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!
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.
July 2023: Preprint A variational LSTM emulator of sea level contribution from the Antarctic ice sheet, led by SciML group Ph.D. student Peter Van Katwyk, is now available on Earth and Space Science (ESS) Open Archive.
June 2022: Summary of our review paper (below) available on Eos: The Big Data Revolution Unlocks New Opportunities for Seismology.
April 2022: Review paper Big Data Seismology, led by Stephen Arrowsmith, accepted for publication in Reviews of Geophysics.
April 2022: SciML Ph.D. student Peter Van Katwyk has been awarded the NSF Graduate Research Fellowship in Geosciences - Artificial Intelligence. Congratulations Peter!
Jan 2022: TWIML AI podcast interview: Machine Learning for Earthquake Seismology with Karianne Bergen [episode page] [watch on YouTube]
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