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Lech Szymanski image

Owheo Building, Room 249
Tel +64 3 479 5691

My research interests are machine learning, deep representation and connectionist models. Our state of the art computation models can learn from examples, but not without the heavy involvement of an expert user, making critical decisions about system architecture, parameters, and appropriate representation. My ultimate objective is to make machine learning into a tool that is easier to use by an average user. However, in order for that to happen, we need to develop truly autonomous machine learning algorithms, capable of forming an appropriate model for the task at hand, completely on their own.

Before my PhD, which I completed in 2012, I worked as a software engineer for a wireless telecommunications company in Ottawa, Canada. My background is in computer and electrical engineering, with a focus on embedded programming and digital signal processing. My interest in artificial neural network models dates back to summer employment, while still an undergraduate student, developing programs for data analysis in a neurobiology laboratory at the National Research Council Canada. Since then I have worked on several aspects of modelling and machine learning, including speech recognition, classification, learning theory and object recognition from images.

For more information and selected publications, see this page


van der Vliet, W., Lal Khakpoor, F., Tetereva, A., Szymanski, L., & Pat, N. (2023). Bypassing parcellations when building predictive models for capturing cognitive abilities from task functional MRI. Proceedings of the Psycolloquy Symposium. (pp. 19). Dunedin, New Zealand: Department of Psychology, University of Otago. Retrieved from

Xu, H., Szymanski, L., & McCane, B. (2023). VASE: Variational assorted surprise exploration for reinforcement learning. IEEE Transactions on Neural Networks & Learning Systems, 34(3), 1243-1252. doi: 10.1109/TNNLS.2021.3105140

Szymanski, L., McCane, B., & Atkinson, C. (2022). Conceptual complexity of neural networks. Neurocomputing, 469, 52-64. doi: 10.1016/j.neucom.2021.10.063

Szymanski, L., & Lee, M. (2021). Coarse facial feature detection in sheep. Proceedings of the 36th International Conference on Image and Vision Computing New Zealand (IVCNZ). IEEE. doi: 10.1109/IVCNZ54163.2021.9653248

Atkinson, C., McCane, B., Szymanski, L., & Robins, A. (2021). Pseudo-rehearsal: Achieving deep reinforcement learning without catastrophic forgetting. Neurocomputing, 428, 291-307. doi: 10.1016/j.neucom.2020.11.050

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