, Ziniu Hu, Xiao Luo, Yadi Cao, Yuanzhou Chen, Yizhou Sun, and Wei Wang.
TANGO: Time-reversal Latent GraphODE for Multi-Agent Dynamical Systems.
In NeurIPS 2023 Workshop DLDE.
Best Paper Award & Spotlight Talk

Haixin Wang, Hao Wu, Jinan Sun, Shikun Zhang, Chong Chen, Xian-Sheng Hua, and Xiao Luo.
IDEA: An Invariant Perspective for Efficient Domain Adaptive Image Retrieval.
In Annual Conference on Neural Information Processing Systems (NeurIPS), 2023. (Accept rate: 26.1%)

Fred Xu, Song Jiang, Zijie Huang, Xiao Luo, Shichang Zhang, Yuanzhou Chen, and Yizhou Sun.
FUSE: Measure-Theoretic Compact Fuzzy Set Representation for Taxonomy Expansion.
In Annual Meeting of the Association for Computational Linguistics - Findings (ACL Findings), 2024.

Weikai Li, Zhiping Xiao, Xiao Luo, and Yizhou Sun.
Fast Inference of Removal-Based Node Influence.
In The Web Conference (TheWebConf), 2024. (Accept rate: 20.2%)

Song Jiang, Zijie Huang, Xiao Luo, and Yizhou Sun.
CF-GODE: Continuous-Time Causal Inference for Multi-Agent Dynamical Systems.
In ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2023. (Accept rate: 22.1%)

Wei Ju, Yusheng Zhao, Yifang Qin, Siyu Yi, Jingyang Yuan, Zhiping Xiao, Xiao Luo, Xiting Yan, and Ming Zhang.
COOL: A Conjoint Perspective on Spatio-Temporal Graph Neural Network for Traffic Forecasting.
Information Fusion, 2024. (IF=14.7, JCR Q1)

Haixin Wang, Huiyu Jiang, Jinan Sun, Shikun Zhang, Chong Chen, Xian-Sheng Hua, and Xiao Luo.
DIOR: Learning to Hash with Label Noise via Dual Partition and Contrastive Learning.
IEEE Transactions on Knowledge and Data Engineering (TKDE), 2023. (IF=8.9, JCR Q1)

Xiao Luo*, Yusheng Zhao*, Yifang Qin*, Wei Ju, and Ming Zhang.
Towards Semi-supervised Universal Graph Classification.
IEEE Transactions on Knowledge and Data Engineering (TKDE), 2023. (IF=8.9, JCR Q1)

Xiao Luo*, Wei Ju*, Yiyang Gu, Zhengyang Mao, Luchen Liu, Yuhui Yuan, and Ming Zhang.
Self-supervised Graph-level Representation Learning with Adversarial Contrastive Learning.
ACM Transactions on Knowledge Discovery from Data (TKDD), 2023. (IF=4.0, JCR Q2)

Xiao Luo*, Daqing Wu*, Yiyang Gu*, Chong Chen, Luchen Liu, Jinwen Ma, Ming Zhang, Minghua Deng, Jianqiang Huang, and Xian-Sheng Hua.
Criterion-based Heterogeneous Collaborative Filtering for Multi-behavior Implicit Recommendation.
ACM Transactions on Knowledge Discovery from Data (TKDD), 2023. (IF=4.0, JCR Q2)

Yiyang Gu, Zihao Chen, Yifang Qin, Zhengyang Mao, Zhiping Xiao, Wei Ju, Chong Chen, Xian-Sheng Hua, Yifan Wang, Xiao Luo, and Ming Zhang.
DEER: Distribution Divergence-based Graph Contrast for Partial Label Learning on Graphs.
IEEE Transactions on Multimedia (TMM), 2024. (IF=8.4, JCR Q1)

Yifang Qin, Wei Ju, Hongjun Wu, Xiao Luo, and Ming Zhang.
Learning Graph ODE for Continuous-Time Sequential Recommendation.
IEEE Transactions on Knowledge and Data Engineering (TKDE), 2024. (IF=8.9, JCR Q1)

