2nd International Conference on Statistics: Theory and Applications (ICSTA’20)
Please see the Keynote Speakers for the 2nd International Conference on Statistics: Theory and Applications (ICSTA'20) listed below:
Dr. Min-ge Xie
Rutgers University, USA
Min-ge Xie is Distinguished Professor of Statistics in Department of Statistics, Rutgers University and Director of Rutgers Office of Statistical Consulting. His main research interest is in the foundation of statistical inference, confidence distribution, fusion learning, estimating equations and developing new statistical methodologies and theories for problems stemming from interdisciplinary research. He received the B.S. degree in mathematics with a high honor from the University of Science and Technology of China (USTC) and the M.S. and Ph.D. degrees in statistics from University of Illinois at Urbana-Champaign (UIUC). His research has been supported in part by grants from the National Science Foundation (NSF), the National Institute of Health (NIH), The Department of Veterans Affairs (VA), the Federal Aviation Administration, among others.
Topic of Keynote: A union of BFF (Bayesian, frequentist and fiducial) inferences by confidence distribution and Monte-Carlo based inference
Dr. Mu Zhu
University of Waterloo, Canada
Mu Zhu is Professor of Statistics at the University of Waterloo, Canada. He obtained his undergraduate degree from Harvard University in 1995, and his doctoral degree from Stanford University in 2001. In 2012, he served as President of the Business and Industrial Statistics Section for the Statistical Society of Canada. In 2019, he was elected Fellow of the American Statistical Association.
Topic of Keynote: Some statistical applications of generative neural networks
Dr. Mohsen Pourahmadi
Texas A&M University, USA
Mohsen Pourahmadi is Professor of Statistics at Texas A&M University, and author of two Wiley monographs, High-Dimensional Covariance Estimation (2013) and Foundations of Time Series Analysis and Prediction Theory (2001). His current research is focused on modeling dependence (covariances) in multivariate, longitudinal and time series data using covariates where the goal is to develop machinery for covariance matrices just like the powerful generalized linear models (GLM) for the mean vector developed over two centuries. The key ideas and tools used are from linear models, multivariate statistics, linear algebra, prediction theory, time series analysis and stochastic processes. His interest in applications includes financial data analysis, analysis of longitudinal and panel data, classification and clustering, fMRI and high-dimensional data.
Topic of Keynote: Recent Trends in High-Dimensional Covariance Estimation: A GLM Perspective
Dr. Hosam M. Mahmoud
George Washington University, USA
Dr. Hosam M. Mahmoud (Professor of Statistics, 1983 Ph.D.) is an elected member of the International Statistical Institute. He currently serves as an Editor of Journal of Applied Probability and Editor of Advances in Applied Probability (publications of the UK Probability Trust). He is also an Associate Editor of the Annals of the Institute of Statistical Mathematics (Japan), an Associate Editor of Methodology and Computing in Applied Probability (USA), and an Associate Editor of Applicable Analysis and Discrete Mathematics (Serbia).
He has research interest in the areas of probabilistic analysis of algorithms, networks, big data, searching and sorting, random structures, and randomized algorithms. He has served as department chair in 1998-2001, and visited numerous institutions worldwide. Dr. Mahmoud is a productive scholar with four books and more than 100 peer-refereed papers, of which 25 are single-authored and many are in premier journals.
Professor Mahmoud spent sabbatical visits at University of Waterloo (Waterloo, Canada, 1990), Institut National de Recherche (Rocquencourt, France, 1997), Princeton University (Princeton, New Jersey, USA, 1998), the Institute of Statistical Mathematics (Tokyo, Japan, 2004), Purdue University (West Lafayette, Indiana, USA, 2012), University of Southern California, USA, 2019) and Center for Complex Network Research (Boston, USA, 2019).
Topic of Keynote: Pólya urns: Probabilistic analysis and statistical questions
Dr. Dirk Husmeier
University of Glasgow, UK
Dirk Husmeier holds a Chair of Statistics at the University of Glasgow. He has made contributions to a wide range of scientific disciplines, including statistical physics (University of Bochum, Germany), neural computation (King’s College London), machine learning (Imperial College London), bioinformatics and systems biology (Biomathematics & Statistics Scotland), statistical methodology, ecology and cell and soft tissue biology (University of Glasgow). This is evidenced by over 130 peer-reviewed publications in international journals and conference proceedings, the publication of two books (on Neural Computation and Bioinformatics), and the successful supervision of 14 former postgraduate students and 8 former postdoctoral research assistants (currently supervising 8 PhD students and 1 postdoc). DH’s international recognition is evidenced by the fact that he has been invited to serve on the programme committees of 16 international workshops and conferences, he is a member of the editorial boards of three journals (Statistics and Computing, IEEE/ACM Transactions on Computational Biology and Bioinformatics, and Statistical Applications in Genetics and Molecular Biology), and he served as an associate editor of the Journal of the Royal Statistical Society, Series C (Applied Statistics) from 2014 to 2018 (editorial board membership is restricted to 4 years for this journal). DH was appointed as external examiner for the Faculty of Mathematics of the University of Cambridge from 2013 to 2016 (Programme: MPhil in Computational Biology), and by the National University of Galway from January to June 2018 to act as reviewer for their research assessment exercise. DH was a member of the senior management group of Biomathematics & Statistics Scotland (BioSS, a research institute associated with the Hutton Institute), from 2004 to 2008, he was a member of the management group of the BBSRC-funded Centre for Systems Biology at Edinburgh, 2004-2008, and he is currently a member of the local executive committee of SoftMech, the EPSRC-funded Centre for Soft Tissue Mechanics (since 2015). DH is currently holding a sabbatical research grant awarded by the Royal Society of Edinburgh (1 July 2019—30 June 2020).
Topic of Keynote: Computationally efficient parameter estimation and uncertainty quantification in complex physiological systems
Dr. Christopher Franck
Virginia Tech, USA
Chris Franck is an assistant professor in the Department of Statistics at Virginia Tech. He received his Ph.D. in Statistics from North Carolina State University in 2010 under the direction of Jason Osborne. His research focuses in Bayesian model selection and averaging, objective Bayes, and spatial statistics. Much of his work has a specific emphasis in health applications. From 2010-2016, He was the assistant director of LISA, the Laboratory for Interdisciplinary Statistical Analysis. In this role he worked on a large variety of research projects spanning medical, psychological, bioinformatic, biomechanical, and other areas. In 2016, he converted to an assistant professor in the Department of Statistics at Virginia Tech.
Topic of Keynote: Hiding in plain sight: latent grouping factors in linear models