We have an excellent lineup of top-class invited speakers covering a diverse range of topics in statistical modelling.
Brendan Murphy | University College Dublin, Ireland
Homepage: people.ucd.ie/brendan.murphy
Talk title (provisional): An unsupervised record linkage approach using household information to enhance individual matching across different databases
Brendan Murphy is a Full Professor of Statistics at the University College Dublin, Ireland. He is a current Editor of the Annals of Applied Statistics and a former President of the Irish Statistical Association. His research spans mixture modelling, cluster analysis, network models, Bayesian methods, and computational statistics.
Ruth King | University of Edinburgh, UK
Homepage: www.ed.ac.uk/profile/ruth-king
Talk title (provisional): Conditional ecological individual heterogeneity models: Accounting for survivorship bias
Ruth King is the Thomas Bayes’ Chair of Statistics, Director of the Bayes Centre, and co-founder of the Centre for Statistics at the University of Edinburgh. She is also an elected Fellow of the Learned Society of Wales, former President of the International Biometrics Society British and Irish Region, and winner of the Royal Statistical Society Barnett Award. Her research interests are broadly in the area of ecological modelling, including state-space models, capture-recapture models, Bayesian inference, and missing data analysis. She is a co-author of the books “Bayesian Analysis for Population Ecology” and “Modelling Population Dynamics”.
Sonja Greven | Humboldt University of Berlin, Germany
Homepage: www.wiwi.hu-berlin.de/en/Professorships/vwl/statistik/team/grevenso
Talk title (provisional): additive density-on-scalar regression in Bayes Hilbert spaces with an application to gender economics
Sonja Greven is a Full Professor of Statistics at HU Berlin. She leading a number of projects funded by the DFG (German Research Foundation Research), including the “DeSBi: Fusing deep learning and statistics” AI Research Unit as Spokesperson; “Flexible density regression methods” as Principal Investigator; and “Statistical modeling using mouse movements to model measurement error and improve data quality in web surveys” as Joint Principal Investigator. Her research interests lie primarily in the areas of functional data analysis and longitudinal data analysis.
Daniele Durante | Bocconi University, Italy
Homepage: danieledurante.github.io/web/
Talk title (provisional): Bayesian modelling of criminal networks
Daniel Durante is an Associate Professor of Statistics in the Department of Decision Sciences at Bocconi University. He is currently leading the ERC Starting Grant NEMESIS on modelling criminal networks and the Italian PRIN (Project of National Interest) CARONTE on competing/co-evolving causes of death. His research covers network science, computational social science, and demography; and he recently won the prestigious COPSS Emerging Leader Award for his contributions in these areas.
Cynthia Rudin | Duke University, USA
Homepage: users.cs.duke.edu/~cynthia/
Talk title (provisional): simpler machine learning models for a complicated world
Cynthia Rudin is a Distinguished Professor of Computer Science at Duke University, and Director of the Interpretable Machine Learning Lab. She previously held positions at MIT, Columbia University, and New York University (NYU), and holds degrees from the University at Buffalo and Princeton University. She has received a variety of prestigious awards and honours throughout her career, most notably the Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity; this is the “Nobel Prize of AI” and carries a monetary award of €1M. Her research is focused on machine learning tools that help humans to make better decisions, through interpretable machine learning and its application to critical societal problems in healthcare, criminal justice, and energy grid reliability.
Laura Sangalli | Politecnico di Milano, Italy
Homepage: https://sangalli.faculty.polimi.it/
Short course title (provisional): Physics-informed statistical learning
Laura Sangalli is Professor of Statistics at the Department of Mathematics of Politecnico di Milano, Milano, Italy, and member of MOX, the Laboratory for Modeling and Scientific Computing of the Department of Mathematics. Her research centers on statistical methods for complex and high-dimensional data, with a focus on physics-informed statistical learning approaches for functional and spatial data. She currently act as co-Editor (Section: Methods) of Statistical Methods and Applications and co-Editor in Chief of Journal of Computational and Graphical Statistics. She acted as panel member for ERC Starting grants 2021 and 2023 for PE1 (Mathematics), and various other international granting schemes. She serves as Elected Vice-President of GRASPA, the Section of the Italian Statistical Society devoted to Statistical Applications to Environmental Problems.