Denis Vrazhnov is a researcher at the V.E. Zuev Institute of Atmospheric Optics at the Siberian Branch of the Russian Academy of Science (IAO SB RAS), and a researcher at the Laboratory of Biophotonics, Tomsk State University. He graduated from Tomsk State University in 2004 with a Master’s degree in Computational Mathematics, and completed a PhD between 2004–2009 at Tomsk State University.
Since 2009, he has been a leading maths engineer at Tomsklabs LLC, a senior researcher at Hypercognition LLC, a researcher at the Laboratory of Biophotonics, TSU, and a researcher at the Laboratory for Remote Sensing of the Environment, IOA SB RAS, Tomsk.
He is a mathematician with more than 10 years of experience in machine learning, data mining, and computer vision. His interests include the development of support and decision systems for medical purposes (data analysis of 1D sound signals for fetal cardio monitoring, the R&D of broncho-pulmonary disease biomarkers including data preprocessing, outlier detection, feature dimensionality reduction, and classification), the R&D of machine learning methods for medical image enhancement (OCT, multi-photon microscopy), and Raman and THz spectra analysis.
Denis Vrazhnov is a researcher at the V.E. Zuev Institute of Atmospheric Optics at the Siberian Branch of the Russian Academy of Science (IAO SB RAS), and a researcher at the Laboratory of Biophotonics, Tomsk State University. He graduated from Tomsk State University in 2004 with a Master’s degree in Computational Mathematics, and completed a PhD between 2004–2009 at Tomsk State University.
Since 2009, he has been a leading maths engineer at Tomsklabs LLC, a senior researcher at Hypercognition LLC, a researcher at the Laboratory of Biophotonics, TSU, and a researcher at the Laboratory for Remote Sensing of the Environment, IOA SB RAS, Tomsk.
He is a mathematician with more than 10 years of experience in machine learning, data mining, and computer vision. His interests include the development of support and decision systems for medical purposes (data analysis of 1D sound signals for fetal cardio monitoring, the R&D of broncho-pulmonary disease biomarkers including data preprocessing, outlier detection, feature dimensionality reduction, and classification), the R&D of machine learning methods for medical image enhancement (OCT, multi-photon microscopy), and Raman and THz spectra analysis.