Leader: Dariusz Plewczyński
The research interests of the Laboratory of Bioinformatics and Computational Genomics are focused on the application of bioinformatics and computational modelling methods in functional and structural genomics. We make use of the vast wealth of data produced by high-throughput genomics projects, such as 4DNucleome, 1000 Genomes Project, UK BioBank, Simons Genome Diversity Project, Earth BioGenome Project, ENCODE, and many others. The major tools that are used in this interdisciplinary research endeavor include statistical data analysis (GWAS studies, clustering, machine learning), genomic variation analysis using diverse experiments (karyotyping, confocal microscopy, aCGH microarrays, short and long reads next generation sequencing), bioinformatics (protein sequence analysis, protein structure prediction), and finally biophysics (polymer theory and simulations) and various genomics data sources (DNA sequence, epigenomics, chromatin organization, topologically associating domains, three dimensional structure of chromatin identified by 3C technologies).
The Laboratory of Bioinformatics and Computational Genomics was founded by and is led by Prof. Dariusz Plewczyński, a researcher from the Department of Information Processing Systems. The laboratory is an independent unit of the Faculty of Mathematics and Information Science of the Warsaw University of Technology. Most of the members of the Laboratory are also employees of the Department of Information Processing Systems.
Leader: Maciej Grzenda
The vision of the Stream Mining and Urban Data Systems Group is to combine research on machine learning methods for data streams with building software platforms, systems, and applications, combining storage and analytical platforms for urban data processing. By applying novel stream mining methods, we aim to address the dynamics of urban systems. To address challenges faced by inter alia urban environments, we develop novel data preprocessing and machine learning methods. By applying Data Science methods to evolving urban systems, we aim to contribute to the social good.
Leader: Paweł Rzążewski
We study the interplay between the structure of graphs and the algorithms that can efficiently solve problems on them. We seek to identify structural properties — like planarity, treewidth, or sparsity — that make otherwise hard problems tractable. We aim to bridge combinatorial insights and computational methods, aiming to understand both what graphs look like and how we can compute on them effectively.