• 165 Citations
  • 7 h-Index
20102019
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Personal profile

Personal profile

Chinese Name : 潘建文

Variants : Poon, K. M. L.

Leonard Kin-Man Poon is an Assistant Professor in the Department of Mathematics and Information Technology at the Education University of Hong Kong. He received his PhD and MPhil degress in computer science and his BEng degree in electronic engineering from the Hong Kong University of Science and Technology. He has previously worked as a part-time lecturer at HKU SPACE Community College, and as a software developer at Reuters and EDS.

Research interests

Clustering, Latent variable models, Probabilistic graphical models, Machine learning

Professional information

ORCID : 0000-0002-8394-1492

Scopus ID : 53464057700

Fingerprint Dive into the research topics where Kin Man POON is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

  • 5 Similar Profiles
Students Engineering & Materials Science
Cluster analysis Engineering & Materials Science
Model-based Clustering Mathematics
Learning systems Engineering & Materials Science
Data mining Engineering & Materials Science
Collaborative filtering Engineering & Materials Science
Classifiers Engineering & Materials Science
Discrete Data Mathematics

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Research Outputs 2010 2019

  • 165 Citations
  • 7 h-Index
  • 19 Paper
  • 10 Chapter
  • 8 Article
  • 2 Other contribution

Applying eye tracking to identify students' use of learning strategies in understanding program code

CHENG, K. S., POON, K. M., LAU, W. W. F. & ZHOU, C. R., 2019, Proceedings of the 2019 3rd International Conference on Education and Multimedia Technology. New York: ACM, p. 140-144

Research output: Chapter in Book/Report/Conference proceedingChapter

learning strategy
programming
student
psychologist
learning

Extracting access patterns with hierarchical latent tree analysis: An empirical study on an undergraduate programming course

POON, K. M., 2019, Integrated uncertainty in knowledge modelling and decision making: 7th International Symposium, IUKM 2019, Nara, Japan, March 27–29, 2019, proceedings. SEKI, H., NGUYEN, C. H., HUYNH, V-N. & INUIGUCHI, M. (eds.). Cham: Springer, p. 380-392

Research output: Chapter in Book/Report/Conference proceedingChapter

Computer programming
Students
Online systems

Learning latent superstructures in variational autoencoders for deep multidimensional clustering

LI, X., CHEN, Z., POON, K. M. & ZHANG, N. L., May 2019

Research output: Other contribution

Deep learning

GPU-accelerated clique tree propagation for pouch latent tree models

POON, K. M., 2018, Network and parallel computing: 15th IFIP WG 10.3 International Conference, NPC 2018, Muroran, Japan, November 29 – December 1, 2018, Proceedings. ZHANG, F., ZHAI, J., SNIR, M., JIN, H., KASAHARA, H. & VALERO, M. (eds.). Cham: Springer, p. 90-102

Research output: Chapter in Book/Report/Conference proceedingChapter

Graphics processing unit
Cluster analysis
Model structures
Program processors
Experiments

UC-LTM: Unidimensional clustering using latent tree models for discrete data

POON, K. M., LIU, A. H. & ZHANG, N. L., 2018, In : International Journal of Approximate Reasoning. 92, p. 392-409

Research output: Contribution to journalArticle

Discrete Data
Latent Variables
Latent Class Model
Attribute
Clustering