STAMPS Research Center Seminar - Natalie Klein
— 2:30pm
Location:
Virtual Presentation - ET
-
Remote Access - Zoom
Speaker:
NATALIE KLEIN
,
AI and Advanced Predictive Modeling Team Lead, Statistics Group, Los Alamos National Laboratory
https://www.linkedin.com/in/natklein/
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.
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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.
Zoom Participation. See announcement.
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For More Information:
tsukiant@andrew.cmu.edu