Assistant Professor of Geophysics; William H. Hurt Scholar
B.S., University of California, Davis, 2009; M.S., California Polytechnic State University, San Luis Obispo, 2011; Ph.D., University of Southern California, 2016. Caltech, 2019-; Hurt Scholar, 2021-.
Observational studies of earthquakes and faults are limited by our general inability to extract information from seismic datasets at scale. My research program directly addresses these challenges by incorporating technology from artificial intelligence, signal processing, and statistics to gain new insights into these complex phenomena. My work aims to facilitate high-resolution imaging of fault zones; connections between structural properties of faults and the earthquake source process; and a better understanding of the physics governing the evolution of seismicity in space and time.
Ge 264. Machine Learning in Geophysics.9 units (3-0-6); third term, 2021-22.Prerequisites: Ge 118 or equivalent.
An overview of machine learning algorithms and their usage in current geophysical research. Both supervised and unsupervised learning will be covered. Algorithms include deep neural networks, ensemble learning, clustering, and dimensionality reduction. The course will address data requirements, current limitations, and the role of machine learning in the future of geophysics.
Instructor: Ross
Ge 271. Dynamics of Seismicity.9 units (3-0-6); second term, 2021-22.
A comprehensive introduction to seismicity of the Earth. Topics covered: Empirical laws for seismicity; spatial and temporal evolution of earthquake sequences; earthquake location algorithms; seismicity in crustal fault zones, subduction zones, creeping faults, volcanoes, and intraplate regions; earthquake triggering; induced seismicity; tectonic tremor and low-frequency earthquakes. Requires a class project.
Instructor: Ross
Ge 264. Machine Learning in Geophysics.9 units (3-0-6); third term, 2020-21.Prerequisites: Ge 118 or equivalent.
An overview of machine learning algorithms and their usage in current geophysical research. Both supervised and unsupervised learning will be covered. Algorithms include deep neural networks, ensemble learning, clustering, and dimensionality reduction. The course will address data requirements, current limitations, and the role of machine learning in the future of geophysics.
Instructor: Ross
Ge 264. Machine Learning in Geophysics.9 units (3-0-6); third term, 2019-20.Prerequisites: Ge 118 or equivalent.
An overview of machine learning algorithms and their usage in current geophysical research. Both supervised and unsupervised learning will be covered. Algorithms include deep neural networks, ensemble learning, clustering, and dimensionality reduction. The course will address data requirements, current limitations, and the role of machine learning in the future of geophysics.
Instructor: Ross
Research Website