Date: Thursday, March 20, 2025 at 1:00 pm - 2:00 pm ET
Zoom Registration Link: https://us02web.zoom.us/webinar/register/WN_O_tP3PKRS8GAAspqOmdkwg
Overview
Join us for the NISS-CANSSI Collaborative Data Science Webinar Series: Changing Climate, Changing Data – A Journey of Statisticians and Climate Scientists on Thursday, March 20, 2025, from 1:00 PM to 2:00 PM ET. This webinar features Claudie Beaulieu (University of California, Santa Cruz) and Rebecca Killick (Lancaster University), with moderation by Emily Casleton (Los Alamos National Laboratory). The discussion will explore how climate change impacts society and the critical role of statistical methods in understanding climate variability and trends. The speakers will highlight their research on whether global warming is accelerating, share insights into their collaboration, and discuss challenges in publishing statistical work in environmental science. Ethical considerations in climate data analysis will also be examined. Don't miss this opportunity to gain valuable perspectives at the intersection of statistics and climate science!
See full details on event page: NISS-CANSSI Collaborative Data Science Webinar: Changing Climate, Changing Data: A journey of statisticians and climate scientists | National Institute of Statistical Sciences
Speakers
Claudie Beaulieu, Associate Professor of Ocean Sciences, University of California, Santa Cruz
Rebecca Killick, Professor of Statistics, School of Mathematical Sciences, Lancaster University
Moderator
Emily Casleton, Statistical Sciences Group, Los Alamos National Laboratory (LANL)
Abstract
Climate change is impacting our society in many different ways. Scientifically and societally, we need to accurately estimate the magnitude of these changes to inform and lead societal adaptation and mitigation to ongoing and future change. Understanding the underlying mechanisms of these changes necessitate robust characterization and quantification of observed and simulated data. This talk will introduce our ongoing work in quantifying climate change and variability, centered around the current debate as to whether global warming is accelerating, or not. We will touch on how our collaboration started and evolved, the pros and cons of publishing statistical work in environmental journals, and ethical quandaries.
About the Speakers
Dr. Claudie Beaulieu is an Associate Professor of Ocean Sciences at the University of California, Santa Cruz, whose groundbreaking work in environmental data science has earned her a Faculty Early Career Development (CAREER) Award from the National Science Foundation (NSF). This prestigious award supports her integrated research and education program, which focuses on understanding climate variability and climate change by leveraging data science techniques. Dr. Beaulieu's research addresses the critical need to comprehend the drivers of oceanic and climatic variability and change. Her work tackles the challenge of analyzing the increasingly complex environmental data made available through advances in climate and ocean monitoring, observational platforms, and Earth system modeling. By applying statistical and machine learning methods, she aims to maximize insights from observational data and model simulations. Dr. Beaulieu earned her Ph.D. in water sciences from the Institut National de la Recherche Scientifique Centre Eau Terre et Environnement in Quebec. She conducted postdoctoral research in atmospheric and oceanic sciences at Princeton University and was a lecturer in the School of Ocean and Earth Science at the University of Southampton before joining the UC Santa Cruz faculty in 2018. Through her research, education, and outreach efforts, Dr. Beaulieu is shaping the future of climate science and environmental data analysis, while inspiring and equipping the next generation of environmental scientists.
Rebecca Killick received their PhD degree in Statistics from Lancaster University, where they hold a Professor and DIrector of Research positions. For 2024/25 Rebecca is also a visiting Professor at UC Santa Cruz. In 2019 they were the first UK recipient of the "Young Statistician of the Year" award from the European Network for Business and Industrial Statistics which recognizes the work of young people in introducing innovative methods, promoting the use of statistics and/or successfully using it in daily practice. Rebecca sees their research as a feedback loop, being inspired by problems in real world applications, creating novel methodology to solve those problems and then feeding these back into the problem domain. Their primary research interests lie in development of novel methodology for the analysis of univariate and multivariate nonstationary time series models. This covers many topics including developing models, model selection, efficient estimation, diagnostics, clustering and prediction. Rebecca is highly motivated by real world problems and has worked with data in a range of fields including Bioinformatics, Energy, Engineering, Environment, Finance, Health, Linguistics and Official Statistics. Rebecca is passionate about ensuring the availability and accessibility of research in the form of open-source software. As part of this they advocate to the statistical community the importance of recognition of research software as an academic output, are co-Editor in Chief of the Journal of Statistical Software and a member of the rOpenSci statistical software peer review board.
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.
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.
Upcoming Webinars
Date: Thursday, March 20, 2025 at 1-2pm ET
Speakers: Claudie Beaulieu, Assistant Professor of Ocean Sciences, University of California, Santa Cruz and Rebecca Killick, Professor of Statistics, School of Mathematical Sciences, Lancaster University; Moderator: Emily Casleton, Statistical Sciences Group, Los Alamos National Laboratory (LANL)
Date: Thursday, April 10, 2025 at 1-2pm ET
Speakers: Kelly Renee Moran, Applied Statistician at Los Alamos National Laboratory (LANL) and Katrin Heitmann, Argonne National Laboaratory (ANL); Moderator: Emily Casleton, Statistical Sciences Group, Los Alamos National Laboaratory (LANL)
Date: Thursday, May 8, 2025 at 1-2pm ET
Speakers: An-Chao Tsai, Department of Computer Science and Artificial Intelligence, National Pingtung University and Anand Paul, LSU Health-New Orleans; Moderator: Qingzhao Yu, Associate Dean for Research at the School of Public Health, Louisiana State University Health, New Orleans
Date: Thursday, June 12, 2025 at 1-2pm ET
Speakers: Lauren Wengerd, Ohio State University, Depart of Rehabilitation Science and Dave Friedenberg, Battelle; Moderator: Nancy McMillan, Battelle
------------------------------------------------
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.
Promoting Science-Led Advancements in Data Science
This initiative promotes the inherently interdisciplinary nature of Data Science, seeking science-led advancements in Data Science and their innovative, significant, or transformative applications in science. It encourages robust collaboration and integration across the broadly defined realms of Science and Data Science via deep domain, broader inter-domain and cross-domain collaborative research. It advocates for collective scientific advancement through novel collaborative and scientific methods and theories that can enrich the knowledge and strengthen the data practices among domain and data scientists.
The NISS-CANSSI Web Series on Collaborative Data Science
The NISS-CANSSI web series on Collaborative Data Science is 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.
Engaging Virtual Seminars Featuring Experts Across Disciplines
Each virtual 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.
Building a Community of Data-Driven Research Excellence
Our mission is to create a vibrant community where data-driven approaches enhance research across fields such as biology, environmental science, engineering, social sciences, and more. By showcasing real-world examples of interdisciplinary teamwork, we aim to empower scientists and data professionals alike to embark on joint ventures that harness the full potential of their combined expertise.
Publishing Opportunities in Data Science in Science
Data Science in Science | Taylor & Francis Online is an open-access, international journal publishing original research and reviews at the intersection of Science and Data Science. NISS Director David S. Matteson is one of the Editors-in-Chief. An additional opportunity of becoming part of a NISS collaboration will be to publish a paper on the research done through this initiative in this journal.
Join Us in Driving Scientific Innovation Through Collaboration
Join us in celebrating the transformative impact of collaboration. Whether you are a seasoned data scientist, a researcher from another discipline, or simply passionate about the possibilities that emerge when diverse minds converge, this seminar series offers invaluable opportunities to learn, connect, and innovate together!
------------------------------
Randy Freret
National Institute of Statistical Sciences
rfreret@niss.org------------------------------