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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 took a Senior Teaching Associate role 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 covers the following topics:
- Educational data mining and AI
- Cognitively inspired AI
- Reinforcement learning
- Statistical methodology
- Natural language processing
- Computational modelling of human behaviour
Adnane has been supervising undergraduate and MSc students in topics related to AI and data science. He is looking to take on self-funded PhD students (there are opportunities to apply for external grants for bright 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 at least be willing to upskill themselves) and a genuine interest in research. If you are interested, get in touch with him (please do not send generic emails).
Publications:
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. https://doi.org/10.1007/s42113-022-00165-y.
Ez-zizi, A., Divjak, D., and Milin, P. (2023). Error-correction mechanisms in language learning: modeling individuals. Language Learning. https://doi.org/10.1111/lang.12569.
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. Proceedings of IEEE Symposium Series on Computational Intelligence, pp. 579-586, Cape Town, https://doi.org/10.1109/SSCI.2015.91.
At Suffolk, Adnane has taught or will be teaching the following modules:
- L7 Introduction to Artificial Intelligence
- L7 SQL and NoSQL Databases
- L7 MSc Dissertation
- L5 Data Mining & Statistics
- L5 NoSQL
- L4 Introduction to Artificial Intelligence and Data Science