Overview

The NISS-CANSSI Collaborative Data Science Webinar: Astronomy & Cosmic Emulation explores the role of statistical modeling and computational techniques in advancing our understanding of the universe. Featuring Kelly Renee Moran (Los Alamos National Laboratory) and Katrin Heitmann (Argonne National Laboratory), this webinar will highlight how statistical emulation accelerates complex astrophysical simulations, enabling researchers to study cosmic structures more efficiently. The discussion will cover key applications in astronomy, from modeling the large-scale structure of the universe to analyzing astrophysical data. Emily Casleton (Los Alamos National Laboratory) will moderate the session, guiding an engaging conversation at the intersection of data science, astronomy, and high-performance computing.

Speakers

Kelly Renee Moran, Los Alamos National Laboratory, LANL

Katrin Heitmann, Argonne National Laboaratory, ANL

Moderator

Emily Casleton, Statistical Sciences Group, Los Alamos National Laboratory (LANL)

Abstract 

Coming Soon!

About the Speakers

Kelly Renee Moran is an applied statistician at Los Alamos National Laboratory (LANL), where she applies statistical modeling and computational tools to tackle complex problems across multiple scientific disciplines. From astrophysics to epidemiology, Moran’s expertise helps researchers extract meaningful insights from their data. Moran joined LANL’s Statistical Sciences Group in 2020 after working intermittently with the lab over five years. Her early interest in applied statistics led her to LANL as an undergraduate at Clemson University, where she engaged with the lab’s epidemiology group. She later pursued a Ph.D. in statistics at Duke University with a Department of Energy Computational Science Graduate Fellowship (DOE CSGF), completing multiple research practicums at Los Alamos before joining full-time. Her research spans a wide array of topics. She has contributed to epidemiology by analyzing internet search data to forecast global disease trends and, during the COVID-19 pandemic, studied how viral variants spread based on demographics and immunity factors. In space science, Moran worked with data from NASA’s Interstellar Boundary Explorer (IBEX) satellite to determine whether different particle detection events could stem from a common heliosphere signal. Additionally, she has played a key role in cosmological modeling, developing an emulator to predict the matter power spectrum from large-scale simulations, enabling researchers to study cosmic structure more efficiently. Beyond research, Moran has also contributed to occupational safety at LANL by automating systems for monitoring employee health and hazard exposure. She is an active member of LANL’s Computational, Computer, and Statistical Sciences (CCS) division, where she helps foster professional development and collaboration among early-career researchers. Moran’s interdisciplinary approach and problem-solving mindset make her an invaluable contributor to LANL’s mission, advancing knowledge across scientific frontiers through data-driven discovery.

Katrin Heitmann is a Physicist and Computational Scientist at Argonne National Laboratory in the High Energy Physics Division. She is also a Senior Associate for the Kavli Institute for Cosmological Physics at the University of Chicago and a member of NAISE at Northwestern. Before joining Argonne, Katrin was a staff member at Los Alamos National Laboratory. Her research currently focuses on computational cosmology, in particular on trying to understand the causes for the accelerated expansion of the Universe. She is responsible for large simulation campaigns with HACC and for the tools in the associated analysis library, CosmoTools. Katrin is a member of several major astrophysical surveys that aim to shed light on this question and was until recently the Spokesperson for the LSST Dark Energy Science Collaboration. Her research Interests include Cosmology, Study of dark energy, dark matter, and inflation; and High-performance computing.

About the Moderator

Emily Casleton 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 post doc 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; a MS in Statistics from West Virginia University, 2006; and a PhD in Statistics from Iowa State University.

About the NISS Collaborative Data Science CoLab

The Collaborative Data Science initiative at NISS brings together experts from various fields to tackle complex data challenges through interdisciplinary teamwork and innovative methodologies.

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.