Agnieszka Niemczynowicz is a senior researcher at the Faculty of Mathematics and Computer Science, University of Warmia and Mazury in Olsztyn, Poland. She earned her Ph.D. in physics from the Faculty of Physics and Applied Informatics, University of Łódź, Poland, in 2014. Her scientific journey commenced in the field of solid-state physics; however, apart mathematical modelling in physics, her current research is centered on employing computational and mathematical methods to extract insights from analytical data. This includes utilizing techniques such as multivariate statistics, chemometric methods, and deep learning. She applies these mathematical tools to design, develop, and deploy statistical and machine learning models across various disciplines including engineering, biology, medicine, molecular spectroscopy, and management. Additionally, she conducted experimental studies utilizing FTIR (Fourier Transform Infrared Spectroscopy) and fluorescence spectroscopy to analyze a diverse range of materials, including biomaterials, to evaluate their quality and characteristics, aiding in defining their physicochemical properties.
She has authored approximately 50 articles, most of which are published in internationally recognized journals. Notably, she, along with scientists from IUMPA UPV, was awarded the 2022 Doak Award (Best Paper Award) in the Journal of Sound and Vibration (Elsevier). Furthermore, she has contributed as an editor for one monograph and co-supervised a Ph.D. student. Her research has been supported by funding from the Polish National Agency for Academic Exchange, the Foundation for Polish Science, as well as various regional and local funding sources. Recently, he has been leading an international scientific grant focused on the mathematical aspects of hypercomplex neural networks. She has undertaken numerous scientific visits, including trips to China, Portugal, Italy, and the Czech Republic.
The visit has been supported by the Polish National Agency for Academic Exchange Strategic Partnership Programme under Grant No. BPI/PST/2021/1/00031