Aravkin works on theoretical and practical problems connected to data science, including theoretical work in convex and variational analysis, robust statistical modeling and algorithm design with applications to high-dimensional inference, machine learning and inverse problems. He was a postdoctoral fellow from 2010 to 2012 at the University of British Columbia, where he worked on robust approaches for seismic inverse problems. He then served as a research staff member at the IBM Thomas J. Watson Research Center and as an adjunct professor in computer science and industrial engineering and operations research at Columbia University. He joined the UW in 2015. Aravkin earning his Ph.D. in mathematics (optimization) at the University of Washington.