Patrick Groenen

Erasmus School of Economics, Netherlands

Patrick J.F. Groenen is a professor of statistics at the Erasmus School of Economics (ESE). He currently is also dean of that school. Professor Groenen’s work focuses on data science techniques and their numerical algorithms. He is the co-author of several textbooks on multidimensional scaling published by Springer and has published articles in the top peer-reviewed journals including, among others, the Journal of Machine Learning Research, the Journal of Marketing Research, Psychological Methods, Psychometrika, the Journal of Classification, Computational Statistics and Data Analysis, the British Journal of Mathematical and Statistical Psychology, and the Journal of Empirical Finance.

https://www.eur.nl/people/patrick-groenen

Sugnet Lubbe

Stellenbosch University, South Africa

Her field of research is focused in general on multivariate data analysis, and more specifically multidimensional visualisation. After 13 years in industry she moved to academia in July 2009 as associate professor at the University of Cape Town. In January 2017 she joined Stellenbosch University as professor in statistics. Her research is both on theoretical developments of methods for visualisation, R software development to implement methods and applications leading to new extensions of current methodology. Together with Prof Niël le Roux and Prof John Gower they published the monograph, Understanding Biplots in 2011. In 2021 the research team was formalised into the Centre for Multi-dimensional Data Visualisation, also known as MuViSU. Since inception she have been heading up this dynamic team of researchers currently consisting of 22 members and 6 post graduate students.

Beatriz Cobo Rodríguez

University of Granada, Spain

Beatriz Cobo Rodríguez holds a PhD in Mathematical and Applied Statistics from the University of Granada and a Technical Engineer in Management Informatics from the University of Jaén. Currently she is University professor at the Department of Quantitative Methods for Economics and Business in the University of Granada. She has focused her research on the use of techniques to reduce social desirability bias, specifically using indirect questioning techniques with the aim of obtaining more efficient estimators for sensitive questions, making use, for example, of auxiliary information and its computational treatment as shown in her articles and published books chapters.

Arnoldo Müller-Molina

University of Chicago, USA

Arnoldo founded simMachines, an award-winning similarity search company. simMachines pioneered the field of Explainable A.I., successfully deploying the technology in Fortune 500 companies.
The company was acquired by InRule Inc. in 2021. Before becoming an entrepreneur, Arnoldo was a postdoc at the Max Planck Institute for Molecular Biomedicine. He began his career at Intel developing data extraction and transformation tools for factory and financial data. Arnoldo holds a Ph.D. and MSc from Kyushu Institute of Technology Japan. His work in AI has been featured in well-known publications such as Forbes and awarded by MIT Technology Review TR35. Arnoldo is the CTO of the TMW Center at the University of Chicago, where he builds an AI powered wearable technology for Early Childhood Education and policy making.

Angela Montanari

University of Bologna, Italy

Angela Montanari is a full professor at Bologna University since 2000; previously, she became researcher at the Faculty of Statistics in 1990 and Associate Professor in 1998. She has also been the Dean of the Faculty of Statistics (Bologna University) in the period 2007-2012. Past-President of the IFCS (2020-2022), she will give the Presidential Addres entitled "(Data oblivious) Random Projections for (data aware) Model-based Clustering" at the IFCS 2024.

Mark de Rooij

Leiden University, The Netherlands

Mark de Rooij is professor of AI and Data Theory at the Methodology and Statistics department at Leiden University. His main research line is about the analysis and visualization of multivariate categorical response variables. Therefore, he combines ideas from various types of logistic regression with those of biplots. Another research line is about multi-view data for supervised analysis, where he specifically investigates so-called late fusion methods.

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Theodorus Chadjipadelis

Aristotle University of Thessalonik, Greece

Theodore Chadjipadelis is Professor of applied statistics and the director of the Laboratory of Applied Political Research. His research interests cover the field of Applied Statistics and mainly refer to issues of experiment design, statistical research training, public opinion, political and electoral behaviour, electoral geography, election systems as well as urban and regional programming and development. Among others projects, he represent A.U.TH in the HORIZON project AI4Gov which is aimed at exploring the possibilities of Artificial Intelligence (AI) and Big Data technologies for developing evidence-based innovations, policies. He has published more than 100 scholarly studies and he has lectured on these subjects in Greece and abroad.

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