Date: Thursday, March 20, 2025
Time: 1:00–2:00 p.m. Eastern time
Location: On Zoom
Join us for the NISS-CANSSI Collaborative Data Science Webinar Series: Changing Climate, Changing Data—A Journey of Statisticians and Climate Scientists. 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!
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 necessitates robust characterization and quantification of observed and simulated data. This talk will introduce our ongoing work in quantifying climate change and variability, centred 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.
Dr. Claudie Beaulieu is an Assistant Professor of Ocean Sciences at the University of California (UC), 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 modelling. By applying statistical and machine learning methods, she aims to maximize insights from observational data and model simulations. Dr. Beaulieu earned her PhD 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 is a Senior Lecturer in Statistics at Lancaster University and joined the Centre for Health Informatics, Computing, and Statistics (CHICAS) in March 2021 following a discipline-hopping award from the Engineering and Physical Sciences Research Council (EPSRC). After completing their PhD in 2012 within the Mathematics and Statistics department, Rebecca was a Postdoctoral Research Associate before obtaining a lectureship in Mathematics and Statistics in 2013. Alongside her departmental role, Rebecca is Head of the Lancaster University Women’s Network and Furness College Advisor. 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 are a member of the rOpenSci statistical software peer review board.
Emily Casleton is currently the Deputy Group Leader of the statistical sciences group at the Los Alamos National Laboratory (LANL), but 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, she has been the PI of a data analytics project under the NA-22 venture MINOS; co-organizer of the invited CCS-6 seminar series; and co-chair of CoDA, the conference that brought her to LANLA a decade ago. 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, 2014.
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.