Artur Yakimovich | Young Investigator Group Leader
Helmholtz-Zentrum Dresden-Rossendorf
Artur Yakimovich obtained his PhD from the University of Zurich, where he trained in two PhD programmes simultaneously: Molecular Life Science and Interdisciplinary PhD program. During that time Artur worked on developing novel machine learning (ML) and computational modelling approaches to understand the biological mechanisms of virus spread between cells. Then, after a short postdoc at the Zurich University Hospital, Artur Yakimovich joined the MRC Laboratory for Molecular Cell Biology at the University College London, where he worked on Deep Learning (DL) approaches to analysing host-pathogen interactions in microscopy images. Next, Dr Yakimovich joined Roche as a Data Scientist, where he worked on the application of DL to biomedical image analysis and literature search. Currently, Artur Yakimovich is a group leader at the Center for Advanced Systems Understanding at the Helmholtz-Zentrum Dresden-Rossendorf and an Associate Professor of Computer Science at the University of Wroclaw. His group is working on ML algorithms for Infection and Disease.
Appearances:
Festival of Biologics Day 2 @ 16:30
Generative AI for Inverse Problems in Biomedical Computational Microscopy
Advanced microscopy techniques, including three-dimensional, super-resolution and quantitative phase microscopy, remain at the forefront of biomedical discovery. These methods enable researchers to visualise complex molecular processes and interactions at the level of single molecules or molecular complexes, capturing yet unseen information and pushing the boundaries of our understanding of health and disease. These innovations have been made possible, among others, through rapid progress in biophotonics, as well as computational processing and analysis of image-based data. However, advanced biophotonics comes at the cost of complex equipment, as well as difficult and lengthy data acquisition and necessitates highly-trained personnel. We demonstrate in several works that this hurdle can be addressed using generative and discriminative AI algorithms by formulating the conversion from conventional microscopy modalities like widefield, to advanced like super-resolution, as a set of inverse problems. We show that incorporating nuance of the data domain into the algorithm design, as well as leveraging synthetic data pre-training, leads to better performance in these algorithms. Among other examples, we demonstrate how Generative AI algorithms can be utilised for Virtual Staining of virus infection in cultured cells, allowing for quasi-label-free detection of infected cells.
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