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Dr. Raviteja Vangara is currently a Postdoctoral Researcher in the Alexandrov lab at the Department of Cellular and Molecular Medicine, UCSD. His current research includes utilizing state-of-art machine learning approaches for mutational scignature analysis for human cancer.

Prior to this, he was a researcher at Theoretical Division, Los Alamos National Laboratory where he worked on various scientific applications that utilize unsupervised machine learning techniques which involve graphical clustering methods, non-negative matrix and tensor factorization techniques for pattern recognition, and latent feature extraction. At LANL, Dr. Vangara was part of a 2021 R&D award winning team, Smart Tensors, that released several open source softwares that utilizes scalable distributed computing methods for high-performance computing scientific applications.

Dr. Vangara received Ph.D. with distinction in 2019 for his work on Coulumbic and non Coulumbic effects of Electric Double Layers and M.S in 2017 from the Petsev lab, Department of Chemical and Biological Engineering, The University of New Mexico.