Tatsiana is a data scientist with current focus on functional genomics (gene expression), but with diverse experience in several fields. Her undergrad studies were in Applied Mathematics and Informatics, specializing in Theory of Probabilities and Mathematical Statistics. She has MSc in Industrial Logistics (production optimization, routing, simulations) and a PhD in Molecular Oncology (applied bioinformatics group).
As bioinformatics scientist she worked in the fields of oncology and neuroscience. Her PhD was focused on analysis of gene expression and clinical data in patients with acute lymphoblastic leukemia during treatment, investigating mechanisms of resistance. As a post-doc she spent few years at Massachusetts General Hospital and Harvard Medical School working with RNA-Seq data from patients and cell lines, where the team was able to uncover a potential molecular mechanism of X-linked Dystonia-Parkinsonism (Aneichyk et al., Cell, 2018). Since 2019 she works as a freelance data scientist working with academic groups and biomedical companies.
Alvaro has a diploma in Engineering Physics and a Master's degree in Medical Imaging Physics. He has worked as Senior Software Engineer in several projects related to last-generation medical imaging devices, image analysis and scientific visualization. Alvaro has vast experience in computer graphics, volume rendering, cloud computing and software architecture. He has participated as a core developer in several high impact open-source scientific libraries such as VTK, ITK and ParaView.
He has also been part of various successful startup companies and currently works as a Principal Engineer at SmartReporting GmbH.
Matthias is a biochemist turned bioinformatician turned data visualization specialist. He started off his research career at the Technische Universität München with PhD studies in proteomics and bioinformatics. For his postdoc he moved to Karolinska Institutet in Stockholm, Sweden. There he has been dedicated to cancer omics analyses and bioinformatic methods development with a strong focus on mass spectrometry based proteomics.
During his journey from wet lab to dry lab, he got passionate about data visualization. In fact, it’s often the visuals which create knowledge and unravel the secrets of biology. However, in order to build impactful data visualizations one needs to cope with the whole process from data generation and data analysis to data design. This is what makes Matthias love data visualization.
Baiba's background includes both experience in experimental biology as well as bioinformatics and computational biology. She started as a biology major, however, during her Master's studies Baiba developed a great interest in bioinformatics and engaged in a database development project, involving the prediction of protein-protein interactions based on correlated mutations in their respective domains, currently part of the DIMA database.
During her PhD, Baiba worked between in silico analysis and wet lab experiments, investigating the regulatory networks of hematopoietic stem cells and their niche. She generated a high-throughput transcriptomics data set, followed by computational analysis involving candidate gene prioritization and biological hypothesis generation, which she then validated experimentally both in vitro and in vivo.
For the last 4-5 years Baiba mainly focused on integrative systems-level investigations linking genetic and transcriptomic data, both in mice and human in the context of coronary artery disease. Most recently she has also been involved in numerous projects related to microbiome (16S rRNA amplicon sequencing) analyses and machine learning approaches.
Catherine is a highly motivated bioinformatician with more than twelve years’ experience of working in UK and German research institutions, honing programming, analytical and statistical skills. She studied Biological Sciences at the University of Birmingham, followed by a Masters in Bioinformatics at
the University of Manchester. Her PhD work was carried out in Professor Sir Tom Blundell’s Biocomputing group at the University of Cambridge, where she sought to identify the molecular mechanisms underlying protein malfunction and disease by developing a program and web server for predicting protein damaging mutations. Since then she has worked in a number of world-class institutes in Germany including the Max Planck Institute for Molecular Genetics and the Max Delbruck Center for Molecular Medicine, focusing on oncology and cardiovascular research respectively.
Catherine has a comprehensive understanding of biological data encompassing structural bioinformatics, genomics, transcriptomics and systems biology. She has a strong publication record with ten first-author and more than 20 co-author scientific papers in international, peer-reviewed journals including Cell, PNAS, Nature Genetics and Nucleic Acids Research. Since July 2020, Catherine works as a freelance bioinformatics consultant.
Gregor Sturm is a PhD student at the Institute of Computational Biology at the Medical University of Innsbruck. His current research focuses on leveraging single-cell multi-omics data to study cell-to-cell communication in cancer and chronic inflammatory diseases.
Before, he obtained a Bachelor's and Master's degree from the joint Bioinformatics programme of University of Munich and Technical University of Munich. He has more than 10 years of coding experience, is a seasoned software developer and data scientist and is strongly committed to fully reproducible research.
Dieudonné is a Data Scientist with a history of research and publication in top journals. He has performed large-scale data analysis using various machine learning methods for answering biological questions in collaboration with other scientists.
He has a BSc in Applied Physics, MSc in Information Technology and Physics, and a PhD in Bioinformatics.
During his PhD studies, he worked in the field of Systems Immunology. His PhD involved different techniques to analyze various datasets (omics and single-cell data) to reveal new insights into mechanisms of human immunity of relevance to environmental exposures. For example, he assessed the repertoire of maternal antibodies in newborn children and found that it targets about 5-10 different viruses (C. Pou and D. Nkulikiyimfura et al, Nature Medicine, 2019). Dieudonné is interested in designing experiments, A/B testing, reproducibility, and building explainable models to reveal new insights of relevance to a question at hand.