Evguenia Kopylova, PhD is the Data Analysis Lead at Arome Science, where she leads the development of computational workflows, algorithmic pipelines, and data visualization tools for complex microbiome, metabolome, and multi-omics datasets. She brings deep expertise in software engineering, computational biology, and longitudinal microbiome-metabolome analysis, helping research teams identify meaningful patterns within high-dimensional biological data.
Dr. Kopylova earned her Ph.D. in Computer Science from the University of Lille 1 and the National Institute for Research in Digital Science and Technology (Inria) in France. During her doctoral research, she developed algorithms for short-read sequence alignment using approximate seeds and created SortMeRNA, a widely used open-source bioinformatics tool for filtering ribosomal RNA from next-generation sequencing (NGS) data.
Prior to her doctoral research, she completed her Master’s degree in Computer Science and Bachelor’s degree in Mathematics and Computer Science at McMaster University. Her earlier work in text compression algorithms, pattern repetition modeling, and discrete mathematics provides a strong foundation for designing efficient computational approaches for large biological datasets.
At Arome Science, Dr. Kopylova transforms complex omics data into clear, interpretable outputs for microbiome and metabolomics research. Her work supports the company’s capabilities in data integration, diagnostic analytics, predictive modeling, and longitudinal analysis of host-microbe and microbiome-metabolome interactions.
