NISS-CANSSI Collaborative Data Science: Bayesian Reconstruction of Ion Temperature and Amplitude Profiles in Inertial Confinement Fusion Diagnostics
Date: Friday, May 8, 2026
Time: 13:00–14:00 Eastern time
Location: On Zoom
Join Us
Join us for “Bayesian Reconstruction of Ion Temperature and Amplitude Profiles in Inertial Confinement Fusion Diagnostics” at the May NISS-CANSSI Collaborative Data Science Webinar.
Presentation Abstract
Inertial confinement fusion (ICF) experiments rely on accurate ion temperature and emission measurements to diagnose plasma conditions and improve performance. However, due to technical challenges and limited signal, existing ion temperature diagnostics lack spatial resolution, integrating measurements over the neutron source. The speakers present a Bayesian framework that uses Gaussian processes to model spatially varying ion temperature and emission amplitude profiles from imaging data. The approach combines a forward physics model with Markov Chain Monte Carlo inference to reconstruct profiles from synthetic datasets generated under realistic conditions, while providing uncertainty quantification through posterior credible intervals. Results show that the GP-based model can recover spatially resolved temperature and amplitude structure with quantified uncertainty, enabling a new capability for ICF experiments.
The webinar will feature Ky D. Potter (Statistics PhD candidate at Simon Fraser University) and Chris Danly (Director’s Postdoctoral Fellow at Los Alamos National Laboratory (LANL)). It will be moderated by Emily Casleton (Los Alamos National Laboratory and Chair of the NISS-CANSSI Collaborative Data Science Webinar Planning Committee).
About the Speakers
Ky D. Potter is a Statistics PhD candidate at Simon Fraser University and a Graduate Student Intern in the Statistical Sciences Group (CAI-4) at Los Alamos National Laboratory. Their work sits at the intersection of Bayesian statistics and physics, with applications spanning inertial confinement fusion, space and ionospheric physics, and astrostatistics. Ky focuses on scalable Gaussian process models, uncertainty quantification, and statistical emulation for complex, noisy data. Ky enjoys collaborative, interdisciplinary research at the interface of statistics and the physical sciences.
Chris Danly is a Director’s Postdoctoral Fellow at Los Alamos National Laboratory. He received his PhD in plasma physics from the University of Rochester and holds master’s degrees in physics and nuclear engineering. Since 2010, Chris has been a member of LANL’s nuclear imaging team, leading development of new imaging techniques to diagnose inertial confinement fusion and high energy density physics experiments. He recently joined the lab’s Analysis division, where his research focuses on applications of fusion ignition, and global security implications of the private fusion R&D boom.About the Moderator
Emily Casleton is Chair of the NISS-CANSSI Collaborative Data Science Webinar Planning Committee. She is a statistician in the statistical sciences group at Los Alamos National Laboratory (LANL), and was recruited to LANL as a summer student at the 2012 Conference on Data Analysis (CoDA). She joined the Lab as a postdoc in 2014 after earning her PhD in Statistics from Iowa State University. Since converting to staff in 2015, Emily has routinely collaborated with seismologists, nuclear engineers, physicists, geologists, chemists, and computer scientists on a wide variety of cool data-driven projects. Most recently, her research focus has been on testing and evaluating large AI models. She holds a BS in Mathematics, Political Science from Washington & Jefferson College, 2003; an MS in Statistics from West Virginia University, 2006; and a PhD in Statistics from Iowa State University.
About the NISS-CANSSI Collaborative Data Science Webinar Series
In an era where data transcends traditional boundaries, fostering interdisciplinary collaboration has never been more crucial. Together with the National Institute of Statistical Sciences (NISS), we are proud to present the NISS-CANSSI Collaborative Data Science webinar series dedicated to showcasing data scientists and domain scientists from diverse scientific fields who collaborate to advance science. This initiative celebrates the power of collaboration, demonstrating how the fusion of data science with various disciplines can drive innovation, solve complex problems, and push the frontiers of knowledge beyond the realm of statistics.
Each session features two speakers: a data scientist and a subject matter expert from another domain who have successfully partnered to achieve impactful results. Through their shared experiences and insights, attendees gain a deeper understanding of the collaborative processes that bridge gaps between different scientific landscapes. These seminars not only highlight successful partnerships but also provide a platform for exchanging ideas, methodologies, and best practices that inspire new collaborations.