Sergey Voronin has a background in applied and computational mathematics and scientific computing in academic and industry settings. He completed Engineering school, went on to defend his Ph.D. in 2012 in Applied and Computational Mathematics, worked in academia in postdoctoral and instructor capacity beween 2012 and 2017 and then worked for an R&D small business between 2017 and 2022 in a signal processing and controls group, where he actively participated in the SBIR program, followed recently by an R&D role at a tech company. He has experience with data analysis, optimization methods, data compression, imaging applications, machine learning, and high performance computing. Previously, he worked as a Research Associate and Instructor in Mathematics at Tufts and in Applied Math at the University of Colorado in Boulder. Prior to that, he was a CNRS Postdoc at the Globalseis group of Geoazur (University of Nice). He obtained an M.A. and Ph.D. in Applied and Computational Mathematics from PACM. His dissertation was on sparsity constrained regularization techniques for large scale inverse problems. His B.S. from the (Fu Foundation) School of Engineering and Applied Science is in Applied Mathematics (with a Computer Science minor). He is a U.S. Citizen.

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