TWIML Podcast (Jan 20, 2022) Machine Learning for Earthquake Seismology with Karianne Bergen
Invited Talks (selected)
Big data for small earthquakes: data mining, deep learning, and explainable AI
USGS Earthquake Science Seminar (2022^^); University of Montana, Missoula, MT (2022^^); University of Oslo, Norway (2022^^); Cornell University, Ithaca, NY (2021^^); University of Utah, Salt Lake City, UT (2021^^); MIT, Cambridge, MA (2021).
Earthquake monitoring, Deep learning and Explainable AI
NSF AI Institute for Data-Driven Discovery in Physics Seminar, Carnegie Mellon University (2022^^)
Explainable AI for Seismology: An interpretable convolutional neural network for earthquake detection
GNEM Seminar, Sandia National Laboratory (2023^^); Symposium on Artificial Intelligence and Earthquake Engineering, EERI San Diego Chapter (2022^^); Brazilian Seismology Symposium, International Congress of the Brazilian Geophysical Society (2021^^).
Big Data Analysis in Geoscience
International Symposium: Frontier of Understanding Earth’s Interior and Dynamics, Tohoku University, Sendai, Japan (2022^^).
Machine Learning in Seismology: A Fireside Chat
Plenary panel with Qingkai Kong and Daniel Trugman, moderated by Bill Walter. Seismological Society of America Annual Meeting (2021^^). [recording]
Advancing solid Earth geoscience with machine learning
Geological Society of Washington, Washington, D.C. (2021^^) [ announcement ]. Winner: 2021 Sleeping Bear Award for good humor at meetings.
Distributed acoustic sensing (DAS) and big scientific data analysis
Distributed Acoustic Sensing Virtual Workshop and Tutorial, IRIS (2020^^). [ workshop website ] [ video ( + captions) ] [ slides ]
Event detection in big sensor data: Applications in earthquake seismology and beyond
Life on Planet Earth: Above and Below Workshop, Mathematical Biosciences Institute, Ohio State University, Columbus, OH (2020^^). [ workshop website ] [ video ]
Big data for small earthquakes: Computational challenges in large-scale earthquake detection
University of Delaware, Newark, DE (2020^^); Boston University, Boston, MA (2020^^); University of California, Santa Barbara, CA (2020); University of British Columbia, Vancouver, Canada (2020); Brown University, Providence, RI (2020); Colorado School of Mines, Golden, CO (2020); University of Texas at Austin, TX (2020); Michigan State University, East Lansing, MI (2020).
Machine learning for data-driven discovery in solid Earth geoscience.
National Academies Committee on Seismology and Geodynamics Fall Meeting. Washington, DC (2019). [ meeting website ] [ video ]
Earthquake monitoring in the age of “big data:” Challenges and opportunities.
Women in Data Science @ Stanford Earth workshop, Stanford University, CA, (2019) [ news release ]; Princeton University, Princeton, NJ (2019); University of Texas at Austin, TX (2019) [ video ] [ slides ].
Data mining for earthquake detection: Lessons for data-driven geoscience.
Machine Learning in Solid Earth Geoscience Conference, Santa Fe, NM (2019). [ conference website ]
Machine Learning in Seismology: Using AI to Improve Earthquake Monitoring.
Congressional briefing (with Zach Ross), hosted by the Seismological Society of America, Washington D.C. (2019). [ SSA Policy Events ] [ abstract ]
Towards data-driven earthquake detection: Extracting weak seismic signals with locality-sensitive hashing.
Conference on Neural Information Processing Systems, Workshop on Machine Learning for Geophysical & Geochemical Signals, Montreal, Canada (2018). [ workshop website ]
Improving earthquake detection with data mining and machine learning.
IRIS Workshop: Foundations, Frontiers & Future Facilities for Seismology, Albuquerque, NM (2018). [ workshop website ] [ abstract ] [ slides ]
Big data for small earthquakes: a data mining approach to large-scale earthquake detection.
University of Washington, Seattle, WA (2019); Columbia University, Palisades, NY (2019); Brown University, Providence, RI (2018); Sandia National Laboratory, Livermore, CA, (2018); MIT, Cambridge, MA (2018). [ recording ]
FAST: Earthquake Detection using Computationally Efficient Similarity Search.
Earthquake Science Seminar (with Clara Yoon), US Geological Survey, Menlo Park, CA (2015). [ recording ]
^^ indicates virtual talk
Machine Learning for Seismology Workshop, Seismological Society of America Annual Meeting, Seattle, WA (2019). [ workshop website ] [ slides ]
Unsupervised Learning for Geoscience Applications, Machine Learning in Solid Earth Geoscience Conference, Santa Fe, NM (2019). [ conference website ] [ slides ]
Introduction to Machine Learning Workshop, SIAM Geosciences Conference, Stanford University (2015). [ conference website ] [ workshop website ]
Harvard FAS Office of Postdoctoral Affairs, Tales from the Battlefront: Q&A Among Survivors and Casualties of the Academic Job Search (2020^^).
Conference Presentations and Posters (selected)
Interpreting and Evaluating Machine Learning-Based Earthquake Monitoring
American Geophysical Union Fall Meeting (2020^^).
Towards robust, reliable earthquake detection with deep neural networks
JpGu-AGU Meeting, Chiba, Japan (2020^^).
How robust is deep learning-based earthquake detection? Insights from adversarial machine learning
American Geophysical Union Fall Meeting, San Francisco, CA (2019).
On the Use of Machine Learning for Seismic Event Detection
Seismological Society of America Annual Meeting, Denver, CO (2017). [ link ] [ student presentation award ]
Data Mining for Earthquake Detection using Computationally Efficient Search for Similar Seismic Signals
Seismological Society of America Annual Meeting, Reno, NV (2016). [ link ] [ slidecast ] [ student presentation award ]
Unsupervised Approaches for Post-Processing in Computationally Efficient Waveform-Similarity-Based Detection
American Geophysical Union Fall Meeting, San Francisco, CA (2015). [ link ] [ student paper award ]