Nan Yin, Li Shen, Huan Xiong, Bin Gu, Chong Chen, Xian-Sheng Hua, Siwei Liu, and Xiao Luo.
Messages Are Never Propagated Alone: Collaborative Hypergraph Neural Network for Time-series Forecasting.
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2023. (IF=20.8, JCR Q1)

Si-Yu Yi, Wei Ju, Yifang Qin, Luchen Liu, Xiao Luo, Yong-Dao Zhou, and Ming Zhang.
Redundancy-Free Self-Supervised Relational Learning for Graph Clustering.
IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2023. (IF=10.2, JCR Q1)

Si-Yu Yi, Zhengyang Mao, Wei Ju, Yong-Dao Zhou, Luchen Liu, Xiao Luo, and Ming Zhang.
Towards Long-Tailed Recognition for Graph Classification via Collaborative Experts.
IEEE Transactions on Big Data (TBD), 2023. (IF=7.5, JCR Q1)

Xinlong Yang, Haixin Wang, Jinan Sun, Shikun Zhang, Chong Chen, Xian-Sheng Hua, and Xiao Luo.
Prototypical Mixup and Retrieval-based Refinement for Label Noise-resistant Image Retrieval.
In International Conference on Computer Vision (ICCV), 2023. (Accept rate: 26.2%)

Yifang Qin, Hongjun Wu, Wei Ju, Xiao Luo, and Ming Zhang.
A Diffusion model for POI recommendation.
ACM Transactions on Information Systems (TOIS), 2023. (IF=5.4, JCR Q2)

Zhengyang Mao, Wei Ju, Yifang Qin, Xiao Luo, and Ming Zhang.
RAHNet: Retrieval Augmented Hybrid Network for Long-tailed Graph Classification.
In ACM International Conference on Multimedia (ACM MM), 2023. (Accept rate: 29.1%)

Jingyang Yuan, Xiao Luo, Yifang Qin, Zhengyang Mao, Wei Ju, and Ming Zhang.
ALEX: Towards Effective Graph Transfer Learning with Noisy Labels.
In ACM International Conference on Multimedia (ACM MM), 2023. (Accept rate: 29.1%)

Fan Zhang, Xian-Sheng Hua, Chen Chong, and Xiao Luo.
Fine-grained Prototypical Voting with Heterogeneous Mixup for Semi-supervised 2D-3D Cross-modal Retrieval.
In IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR), 2024.

Wei Ju, Yifang Qin, Siyu Yi, Zhengyang Mao, Kangjie Zheng, Luchen Liu, Xiao Luo, and Ming Zhang.
Zero-shot Node Classification with Graph Contrastive Embedding Network.
Transactions on Machine Learning Research (TMLR), 2023.

Wei Ju, Zequn Liu, Yifang Qin, Bin Feng, Chen Wang, Zhihui Guo, Xiao Luo, and Ming Zhang.
Few-shot Molecular Property Prediction via Hierarchically Structured Learning on Relation Graphs.
Neural Networks, 2023. (IF=6.0, JCR Q1)

Xiao Luo, Wei Ju, Yiyang Gu, Yifang Qin, Daqing Wu, Si-Yu Yi, Luchen Liu, and Ming Zhang.
Towards Effective Semi-supervised Node Classification with Hybrid Curriculum Pseudo-labeling.
ACM Transactions on Multimedia Computing Communications and Applications (TOMM), 2023. (IF=5.2, JCR Q1)

Zhonghui Gu*, Xiao Luo*, Jiaxiao Chen, Minghua Deng, and Luhai Lai.
Hierarchical Graph Transformer with Contrastive Learning for Protein Function Prediction.
Bioinformatics, 2023. (IF=4.4, JCR Q1)

Haixin Wang, Jinan Sun, Xiao Luo, Shikun Zhang, Wei Xiang, Chong Chen, and Xian-Sheng Hua.
Towards Effective Domain Adaptive Retrieval.
IEEE Transactions on Image Processing 2023. (IF=10.8, JCR Q1)

Yusheng Zhao*, Xiao Luo*, Wei Ju, Chong Chen, Xian-Sheng Hua and Ming Zhang.
Dynamic Hypergraph Structure Learning for Traffic Flow Forecasting.
In IEEE International Conference on Data Engineering (ICDE), 2023.

