Department of Data Science and Engineering Faculty of Automatic Control, Electronics and Computer Science

Science discipline

Information and Communication Technology

Biographical note

Information on employment in research institutes

• 10.2014-present – Assistant professor at the Department of Data Science and Engineering, Silesian University of Technology in Gliwice
• 10.2020-present – Adjunct Assistant Professor at Yale Breast Medical Oncology Group, Yale University, New Haven, CT, USA
• 12.2017-09.2020 – Research Associate at Yale Breast Medical Oncology Group, Yale University, New Haven, CT, USA
• 10.2013-09.2014 – Assistant at the Department of Data Science and Engineering, Silesian University of Technology in Gliwice
• 05.2013-12.2013 – Junior specialist at the Department of Thoracic Surgery, Medical University of Gdansk, Gdansk
• 05.2013-08.2013 – Research internship at the Department of Molecular Biology, The Wenner -Gren Institute, Stockholm University, Stockholm, Sweden
• 04.2009-05.2009 – Research internship at the Faculty of Mathematical Sciences, Chalmers University of Technology, Gothenburg, Sweden
  
  

Diplomas, degrees conferred in specific areas of science

• 09.2013 – Doctorate with honors in the field of biocybernetics and biomedical engineering, Silesian University of Technology. Thesis topic: “Processing and classification of data obtained with the use of high throughput molecular biology techniques “, Supervisor: prof. Joanna Polanska
• 09.2008 – Master’s degree in Automatic Control and Robotics, specialization: Information Processing for Control, Silesian University of Technology. Thesis topic: ” Peak alignment in protein spectra”, Supervisor: prof. Joanna Polanska

Scientific interests

• Bioinformatics, biostatistics, and exploratory data analysis
• Pre-processing and analysis of oligonucleotide DNA microarrays, qRT-PCR arrays, and sequencing data (RNA-seq, scRNA-seq, ATAC-seq, WGS)
• Pre-processing and analysis of medical imaging data, including stained histopathological images, X-rays, and computed tomography images
• Application of Gaussian mixtures for modeling 1D spectra (MALDI-ToF, NMR) and 2D images (2DGE, H&E)
• Development of statistical, machine learning, and deep learning models for supporting cancer research