Somoeun Mut

Somoeun Mut

Mr. Somoeun Mut

Vice Dean of the Faculty of Arts, Humanities and Social Sciences

Mr. Mut Somoeun is a Vice Dean of the Faculty of Arts, Humanities and Social Sciences. He graduated BA in Educational Administration from Preah Sihanouk Raja Buddhist University and BA in Law from University of Cambodia, and MA in Educational Administration specializing in School-based management from Universitas Pendidikan Indonesia (UPI). He has been actively involved in academic counseling, academic administration, curriculum development, and teaching with general education and higher education institutions in Cambodia for many years. In addition, Somoeun has practical experience in policy development with the Department of Policy (DoPo), and the Ministry of Education, Youth and Sport (MoEYS) as a National Technical Assistant and officially employed officer for over 2 years. At DoPo, he held key duties in policy research, policy analysis, and policy development. Somoeun is interested in teaching: School-based Management, Education Management, Education Policy, Teaching Methodology, Khmer Cultural Studies, and Religious Studies.

Research Interests

Teaching and Supervision

Qualifications

  • Teaching Pedagogy
  • Khmer Studies
  • Education Administration
  • Khmer Cultural Studies
  • Education Management
  • Teaching Methodology
  • Ph.D in Public Administration, The University of Cambodia 
  • M.A in Educational Administration, Indonesia University of Education, Indonesia
  • LL.B in Laws, The University of Cambodia
  • B.A in Educational Administration, Preah Sihanouk Raja Buddhist University, Cambodia

Publications

Contact Details

Publications

1. Park, S. M., Yu, X., Chum, P., Lee, W. Y., & Sim, K. B. (2017). Symmetrical feature for interpreting motor imagery EEG signals in the brain-computer interface. Optik, 129, 163–171. https://doi.org/10.1016/j.ijleo.2016.10.047
2. Yu, X., Chum, P., & Sim, K. B. (2014). Analysis the effect of PCA for feature reduction in non-stationary EEG based motor imagery of BCI system. Optik, 125(3), 1498–1502. https://doi.org/10.1016/j.ijleo.2013.09.013
3. Pharino, C. (2015). A Comparison Study for an Optimal Common Spatial Pattern Algorithm for EEG Signal Classification applicable to BCI Systems. 8TH AUN/SEED-NET INT’L CONFERENCE ON EEE 2014, 1–14.
4. Yu, X., Park, S. M., Ko, K.-E., Chum, P., & Sim, K. (2013). A study on STFT Feature Extraction of Motor Imagery Brain-Computer Interface. Proceedings of KIIS Spring Conference, 23(1), 8726.
5. Pharino Chum, Park, S.-M., & Kwang-Eun Ko and Kwee-Bo Sim. (2013). VCSP Method for EEG Feature Extraction of Motor Imagery Brain-Computer Interface. Proceedings of KIIS Spring Conference, 23(1), 115–116.
6. Yu, X., Park, S., Ko, K., Pharino, C., & Sim, K. (2012). Discriminative Power Band Feature Selection using PCA for Motor Imagery Classification in EEG-based BCI System. Proceedings of KIIS Fall Conference, 37–38.

7. Chum, P., Park, S.-M., Ko, K.-E., & Sim, K.-B. (2012). Particle swarm optimization based optimal spatial-spectral-temporal component search in motor imagery brain-computer interface. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Vol. 7425 LNCS. https://doi.org/10.1007/978-3-642-32645-5_59
8. Chum, P., Park, S. M., Ko, K. E., & Sim, K. B. (2012). Particle swarm optimization based optimal spatial-spectral-temporal component search in motor imagery brain-computer interface. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7425 LNCS, 469–476. https://doi.org/10.1007/978-3-642-32645-5_59
9. Chum, P., Park, S., Ko, K., & Sim, K. (2012). Improved Method for Extracting Optimal Time-Frequency EEG Signal Feature. Proceedings of KIIS Spring Conference 2012, 22(1), 143–144.
10. Chum, P., Park, S., Ko, K., & Sim, K. (2012). Orthonormal Polynomial based Optimal EEG Feature Extraction for Motor Imagery Brain-Computer Interface. Journal of Korean Institute of Intelligent Systems, 22(6), 793–798.
11. Chum, P., Park, S., Ko, K., & Sim, K. (2012). Orthonormal Polynomial based Optimal EEG Feature Extraction for Motor Imagery Brain-Computer Interface. Proceedings of KIIS Fall Conference 2012, 22(2), 19–20.

12. Chum, P., Park, S. M., Ko, K. E., & Sim, K. B. (2012). Optimal EEG feature selection by genetic algorithm for classification of imagination of hand movement. IECON Proceedings (Industrial Electronics Conference), 1561–1566. https://doi.org/10.110/IECON.2012.6388508
13. Chum, P., Kim, J., Park, S., Ko, K., & Sim, K. (2012). Particle Swarm Optimization based Automatic Feature Selection for Spatio-Spectral- Temporal Filtering in Brain Computer Interface. International Conference on Smart Convergence Technologies and Applications 2012, 2(3), 2012.
14. Chum, P., Park, S.-M. M., & Sim, K. B. (2013). Parallel model feature extraction to improve performance of a BCI system. Journal of Institute of Control, Robotics and Systems, 19(2012), 1022–1028. https://doi.org/10.5302/J.ICROS.2013.13.1930.

Service and Leadership:
• Cambodia Robocon Committee

Contact Details:
• Email: pharino.chum@gmail.com
• Tel: (855) 89-910-904
• Linkedin: www.linkedin.com/in/pharino-chum-446b5a85

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