Members’ News

Congratulations: 2026 - 1st Round Newly Elected Members of the ISI

27 March 2026
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The International Statistical Institute is pleased to announce the first round of Elected Members for 2026. Please join us in congratulating these professionals, whose dedication and impact in the field of statistics and data science. 

These individuals have been awarded the title of Elected Member of the International Statistical Institute.

Find a list of our newest Elected Members below:

Australia

  •  Binkowski, Karol Patryk
  • Wu, Paul Pao-Yen

Brazil

  •  Bayer, Fabio Mariano

Canada

  •  Deardon, Rob
  • Newlands, Nathaniel Kenneth

China

  •  Mei, Hao
  • Qiu, Yumou
  • Yu, Mengxin

India

  •  Mannepalli, Peter Johnson
  • Sanka, Praveen Gupta
  • Uddandarao Dharmateja Priyadarshi
  • Vaidyanathan VS

Taiwan

  •  Su Pei-Fang

US

  •  Chakroborty Sounak
  • Crippa Paola
  • Ghosh Sujit Kumar
  • Mcgee Monnie
  • Qiao Wanli
  • Xu Lihu
  • Zhang Panpan
  • Zou Xin

 

Read some of our Elected Members' biographies:

 

Fabio Mariano Bayer

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Fábio M. Bayer received the B.Sc. degree in Mathematics from the Federal University of Santa Maria (UFSM), Brazil, in 2006, and the D.Sc. degree in Statistics from the Federal University of Pernambuco (UFPE), Brazil, in 2011. He is an Associate Professor with the Department of Statistics at UFSM and a Researcher with the Santa Maria Space Science Laboratory (LACESM). He has held visiting research positions at the University of Pavia, Italy (2019), and at the Blekinge Institute of Technology (BTH), Sweden (2025). He has served as an Associate Editor for leading journals, including the IEEE Transactions on Geoscience and Remote Sensing and Statistics, Optimization and Information Computing (SOIC). His research spans digital signal processing, statistical signal and image processing, regression and dynamic models, and statistical computing. He has authored over 100 peer-reviewed articles in high-impact international journals and has a strong track record in mentoring young researchers, with several of his students receiving awards from scientific societies.

Mengxin Yu

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Mengxin Yu is an Assistant Professor at Washington University in St. Louis, starting in 2025. Previously, she was a postdoctoral researcher at the University of Pennsylvania and received her Ph.D. in Operations Research and Financial Engineering (Statistics track) from Princeton University. Her research focuses on uncertainty quantification for ranking problems and black-box model predictions, as well as causal inference and robust high-dimensional statistics. Her work aims to develop principled statistical methodologies that are both theoretically grounded and practically applicable, particularly in settings involving complex data structures, synthetic data, and decision-making under uncertainty.
 

Interdisciplinary Contributions. Beyond methodological developments, she has led and actively participated in interdisciplinary collaborations with clinicians and domain scientists, resulting in applications in areas such as cerebral malaria, digital health interventions for cancer patients, and Alzheimer’s disease. These collaborations bridge statistical innovation with real-world challenges, enabling the development of data-driven solutions that improve understanding, prediction, and decision-making in critical health contexts.

Professional Service and Broader Impact. She has also demonstrated sustained service to statistical and professional organizations at the local, national, regional, and international levels. Her contributions include peer-review service for leading journals and conferences, leadership roles in conference organization and session chairing (e.g., JSM, INFORMS), participation in professional committees (such as ASA student paper award committees, conference organization, graduate course design, and seminar coordination), and mentoring of early-career students and researchers.

Monnie McGee

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Monnie McGee received her PhD in statistics from Rice University in Houston, TX and is currently an associate professor at Southern Methodist University in Dallas, TX. Her research focuses on developing statistical methods for complex, high-dimensional, and real-world data, with applications spanning biomedicine, sports analytics, and artificial intelligence. Her work advances modeling for compositional data, including Dirichlet-based approaches that address dependence, zeros, and evolving structures. She has also contributed to the analysis of high-throughput biological data, integrating Bayesian, nonparametric, and machine learning techniques. More recently, her research examines the role of generative AI in statistics, including the reliability and variability of large language models in statistical tasks and education. Across all areas, her work emphasizes practical impact, interdisciplinary collaboration, and the integration of statistical rigor with modern data science.

