Wei Zhu’s Homepage, University of Rochester

Hi, I am Wei Zhu, a 3-rd year Ph. D. student from the department of computer science, University or Rochester Rochester, NY. My advisor is Prof. Jiebo Luo. Prior to coming to UR, I received my M.S. degree from Northwestern Polytechnical University, Xi’an China in 2018 advised by Prof. Feiping Nie and Prof. Xuelong Li. I am interested in representation learning, federated learning, few-shot learning, fairness/debiasing and their applications in healthcare data, natural images, multivaraite time series data, and medical images.

last update: Dec. 2020

Publications: Google Scholar

Preprint Papers

  1. H. Zheng, K. Wu, J. Park, W. Zhu, and J. Luo, “Personalized Fashion Recommendation from Personal Social Media Data: An Item-to-Set Metric Learning Approach”. paper [metric learning], [representation learning]
  2. Y. Feng, F. Peng, X. Zhang, W. Zhu, S. Zhang, Z. Li, T. Duerig, S. Chang, and J. Luo, “Unifying Specialist Image Embedding into Universal Image Embedding”. paper [representation learning]

Conference Papers

  1. 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. paper [few-shot learning], [contrastive learning], [metric learning]
  2. W. Zhu, F. Shi and J. Luo, “Modeling Heterogeneity in Feature Selection for MCI Classification”, ISBI 2020. paper [feature selection], [clustering]
  3. 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]
  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]

Journal Papers

  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. F. Nie, W. Zhu and X. Li, “Decision Tree SVM: An Extension of Linear SVM for Non-linear Classification,” Neurocomputing 2020. paper [SVM]
  3. F. Nie, W. Zhu and X. Li, “Unsupervised Large Graph Embedding Based on Balanced and Hierarchical K-means,” TKDE 2020. paper [clustering], [metric learning]
  4. F. Nie, W. Zhu and X. Li, “Structured Graph Optimization for Unsupervised Feature Selection,” TKDE 2019. paper [feature selection]


  • Ph.D. in Computer Science (Sep. 2018 - May. 2023 expected)
    • Department of Computer Science, University of Rochester, NY
    • Advisor: Prof. Jiebo Luo
  • M.S. in Computer Science (Sep. 2015 - May. 2018)
  • B.S. in Software Engineering (Sep. 2011 - Jul. 2015)
    • School of Software and Microelectronics, Northwestern Polytechnical University, Xi’an

Research Experiences

  • Work with Prof. Jiebo Luo as a reserach assistant at the University of Rochester, NY (2018 - now)
  • Work with Prof. Dongjin Song and Dr. Yuncong Chen as a summer Intern at NEC American Lab, Princeton, NJ (May. 2020 - Aug. 2020)
  • Work with Dr. Zhoubing Xu as a summer Intern at Siemens Healthineer, Princeton, NJ (May. 2019 - Aug. 2019)
  • Work with Dr. Feng Shi as a summer Intern at United-imaging Inc. Shanghai, China (May. 2018 - Aug. 2018)
  • Work with Prof. Feiping Nie at the Northwestern Polytechnical University, Xi’an, China (2015 - 2018)