Xingquan (Hill) Zhu

Xingquan (Hill) Zhu

Professor

Department of Electrical Engineering and Computer Science

777 Glades Road, EE 503B

Boca Raton, FL 33431-0991

p: 561.297.3452

xzhu3@fau.edu

Education

  • Ph.D., Computer Science, Fudan University, China

Research Interests

  • Data Mining and Machine Learning
  • Information Retrieval
  • Bioinformatics

Research Sites

Current Sponsored Research

  1. RAPID: COVID-19 Coronavirus Testbed and Knowledge Base Construction and Personalized Risk Evaluation
    • National Science Foundation (IIS-2027339)
    • Xingquan Zhu (PI), Michael DeGiorgio (Co-PI), and Massimo Caputi (Co-PI)
    • Amount: $90,000, Duration: 2020-2021
  2. III: Medium: Collaborative Research: KMELIN: Knowledge Mining and Embedding Learning for Complex Dynamic Information Networks
    • National Science Foundation (IIS-1763452)
    • Xingquan Zhu (PI), Ankur Agarwal (Co-PI), and Dingding Wang (Co-PI)
    • Amount: $599,983, Duration: 2018-2022
  3. MRI: Acquisition of Artificial Intelligence & Deep Learning (AIDL) Training and Research Laboratory
    • National Science Foundation (CNS-1828181).
    • Xingquan Zhu (PI), Taghi Khoshgoftaar (Co-PI), Dimitris Pados (Co-PI), Hanqi Zhuang (Co-PI), and Laurent Cherubin (Co-PI)
    • Amount: $652,850, Duration: 2018-2021
  4. FAU Bidtellect Laboratory - Industry Research Collaboration
    • Bidtellect Inc.
    • Lead Lab Director: Xingquan Zhu
    • Amount: $300,000, Duration: 2017-2022

previous sponsored research

Recent Publications

Book
  • Xingquan Zhu, Haicheng Tao, Zhiang Wu, Jie Cao, Kris Kalish, and Jeremy Kayne, "Fraud Prevention in Digital Advertising, Springer Briefs in Computer Science", ISBN 978-3-319-56792-1, 2017.
Journal Articles (Peer Reviewed)
  1. Jia Wu, Shirui Pan, Xingquan Zhu, Chengqi Zhang, and Philip S. Yu, "Multiple Structure-View Learning for Graph Classification, IEEE Transactions on Neural Networks and Learning Systems," Accepted, In Press.
  2. Jia Wu, Shirui Pan, Xingquan Zhu, Chengqi Zhang, and Xindong Wu, "Towards Multi-instance Learning with Discriminative Bag Mapping. IEEE Transactions on Knowledge and Data Engineering, Accepted," In Press.
  3. Yisen Wang, Shu-Tao Xia, Qingtao Tang, Jia Wu, and Xingquan Zhu, "A Novel Consistent Random Forest Framework: Bernoulli Random Forests, IEEE Transactions on Neural Networks and Learning Systems," Accepted, In Press.

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