Alamgir HossainAugust 16, 2021 2021-12-17 8:56
Dr. Alamgir Hossain
Vice President for Academic Affairs and Research,
Professor of Artificial Intelligence
Professor Alamgir Hossain received his PhD degree from the Department of Automatic Control & Systems Engineering, University of Sheffield, UK. He is currently serving as the Professor of Artificial Intelligence and Lead of the Centre for Digital Innovation at Teesside University (TU). Prior to this, he also served at Anglia Ruskin University (as the Director of the IT Research Institute), Head of Digital Research and Innovation of the National Horizon Centre (TU), University of Northumbria at Newcastle (as head of Computational Intelligence Group), University of Bradford, University of Sheffield, Sheffield Hallam University and the University of Dhaka (as the head of the Department of Computer Science and Engineering). He has extensive research experience in artificial intelligence, medical informatics, big data analysis, mobile enabled expert systems, cyber security, real-time algorithm design, decision support systems, robotics and adaptive active noise and vibration control systems. Professor Hossain has led many large EU funded projects as an international lead/co-investigator, worth over £16 million. With a publication in Nature, he has published over 300 refereed research articles, contributed in 12 books, received the “F C Williams 1996” award for an IET journal (UK) and ‘Best Paper Awards’ for five conference articles. See further details of articles and citations in his Google Scholar account.
Teaching and Supervision
- AI Applications
- Health Technology
- Cyber Security
- System Automation
- Computer Fundamentals
- Artificial Intelligence
- Research Project Supervision
- Ph.D in Real-time Intelligent System, university of Sheffield, UK
- PgCe Learning and Teaching, Sheffield Hallam University, UK
- BSc. & MSc. in Electronic Engineering, University of Dhaka, Bangladesh
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
• Email: email@example.com
• Tel: (855) 89-910-904
• Linkedin: www.linkedin.com/in/pharino-chum-446b5a85