Aleksandr Smirnov, PhD is Chief Technology Officer at Arome Science, where he leads computational development for high-dimensional metabolomics and multi-omics data. He brings more than 12 years of full-stack software and algorithm development experience, with expertise in signal processing, Multivariate Curve Resolution (MCR), machine learning, and high-performance numerical methods.
Dr. Smirnov earned his Ph.D. in Applied Mathematics from Louisiana State University. His contributions to computational metabolomics were recognized in 2021 with the Mark P. Styczynski Early Career Award in Computational Metabolomics. He also played a leading role in developing ADAP-KDB, a spectral knowledgebase for tracking and prioritizing unknown GC-MS spectra within the NIH’s Metabolomics Data Repository.
His technical work focuses on processing raw untargeted mass spectrometry data, deconvoluting complex chemical mixtures, prioritizing unknown compounds, and mapping relationships between foods, metabolites, pathways, and disease-related signatures.
At Arome Science, Dr. Smirnov applies machine learning and computational bioinformatics to help turn complex mass spectrometry outputs into structured, interpretable data. His work supports scalable metabolomics workflows for biotech, pharma, and academic research teams.
