Date: Thursday, April 10, 2025
Time: 1:00–2:00 p.m. Eastern time
Location: On Zoom
Join us for the second NISS-CANSSI Collaborative Data Science Webinar: Astronomy & Cosmic Emulation. This webinar explores the role of statistical modelling and computational techniques in advancing our understanding of the universe. Featuring Kelly Renee Moran (Los Alamos National Laboratory) and Katrin Heitmann (Argonne National Laboratory), the presentation will highlight how statistical emulation accelerates complex astrophysical simulations, enabling researchers to study cosmic structures more efficiently. Key statistical concepts discussed include iterative space-filling designs, statistical smoothing techniques, and Gaussian process based emulation and calibration. 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.
REGISTER ON ZOOM
Kelly Renee Moran is an applied statistician at Los Alamos National Laboratory (LANL), where she applies statistical modelling 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 PhD 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 modelling, 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 University. 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 (Hardware/Hybrid Accelerated Cosmology Code) 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.
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 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 and 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.
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 will feature 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 will gain a deeper understanding of the collaborative processes that bridge gaps between different scientific landscapes. These seminars will not only highlight successful partnerships but also provide a platform for exchanging ideas, methodologies, and best practices that inspire new collaborations.