Overview:

(Coming Soon)

Speakers

Ky D. Potter, Statistics PhD candidate at Simon Fraser University; Graduate Student Intern, Statistical Sciences Group (CAI-4) at Los Alamos National Laboratory

Chris Danly, Graduate Researcher at Los Alamos National Lab

Moderator

Emily Casleton, Chair of NISS-CANSSI Collaborative Data Science Webinar Planning Committee; and Los Alamos National Laboratory

Abstract

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, Graduate Researcher at Los Alamos National Lab (bio coming soon!)

About the Moderator

About the NISS-CANSSI Collaborative Data Science Web Series:

The NISS-CANSSI Collaborative Data Science initiative that the National Institute of Statistical Sciences (NISS) in collaboration with the Canadian Statistical Sciences Institute (CANSSI) brings together experts from various fields to tackle complex data challenges through interdisciplinary teamwork and innovative methodologies.

Goals of the Initiative

The goal is to foster progress in:

• Developing new ideas for experimental and observational data-driven learning and discovery that address key questions at the cutting edge of science and scientific deduction;
• Quantifying and summarizing uncertainty in data-driven theories, as well as complex Data Science models, algorithms, and workflows; and
• Establishing new practices for scientific reproducibility and replicability through Data Science.