Ewa SzczurekCo-director of the Institute for AI for Health, leading joint labs at Helmholtz Munich and at the Faculty of Mathematics, Informatics and Mechanics of the University of Warsaw, Poland
Our lab focuses on artificial intelligence, in particular probabilistic graphical models and deep generative models, and their applications in computational medicine. Specific applications of our research include oncology, pulmonology and the AI-driven design of antimicrobial peptides, a work for which we were recently awarded the ERC Consolidator grant. Have a glimpse on our example projects:
- Antimicrobial resistance: variational autoencoders to the rescue! We work out specialized deep generative models to produce synthetic antimicrobial peptides that can kill antibiotic-resistant bacteria. Methods: deep learning, generative models.
- Modeling tumor microenvironment. What is the spatial organization of the tumor and its neighborhood? How do they interact? These interesting questions are solved in our lab by analyzing spatial transcriptomics, digitized tumor imaging, or imaging mass spectrometry data. This work is in collaboration with colleagues from the Oncology Bioinformatics department at Merck, Germany, and with an international consortium called IMMUcan. Methods: probabilistic graphical models, deep learning, machine learning models.
- Drawing the genealogical trees of tumors. Which cancer mutations come first? Which events and how drive tumor evolution: single nucleotide, or copy number variants? How does drug resistance appear in cancer? These and many more questions about the family history of tumor cells we find very exciting! Methods: probabilistic graphical models.
A. Geras, S. Darvish Shafighi, K. Domza l, I. Filipiuk, A. Raczkowska, H. Toosi, L. Kaczmarek, L. Koperski, J. Lagergren, D. Nowis, E. Szczurek, Celloscope: a probabilistic model for marker gene-driven cell type deconvolution in spatial transcriptomics data. Genome Biol, 2023, 24, 120
P. Szymczak, M. Mozejko, T. Grzegorzek, M. Bauer, D. Neubauer, M. Michalski, J. Sroka, P. Setny, W. Kamysz, E. Szczurek, Discovering highly potent antimicrobial peptides with deep generative model HydrAMP. NatCommun, 2023, 14, 1453
M. Markowska, T. Caka la, B. Miasojedow, B. Aybey, D. Juraeva, J. Mazur, E. Ross, E. Staub, E. Szczurek, CONET: Copy number event tree model of evolutionary tumor history for single-cell data. Genome Biology, 2022, 23 (1), 1-35
Deep learningGenerative models Probabilistic graphical models
Machine learning models
Master degree in Computer Science from the Uppsala University, Sweden.
Master degree in Computer Science from the University of Warsaw, Poland.
Obtained her postdoctoral fellowship in Switzerland at ETH Zurich.
Obtained her doctoral degree from the Max Planck Institute for Molecular Genetics in Berlin, Germany.
Visiting fellow at the Center for Interdisciplinary Research, Bielefeld, Germany.
Habilitated at the University of Warsaw, Poland.
Visiting associate professor at Northwestern University in the United States.
Honors and Awards
In 2023, Prof. Szczurek received the Consolidator Grant awarded by the European Research Council (ERC) for the Deep Optimised Generation of AntiMicrobial Peptide (DOG-AMP) project.
Prof. Szczurek was a recipient of the distinction, scientific and didactic award from the Rector of the University of Warsaw.
Prof. Szczurek was a recipient of the distinction, scientific and didactic awards from the ETH Zurich and IMPRS fellowships for her postdoctoral and doctoral research.
- Program Committee member for the ISMB and RECOMB-CCB Conferences.
- Member of the ELLIS, pan-European AI network of excellence.
- Member of the Polish Bioinformatics Society.
- Associate editor for Genome Biology.