Welcome to AMyC2024
The VI International Workshop on Proximity Data, Multivariate Analysis and Classification will take place during November, 28-29, 2024 in Girona (Spain). It is organized by the Multivariate Analysis and Classification Spanish SEIO Group (AMyC-SEIO).
AMyC is a Working Group of around 100 researchers from all the Spanish universities. Every year, the Working Group organizes a meeting to promote the communication among its members and between them and other researchers, and to contribute to the development of the Multivariate Analysis and Classification field and related problems and applications.
Looking forward to meeting you in Girona!
Keynote speakers
Prof. Victor Elvira, University of Edinburgh
Victor Elvira is a Professor with a Personal Chair in Statistics and Data Science at the School of Mathematics at the University of Edinburgh since 2023. Previously, he was a Reader (2019-2023) and the Director of the Centre for Statistics (2022) also at the University of Edinburgh (UK). From 2016 to 2019, he was an Associate Professor at the engineering school IMT Lille Douai (France). From 2013 to 2016, he was an Assistant Professor at University Carlos III of Madrid (Spain). He has also been a visiting researcher at several institutions such as Columbia University (USA), University of Sydney (Australia), and Paris-Dauphine University (France). Prof. Elvira received his Ph.D. degree in statistical signal processing in 2011 from the University of Cantabria (Spain). Prof. Elvira's has co-authored more than 150 journal and refereed conference papers. His research interests are mostly in the fields of computational statistics, statistical signal processing, and probabilistic machine learning, in particular in Bayesian inference and Monte Carlo methods with different applications including sensor networks, wireless communications, target tracking, ecology, and biomedicine. He is a Fulbright Fellow, Marie Curie Fellow, Leverhulme Fellow, Alan Turing Fellow, and IEEE Senior Member. He has also received the 2024 EURASIP Early Career Award, from the European Association for Signal Processing, for contributions in the theory and practice of importance sampling and particle filtering methodologies.
Prof. Michele Gallo, University of Naples
In 1995, he graduated in Economics and Business from the University of Naples - Federico II, where he also earned a Doctorate in "Total Quality Management" in 1999. Since October 2020, he has been a Full Professor of Statistics in the Department of Human and Social Sciences at the University of Naples - L'Orientale, teaching a basic Statistics course for undergraduate students and an advanced course on multidimensional methods for graduate students. Since January 2020, he has also been the Chancellor’s Delegate for Orientation and Tutoring Services. Previously, he served as an Associate Professor of Statistics in the Department of Human and Social Sciences (formerly the Faculty of Political Sciences) from January 2005 to December 2011, and as an Assistant Professor of Statistics in the Faculty of Political Sciences.
He was a Researcher Assistant in "Statistical Methods for the Total Quality Management" in the Department of Mathematical and Statistical Sciences at the University of Naples - Federico II from November 2000 to December 2001. At the University of Naples - L'Orientale, Michele Gallo was the coordinator of the three-year degree course "Political Science and International Relations" (L-36 Political Science and International Relations) from November 2012 to October 2016 and coordinator of the Ph.D. program in “Institution, Law and Economics of Public Services” from November 2008 to October 2014. He also served as President of the Interdepartmental Service Centre for Telematics and Informatics from January 2007 to December 2011 and as Quality Assurance Manager for Scientific Research from June 2004 to December 2010. Michele Gallo's primary research interests include: - Multivariate data analysis - Tensor analysis - Compositional analysis - Rasch analysis - Applied Statistics.
Prof. Mia Hubert, KU Leuven
Mia Hubert is professor at the KU Leuven, department of Mathematics, section of Statistics and Data Science. Her research focuses on robust statistics, outlier detection, data visualization, depth functions, and the development of statistical software. She is an elected fellow of the ISI and has served as associate editor for several journals such as JCGS, CSDA, and Technometrics. She is co-founder and organizer of The Rousseeuw Prize for Statistics, a biennial prize which awards pioneering work in statistical methodology.
Dr. Nirian Martín, Complutense University of Madrid
Nirian Martín is an Associate Professor in the department of Statistics & Operations Research and the Interdisciplinary Mathematics Institute at the Mathematics Faculty in the Complutense University of Madrid since 2023. Previously, she was an Associate Professor (2015-2023) at the Commerce and Tourism Faculty in the same university. From 2005 to 2015, she was an Assistant Professor in Carlos III University of Madrid (2010-2015) and in Complutense University of Madrid (2005-2009). She has had several reasearch appointments as Visiting Scientist position at Harvard University and Dana Farber Cancer Institute (whole academic year 2008-2009, Departments of Biostatistics and Biostatistics & Computational Biology), Guest researcher in McMaster University (June-July 2011, Department of Mathematics & Statistics), and Visiting Professor in McMaster University (March-August 2019, Department of Mathematics & Statistics). Prof. Nirian Martín has published more than 70 refereed journal papers and has served as associate editor for Journal of Multivariate Analysis, Communications in Statistics - Theory & Methods, Communications in Statistics - Simulation & Computation, Communications in Statistics - Case Studies, Data Analysis and Applications. Her research interests are mostly in the fields of Categorical data analysis, Generalized linear models, Statistical methods in public health for cancer surveillance, Change points, Empirical likelihood, Small area estimation, Order restricted statistical inference, Regression diagnostic, Robust statistics and Reliability.
