Full details will become available soon!

Speakers

Claudie Beaulieu, Assistant Professor of Ocean Sciences at the University of California, Santa Cruz

Rebecca Killick, Professor of Statistics, School of Mathematical Sciences, Lancaster University

Moderator

David S. Matteson, NISS Director and Professor of Statistics at Cornell University

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

David S. Matteson is the incoming Director of NISS and an Associate Professor and Associate Department Chair of Statistics and Data Science at Cornell University, where he is a member of the ILR School, Computing and Information Science, the Center for Applied Mathematics, the Fields of Computer Science and Operations Research, and the Program in Financial Engineering. Professor Matteson received his PhD in Statistics from the University of Chicago and his BSB in Finance, Mathematics, and Statistics from the University of Minnesota. He received a CAREER Award from the National Science Foundation (2015), the Chancellor’s Award for Scholarship and Creative Activities from the State University of New York (SUNY, 2022), the Ann S. Bowers Research Excellence Award from the Bowers College of Computing and Information Science (2022), Faculty Research Awards from the Xerox/PARC Foundation (2014) and LinkedIn (2022), and he and his students have received numerous Best Paper Awards. He is currently founding Editor-in-Chief for Data Science in Science and an Associate Editor for the Journal of Econometrics and Statistica Sinica, as well as a former Associate Editor for the Journal of the American Statistical Association-Theory & Methods, The American Statistician, and Biometrics. He is an elected Officer for the Business and Economic Statistics Section of the American Statistical Association, a member of the Institute of Mathematical Statistics, the International Biometric Society, the International Society for Bayesian Analysis, the Association for Computing Machinery, the Society for Industrial and Applied Mathematics, and the American Geophysical Union. He is coauthor of Statistics and Data Analysis for Financial Engineering, and MOOC instructor for Introduction to Time Series Analysis. He is lead PI and Director of the NSF funded PRISM Institute for Trans-domain Systemic Risk, lead PI and co-Director of the NSF funded TRIPODS Greater Data Science Cooperative Institute (GDSC), co-PI of the NSF funded Atomic-Level Structural Dynamics in Catalysts Institute, co-PI of a USAID funded Feed-the-Future team, an Executive Committee Member for the Cornell Center for Data Science for Enterprise & Society, and a Visiting Scholar at the Institute for Mathematics and its Applications. He is also a strong supporter of the National Institute of Statistical Sciences and its mission. 

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.