Machine Learning Lead
Dr. Aleksandr Smirnov is the Computational Development Lead at our company, bringing over 12 years of full-stack software and algorithm development experience with a strong focus on Signal Processing in Bioinformatics, Multivariate Curve Resolution, Machine Learning, and numerical methods for solving partial differential equations (PDE). He earned his Ph.D. in Applied Mathematics from Louisiana State University and has accumulated over eight years of research experience, demonstrating exceptional mathematical and programming skills that allow him to solve a wide range of complex problems across various scientific fields.
In his role, Dr. Smirnov is instrumental in driving the development of innovative algorithms and software tools, particularly in the realm of Metabolomics. His expertise includes the processing of raw untargeted Mass Spectrometry data, tracking and prioritizing unknown compounds, and visualizing intricate relationships between foods, compounds, pathways, and diseases. His significant contributions have been recognized with the Mark P. Styczynski Early Career Award in Computational Metabolomics in 2021.
Dr. Smirnov is not only a distinguished researcher but also an effective leader with strong team management and diplomacy skills, consistently fostering win-win results for both customers and the company. His extensive publication record highlights his impact on the field, with key works such as the development of ADAP-KDB: A Spectral Knowledgebase for Tracking and Prioritizing Unknown GC–MS Spectra in the NIH’s Metabolomics Data Repository and other influential contributions published in top scientific journals. As Machine Learning Lead, Dr. Smirnov continues to enhance our company’s capabilities in computational bioinformatics, leveraging his deep expertise and leadership to advance our mission and deliver cutting-edge solutions in Metabolomics and beyond.