Dr. McGee is the current Chair of the ASA Committee on Publications, Associate Editor for the Journal of Data Science, Statistics, and Visualization, and Editorial Board Member for Real World Data Science. She has held leadership roles in the American Statistical Association, including Program Chair for the Section on Statistics in Sports, and has served on program committees for the Symposium for Data Science and Statistics and the Virtual Sports Analytics Conference. She regularly contributes to national initiatives such as a National Academies panel on the future of statistics.

Nathaniel Newlands

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Dr. Newlands is a Senior Research Scientist (Ecosystem Modeler) and Team Lead of Data Science and Omics within the Government of Canada (Agriculture and Agri-Food Canada) in the Summerland Research and Development Centre, British Columbia, Canada, and Adjunct Professor in Geography with the University of Victoria, Canada. He is the past recipient of a prestigious Government of Canada national team award, the Public Service Award of Excellence in Innovation.

He is author of the book, “Future Sustainable Ecosystems: Complexity, Risk, Uncertainty” published by Taylor and Francis LCC in 2016, and Co-editor of Urban Food Security in a Crisis Prone World: The Urban, Water, and Food Nexus (Springer Nature, UNFAO and AAFC). He was a Co-chair (2021-2024) of the Group on Earth Observations (GEO)’s Disaster Risk Reduction Working Group and Subgroup 2 Lead on UNDRR Sendai Framework Monitoring and Global Assessments. Currently, he serves as the President of the International Environmetrics Society (TIES), Deputy-Chair of the Global Expert Working Group in Nature-Based Solutions (UN-FAO), and Editor-in-Chief of the international Journal of Applied Statistics: Environmental Statistics and Data Science.

His research work addresses public-good food-water-energy nexus issues and tackles broad, integrated, complex global problems to help support and advance global sustainable development.

Panpan Zhang

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Panpan Zhang is an Assistant Professor of Biostatistics at Vanderbilt University Medical Center and co-leader of the Data Management and Statistics (DMS) Core of the Vanderbilt Alzheimer’s Disease Research Center. His research focuses on developing rigorous statistical and machine learning methods for high-dimensional, multimodal biomedical data arising from multi-center cohort studies, with a particular emphasis on Alzheimer’s disease and related dementias (ADRD).

Dr. Zhang’s methodological contributions span longitudinal data analysis, missing data methods, causal inference, and network-based modeling. He has particular expertise in modeling high-dimensional, multimodal data and characterizing longitudinal trajectories of neuroimaging and cognitive markers to better understand disease heterogeneity and progression in aging populations. His work leverages large-scale cohort studies and national research consortia.

In addition to his research, Dr. Zhang is actively engaged in professional service and leadership. He serves as Program Chair-Elect of the ASA Statistics and Data Science in Aging Interest Group and as an early-career representative on the Data Core Steering Committee of the National Alzheimer’s Coordinating Center (NACC). His work aims to bridge methodological innovation with clinical impact, advancing early detection, risk prediction, and mechanistic understanding of ADRD.

Paola Crippa

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Dr. Paola Crippa is an Assistant Professor at the University of Notre Dame, with a joint appointment in Statistics and Civil and Environmental Engineering. She has established an interdisciplinary research program at the interface of atmospheric science, statistics, and data science, with applications spanning air quality, extreme events, and renewable energy. Her research aims to improve the prediction and uncertainty quantification in complex atmospheric and climate systems, with a focus on environmental hazards and human exposure. Her work advances environmental statistics through the development and application of Bayesian, non-Gaussian, functional data, and machine-learning methods.

Her work has been supported by major funding agencies including the U.S. National Science Foundation and NASA, and contributes to advancing data-driven approaches for environmental decision-making, energy systems, and public health. Among her recognitions, she received the 2015 L’Oréal-UNESCO UK and Ireland Fellowship For Women In Science for research on the societal impacts of wildfires in Southeast Asia and the 2023 NSF CAREER Award for developing multiscale modeling frameworks for the atmospheric boundary layer.