Welcome to Karianne's Website!
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, and my B.Sc. in Applied Mathematics from Brown University. 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!
December 2023: The SciML Group's most recent paper, A Variational LSTM Emulator of Sea Level Contribution from the Antarctic Ice Sheet, has been published in the Journal of Advances in Modeling Earth Systems (JAMES).
August 2023: Review paper on Scientific discovery in the age of artificial intelligence, co-authored by SciML group Ph.D. student Peter Van Katwyk, is published in Nature.
November 2023: Artificial Intelligence and Machine Learning in Geophysics - Are We Beyond the Black Box? National Academies Committee on Solid Earth Geophysics Fall 2023 Meeting
July 2023: Computational Science Session, Computability in Europe (CiE), Batumi, Georgia [virtual]
April 2023: Women in Data Science Conference (WiDS@AUB), American University of Beirut, Lebanon [virtual]
Feb 2023: Brown DSI hosts Women in Data Science (WiDS) Providence Datathon.
Nov 2022: NSF AI Planning Institute for Data-Driven Discovery in Physics Seminar Series, Carnegie Mellon [virtual]
Nov 2022: Earthquake Science Center Seminar, USGS.
More details on the Presentations page