Nikolay is a bioinformatician, computational biologist, and data scientist working at the intersection of biology, medicine, statistics, and artificial intelligence. His research focuses on applying mathematical statistics, machine learning, and deep learning methods to complex biological and biomedical datasets, including genomics, transcriptomics, microbiome research, single-cell data, metagenomics, and multi-omics integration.
Nikolay has a PhD in theoretical physics from 2007, he transition to the Life Sciences in 2011. He currently leads the Metabolic Research Group (MRG) within the TARGETWISE project at the National Institute of Research and Innovation in Latvia, and having a teaching position at Lund University, Sweden, he has previously held research positions at the Danish Technical University, University of North Carolina, Lund University and the National Bioinformatics Infrastructure Sweden (NBIS/SciLifeLab).
Nikolay has more than 20 years of teaching experience and is widely recognised for his ability to communicate advanced statistical and computational methods to researchers from diverse scientific backgrounds. His expertise spans both frequentist and Bayesian statistics, machine learning, dimensionality reduction, clustering, bioinformatics, and scientific programming in R and Python. He has delivered numerous international workshops, summer schools, and professional training courses in computational biology, genomics, and AI-driven biomedical research.
Education & Career
- PhD in Theoretical Physics (2007)
• Transitioned from theoretical physics to bioinformatics and computational biology in 2011
• Group Leader (PI), Metabolic Research Group, TARGETWISE Project, Latvia
• Former researcher and bioinformatician at Lund University and NBIS/SciLifeLab, Sweden
• Author of more than 60 peer-reviewed scientific publications with extensive international collaborations in computational biology and biomedical research
Research Focus
Nikolay’s work centres on extracting biological insight from large-scale, high-dimensional datasets using advanced statistical and machine learning approaches. His research interests include:
- Machine learning and deep learning for biomedical and omics data
• Multi-omics integration and systems biology
• Single-cell transcriptomics and dimensionality reduction methods
• Population genomics and evolutionary biology
• Microbiome, environmental DNA, and ancient DNA analysis
• Statistical modelling and Bayesian approaches for complex biological systems
• AI applications in precision medicine and drug discovery
Current Projects
- Development of machine learning methods for multi-omics data integration and drug discovery in metabolic diseases
• AI-driven approaches for genomics and computational biology
• Statistical and computational methods for ancient and environmental DNA research
• Machine learning analysis workflows for single-cell and population genomics datasets
• Research on metabolic diseases through integrative bioinformatics and systems biology approaches
Professional Consultancy
Nikolay provides expert consultancy in biological and biomedical data analysis, supporting academic researchers, healthcare scientists, and industry teams. His consultancy expertise includes:
- Bioinformatics and computational biology
• Medical genomics and precision medicine
• Single-cell and multi-omics data analysis
• Metagenomics and population genomics
• Frequentist and Bayesian statistical modelling
• Machine learning and deep learning applications
• Scientific programming in R, Python, Bash, and C++
• Study design, data analysis pipelines, and reproducible research workflows
Teaching & Skills
- More than 20 years of teaching experience in statistics, machine learning, and computational biology
• Teaches topics including machine learning, deep learning, Bayesian statistics, dimensionality reduction, clustering, single-cell analysis, genomics, and bioinformatics
• Instructor for international courses and workshops through organisations including Instats, Physalia, NBIS SciLifeLab, TARGETWISE, and RaukR
• Strong advocate for rigorous statistical thinking, reproducible research, and accessible scientific education
• Experienced in translating advanced computational methods into practical tools for life scientists and healthcare researchers
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