A Conversation with Vijay Nair
Vijayan N. Nair (Vijay) was born in Malaysia. He received his undergraduate degree in Economics from the University of Malaya and his Ph.D. in Statistics from the University of California, Berkeley. He spent 15 years as a Research Scientist at Bell Labs in New Jersey before turning to academic life at the University of Michigan in 1993. He was Donald A. Darling Collegiate Professor of Statistics and Professor of Industrial and Operations Engineering at Michigan, and he also served as department chair of Statistics for over 10 years and cofounded the Michigan Institute for Data Science. After retiring from Michigan in 2016, he moved to Wells Fargo, where he is currently Head of a Research and Development Group. Vijay has broad research interests and has contributed to industrial and engineering statistics, reliability, design and analysis of experiments, network tomography and behavioral interventions. His current research interests include risk modeling, machine learning and generative AI. He is a Fellow of the American Statistical Association, the Institute of Mathematical Statistics, the American Society for Quality and the American Association for the Advancement of Science. He delivered the Deming Lecture at the Joint Statistical Meetings in 2013 and the Gosset Lecture at the World Statistics Congress in 2017. His extensive record of service to the profession includes serving as Coeditor-in-Chief of the International Statistical Review for two terms, Editor of Technometrics, President of the International Statistical Institute and President of the International Society for Business and Industrial Statistics.
Authors
- Elizaveta Levina (ORCID: https://orcid.org/0000-0002-0713-3450)
- Naveen N. Narisetty (ORCID: https://orcid.org/0000-0002-8552-5580)
Institutions
- University of Michigan (US)
Publication Details
- Journal
- Statistical Science
- Published
- 2025-05-01
- DOI
- https://doi.org/10.1214/25-sts992
- Primary Topic
- Optimal Experimental Design Methods
- Type
- article
- Field-Weighted Citation Impact
- 0.00