Proceedings of the 6^th International Conference
on Statistics: Theory and Applications (ICSTA 2024)

Dr. Michael Evans
University of Toronto, Canada

Michael Evans is a Professor of Statistics at the University of Toronto. He received his Ph.D. from the University of Toronto in 1977 and has been employed there ever since with leaves spent at Stanford University and Carnegie Mellon University. He is a Fellow of the American Statistical Association, he served as Chair of the Department of Statistcs 1992-97, Interim Chair 2022-23 and as President of the Statistical Society of Canada 2013-2014. He has served in a number of editorial capacities as follows: Associate Editor of JASA Theory and Methods 1991-2005, Associate Editor of the Canadian Journal of Statistics 1999-2006 and 2017-present, Associate Editor of the journal Bayesian Analysis 2005-2015, and as an Editor 2015-2021, Subject matter Editor for the online journal FACETS (current) and Associate Editor of the New England Journal of Statistics in Data Science (current).

Michael Evans’ research has been concerned with multivariate statistical methodology, computational statistics, and the foundations of statistics. A current focus of research is the development of a theory of inference based upon an explicit definition of how to measure statistical evidence and the development of tools to deal with criticisms of statistical methodology associated with its inherent subjectivity. He has authored, or coauthored, numerous research papers as well as the books Approximating Integrals via Monte Carlo and Deterministic Methods (with T. Swartz) published by Oxford in 2000, Probability and Statistics: The Science of Uncertainty (with J. Rosenthal) published by W.H. Freeman in 2004 and 2010 and Measuring Statistical Evidence Using Relative Belief published by CRC Press/Chapman and Hall in 2015.

Topic of keynote:
The Concept of Statistical Evidence: Historical Roots and Current Developments

Dr. Christopher Franck
Virginia Tech, USA

Chris Franck is an Associate Professor in the Department of Statistics at Virginia Tech. He is an application-oriented methodologist who focuses on statistical problems in health applications, behavioral economics, probabilistic forecasting, bioinformatics, and other areas. His work includes Bayesian statistical methodologies that can be implemented automatically and/or with objective prior information, Model selection and averaging approaches and practical methods by which to assess their sensitivities, and finally, in cases where historical data is inadequate and contemporary information is available, he develops Bayesian methods that allow researchers to formally incorporate subjective information into their analyses and predictions.

Topic of Keynote:
The Responsible Emboldening Of Probability Forecasts

Dr. Marco Grzegorczyk
University of Groningen, Netherland

Marco Grzegorczyk is Associate Professor for Computational Statistics at the Bernoulli Institute of Groningen University (NL). He received his PhD in Statistics from the TU Dortmund University in Germany in 2006. Since then his main research focus has been on Bayesian network structure learning with a special focus on learning gene regulatory pathways and protein activation cascades from molecular data. He is Associate Editor of the journals `Computational Statistics’, ‘The Journal of Applied Statistics’ and `Statistica Neerlandica’. Also he is involved in the Statistical Modelling Society (treasurer) and the International Biometric Society (representative council).

Topic of Keynote:
The Flexibility of Gaussian Bayesian Networks

Dr. Hosam M. Mahmoud
George Washington University, USA

N/A

Topic of Keynote:
Recursive Trees with Generalized Affinities

Dr. Min-ge Xie
Rutgers University, USA

Min-ge Xie, PhD is a Distinguished Professor at Rutgers, The State University of New Jersey. Dr. Xie received his PhD in Statistics from University of Illinois at Urbana-Champaign and his BS in Mathematics from University of Science and Technology of China. He is the current Editor of The American Statistician and a co-founding Editor-in-Chief of The New England Journal of Statistics in Data Science. He is a fellow of ASA, IMS, and an elected member of ISI. His research interests include theoretical foundations of statistical inference and data science, fusion learning, finite and large sample theories, parametric and nonparametric methods. He is the Director of the Rutgers Office of Statistical Consulting and has a rich interdisciplinary research experiences in collaborating with computer scientists, engineers, biomedical researchers, and scientists in other fields.

Topic of Plenary:
Repro Samples Method for Addressing Irregular Inference Problems and for Unraveling Machine Learning Blackboxess