SIPTA Online School

Fabio Cozman: Credal Networks: Specification, Algorithms, Complexity

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Fabio G. Cozman is a Full Professor at Escola Politécnica, Universidade de São Paulo (USP), Director of the Center for Artificial Intelligence at USP, and researcher with an interest in machine learning and knowledge/uncertainty representation. Engineer (USP) and PhD (Carnegie Mellon University, USA), he has served, amongst other duties, as Program and General Chair of the Conference on Uncertainty in Artificial Intelligence, Area Chair of the International Joint Conference on Artificial Intelligence, and Associate Editor of the Artificial Intelligence Journal, the Journal of Artificial Intelligence Research, and the Journal of Approximate Reasoning. He chaired the Special Committee on Artificial Intelligence of the Brazilian Computer Society, and received the Prize for Scientific Merit in Artificial Intelligence by that society.

Denis D. Mauá is an Assistant Professor at the Department of Computer Science of the Institute of Mathematics and Statistics of the University of São Paulo. He obtained his PhD from the University of Lugano (2013), and his Masters (2009) and Engineering degrees (2007) from the University of São Paulo. He has authored and co-authored over 50 articles in journals and conference proceedings.
His research focus on the theory of probabilistic reasoning, and its application to artificial intelligence and machine learning.

Glenn Shafer: Game theoretic foundations for statistical testing and imprecise probablities

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Dr. Glenn Shafer is the co-creator of the Dempster-Shafer Theory. He is a University Professor and Board of Governors Professor at Rutgers University. He received a bachelor's degree in mathematics from Princeton University, then entered the Peace Corps, serving in Afghanistan. He returned to Princeton, earning a PhD in mathematical statistics in 1973. During the 1970s and 1980s he expanded a theory first introduced by Arthur P. Dempster to create the Dempster-Shafer Theory, also described as the theory of belief functions or evidence theory. It is a general framework for reasoning with uncertainty. The theory and its extensions have been of particular interest to the artificial intelligence community

Ignacio Montes: Introduction to imprecise probability

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Ignacio Montes is an Assistant Professor at the Department of Statistics and Operations Research at the University of Oviedo. He got the PhD degree on Mathematics and Statistics in 2014 with a thesis entitled “Comparison of alternatives under uncertainty and imprecision”. After finishing his PhD, he was a postdoctoral student at the HEUSIASYC Research Unit of the Technologic University of Compiegne and an Assistant Professor at the Carlos III University of Madrid. His research focuses on different theoretical aspects of the imprecise probability theory.

Ryan Martin: Inferential models

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Dr. Ryan Martin is a Professor in the Department of Statistics at North Carolina State University, USA. His research interests include asymptotics, empirical Bayes analysis, high- and infinite dimensional inference problems, foundations of statistics, imprecise probability, mixture models, etc. He is co-author of the monograph Inferential Models and co-founder of the Researchers. One peer review and publication platform.

Seamus Bradley: Expert aggregation

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Dr. Seamus Bradley is currently a Marie Curie Individual Fellow at the University of Leeds, with interests in Epistemology, Decision Theory, Philosophy of Science and Logic. Before working at the University of Leeds, Dr Bradley was an assistant professor at the University of Tilburg Philosophy Department, and before that was a postdoctoral research fellow at the Munich Centre for Mathematical Philosophy. Dr Bradley completed a philosophy PhD at the London School of Economics in 2012 with a thesis title of "Scientific Uncertainty and Decision Making”.