Sergey Voronin has a background in applied mathematics and scientific computing in academic and industry settings. He defended his Ph.D. in 2012 in Applied and Computational Mathematics, worked in academia in postdoctoral capacity beween 2012 and 2017 and then worked for an R&D small business between 2017 and 2022, where he actively participated in the SBIR program. He has experience with data analysis, optimization methods, data compression, imaging applications, machine learning, and high performance computing.

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