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Speakers

Claudie Beaulieu, Assistant 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

(Abstract Coming Soon!)

About the Speakers

Dr. Claudie Beaulieu is an assistant 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 is a Senior Lecturer in Statistics and joined CHICAS in March 2021 following a disciplne hopping award from EPSRC. After completing their PhD in 2012 within the Mathematics & Statistics department, Rebecca was a PDRA before obtaining a lectureship in Mathematics & 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 a member of the rOpenSci statistical software peer review board.

About the Moderator

Emily Casleton is currently the deputy group leader of the statistical sciences group, but 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, 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 here a decade ago.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, 2014.

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.