Invited Talks 🎤

MIT ERL Presents: Geo-Solutions for the Energy Transition, Cambridge, MA: Continental-scale subsurface resistivity imaging of Australia: Implications for groundwater and critical mineral systems.
Department of Geology and Geophysics, University of Wyoming, Laramie, WY: AI-enabled electromagnetic imaging with quantified uncertainty from near-surface to deep Earth.
School of Earth Sciences, The Ohio State University, Columbus, OH: AI-enabled electromagnetic imaging with quantified uncertainty from near-surface to deep Earth.

Guest Instructor in NSF Data Science Corps Summer Camp, online: Data science for energy transition.
Department of Petroleum Engineering Weekly Seminar, University of Houston, Houston, TX: Advancing subsurface characterization with deep generative models.

Geophysical Society of Houston Potential Fields Special Interest Group, Houston, TX: Advancing the interpretation of potential field geophysical data with deep generative models.
Guest Instructor in NSF Data Science Corps Summer Camp, online: Data science for energy transition.
The United States Geological Survey, online: Airborne electromagnetic data inversion and uncertainty quantification using deep learning.

Amazon Web Services, Houston, TX: Generative AI for Geoscience Applications.
China Aero Geophysical Survey & Remote Sensing Center for Natural Resources, Beijing, China: Deep learning-based inversion and uncertainty quantification of airborne electromagnetic data.
National Institute of Natural Hazard, Ministry of Emergency Management of the People’s Republic of China, Beijing, China: Deep learning-based inversion and uncertainty quantification of airborne electromagnetic data.