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Dr Adnane Ez-zizi
Senior Lecturer and Course Leader for Data Science and Artificial Intelligence
- Phone
- +44 (0)1473 338912
- a.ez-zizi@uos.ac.uk
- School/Directorate
- School of Technology, Business and Arts
- Adnane Ez-zizi ORCID
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Dr Adnane Ez-zizi is a Senior Lecturer in Artificial Intelligence and Course Leader for the MSc Data Science and Artificial Intelligence at the School of Technology, Business and Arts. He holds a PhD in Experimental Psychology from the University of Bristol, where he was part of a large interdisciplinary research group that brought together psychologists, mathematicians, computer scientists and biologists to study human and animal decision making. He also obtained an MRes in Statistics from the University of Pierre et Marie Curie (now Sorbonne University), an MSc in Random modelling from the University of Paris Diderot and a BSc in Statistical techniques from the University of Paris Descartes. Adnane has recently completed a Postgraduate Certificate in Academic Practice and has become Fellow of Higher Education Academy.
Before joining the University of Suffolk, Adnane worked at the University of Birmingham as a Research Associate in Machine learning within another large interdisciplinary project that combines linguistics, psychology, computer science and statistics to better understand language learning and to improve language teaching. He also served as a Senior Teaching Associate at the University of Bristol for one year, where he taught statistics to undergraduate students.
Adnane’s research work is interdisciplinary in nature, where he has resorted to experimental, statistical, machine learning and natural language processing methods to analyse various types of data. His research interests include:
- Machine learning applications in healthcare, education and cyber security
- Reinforcement learning
- Natural language processing
- Responsible AI
- Cognitively inspired AI
- Statistical methodology
- Computational modelling of human behaviour
Adnane has supervised 25 undergraduate and postgraduate final projects in topics related to AI and data science. He is looking to take on self-funded PhD students with interest in a topic related to the above areas or AI/data science in general. Prospective students should have strong skills in programming and/or mathematics (or be willing to upskill themselves) and a genuine interest in research. Interested students should contact Adnane directly with specific research ideas (please do not send generic emails).
List of publications:
Madamidola, O., Ngobigha, F., and Ez-zizi, A. (2025). Detecting unseen obfuscated malware variants: A lightweight and interpretable machine learning approach. Intelligent Systems with Applications, 25, 200472. https://doi.org/10.1016/j.iswa.2024.200472.
Adeyemi, T.O., Ngobigha, F., and Ez-zizi, A. (2025). Future-proofed intrusion detection for Internet-of-things with machine learning. In 4th IEEE International Conference on AI in Cybersecurity (ICAIC). https://doi.org/10.1109/ICAIC63015.2025.10848845.
Jamil, M., Manthorpe, S., MacDonald, D., and Ez-zizi, A. (2025). An examination of how fielding outcomes in international and franchise T20 and 50-over cricket are associated with bowling performances and field positions. Performance Analysis in Sport, 1-16. https://doi.org/10.1080/24748668.2025.2455276.
Ez-zizi, A., Divjak, D., and Milin, P. (2024). Error-correction mechanisms in language learning: modeling individuals. Language Learning, 74(1), 41-77. https://doi.org/10.1111/lang.12569.
Ez-zizi, A., Farrell, S., Leslie, D., Malhotra, G., and Ludwig, C. (2023). Reinforcement learning under uncertainty: expected versus unexpected uncertainty and state versus reward uncertainty. Computational Brain & Behavior, 6(4), 626-650. https://doi.org/10.1007/s42113-022-00165-y.
Romain, L. *, Ez-zizi, A. *, Milin, P. and Divjak, D. (2022). What makes the past perfect and the future progressive? Experiential coordinates for a learnable, context-based model of tense and aspect. Cognitive Linguistics, 33(2), 251-289. https://doi.org/10.1515/cog-2021-0006. * Joint first author.
Divjak, D., Milin, P., Ez-zizi, A., Józefowski, J., and Adam, C. (2021). What is learned from exposure: an error-driven approach to productivity in language. Language, Cognition and Neuroscience, 36(1), 60-83. https://doi.org/10.1080/23273798.2020.1815813.
Ez-zizi, A., McNamara, J. M., Malhotra, G., and Houston, A. I. (2018). Optimal gut size of small birds and its dependence on environmental and physiological parameters. Journal of Theoretical Biology, 454, 357-366. https://doi.org/10.1016/j.jtbi.2018.05.010.
Ez-zizi, A., Farrell, S., and Leslie, D. (2015). Bayesian Reinforcement Learning in Markovian and non-Markovian Tasks. In IEEE Symposium Series on Computational Intelligence, pp. 579-586, Cape Town, https://doi.org/10.1109/SSCI.2015.91.
At the University of Suffolk, Adnane has led and taught various modules, including:
- L7 Introduction to Artificial Intelligence
- L7 Data Mining and Statistical AI
- L7 SQL and NoSQL Databases
- L7 MSc Dissertation
- L5 Data Mining and Statistics
- L5 NoSQL
- L4 Introduction to Artificial Intelligence and Data Science