Dr David Adama is a Lecturer with the Department of Computer Science at Nottingham Trent University, where he is the course leader for the Data Scientist Degree Apprenticeship and also a member of the Computational Intelligence and Applications (CIA) research group.
David received his BEng (Hons) degree in Electrical and Electronics Engineering and MSc in Engineering (Cybernetics and Communication) from UAM and Nottingham Trent University, United Kingdom, respectively. He received his PhD degree in Computational Intelligence for his research on fuzzy transfer learning in human activity recognition from Nottingham Trent University.
Prior to his current role, David has held other positions including researcher in human activity recognition and data scientist roles. He also worked as a software developer in computational intelligence at Nottingham Trent University in which he was part of the Si-Elegans EU-FP7 project.
David also worked as part-time lecturer in teaching various computer science related subjects in higher education at different institutions.
David's area of research is in applications of artificial intelligence, machine learning, deep learning and transfer learning, in developing systems for human-robot interaction, human behaviour monitoring and recognition, independent assisted living and computer vision.
PhD Applicants: Opportunities arise to carry out postgraduate research towards an MPhil / PhD in the areas identified above. Further information may be obtained from the NTU Doctoral School.
- Member of the British Computer Society (BCS) (2020 - present).
- Member of the Institution of Engineering and Technology (IET), UK (2019 - present).
- Member of the Institute Electrical and Electronics Engineers (IEEE) (2016 - present).
ADAMA, D. A., LOTFI, A. and RANSON, R.. Adaptive segmentation and sequence learning of human activities from skeleton data. Expert Systems with Applications. August 2020.
ADAMA, D.A., LOTFI, A., LANGENSIEPEN, C., LEE, K. and TRINDADE, P., 2018. Human activity learning for assistive robotics using a classifier ensemble. Soft Computing: a Fusion of Foundations, Methodologies and Applications.
ADAMA, D.A., LOTFI, A., RANSON, R. and TRINDADE, P., 2019. Transfer learning in assistive robotics: from human to robot domain. In: Proceedings of the 2nd UK-RAS Robotics and Autonomous Systems Conference, Loughborough, 24 January 2019. London: UK-RAS Network, pp. 60-63.
ADAMA, D.A., LOTFI, A. and RANSON, R., 2019. Fuzzy transfer learning of human activities in heterogeneous feature spaces. In: Proceedings of the 12th ACM International Conference on PErvasive Technologies Related to Assistive Environments - PETRA '19, Rhodes, Greece, 5-7 June 2019. New York: ACM, pp. 593-598.
ADAMA, D.A., LOTFI, A. and LANGENSIEPEN, C., 2018. Key frame extraction and classification of human activities using motion energy. In: A. LOTFI, H. BOUCHACHIA, A. GEGOV, C. LANGENSIEPEN and M. MCGINNITY, eds., Advances in computational intelligence systems: contributions presented at the 18th UK Workshop on Computational Intelligence, September 5-7, 2018, Nottingham, UK. Advances in intelligent systems and computing (AISC), 840 . Cham, Switzerland: Springer, pp. 303-311.
ADAMA, D.A., LOTFI, A., LANGENSIEPEN, C., LEE, K. and TRINDADE, P., 2017. Learning human activities for assisted living robotics. In: Proceedings of the 10th International Conference on PErvasive Technologies Related to Assistive Environments, PETRA '17, Island of Rhodes, Greece, 21-23 June 2017. New York: ACM, pp. 286-292.
ADAMA, D.A., LOTFI, A., LANGENSIEPEN, C. and LEE, K., 2017. Human activities transfer learning for assistive robotics. In: F. CHAO, S. SCHOCKAERT and Q. ZHANG, eds., Advances in computational intelligence systems: contributions presented at the 17th UK Workshop on Computational Intelligence, Cardiff, 6-8 September 2017. Advances in intelligent systems and computing (650). Cham: Springer, pp. 253-264.
MOHMED, G., ADAMA, D.A. and LOTFI, A., 2019. Fuzzy feature representation with bidirectional long short-term memory for human activity modelling and recognition. In: Z. JU, L. YANG, C. YANG, A. GEGOV and D. ZHOU, eds., Advances in computational intelligence systems. UKCI 2019. Advances in intelligent systems and computing (1043). Cham: Springer, pp. 15-26.
MACHADO, P., COSTALAGO MERUELO, A., PETRUSHIN, A., FERRARA, L., LAMA, N., ADAMA, D., APPIAH, K., BLAU, A. and MCGINNITY, T.M., 2016. Si elegans: evaluation of an innovative optical synaptic connectivity method for C. elegans phototaxis using FPGAs. In: Proceedings of the 2016 IEEE International Joint Conference on Neural Networks (IJCNN), Vancouver, Canada, 24-29 July 2016. Piscataway, New Jersey: IEEE, pp. 185-191.