Wei Zhu’s Homepage, University of Rochester

Hi, I am Wei Zhu, a 4-th year Ph. D. student from the department of computer science, University or Rochester(UR), NY. My advisor is Prof. Jiebo Luo. I also work with Prof. Andrew D. White from the department of chemical engineering, UR. I am interested in representation learning, fairness/debiasing/domain generalization, federated learning, graph neural network, few-shot learning and their applications in natural images, molecular graph, multivariate time series data, and medical images.

Email: zwvews@gmail.com

last update: May. 2022

Publications: Google Scholar

Domain Generalization/Fairness/Debiasing

  1. W. Zhu, L. Lu, J. Xiao, M. Han, J. Luo, and A. P. Harrison, “Localized Adversarial Domain Generalization,” CVPR 2022. paper, code [domain generalization], [adversarial learning]
  2. W. Zhu, Z. Zheng, H. Liao, W. Li, and J. Luo, “Learning Bias-Invariant Representation by Cross-Sample Mutual Information Minimization,” ICCV 2021. paper, code [debiasing/fairness], [mutual information estimation]

Federated Learning

  1. W. Zhu and J. Luo, “Federated Medical Image Analysis with Virtual Sample Synthesis,” MICCAI 2022 (early accept). paper, code [federated learning], [medical image analysis], [adversarial training]
  2. W. Zhu, Andrew White, and J. Luo, “Federated Learning of Molecular Properties in a Heterogeneous Setting,” Patterns 2022. paper, code [federated learning], [graph neural network]

Few-Shot Learning

  1. W. Zhu, W. Li, H. Liao, and J. Luo, “Temperature Network for Few-shot Learning with Distribution-aware Large-margin Metric,” Pattern Recognition 2021. paper, code [few-shto leanring], [metric learning]
  2. W. Zhu, H. Liao, W. Li, W. Li, and J. Luo, “Alleviating the Incompatibility between Cross Entropy Loss and Episode Training for Few-shot Skin Disease Classification,” MICCAI 2020 (early accept). paper [few-shot learning], [contrastive learning], [metric learning]

Spectral-based Methods

  1. W. Zhu, F. Shi and J. Luo, “Modeling Heterogeneity in Feature Selection for MCI Classification”, ISBI 2020. paper [feature selection], [clustering]
  2. F. Nie, W. Zhu and X. Li, “Unsupervised Large Graph Embedding Based on Balanced and Hierarchical K-means,” TKDE 2020. paper [clustering], [metric learning]
  3. F. Nie, W. Zhu and X. Li, “Structured Graph Optimization for Unsupervised Feature Selection,” TKDE 2019. paper [feature selection]
  4. W. Zhu, F. Nie and X. Li, “Fast Spectral Clustering with Efficient Large Graph Embedding,” ICASSP 2017. paper [clustering], [metric learing]
  5. F. Nie, W. Zhu and X. Li, “Unsupervised Large Graph Embedding,” AAAI 2017. paper [metric learning]
  6. F. Nie, W. Zhu and X. Li, “Unsupervised Feature Selection with Structured Graph Optimization,” AAAI 2016, 1302-1308. paper [feature selection]

Others

  1. W. Zhu, Z. Zheng, H. Zheng, H. Lyu, and J. Luo, “Learning to Aggregate and Refine Noisy Labels for Visual Sentiment Analysis”, ICPR 2022 (early accept). paper [noisy labels]
  2. W. Li, W. Zhu, R. Dorsey, J. Luo, “Predicting Parkinson’s Disease with Multimodal Irregularly Collected Longitudinal Smartphone Data”, ICDM 2020. paper [multivarite time series]
  3. F. Nie, W. Zhu and X. Li, “Decision Tree SVM: An Extension of Linear SVM for Non-linear Classification,” Neurocomputing 2020. paper [SVM]

Educations

  • Ph.D. in Computer Science (Sep. 2018 - May. 2023 expected)
    • Department of Computer Science, University of Rochester, Advisor: Prof. Jiebo Luo
      • Representation Learning, Federated Learning, and Domain Generalization/Debiasing
    • I also work with Prof. Andrew D. White from the department of chemical engineering, University of Rochester
      • Federated Learning, Graph Neural Network, and Molecular Property Prediction

Research Experiences

Summer intern

  • Summer intern @PAII, MD: work with Dr. Adam P. Harrison (May. 2021 - Aug. 2021)
    • Domain Generalization for Steatosis Diagnosis based on Ultrasound Images
  • Summer intern @NEC, NJ: work with Prof. Dongjin Song and Dr. Yuncong Chen (May. 2020 - Aug. 2020)
    • Federated Anomaly Detection
  • Summer intern @Siemens, NJ: work with Dr. Zhoubing Xu (May. 2019 - Aug. 2019)
    • Self-supervised learning with CT images
  • Summer intern @United-Imaging, Shanghai: work with Dr. Feng Shi (May. 2018 - Aug. 2018)
    • MCI Diagnosis

Professonal Activities

  • Reviewer for CVPR, AAAI, ECCV, ICPR, IEEE TNNLS, IEEE TII, IEEE TSP, IEEE/CAA JAS, International Journal of Intelligent Systems, WSDM workshops