NISS-CANSSI Collaborative Data Science: Bayesian Reconstruction of Ion Temperature and Amplitude Profiles in Inertial Confinement Fusion Diagnostics
Date: Thursday, March 12, 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 next NISS-CANSSI Collaborative Data Science Webinar.
Presentation Abstract
This webinar explores Bayesian methods for reconstructing ion temperature and amplitude profiles in inertial confinement fusion diagnostics. The session will highlight cutting‑edge statistical modelling at the intersection of data science and physics. Attendees will gain insight into how advanced Bayesian tools support uncertainty quantification and improve interpretation of complex, noisy fusion data.
The webinar will feature Ky D. Potter (Statistics PhD candidate at Simon Fraser University) and Chris Danly (Graduate Researcher at Los Alamos National Laboratory). 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 Graduate Researcher at Los Alamos National Lab (bio coming soon!).
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.