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Empowering Biologists with Deep Learning Approaches for Image Analysis

Award Imaging Scientist

Funding Cycle Cycle 2


Katarzyna Kedziora, PhD

University of North Carolina at Chapel Hill (Bioinformatics & Analytics Research Collaborative)


Dr. Kedziora is a microscopist and image analyst with extensive experience in developing and applying advanced microscopy techniques, designing new image analysis algorithms, automating analysis pipelines, and teaching microscopy on all levels—from third graders to postdocs. She became interested in microscopy during her studies in biophysics at the Jagiellonian University and biomedical engineering at the University of Science and Technology in Krakow, Poland. She completed her PhD at The Netherlands Cancer Institute in Amsterdam, where she developed and applied advanced microscopy techniques to study motility and signaling of individual cancer cells. As an imaging scientist associated with The Bioinformatics and Research Collaborative at the University of North Carolina (UNC) at Chapel Hill, she collaborates with microscopy cores to provide support for image analysis to researchers studying diverse questions in cell biology. She is fascinated with how machine and deep learning has changed the field of computer vision and is passionate about making these state-of-the-art analysis methods accessible to biologists working with microscopy images.

Project Description

Dr. Kedziora provides support for image analysis to the UNC community of biological and biomedical researchers and their network of collaborators. She develops customized and automated data analysis and visualization pipelines and trains scientists to use them in their quantitative microscopy projects. She will collaborate with the information technology and research computing teams to provide the imaging community access to tools and resources to improve data and metadata handling, data quality control and reproducibility, and the sharing of imaging datasets. She will focus on identifying and supporting projects that could benefit most from machine and deep learning approaches. She also plans to use her teaching experience to develop a workshop focused on deep learning in microscopy image analysis.