I am a Data Science Initiative Postdoctoral Fellow at Harvard University and a Visiting Assistant Professor at Brown University. I am affiliated with the School of Engineering and Applied Sciences (SEAS/Computer Science) at Harvard, and I am affiliated with the Data Science Initiative (DSI) and Department of Earth, Environmental and Planetary Sciences (DEEPS) at Brown.
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 earned my Ph.D in Computational and Mathematical Engineering at Stanford University, where I was advised by Greg Beroza, Professor of Geophysics. Prior to starting my graduate studies, I worked as a research data scientist in the Biodefense Systems group at MIT-Lincoln Laboratory. I hold a B.Sc. in Applied Mathematics from Brown University and a M.S. in Computational and Mathematical Engineering from Stanford University.
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!
Sept 8, 2020: Brown welcomes talented group of 59 new faculty members
June 25, 2019: My colleague Clara Yoon's paper, "Unsupervised Large-Scale Search for Similar Earthquake Signals," was published in BSSA.
Mar 21, 2019: My review paper, "Machine learning for data-driven discovery in the solid Earth geosciences," was published in Science.
Mar 2, 2018: I passed my thesis defense! Title: "Big Data for Small Earthquakes: Detecting Earthquakes Over a Seismic Network with Waveform Similarity Search"
Sept 2020: SCEC Annual Meeting [virtual]
Aug 2020: Distributed Acoustic Sensing Virtual Workshop and Tutorial, hosted by IRIS [virtual]
July 2020: JpGU-AGU Meeting [virtual]
Oct 2019: National Academies' Committee on Seismology and Geodymanics Fall Meeting in Washington, DC.
Meeting Theme: Beyond the Black Box: The Future of Machine Learning and Data-Intensive Computing in Solid Earth Geosciences
Oct 2019: Workshop on Interpretable Learning in the Physical Sciences at the Institute for Pure and Applied Mathematics, UCLA.
Mar 2019: Machine Learning in Solid Earth Geoscience in Santa Fe, NM
Feb 2019: SSA Policy Briefing in Washington, DC.
Topic: "Machine Learning in Seismology: Using AI to Improve Earthquake Monitoring," with Zach Ross [ abstract ]
Dec 2018: Machine Learning for Geophysical & Geochemical Signals workshop at NeurIPS in Montreal, Canada.
June 2018: 2018 IRIS Workshop , Albuquerque. NM.
Jan 2018: Fundamentals of Data Science ICME Summer Workshops @ Santiago.
More details on the Presentations page