Rate My Professor Alina Bialkowski

Dr Bialkowski is recognised as a computer vision and machine learning researcher who develops interpretable machine learning models to boost the performance and transparency of artificial intelligence decision-making. Her academic interests include quantifying and extracting actionable knowledge from complex data and imparting human-understandable explanations to AI models through feature visualisation and attribution methods. These approaches have been applied across multidisciplinary domains, including medical imaging—such as detecting strokes in the brain via electromagnetic imaging—modelling human attention in driving, intelligent transport systems, intelligent surveillance, and sports analytics. The impact of her work is demonstrated by over 1600 citations and an h-index of 20 on Google Scholar. She earned a best paper prize at the 2017 Winter Conference on Applications of Computer Vision (WACV). Bialkowski has contributed to six international patents filed with Disney Research, Toyota Motor Europe, University College London, and the University of Queensland. Notable publications encompass 'Medical microwave imaging using physics-guided deep learning part 1: the forward solver' (IEEE Transactions on Medical Imaging, 2025), 'Medical microwave imaging using physics-guided deep learning part 2: the inverse solver' (IEEE Transactions on Medical Imaging, 2026), 'Conditional synthetic signal generation for microwave head imaging using diffusion models' (IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology, 2025), and 'Representing team behaviours from noisy data using player role' (book chapter, 2014). She has obtained funding through ARC Discovery Projects, including Next-Generation Solvers for Complex Microwave Engineering Problems (2024-2027), and MRFF NCRI grants such as BioMotionAi (2024-2029). Additionally, she supervises PhD students on topics like robust deep learning for microwave imaging and knowledge distillation for computer vision models.