Public Webinars & Hybrid Events
Webinars are held monthly and are open to all interested members of the scientific community.
Unless otherwise stated, these webinars will take place on Zoom Fridays once a month at 1:30-2:30 PM ET. Some webinars will be hybrid events with an in-person component at the CMU campus.
To join, you must be subscribed to our webinar mailing list. You can also add the webinars to your calendar by subscribing to the Google Calendar below. Past webinar recordings and information are available in the online archive and on the STAMPS YouTube Channel.
Upcoming Webinars & Hybrid Events
Nov 7th, 2025
Natalie Klein, Los Alamos National Laboratory (LANL)
Location: Zoom
Title: From Earth to Mars: Statistical Challenges in Analyzing Rover Spectroscopy Data
Abstract: NASA’s Curiosity and Perseverance rovers have collected rich spectroscopic data from the Martian surface using instruments such as ChemCam and SuperCam. These multimodal datasets (spanning LIBS, infrared, and Raman measurements) pose unique challenges for calibration, interpretation, and data integration across vastly different environments. This talk will highlight statistical and machine learning methods developed to meet these challenges, including Bayesian neural networks for uncertainty-aware prediction, optimal transport for aligning Earth and Mars data, multimodal fusion with interpretability metrics, and density-ratio weighting for combining heterogeneous observations. I’ll also discuss generative models for LIBS spectra and ongoing work using fast simulators for model pretraining. Together, these advances illustrate how planetary science data drive new ideas in uncertainty quantification, domain adaptation, and the fusion of physical and statistical modeling.
Bio: Dr. Natalie Klein is the AI and Advanced Predictive Modeling Team Lead in the Statistics Group at Los Alamos National Laboratory, where she has been a staff member since 2019. Her research focuses on integrating statistical methodology with machine learning to address challenges in scientific domains such as remote sensing and planetary exploration. She holds a joint Ph.D. in Statistics and Machine Learning from Carnegie Mellon University.
Dec 12th, 2025
Jonathan Lilly, Planetary Science Institute
Location: Zoom
Title: TBD
Abstract: TBD
Bio: TBD