I am a Data Science Initiative Postdoctoral Fellow at Harvard University. I am affiliated with the School of Engineering and Applied Sciences (SEAS/Computer Science) and Department of Earth and Plantary Sciences (EPS).
My research interest is in scientific machine learning. 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.
This portfolio contains information about my professional work, including my research projects and teaching experiences.
Thanks for visiting!
Karianne
Fall 2019 - I'm on the job market (junior faculty or research scientist roles)!
Application materials are available here.
News:
- June 25, 2019 - My colleague Clara Yoon's paper, "Unsupervised Large-Scale Search for Similar Earthquake Signals," was published in BSSA.
- Mar 21, 2018 - My review paper, "Machine learning for data-driven discovery in the solid Earth geosciences," was published in Science.
- Nov 12, 2018 - I was selected for the 2018 GJI Student Author Award by the Editorial Board of Geophysical Journal International for my recent paper.
- Mar 2, 2018 - I passed my thesis defense! Title: "Big Data for Small Earthquakes: Detecting Earthquakes Over a Seismic Network with Waveform Similarity Search"
Upcoming Events:
- Dec 9-13, 2019: AGU Fall Meeting in San Francisco, CA.
- Oral Presentation (S53A-03): "How robust is deep learning-based earthquake detection? Insights from adversarial machine learning," Friday, December 13 at 2:10pm, Moscone South 158
Recent Events:
- Nov 1, 2019: Women in Data Science @ Stanford Earth Workshop at Stanford University. Read about it here.
- Oct 28, 2019: National Academies' Committee on Seismology and Geodymanics Fall Meeting in Washington, DC.
- Oct 14-18, 2019: Workshop on Interpretable Learning in the Physical Sciences at the Institute for Pure and Applied Mathematics, UCLA.
- Sept 27, 2019: UTIG Seminar at the Jackson School of Geosciences, University of Texas at Austin. [ slides ]
- Mar 18-22, 2019: Machine Learning in Solid Earth Geoscience in Santa Fe, NM
- Feb 13, 2019: SSA Policy Briefing in Washington, DC.
- Topic: "Machine Learning in Seismology: Using AI to Improve Earthquake Monitoring," with Zach Ross [ abstract ]
- Dec 7, 2018: Machine Learning for Geophysical & Geochemical Signals workshop at NeurIPS in Montreal, Canada.
- June 12-14, 2018: 2018 IRIS Workshop , Albuquerque. NM.
- Plenary Talk: "Improving earthquake detection with data mining and machine learning" [ abstract ] [ presentation ]
- Jan 8-12, 2018: Fundamentals of Data Science ICME Summer Workshops @ Santiago.
- I taught the Introduction to Machine Learning workshop (with Alex Ioannidis) at Pontificia Universidad Católica de Chile (UC), Santiago, Chile, and gave a seminar, "Scalable Similarity Search for Earthquake Detection," at the Institute for Mathematical and Computational Engineering, UC.