Presentations

Invited Talks (selected)

Big data for small earthquakes: data mining, deep learning, and explainable AI

  • Cornell University, Ithaca, NY (2021^^); University of Utah, Salt Lake City, UT (2021^^); MIT, Cambridge, MA (2021).

Explainable AI for Seismology: An interpretable convolutional neural network for earthquake detection

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

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.

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 Seismology: Using AI to Improve Earthquake Monitoring.

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.

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.

^^ indicates virtual talk

Conference Tutorials

Panels

  • 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

Data Mining for Earthquake Detection using Computationally Efficient Search for Similar Seismic Signals

Unsupervised Approaches for Post-Processing in Computationally Efficient Waveform-Similarity-Based Detection