ASA Connect

The Department of Statistics, Actuarial and Data Sciences is pleased to announce that Rebecca Nugent from Carnegie Mellon University will be the fourth and final speaker of the 2022-2023 Data Science Speaker Series.

Title:                            Data, huh, Yeah.  What is it good for?  Absolutely Everything!

Speaker:                      Rebecca Nugent, Professor of Statistics & Data Science and Head of the Carnegie Mellon Department of Statistics & Data Science

Time and Date:           11:00 am – Noon, Friday, April 7, 2023

Location:                     Pearce Hall 138 or by WebEx at https://cmich.webex.com/meet/famoy1kf

Abstract: The Data Science Pipeline - far more than just a set of AI/ML algorithms.  The first questions we ask, the early decisions we make, the final use and interpretation of our results - all of these play a crucial role when leveraging data-informed decision making for any problem.  In this talk, we'll unpack what data science is with an emphasis on thinking about the entire data life cycle. We'll explore how data science is being used to tackle problems in transportation logistics, retail, travel, professional sports, as well as take an insider's look at modeling influenza and the COVID-19 pandemic.  We'll also describe our current research on "the science of data science" supported by the Integrated Statistics Learning Environment (http://www.stat.cmu.edu/isle). An interactive, web-based e-learning platform used by thousands of students and industry practitioners, ISLE tracks the entire data analysis process including written work and group collaboration, helping us capture behavioral information that allows us to study data science:  How should we teach it?  Where are the most common mistakes made and why? How do people best collaborate using data?  Optimizing the use of data science requires understanding the people who do it and the decisions they make.  Because, at its heart, data science is all about people.

BIO:  Rebecca Nugent is the Stephen E. and Joyce Fienberg Professor of Statistics & Data Science and Head of the Carnegie Mellon Department of Statistics & Data Science.  She received her PhD in Statistics from the University of Washington, her M.S. in Statistics from Stanford University, and her B.A. in Mathematics, Statistics, and Spanish from Rice University.   Dr. Nugent has expertise in designing and implementing data science/AI professional development programs for business leaders in industries including health care, finance, automotive/manufacturing, and life sciences.  She was the faculty co-Director of the Moderna AI Academy and the Founding Director of the Statistics & Data Science Corporate Capstone program. She has won several national and university teaching awards including the American Statistical Association Waller Award for Innovation in Statistics Education and serves as one of the co-editors of the Springer Texts in Statistics.  She recently served as the co-chair for the National Academy of Sciences study on Improving Defense Acquisition Workforce Capability in Data Use and served on the NAS study on Envisioning the Data Science Discipline: The Undergraduate Perspective. Dr. Nugent has worked extensively in clustering and classification methodology with an emphasis on high-dimensional, big data problems and record linkage applications.  Her current research focus is the development and deployment of low-barrier data analysis platforms that allow for adaptive instruction and the study of data science as a science.