Haibo He

Haibo He

  • Professor
  • Electrical, Computer and Biomedical Engineering
  • Office: Morrill 318
  • Phone: 401.874.5844
  • Fax: 401.782.6422
  • Email: he@ele.uri.edu
  • Mailing Address: 51 Lower College Rd.
    Pastore 127
    Kingston, RI 02881
  • Research Links
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  • Lab: Computational Intelligence and Self-Adaptive Systems (CISA) Laboratory

research

He is the Director of the Computational Intelligence and Self-Adaptive Systems (CISA) Laboratory. His research interests include computational intelligence, self-adapative systems, machine learning and data mining, hardware design for machine intelligence (VLSI and FPGA), and various application fields including smart grid, sensor networks, biomedical applications, and cognitive communication networks The long-term research goal is to advance the fundamental principles and mathematical foundations of general-purpose brain-like intelligence, develop systematical methodologies to potentially replicate certain levels of such intelligence, and demonstrate its wide application to real-world complex problems to bring this level of intelligence closer to reality.

education

  • Ph.D. Ohio University, 2006

publications

Cao, Y., & He, H. (2012). SSC: A classifier combination method based on signal strength. IEEE Transactions Neural Networks and Learning Systems, 23, 1100-1117.

Cao, Y., He, H., & Hong, M. (2012). Somke: kernel density estimation over data streams by sequences of self-organizing maps. IEEE Transactions Neural Networks and Learning Systems, 23, 1254-1268.

Li, H., Sun, H., Wen, J., Cheng, S., & He, H. (2012). A fully decentralized multi-agent system for intelligent restoration of power distribution network incorporating distributed generations. IEEE Computational Intelligence Magazine, 7, 66-76.

Ni, Z., He, H., & Wen, J. (2013). Adaptive learning in tracking control based on the dual critic network design. IEEE Transactions Neural Networks and Learning Systems, 24, 913-928.

Tang, Y., He, H., Ni, Z., Wen, J., & Sui, X. (2013). Reactive power control of grid-connected wind farm based on adaptive dynamic programming. Neurocomputing, .

Tang, Y., Ju, P., He, H., C. Qin, & Feng, W. (2013). Optimized control of dfig-based wind generation using sensitivity analysis and particle swarm optimization. IEEE Transactions on Smart Grid, 4, 509-520.

Xu, X., Hou, Z., Lian, C., & He, H. (2013). Online learning control using adaptive critic designs with sparse kernel machines. IEEE Transactions Neural Networks and Learning Systems, 24, 762-775.

Yan, J., Zhu, Y., He, H., & Sun, Y. (2013). Multi-contingency cascading analysis of smart grid based on self-organizing map. IEEE Transactions Information Forensics and Security, 8, 646-656.

Personal website: http://www.ele.uri.edu/faculty/he/