Abstract

(Coming soon!)

Speakers

Maria Nareklishvili Assistant Professor of Econometrics at the University of Glasgow

Research Collaborator  (coming soon!)

Moderator

Elizabeth Eisenhauer, Senior Statistical Associate, Statistics and Data Science at Westat

Registration Coming Soon!

About the Speakers

Dr. Maria Nareklishvili is an Assistant Professor of Econometrics at the University of Glasgow. Her research interests include econometrics, statistics, and data science, with a particular focus on personalized policy analysis and data-driven decision-making. Maria served as a Postdoctoral Scholar at the Stanford Graduate School of Business (2024–2025) and is presently affiliated with the institution. She earned her Ph.D. in Econometrics from the University of Oslo. See Profile

Research Collaborator (coming soon!)

About the Moderator

Elizabeth Eisenhauer, PhD, is a Senior Statistical Associate for Statistics and Data Science with 10 years of experience in causal inference, including experiments and quasi-experiments such as interrupted time series and regression discontinuity, and survey sampling and weighting. She integrates quantitative and qualitative perspectives to connect statistical rigor with real-world evaluation practice, applying statistical methods to help answer questions in public health, education, and ecology contexts. Eisenhauer has worked to advance collaboration between statisticians and evaluators by organizing international panels and contributing an article to Amstat News on the topic. As a part-time instructor at Penn State University, Eisenhauer designs and teaches courses in artificial intelligence (AI), machine learning (ML), survey statistics, and introductory statistics at the undergraduate and graduate levels. She is the 2026 vice chair of the American Statistical Association’s (ASA) Committee on Career Development and a 2026 ASA StatsForward fellow. She is also a member of the National Institute of Statistical Sciences-Canadian Statistical Sciences Institute (NISS-CANSSI) Collaborative Data Science Committee. In earlier work as a statistical consultant with Penn State University, Eisenhauer developed and vetted statistical models for researchers across disciplines. Her research included advanced parametric and nonparametric models, sampling designs in Bayesian and frequentist inference frameworks, and survey instrument development to assess students’ attitudes. See Profile

About the NISS-CANSSI Collaborative Data Science Web Series:

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.

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.

Learn more: NISS-CANSSI Collaborative Data Science