Speakers

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Speakers

Tomasz Żądło (University of Economics in Katowice)

Tomasz Żądło is employed as an associate professor at the Department of Statistics, Econometrics and Mathematics at the University of Economics in Katowice. His research interests focus on small-area estimation, mixed models and bootstrap methods. He is a co-author of the R package “qape”, available on CRAN, designed for prediction, accuracy estimation and Monte Carlo analysis under linear mixed models. He has published numerous papers in international journals, including International Journal of Machine Learning and Cybernetics, The R Journal, Social Indicators Research, Journal of Official Statistics, Australian and New Zealand Journal of Statistics and Statistical Papers. He is an elected member of the International Statistical Institute, a country representative of the International Association of Survey Statisticians, and a member of the Scientific Statistical Council, an advisory and opinion-making body for statistical research methodology supporting the President of Statistics Poland. 

Anastasija Tetereva (Erasmus University Rotterdam)

Anastasija Tetereva is an Assistant Professor at the Erasmus School of Economics, Erasmus University Rotterdam. Her research lies at the intersection of financial econometrics, asset pricing, and machine learning, with a focus on developing data-driven methods for forecasting, portfolio allocation, and risk measurement. She designs econometric and machine-learning frameworks that exploit rich financial information - such as high-dimensional firm characteristics, macro-financial variables, textual disclosures, and other unstructured data - to improve prediction and economic decision-making. Her work includes tree-based and ensemble approaches for volatility and tail-risk forecasting, machine-learning methods for asset allocation and stochastic discount factor construction. Her research emphasizes interpretable, economically grounded machine-learning tools that deliver measurable improvements.

Marcin Szymkowiak (Poznań University of Economics and Business)

Professor at the Department of Statistics, Poznań University of Economics and Business (Institute of Informatics and Quantitative Economics), Deputy Director at the Statistical Office in Poznań, United Nations Expert in the Expert Group on the Revision of the Principles and Recommendations for Population and Housing Censuses for the 2030 Round, member of the Main Council of the Polish Statistical Association and Chair of the Council of the Poznań Branch of the Polish Statistical Association, with many years of experience in statistical data analysis. He specializes in small area statistics, methods for handling non-response (imputation and calibration), survey sampling methods (including non-probability samples and big data), multivariate data analysis, and statistical data integration methods (probabilistic record linkage and statistical matching). His main research interests focus on the application of statistical methods in economics, including poverty, the labour market, and disability. He has participated in numerous national and international research projects in collaboration with Statistics Poland, the United Nations, the World Bank, and Eurostat (including Eurarea, ESSnet on Small Area Estimation, MEETS, VIP Admin, Memobust, Improving the Quality of EU Censuses from 2021, Foreigners in the Polish Regional Labour Market, and Extending Labour Market and Education Indicators in the Labour Force Survey). He is also the author and co-author of numerous articles and reports in the fields of small area statistics, calibration, data integration, and the application of statistics in economics and management. A passionate chess player, table tennis enthusiast, and lover of high-mountain literature.

Marco Puts (Statistics Netherlands)

Marco Puts is a methodologist at Statistics Netherlands. His work focuses on the methodological foundations of using Machine Learning in official statistics. He has contributed important ideas to survey methodology, including work related to the Total Machine Learning Error Framework, which extends classical survey error thinking to AI-based statistical production. Marco’s research aims to support the responsible and transparent use of machine learning in modern statistical systems.

Danutė Krapavickaitė (Vilnius Gediminas Technical University)

Danutė Krapavickaitė worked as a professor at the Vilnius Gediminas Technical University and authored several textbooks in Lithuanian. Currently she is a data analyst and researcher at the department of Mathematical Statistics, specializing in the survey methodology and official statistics. Her work focuses on sampling design, survey estimation, and the application of statistical methods in socio‑economic data analysis. Danutė has extensive experience in the field, having worked at Statistics Lithuania for almost twenty years, where she played a key role in the development of the social surveys. Danutė Krapavickaitė is a member of IASS, she has been a long term editor of the IASS newsletter The Survey Statistician and editor of Lithuanian Journal of Statistics. She is one of the creators of the BNU Network on Survey Statistics.

Henri Luomaranta-Helmivuo (Statistics Finland)

Henri Luomaranta-Helmivuo is the head of Methodology at Statistics Finland. He has specialized knowledge in statistics production processes and has developed many new statistical products using innovative methodologies, and examples of his work include nowcasting or new experimental trade-in-value added statistics. Much of his work involves the use of AI in statistics production, as Henri helps to bridge the gap between slow-moving official reports and the need for immediate economic insights in a rapidly changing world.

Kaja Sõstra (Statistics Estonia)

TBA

Kristi Lehto (Statistics Estonia)

TBA

Contributed papers

TBA