I am PhD student at Modelling and Machine Learning Group (Wrocław University of Technology, Poland). My major research interests lie in the field of theoretical and applied machine learning. Most recently my work is focused on improvement of state-of-the-art object detection methods by utilizing novel deep models. Additionally, I greatly appreciate all opportunities that allow me to apply my machine learning skills in interdisciplinary projects. So far, I have spent one year working in the field of systems biology and bioimaging (Max Planck Institute of Molecular Cell Biology and Generics, Development of image processing methods for confocal microscopy), as well as two years in the field of structural biology (in collaboration with ETH Zurich, Automated analysis of Nuclear Magnetic Resonance spectra of proteins for structural studies).

You can download my CV and visit my LinkedIn profile.




Computer-vision based analysis of protein NMR spectra 

GlycoNMRSearch: chemical shift-based identification of monosaccharide spin-systems 

Image analysis for confocal microscopy in the context of 3D liver tissue architecture studies

Other projects

1 X 2014 - ...

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1 II 2016 - ...

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15 VII 2013 - 30 VII 2014

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A Benchmark for Automated Peak Picking of Protein NMR Spectra, IEEE CIBCB 2015, August 2015
In drug discovery field, one of the major used techniques is Nuclear Magnetic Resonance spectroscopy (NMR). To date, most of the steps in NMR analysis have been automated. The remaining step, peak picking, is still performed manually, what is extremely time-consuming and constitutes limiting factor in NMR-based drug discovery. Peak picking is a process of recognition of 2D/3D patterns in NMR image. To help automation of peak picking we have prepared a considerable benchmark, which enables testing various approaches. We also propose a baseline framework based on object detection scheme, which is well studied in the field of computer vision, and addresses automation of peak picking. Finally, we present the results of empirical studies, where a few different methods were compared, and show that peak pickers based on our framework significantly outperform the remaining methods for complex spectra and behave comparably for average ones.

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Protein NMR Peak Picking Software, August 2015
Peak Picker is standalone software, written in Java, which automate peak picking of 2D/3D protein NMR spectra. Peak Picker takes spectral data in the *.ucsf format (Sparky file) and performs analysis using computer vision based approach described in (Klukowski et al., Bioinformatics 2015).

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Computer Vision – Based Automated Peak Picking Applied to Protein NMR Spectra, Bioinformatics, May 2015
We present a novel approach using computer vision (CV) methodology which could be better adapted to the problem of peak recognition. After suitable “training” we successfully applied the CV algorithm to spectra of medium sized soluble proteins up to molecular weights of 26 kDa and to a 130 kDa complex of a tetrameric membrane protein in detergent micelles. Our CV approach outperforms commonly used programs. With suitable training data sets the application of presented method can be extended to automated peak picking in multidimensional spectra of nucleic acids or carbohydrates and adapted to solid state NMR spectra.

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Telephone: +48 71 320 4453
Building C-3, room 121
Department of Computer Science
Wrocław University of Technology
Wybrzeże Stanisława Wyspiańskiego 27
50-370 Wrocław, Poland