dr hab. inż. Radosław Wajman

Dr. Radosław Wajman, DSc, Eng., Associate Professor (ORCID: 0000-0002-6372-5960), is a researcher at the Institute of Applied Computer Science at Lodz University of Technology. From the beginning, his scientific development has been related to technical computer science, data processing, artificial intelligence, process tomography, and the diagnosis of complex physical and biological processes. He received the degree of Doctor habilitatus in technical sciences in the discipline of computer science in 2020. He is a co-author of more than 100 publications in JCR-indexed journals and chapters in English-language monographs. His Hirsch index in the Scopus database is 12.

He has experience in leading and carrying out national and international R&D projects. He led an NCN project on intelligent control of two-phase flow using capacitance tomography diagnostics. He was a principal investigator or key researcher in the DENIDIA, TOMOCON, PLATOM, UROTOM, Assist, CadrioSense, and LIFE Mercury-Free projects. These projects covered industrial tomography, measurement systems, automation, data analysis, prediction of cardiovascular events using artificial intelligence and machine learning, and the reduction of mercury pollution in the environment. In his R&D work, he combines computer science skills with practical engineering experience. His competences include data acquisition from many sources, measurement synchronization, signal filtering, data and feature fusion, anomaly detection, and the design of decision algorithms. He also works on measurement systems, hardware and software integration, data visualization, prototype testing, and system validation in real conditions.

The main area of his research has become computer-based methods for non-invasive diagnosis and control of gas–liquid two-phase flow processes. In this area, he developed algorithms for 3D image reconstruction, sensitivity matrix calculation, tomographic sensor design, flow structure identification, and fuzzy control of the process. These results have both scientific and practical value. They have been used in diagnostic systems, research software, industrial solutions, and a European patent concerning the measurement of component fractions in multiphase flow.

The next stage of his scientific development was the transfer of experience from process tomography, modelling, and artificial intelligence to medical diagnostics. His current research focuses on intelligent methods for non-invasive monitoring and diagnosis of functional disorders of the lower urinary tract in children. This work combines ultrasound tomography, impedance tomography, physiological modelling, and machine learning. The aim is to analyse changes in bladder volume and morphology over time. The goal is to develop computer-based tools that support physicians in assessing bladder function and classifying voiding disorders. These tools are also intended to reduce the need for painful and psychologically stressful urodynamic examinations in children.

In addition to his research work, he has many years of experience as an academic teacher. His teaching achievements include educating students in applied computer science, data analysis, and artificial intelligence. They also include supervision of doctoral and diploma theses, as well as support for the development of young researchers. This was confirmed by the Faculty Student Council, which twice awarded him the prize for the best academic teacher, in 2024 and 2026.

His current research directions also include the analysis of biomedical time-series data, prediction of physiological parameters, and the development of solutions for telecare and systems supporting people who require care. In his scientific activity, Radosław Wajman combines computer science, artificial intelligence, biomedical modelling, and practical clinical needs. He develops technologies that aim to improve the safety, comfort, and effectiveness of patient diagnostics.

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