Ziyue Qiao*, Xiao Luo*, Meng Xiao*, Hao Dong, Yuanchun Zhou and Hui Xiong.
Semi-supervised Domain Adaptation in Graph Transfer Learning.
In International Joint Conference on Artificial Intelligence (IJCAI), 2023. (Accept rate: 15%)

Nan Yin, Mengzhu Wang, Zhenghan Chen, Li Shen, Huan Xiong, Bin Gu, and Xiao Luo.
DREAM: Dual Structured Exploration with Mixup for Open-set Graph Domain Adaption.
In International Conference on Learning Representations (ICLR), 2024. (Accept rate: 31.0%)

Haixin Wang, Jinan Sun, Wei Xiang, Shikun Zhang, Chong Chen, Xian-Sheng Hua, and Xiao Luo.
DANCE: Learning A Domain Adaptive Framework for Deep Hashing.
In The Web Conference (TheWebConf), 2023. (Accept rate: 19.6%)

Jingyang Yuan*, Xiao Luo*, Yifang Qin, Yusheng Zhao, Wei Ju, and Ming Zhang.
Learning on Graphs under Label Noise.
In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023.

Wei Ju, Yiyang Gu, Binqi Chen, Gongbo Sun, Yifang Qin, Xingyuming Liu, Xiao Luo, and Ming Zhang.
GLCC: A General Framework for Graph-level Clustering.
In AAAI Conference on Artificial Intelligence (AAAI), 2023.

Wei Ju, Yiyang Gu, Xiao Luo, Yifan Wang, Haochen Yuan, Huasong Zhong, and Ming Zhang.
Unsupervised Graph-level Representation Learning with Hierarchical Contrasts.
Neural Networks, 2022. (IF=6.0, JCR Q1)

Weicong Liang, Yuhui Yuan, Weihong Lin, Xiao Luo, Henghui Ding, Ding Jia, Zheng Zhang, Chao Zhang, and Han Hu.
Expediting Vision Transformer for Dense Prediction without Fine-tuning.
In Annual Conference on Neural Information Processing Systems (NeurIPS), 2022. (Accept rate: 25.6%)

Wei Ju, Yifang Qin, Ziyue Qiao, Xiao Luo, Yifan Wang, Yanjie Fu, and Ming Zhang.
Kernel-based Substructure Exploration for Next POI Recommendation.
In IEEE International Conference on Data Mining (ICDM), 2022. (Accept rate: 9.7%)
Best Paper Finalist

Jinan Sun, Haixin Wang, Xiao Luo, Shikun Zhang, Wei Xiang, Chong Chen, and Xian-Sheng Hua.
HEART: Towards Effective Hash Codes Against Label Noise.
In ACM International Conference on Multimedia (ACM MM), 2022. (Accept rate: 27.6%)

Yingjie Chen, Chong Chen, Xiao Luo, Jianqiang Huang, Xian-Sheng Hua, Tao Wang, and Yun Liang.
Pursuing Knowledge Consistency: Supervised Hierarchical Contrastive Learning for Facial Action Unit Recognition.
In ACM International Conference on Multimedia (ACM MM), 2022. (Accept rate: 27.6%)

Xiao Luo*, Wei Ju*, Meng Qu, Yiyang Gu, Chong Chen, Minghua Deng, Xian-Sheng Hua, and Ming Zhang.
CLEAR: Cluster-enhanced Contrast for Self-supervised Graph Representation Learning.
IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2022. (IF=10.2, JCR Q1)

Wei Ju*, Xiao Luo*, Meng Qu, Yifan Wang, Chong Chen, Minghua Deng, Xian-Sheng Hua, and Ming Zhang.
TGNN: A Joint Semi-supervised Framework for Graph-level Classification.
In International Joint Conference on Artificial Intelligence (IJCAI), 2022. (Accept rate: 14.9%)

