Krzysztof Jajuga
Wrocław University of Economics and Business
Title of the talk: Data Science and Artificial Intelligence – historical links and future challenges
Krzysztof Jajuga is a full professor of finance at Wroclaw University of Economics and Business, Poland. He holds master, doctoral and habilitation degrees from Wroclaw University of Economics and honorary doctorates from Cracow University of Economics and Academy Higher School of Business. His research focuses on multivariate statistics with applications in economics and finance, financial markets, risk management and household finance. He is editor in chief of Argumenta Oeconomica (JCR journal). He was chairperson of Section of Classification and Data Analysis of Polish Statistical Society (SKAD). Since 1993 he has been a member of IFCS Council. In the period between 2002 and 2006 he served as IFCS Publication Officer and was a member of IFCS Executive Committee. He holds the position of IFCS President for the period of 2025-2026.
Krzysztof Jajuga is a full professor of finance at Wroclaw University of Economics and Business, Poland. He holds master, doctoral and habilitation degrees from Wroclaw University of Economics and honorary doctorates from Cracow University of Economics and Academy Higher School of Business. His research focuses on multivariate statistics with applications in economics and finance, financial markets, risk management and household finance. He is editor in chief of Argumenta Oeconomica (JCR journal). He was chairperson of Section of Classification and Data Analysis of Polish Statistical Society (SKAD). Since 1993 he has been a member of IFCS Council. In the period between 2002 and 2006 he served as IFCS Publication Officer and was a member of IFCS Executive Committee. He holds the position of IFCS President for the period of 2025-2026.
Laura Sangalli
Politecnico di Milano
Title of the talk: Physics-Informed Statistical Learning for spatial and functional data
Laura Sangalli is Professor of Statistics at Politecnico di Milano, Italy, and a member of MOX, the Laboratory for Modeling and Scientific Computing of the Department of Mathematics. Her research focus on statistical methods for complex and high-dimensional data. She serves as panel member for ERC Starting Grants (PE1, Mathematics) and other international funding boards, and is Vice-President of GRASPA, the environmental statistics section of the Italian Statistical Society. She currently acts as co-Editor of Statistical Methods and Applications and co-Editor-in-Chief of the Journal of Computational and Graphical Statistics.
Laura Sangalli is Professor of Statistics at Politecnico di Milano, Italy, and a member of MOX, the Laboratory for Modeling and Scientific Computing of the Department of Mathematics. Her research focus on statistical methods for complex and high-dimensional data. She serves as panel member for ERC Starting Grants (PE1, Mathematics) and other international funding boards, and is Vice-President of GRASPA, the environmental statistics section of the Italian Statistical Society. She currently acts as co-Editor of Statistical Methods and Applications and co-Editor-in-Chief of the Journal of Computational and Graphical Statistics.
Brendan Murphy
University College Dublin
Title of the talk: Partial membership models for model-based soft clustering
Brendan Murphy is Professor of Statistics in University College Dublin, Ireland and he works on statistical modelling in a wide range of domains, including the biomedical and social sciences. He has recently served as head of the School of Mathematics & Statistics at University College Dublin. He has been a visiting fellow at the Center for Statistics & Social Sciences at University of Washington and Institut d’Études Avancées de Lyon. He has served as Area Editor of the Annals of Applied Statistics and he is currently a member of the Statistical and Methodology Review Committee for Proceedings of the National Academy of Sciences. He was recently elected as Fellow of the American Statistical Association and has been selected as the Honorary Officer for Journals by the Royal Statistical Society.
Brendan Murphy is Professor of Statistics in University College Dublin, Ireland and he works on statistical modelling in a wide range of domains, including the biomedical and social sciences. He has recently served as head of the School of Mathematics & Statistics at University College Dublin. He has been a visiting fellow at the Center for Statistics & Social Sciences at University of Washington and Institut d’Études Avancées de Lyon. He has served as Area Editor of the Annals of Applied Statistics and he is currently a member of the Statistical and Methodology Review Committee for Proceedings of the National Academy of Sciences. He was recently elected as Fellow of the American Statistical Association and has been selected as the Honorary Officer for Journals by the Royal Statistical Society.
Mohamed Nadif
Université Paris Cité
Title of the talk: Boosting Clustering Performance: Approaches and Key Insights
Mohamed Nadif is a Full Professor of Machine Learning and Data Science at Université Paris Cité, where he is a member of the Centre Borelli (CNRS UMR 9010). He earned his PhD in 1991 from University of Lorraine (France) with a dissertation focused on clustering and missing data. He leads the “Artificial Intelligence for Data Science and Cybersecurity” research team. He served as President of the Francophone Classification Society (Société Francophone de Classification) until 2020. His research focuses on diverse approaches for clustering including block models, deep learning, representation learning, and spectral or factorization methods. His work has been presented at major conferences, including AAAI, NeurIPS, ICML, and published in leading scientific journals.
Mohamed Nadif is a Full Professor of Machine Learning and Data Science at Université Paris Cité, where he is a member of the Centre Borelli (CNRS UMR 9010). He earned his PhD in 1991 from University of Lorraine (France) with a dissertation focused on clustering and missing data. He leads the “Artificial Intelligence for Data Science and Cybersecurity” research team. He served as President of the Francophone Classification Society (Société Francophone de Classification) until 2020. His research focuses on diverse approaches for clustering including block models, deep learning, representation learning, and spectral or factorization methods. His work has been presented at major conferences, including AAAI, NeurIPS, ICML, and published in leading scientific journals.