Neeraja Vasa
University of Utah
SOURCES Intern
Expertise: Artificial Intelligence, Machine Learning, Data Science, Software Development
About Me
Publications
Neeraja Vasa is a Computer Science student at the University of Utah. She brings a strong foundation in artificial intelligence, computer vision, and data science to earthquake research.
As a SOURCES Intern at the Statewide California Earthquake Center, Neeraja developed machine learning algorithms to automate post-processing of GIS field mapping data from major earthquakes, including the 2014 South Napa and 2019 Ridgecrest events. Her work reduced manual data parsing time while improving accuracy of emergency response datasets. She engineered data preprocessing pipelines to transform geospatial and photographic field survey data into ML-ready formats, and built supervised learning models that automatically convert unstructured field observations into standardized database schemas.
Her research experience extends to computational materials science, where she applies deep learning and graph neural networks to predict mechanical properties from molecular dynamics simulations. Neeraja is passionate about leveraging computational methods to solve real-world problems in earthquake science and disaster response.
During her free time, she likes to travel, hike, and dance.
As a SOURCES Intern at the Statewide California Earthquake Center, Neeraja developed machine learning algorithms to automate post-processing of GIS field mapping data from major earthquakes, including the 2014 South Napa and 2019 Ridgecrest events. Her work reduced manual data parsing time while improving accuracy of emergency response datasets. She engineered data preprocessing pipelines to transform geospatial and photographic field survey data into ML-ready formats, and built supervised learning models that automatically convert unstructured field observations into standardized database schemas.
Her research experience extends to computational materials science, where she applies deep learning and graph neural networks to predict mechanical properties from molecular dynamics simulations. Neeraja is passionate about leveraging computational methods to solve real-world problems in earthquake science and disaster response.
During her free time, she likes to travel, hike, and dance.
