Distinguished Professor, Quetelet Endowed Professor of Public Health, Associate Dean for Science, Director of Office of Energetics, and Director of the Nutrition Obesity Research Center; Authored over 450 peer-reviewed publications and five books. 2012 IOM Member. 2002 ILSI Board of Trustees. Elected Fellow ASA, APA, & AAAS. 2002 Andre Mayer Award from IASO; 2009 ASN Centrum Award; 2002 Lilly Scientific Achievement Award & 2009 TOPS Award from the Obesity Society; 2006 NSF PAESMEM Award.
Assistant Professor of Computation Genetics at Albert Einstein College of Medicine, NY.
Gary has research interests primarily focussed on statistical (and reporting) aspects in developing and validating multivariable prediction models. He has published over 100 papers on clinical trials, observational studies, systematic reviews, quality of life, propensity scores and prediction models.
Gary is a statistical editor ("hanging committee") for the BMJ.
Gary also led the development of the TRIPOD Statement for reporting clinical prediction models - www.tripod-statement.org.
Xiangqin Cui is an associate professor in the Biostatistics Department, Section on Statistical Genetics, at University of Alabama at Birmingham. She received her Ph.D in Genetics at Iowa State University in 2001 and a three-year postdoctoral training in statistical genetics at the Jackson Laboratory afterwards. Her research is on experimental design and data analyses of high-throughput experiments including microarray, next-generation sequencing, statistical genetics/genomics, epigenomics.
Dr Martin Daumer: Director of the SLCMSR e.v. - The Human Motion Institute in Munich and managing director of the IT company, Trium Analysis Online GmbH. He is also visiting lecturer for Telemedicine and Clinical Applications of Computational Medicine at the Technical University Munich.
Dr Daumer received a diploma in Physics in 1990 and a Ph.D. in mathematics from the Ludwig-Maximilians-University Munich in 1995, after having worked at CERN, Switzerland, and Rutgers University, USA.
Professor of Structural Biology and Director of the Systems Approaches to Biomedical Sciences Industrial Doctoral Centre at Oxford University.
Senior Research Fellow at the Harvard Forest and Adjunct Professor at the University of Massachuestts; Editor-in-Chief of Ecological Monographs; Presidential Faculty Fellow (US National Science Foundation), Eminent Ecologist (Kellogg Biological Station), Distinguished Visiting Professor (University of Miami), Distinguished Ecologist (Michigan Technical University), Ledermann Lecturer in Natural History and Cosnervation Biology (University of Rhode Island), Fellow (Ecological Society of America)
Tampere University of Technology. Head of the Computational Medicine and Statistical Learning Laboratory.
Assistant Professor of Computer Science at Princeton University. My group develops statistical models and methods for high-dimensional genomic data, modeling human genetic variation and its impact on gene expression and splicing, with the goal of identifying mechanisms of human disorders and diseases.
Dr. Kattan's research is primarily focused on the development, validation, and use of prediction models. Most of these models are available online, and designed for physician use, at http://rcalc.ccf.org/. He is also interested in quality of life assessment to support medical decision making (such as utility assessment), decision analysis, cost-effectiveness analysis, and comparative effectiveness.
Professor of Systems Engineering and Operations Research and Associate Director of the Center of Excellence in Command, Control, Communications, Computing and Intelligence at George Mason University. Member of Board of Directors, Association for Uncertainty in Artificial Intelligence
Ben Letcher is a quantitative stream ecologist working at the interface of field studies and mathematical models of population and evolutionary dynamics. My group is combining information from long-term intensive studies of stream fish with extensive studies to develop broad scale models of population response to environmental change.