I’m a group leader of Computational Genomics and Transcriptomics group. My main reserach is the BMBF funded Sys_CARE project about alternative splicing in cardiac and renal diseases. I graduated from Lomonosov Moscow State University and did PhD in Kharkevich Institute in Moscow at professor Gelfand lab. There I studied the evolution of transcriptional regulatory networks in bacteria. During my PhD I shortly stayed at Stowers Institute for Medical Research, Kansas-City, and in Okinawa Institute iof Science and Technology where I went on working on regulation of bacterial metabolic pathways. As a research scientist, I worked in Skolkovo Institute of Science and Technology, Moscow on 3D chromatin structure analysis.

I'm a PhD candidate at Cosy.Bio. My project is the study of alternative splicing in heart and kidney using machine learning to analyze multi-omics data. I studied cellular biology, specializing in the mitochondria, during my Master's at LMU. For my Master's thesis, I explored splicing within ribosomal protein genes, in the context of the biogenesis of mitochondria.

Since April 2020 I am a Ph.D. student at CoSy.Bio within the Sys_Care project. Before, I have been a biology master student from LMU Munich, where I have experience in working with sequencing data analysis. In my master thesis, I work at the Chair of Experimental Bioinformatics on time course network enrichment analysis on X-irradiated human fibroblast using TiCoNE and machine learning.

I am a Ph.D. student at The University of Hamburg and Technical University of Munich. I developed the software DIGGER, NEASE, and Deep Splicing Code. My work mainly focuses on transcriptomics data analysis, with emphasis on the role of alternative splicing.

As a student of bioinformatics at the University of Hamburg, I focused on the intersection of data science and machine learning within the field of bioinformatics. I hold a Bachelor's degree in Molecular Life Sciences and am currently pursuing my Master's degree. My Master's thesis delves into the optimization of pipelines to identify isoforms in single-cell RNA sequencing data, a crucial aspect of understanding gene expression at the cellular level. I am excited to see the potential discoveries and advancements that my research may uncover.