Lecturer in Computer Science

Dr Massoud Khodadadzadeh, a Lecturer in Computer Science at the University of Bedfordshire, holds a PhD in Computer Science from Ulster University. His research journey includes impactful roles such as a Research Associate in Artificial Intelligence for Data Science at The Bath Institute for the Augmented Human, University of Bath, and previously at the Intelligent Systems Research Centre, Ulster University.

With international collaborations, he has contributed to pioneering projects, including a visiting researcher position at the Centre for Complex Systems and Brain Sciences, Florida Atlantic University. His work encompasses cutting-edge developments in machine learning and deep learning methods for various applications, such as Remote Sensing, Brain-Computer Interface, advanced techniques for Inner Speech Classification, and identifying emergent agency in infants.

Teaching Expertise/Areas

• Artificial Intelligence
• Programming
• Data mining
• Computer Vision

Research Interests/Areas

• Deep/Machine Learning
• Remote Sensing
• Brain Computer Interface (BCI)
• Signal/Image Processing

Publications

• Sloan, A. T., Jones, N. A., Boiten, N., Friston, K., Khodadadzadeh, M., Coyle, D., Gudibanda, K., Jirsa, V., & Kelso, J. A. S. (2023). “Coordination Dynamics meets Active Inference and Artificial Intelligence (CD + AI2): A multi-pronged approach to understanding the dynamics of brain and the emergence of conscious agency”. Society for Neuroscience (SfN), https://doi.org/10.1016/j.tics.2016.04.004
• Khodadadzadeh, M., Sloan, A. T., Jones, N. A., Coyle, D., & Kelso, J. A. S. (2023). 2D Capsule Networks Detect Perceived Changes in Infant∼Environment Relationship Reflected in 3D Movement Dynamics. Scientific Reports, Nature, https://doi.org//10.21203/rs.3.rs-3088795/v1
• M. Khodadadzadeh, N. deBois, and D. Coyle, “Knowledge Extraction using Capsule Deep Learning Approaches.” UK AI Fellows Conference. Accessed: May. 24, 2023.
• M. Khodadadzadeh and D. Coyle, “Imagined Speech Classification from Electroencephalography with a Features-Guided Capsule Neural Network.” 15th Irish Human Computer Interaction (iHCI) Symposium, Dec. 18, 2022. Accessed: Feb. 26, 2023.
• M. Khodadadzadeh, X. Ding, P. Chaurasia, and D. Coyle, “A Hybrid Capsule Network for Hyperspectral Image Classification,” IEEE J Sel Top Appl Earth Obs Remote Sens, vol. 14, pp. 11824–11839, 2021.
• Korik A, McCreadie K, McShane N, Du Bois N, Khodadadzadeh M, Stow J, et al., “Competing at the Cybathlon championship for people with disabilities: long-term motor imagery brain–computer interface training of a cybathlete who has tetraplegia,” J Neuroeng Rehabil, vol. 19, no. 1, pp. 1–22, Dec. 2022.
• M. Khodadadzadeh, X. Ding, P. Chaurasia, and D. Coyle, “Data Analysis with Capsule Deep Learning Approaches,”Intelligent Systems Research Centre, Faculty of Computing, Engineering and Built Environment, Ulster University, Confirmation report, 2019.
• M. Khodadadzadeh, X. Ding, P. Chaurasia, and D. Coyle, “Intelligent Data Analysis with Capsule Neural Networks,”Intelligent Systems Research Centre, Faculty of Computing, Engineering and Built Environment, Ulster University, Initial assessment report, 2018.
• M. Khodadadzadeh and H. Gholizadeh-Narm, “Improvement of chaotic secure communication scheme based on steganographic method and multimodal dynamic maps,” International Journal of Systems, Control and Communications, vol. 6, no. 4, pp. 305–320, 2015.

Contact Details

E: Massoud.Khodadadzadeh@beds.ac.uk