Wei Ju, Yiyang Gu, Zhengyang Mao, Ziyue Qiao, Yifang Qin, Xiao Luo, Hui Xiong, and Ming Zhang.
GPS: Graph Contrastive Learning via Multi-scale Augmented Views from Adversarial Pooling.
Science China Information Sciences (SCIS), 2024. (IF=7.3, JCR Q1)

Xiao Luo*, Wei Ju*, Meng Qu, Chong Chen, Minghua Deng, Xian-Sheng Hua, and Ming Zhang.
DualGraph: Improving Semi-supervised Graph Classification via Dual Contrastive Learning.
In IEEE International Conference on Data Engineering (ICDE), 2022. (Accept rate: 27.1%)

Xiao Luo*, Xinming Tu*, Yang Ding, Ge Gao, and Minghua Deng.
Expectation Pooling: An Effective and Interpretable Pooling Method for Predicting DNA-protein Binding .
Bioinformatics, Volume 36, Issue 5, March 2020, Pages 1405–1412. (IF=4.4, JCR Q1)

Xiao Luo*, Weilai Chi*, and Minghua Deng.
Deepprune: Learning Efficient and Interpretable Convolutional Networks Through Weight Pruning for Predicting DNA-Protein Binding .
Frontiers in Genetics, Nov 2019. (IF=2.8, JCR Q2)

Tutorial

Xiao Luo, Haixin Wang, Daqing Wu, Chong Chen, Minghua Deng, Jianqiang Huang, and Xian-Sheng Hua.
A Survey on Deep Hashing Methods.
ACM Transactions on Knowledge Discovery from Data (TKDD), 2023. (IF=4.0, JCR Q2)

Wei Ju, Zheng Fang, Yiyang Gu, Zequn Liu, Qingqing Long, Ziyue Qiao, Yifang Qin, Jianhao Shen, Fang Sun, Zhiping Xiao, Junwei Yang, Jingyang Yuan, Yusheng Zhao, Yifan Wang, Xiao Luo, and Ming Zhang.
A Comprehensive Survey on Deep Graph Representation Learning.
Neural Networks, 2024. (IF=6.0, JCR Q1)

Selected Honors & Awards

  • Institute for Digital Research and Education Postdoctoral Fellowship, 2023
  • Outstanding Graduates, Beijing, 2023 (top 1 per department)
  • National Scholarship, China, 2020 (top 0.1% nation-wide)
  • President Scholarship, 2019, 2020, 2021 (top 1% university-wide)
  • Merit Student, Peking University, 2018, 2019, 2020, 2021
  • Outstanding Graduates, Nanjing University, 2017
  • National Scholarship, China, 2016 (top 0.1% nation-wide)

Teaching

Teaching Assistant for
Probability and Statistics, Advanced Mathematics, Linear Algebra, Deep Learning for Bioinformatics

Academic Services

Associate Editor/Editorial Board Member: BMC Bioinformatics (2023- ), PLOS ONE (2023- )
Area Chair: ACM MM 2024
PC Member/Conference Reviewer: ECCV 2022, CVPR 2023, IJCAI 2023, KDD 2023, SIGIR 2023, NeurIPS 2023, NeurIPS 2023 Track Datasets and Benchmarks, ACM MM 2023, CIKM 2023, EMNLP 2023, ICDM 2023, ACL ARR 2023, WSDM 2024, AAAI 2024, LOG 2024, DASFAA 2024, ICLR 2024, TheWebConf 2024, SDM 2024, ICASSP 2024, CVPR 2024, RECOMB 2024, IJCAI 2024, ICML 2024, KDD 2024, ECCV 2024, CIKM 2024, NeurIPS 2024, NeurIPS 2024 Track Datasets and Benchmarks, ACL ARR 2024, WSDM 2025, AAAI 2025, KDD 2025
Journal Reviewer: TPAMI, TIP, TKDE, TIFS, TNNLS, TMM, TCVST, TOIS, TKDD, TITS, TMLR, TETCI, DMLR, JAIR, CVIU, Neural Networks, Information Fusion, Knowledge-Based Systems, Expert Systems With Applications
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