Professor of Artificial Intelligence, University of Bath, UK
Deputy Head of Department, Department of Computer Science, UKRI CDT in Accountable, Responsible and Transparent AI, Centre for Mathematics and Algorithms for Data (MAD)
Vita
Özgür Şimşek is the Deputy Head of the Department of Computer Science, where she leads the Artificial Intelligence Research Group. From 2018 to 2020, she served as Deputy Director at IMI. Before joining the University of Bath in 2017, Özgür was a research scientist at the Center for Adaptive Behaviour and Cognition at the Max Planck Institute for Human Behaviour in Berlin, Germany. She received her PhD in Computer Science in 2008 from the University of Massachusetts Amherst.
Özgür’s research spans a broad range of areas in machine learning, including reinforcement learning, supervised learning, learning from small data sets, and bounded rationality. As part of her IMI fellowship, Özgür will develop applications of reinforcement learning in healthcare with IMI fellows Dr Will Tillett and Dr Raj Sengupta.
Research Interests
- Machine learning
- Artificial intelligence
- Reinforcement learning
- Bounded rationality
- Network science
Selected publications
Sanchez-Bornot, J., Sotero, R. C., Kelso, J. S., Şimşek, Ö., & Coyle, D. (2024). Solving large-scale MEG/EEG source localisation and functional connectivity problems simultaneously using state-space models. NeuroImage, 285, 120458.
Saunders, J., Prenevost, L., Şimşek, Ö., Hunter, A., & Li, W. (2023). Resource-Constrained Station-Keeping for Helium Balloons using Reinforcement Learning. arXiv preprint arXiv:2303.01173.
Katsikopoulos, K. V., Şimşek, Ö., Buckmann, M., & Gigerenzer, G. (2022). Transparent modeling of influenza incidence: Big data or a single data point from psychological theory?. International Journal of Forecasting, 38(2), 613-619.
Lichtenberg, J. M. , & Şimşek, Ö. (2019). Regularization in directable environments with application to Tetris. In Proceedings of the Thirty-Sixth International Conference on Machine Learning (ICML).
Şimşek, Ö., Algorta, S., & Kothiyal, A. (2016). Why Most Decisions Are Easy in Tetris—And Perhaps in Other Sequential Decision Problems, As Well. In Proceedings of the Thirty-Third International Conference on Machine Learning (ICML).