My research interest is in scientific machine learning (SciML). My past work has focused on machine learning for pattern recognition and discovery in large, noisy sensor data sets, with applications in earthquake seismology and biodefense.
I did my postdoctoral training at Harvard University as a Data Science Initiative Postdoctoral Fellow in Computer Science. I earned my Ph.D (and M.S.) in Computational and Mathematical Engineering at Stanford University, where I was advised by Greg Beroza, Professor of Geophysics. I am an alumna of Brown University, where I earned a B.Sc. in Applied Mathematics. I have also worked as a data scientist at MIT-Lincoln Laboratory.
I am also passionate about data science education and diversity and inclusion in data science. I have taught a number of courses and workshops that aim to make data science accessible to students and professionals from a range of disciplinary backgrounds.
Thanks for visiting!
Feb 25, 2021: Quoted in Eos article A Promising Forecast for Predictive Science.
Jan 1, 2021: I'm starting a new position as an Assistant Professor of Data Science and Earth, Environmental & Planetary Sciences at Brown University!
Sept 8, 2020: News from Brown: Brown welcomes talented group of 59 new faculty members
Dec 2021: AGU Fall Meeting (virtual) talk: Explainable AI (XAI) for Seismology: An interpretable convolutional neural network for earthquake detection [a recording of this talk is available by request]
April 2022: ML tutorial at SSA Annual Meeting in Bellevue, WA
Nov 2021: Keynote talk at Brazilian Seismology Symposium, part of the International Congress of the Brazilian Geophysical Society [virtual]
Oct/Nov 2021: Seminars at MIT [in-person!], University of Utah, and Cornell University.
More details on the Presentations page