Rate My Professor Tomasz Woloszynski

His research specializations encompass artificial intelligence, control systems, and tribology, with specific interests in dynamic ensemble selection, classifier competence measures, hydrodynamic lubrication, cavitation in lubrication, surface texturing, osteoarthritis detection and prediction, trabecular bone texture analysis, X-ray image analysis, and wear classification. Key publications highlight his contributions: 'A probabilistic model of classifier competence for dynamic ensemble selection' (Pattern Recognition, 2011), 'Efficient solution to the cavitation problem in hydrodynamic lubrication' (Tribology Letters, 2015), 'A measure of competence based on random classification for dynamic ensemble selection' (Information Fusion, 2012), 'Prediction of progression of radiographic knee osteoarthritis using tibial trabecular bone texture' (Arthritis & Rheumatism, 2012), 'Detection and prediction of osteoarthritis in knee and hand joints based on the X-ray image analysis' (Biosurface and Biotribology, 2016), and 'A signature dissimilarity measure for trabecular bone texture in knee radiographs' (Medical Physics, 2010). These works demonstrate his impact on advancing machine learning applications in engineering and medical diagnostics through precise modeling and analysis techniques. Woloszynski also teaches courses such as Linear Systems and Control, contributing to the education of engineering students.