World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
42
Citations
8365
World Ranking
8317
National Ranking
3565

Overview

Jiayu Zhou is affiliated with Michigan State University in the United States. Their research spans primarily the fields of computer science and medicine, with a substantial focus on artificial intelligence, molecular biology, and related interdisciplinary areas.

The scientist has contributed extensively to topics such as privacy-preserving technologies in data, head and neck cancer studies, domain adaptation and few-shot learning, adversarial robustness in machine learning, machine learning applications in healthcare, dementia and cognitive impairment research, and computational drug discovery methods.

Some of Jiayu Zhou's recent notable publications include:

  • Transfer Learning in Deep Reinforcement Learning: A Survey, 2023, IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Transfer Learning in Deep Reinforcement Learning: A Survey, 2020, arXiv (Cornell University)
  • Cosmo, 2022, Proceedings of the 28th Annual International Conference on Mobile Computing And Networking
  • Margin Preserving Self-Paced Contrastive Learning Towards Domain Adaptation for Medical Image Segmentation, 2022, IEEE Journal of Biomedical and Health Informatics
  • A phenylalanine ammonia lyase from Fritillaria unibracteata promotes drought tolerance by regulating lignin biosynthesis and SA signaling pathway, 2022, International Journal of Biological Macromolecules

Jiayu Zhou has frequently published in venues such as:

  • arXiv (Cornell University)
  • Scientific Reports
  • PubMed
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • SSRN Electronic Journal

Collaborations have been prominent in Jiayu Zhou's work, with frequent coauthors including Junyuan Hong, Hai Liao, Yingying Huang, Ze-Jiang Zhan, and Xing Lv.

Their research emphasizes methods and applications across computer vision and pattern recognition, electrical and electronic engineering, plant science, and other subfields supporting their main disciplinary scopes.

Best Publications

  • Patient Subtyping via Time-Aware LSTM Networks

    Inci M. Baytas;Cao Xiao;Xi Zhang;Fei Wang

  • 2014 IEEE International Conference on Data Mining

    Aleksandr Aravkin;Aurelie Lozano;Ronny Luss;Prabhajan Kambadur

  • Graph convolutional networks for computational drug development and discovery.

    Mengying Sun;Sendong Zhao;Coryandar Gilvary;Olivier Elemento

  • Efficient Large-Scale Fleet Management via Multi-Agent Deep Reinforcement Learning

    Kaixiang Lin;Renyu Zhao;Zhe Xu;Jiayu Zhou

  • Differentially Private Generative Adversarial Network.

    Liyang Xie;Kaixiang Lin;Shu Wang;Fei Wang

  • Integrating low-rank and group-sparse structures for robust multi-task learning

    Jianhui Chen;Jiayu Zhou;Jieping Ye

  • A multi-task learning formulation for predicting disease progression

    Jiayu Zhou;Lei Yuan;Jun Liu;Jieping Ye

  • Clustered Multi-Task Learning Via Alternating Structure Optimization

    Jiayu Zhou;Jianhui Chen;Jieping Ye

  • Modeling disease progression via multi-task learning

    Jiayu Zhou;Jun Liu;Vaibhav A. Narayan;Jieping Ye

  • Analysis of sampling techniques for imbalanced data: An n = 648 ADNI study.

    Rashmi Dubey;Jiayu Zhou;Yalin Wang;Paul M. Thompson

  • Modeling disease progression via fused sparse group lasso

    Jiayu Zhou;Jun Liu;Vaibhav A. Narayan;Jieping Ye

  • Missing Modalities Imputation via Cascaded Residual Autoencoder

    Luan Tran;Xiaoming Liu;Jiayu Zhou;Rong Jin

  • ClusterFL: a similarity-aware federated learning system for human activity recognition

    Xiaomin Ouyang;Zhiyuan Xie;Jiayu Zhou;Jianwei Huang

  • Lasso Screening Rules via Dual Polytope Projection

    Jie Wang;Jiayu Zhou;Peter Wonka;Jieping Ye

  • Data-Free Knowledge Distillation for Heterogeneous Federated Learning

    Zhuangdi Zhu;Junyuan Hong;Jiayu Zhou

  • Drug Similarity Integration Through Attentive Multi-view Graph Auto-Encoders

    Tengfei Ma;Cao Xiao;Jiayu Zhou;Fei Wang

  • Transfer Learning in Deep Reinforcement Learning: A Survey

    Zhuangdi Zhu;Kaixiang Lin;Jiayu Zhou

  • Comparison of nine tractography algorithms for detecting abnormal structural brain networks in Alzheimer's disease.

    Liang Zhan;Jiayu Zhou;Yalin Wang;Yan Jin

  • From micro to macro: data driven phenotyping by densification of longitudinal electronic medical records

    Jiayu Zhou;Fei Wang;Jianying Hu;Jieping Ye

  • An RNN Architecture with Dynamic Temporal Matching for Personalized Predictions of Parkinson's Disease.

    Chao Che;Cao Xiao;Jian Liang;Bo Jin

  • Who, What, When, and Where: Multi-Dimensional Collaborative Recommendations Using Tensor Factorization on Sparse User-Generated Data

    Preeti Bhargava;Thomas Phan;Jiayu Zhou;Juhan Lee

Frequent Co-Authors

Jieping Ye
Jieping Ye Alibaba Group (China)
Pang-Ning Tan
Pang-Ning Tan Michigan State University
Zhangyang Wang
Zhangyang Wang The University of Texas at Austin
Thomas S. Huang
Thomas S. Huang University of Illinois at Urbana-Champaign
Anil K. Jain
Anil K. Jain Michigan State University
Shiyu Chang
Shiyu Chang University of California, Santa Barbara
Cao Xiao
Cao Xiao General Electric (United Kingdom)
Paul M. Thompson
Paul M. Thompson University of Southern California
Jianying Hu
Jianying Hu IBM (United States)
Jieping Ye
Jieping Ye Alibaba Group (China)

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