Dr. Raviteja Vangara, Ph.D. is a Postdoctoral Researcher specializing in the advanced field of machine learning applications for scientific research. He currently holds a position at the esteemed Alexandrov lab within the Department of Cellular and Molecular Medicine at the University of California, San Diego (UCSD). Here, his research focuses on leveraging state-of-the-art machine learning methodologies to conduct insightful mutational signature analyses related to human cancer.

Before joining UCSD, Dr. Vangara contributed his extensive knowledge and innovative approach to research at the Theoretical Division of Los Alamos National Laboratory. His work at LANL covered a broad spectrum of scientific applications, predominantly utilizing unsupervised machine learning techniques such as graphical clustering methods, and non-negative matrix and tensor factorization techniques for advanced pattern recognition and latent feature extraction. During his tenure, Dr. Vangara played a crucial role in the Smart Tensors team, a 2021 R&D award winning collective responsible for releasing several groundbreaking open-source software tools optimized for high-performance computing scientific applications, focusing on scalable distributed computing methods.

Dr. Vangara has been recognized for his academic excellence, earning his Ph.D. with distinction in 2019 for his significant work on Coulombic and non-Coulombic effects of Electric Double Layers. He obtained his M.S. in 2017 from the University of New Mexico’s Petsev lab, Department of Chemical and Biological Engineering, The University of New Mexico.

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