Founded in 1885, the International Statistical Institute (ISI) is a prestigious non-profit, non-governmental organization with individual and institutional members spanning over 150 countries. Headquartered in The Hague, Netherlands, the ISI serves as the core global network for statistics, dedicated to promoting the understanding, development, and good practice of statistics worldwide. With consultative status with the United Nations Economic and Social Council since 1947, the ISI brings together statisticians and data scientists from government, academia, and the private sector. The Institute comprises seven specialized Associations, each focusing on a distinct area of statistics, and hosts the renowned biennial World Statistics Congress. Through its publications, capacity development programs, and leadership initiatives, the ISI has remained a truly international force in fostering collaboration, advancing statistical knowledge, and shaping best practices across the global statistical community for nearly 150 years.
ISI Elected Membership is a distinguished honour reserved for individuals who are established in their careers and have made very significant and consistent contributions to the statistical profession. The election process is conducted by the ISI Membership Elections Committee (MEC).
The ISI selects Elected Members through three election rounds each year, ensuring a continuous pipeline of recognition for outstanding statisticians and data scientists worldwide. In 2025, the Institute elevated approximately 40 professionals to this prestigious status, underscoring its commitment to honouring excellence across all branches of statistics and data science.
The ISI has now announced the results of the 2026 first round of Membership Elections. This diverse cohort of around 20 statisticians and data scientists represents nine countries across five continents, reflecting the ISI’s truly global mission. The newly elected members earning this prestigious distinction are Karol Patryk Binkowski, Paul Pao-Yen Wu, Fabio Mariano Bayer, Dharmateja Priyadarshi Uddandarao, Rob Deardon, Nathaniel Kenneth Newlands, Hao Mei, Yumou Qiu, Mengxin Yu, Peter Johnson Mannepalli, Praveen Gupta Sanka, VS Vaidyanathan, Pei-Fang Su, Sounak Chakroborty, Paola Crippa, Sujit Kumar Ghosh, Monnie McGee, Wanli Qiao, Lihu Xu, Panpan Zhang, and Xin Zou.
Spotlight on Select Elected Members
Fábio Mariano Bayer is an Associate Professor at the Federal University of Santa Maria and a researcher with the Santa Maria Space Science Laboratory. His research spans digital signal processing, statistical computing, and regression models, and he has authored over 100 peer-reviewed articles in high-impact international journals.
Dharmateja Priyadarshi Uddandarao is an expert Statistician and Data Scientist who currently works at Amazon and is one of the few industry professionals to earn this honor. He is among a select group of statisticians worldwide to hold the trifecta of professional accreditations: AdvDSP from the Alliance for Data Science Professionals, PStat from ASA, and CStat from RSS. He has contributed numerous tech articles and books on novel causal inference methods, including pre-balanced causal techniques to advance the frontier of observational study designs. Dharmateja also actively contributes to the field through mentoring and committee roles in various professional organizations and conferences.
Mengxin Yu is an Assistant Professor at Washington University in St. Louis, specializing in uncertainty quantification, causal inference, and robust high-dimensional statistics. Her interdisciplinary collaborations with clinicians have advanced data-driven solutions in cerebral malaria, digital health, and Alzheimer’s disease research.
Monnie McGee is an Associate Professor at Southern Methodist University, whose research develops statistical methods for complex, high-dimensional data with applications in biomedicine, sports analytics, and artificial intelligence. She currently chairs the ASA Committee on Publications and serves on a National Academies panel on the future of statistics.
Nathaniel Kenneth Newlands is a Senior Research Scientist and Team Lead of Data Science within Agriculture and Agri-Food Canada, and Adjunct Professor at the University of Victoria. He currently serves as President of the International Environ-metrics Society (TIES) and Editor-in-Chief of the journal Applied Statistics: Environmental Statistics and Data Science.
Panpan Zhang is an Assistant Professor of Biostatistics at Vanderbilt University Medical Center and co-leader of the Data Management and Statistics Core of the Vanderbilt Alzheimer’s Disease Research Center. His methodological contributions span longitudinal data analysis, causal inference, and network-based modelling for neuroimaging and cognitive markers.
Paola Crippa is an Assistant Professor at the University of Notre Dame with a joint appointment in Statistics and Civil and Environmental Engineering. A recipient of the 2023 NSF CAREER Award and the 2015 L’Oréal-UNESCO Fellowship for Women in Science, her research advances environmental statistics through Bayesian, non-Gaussian, and machine-learning methods.
Rob Deardon is a Professor of Biostatistics at the University of Calgary, holding a joint appointment in the Faculty of Veterinary Medicine and the Department of Mathematics & Statistics. He is also President of Statistical Society of Canada. His research focuses on infectious disease modelling, spatial epidemiology, and Bayesian statistical methods, with applications spanning animal and public health.
Praveen Gupta Sanka is a seasoned Data Scientist with over a decade of experience in analytics, specializing in data-driven decision-making and innovation across complex business challenges. He has authored articles on artificial intelligence, agentic AI platforms, and machine learning. His work bridges advanced analytical methods with practical business applications, reflecting a commitment to advancing the data science profession through both practice and knowledge-sharing. Beyond his industry work, Praveen is actively engaged in the broader data science community through mentoring, peer reviewing, and leadership roles in professional organizations such as IEEE and the ASA
Karol Patryk Binkowski is a statistician and academic at Macquarie University in Sydney, with a background in quantitative risk analysis. His research includes stochastic modelling, commodity pricing, and statistical methods for financial applications, with contributions spanning Ornstein-Uhlenbeck processes and futures contract pricing. He specializes in a distinctive blend of industry experience and academic rigour to the statistical profession.
