Assistant Professor of Computation Genetics at Albert Einstein College of Medicine, NY.
I am a Biostatistician working in descriptive cancer epidemiology in Queensland, Australia, with a strong research interest in describing temporal and spatial patterns of cancer. My work has a particular focus on health inequalities in cancer-related outcomes. I have published over 180 peer-reviewed journal articles in addition to 20+ commissioned peer-reviewed monographs, and been a chief investigator on externally funded research grants totaling over $7 million. I regularly review scientific manuscripts and grant applications for national and international funding bodies.
Professor in Scientific Computing; Training as an evolutionary biologist working with water frogs in the Mediterranean Sea; Distributor of the Bayesian population genetics inference program MIGRATE.
Interested in computational biology, in particular in computational population genetics and phylogenetics
Dr. Berghout received her PhD in Biochemistry from McGill University in Montreal, QC where she researched the genetics of complex traits and susceptibility to infectious disease in humans and mouse models. Following that, she spent three years as the Outreach Coordinator for the Mouse Genome Informatics (MGI) database in Bar Harbor, ME. There, she trained researchers in genetics, genomics, data structures and data mining to answer biological questions, and worked closely with other members of the MGI group to develop and optimize the MGI resource. Now her research interests include genetics of all kinds, personalized medicine, big data, and scientific communication. She is currently pursuing projects in precision medicine for analysis of transcriptome data from patients with rare lung diseases (Sarcoidosis, Coccidiomycosis), and integrative network analysis of complex traits including Alzheimer's Disease. She is currently appointed at the University of Arizona's Center for Biomedical Informatics and Biostatistics (CB2) and The Center for Genetics and Genomic Medicine (TCG2M) in Tucson, AZ.
Karl Broman is Professor in the Department of Biostatistics & Medical Informatics at the University of Wisconsin–Madison; research in statistical genetics; developer of R/qtl (for R).
Karl received a BS in mathematics in 1991, from the University of Wisconsin–Milwaukee, and a PhD in statistics in 1997, from the University of California, Berkeley; his PhD advisor was Terry Speed. He was a postdoctoral fellow with James Weber at the Marshfield Clinic Research Foundation, 1997-1999. He was a faculty member in the Department of Biostatistics at Johns Hopkins University, 1999-2007. In 2007, he moved to the University of Wisconsin–Madison, where he is now Professor.
Karl is a Senior Editor for Genetics, Academic Editor for PeerJ, and a member of the BMC Biology Editorial Board.
Karl is an applied statistician focusing on problems in genetics and genomics – particularly the analysis of meiotic recombination and the genetic dissection of complex traits in experimental organisms. The latter is often called “QTL mapping.” A QTL is a quantitative trait locus – a genetic locus that influences a quantitative trait. Recently he has been focusing on the development of interactive data visualizations for high-dimensional genetic data.
Chris Brown is a clinical trial bio-statistician at the NHMRC Clinical Trails Centre at the University of Sydney. His main area of expertise is in oncology trials but also has experience in cardiology and neonatal research. His main areas of research are in pharmacoepidemiology and statistical methods.
Associate Research Professor of Statistics & Biostatistics at Rutgers University, with adjunct appointments in the Deptartment of Genetics and the Center of Alcohol Studies. Particular interest in how statistics is applied, especially in Biology, Medicine, and particularly Human Genetics.
Associate Professor Biostatistics and Informatics Colorado School of Public Health; Associate Chair for Research Biostatistics and Informatics; Director Colorado Biostatistics Consortium; Director Clinical and Translational Science Institute Biostatistics, Epidemiology and Research Design Program; Theme Chair, Biostatistics Special Interest Group, Association for Clinical and Translational Science
Head of Human and Comparative Genomics Laboratory in the Biodesign Institute at Arizona State University. Affiliated faculty with the Center for Evolution and Medicine, ASU.
My research is at the interface of genetics, statistics, and software development. I am primarily interested in developing statistical models to estimate evolutionary process from large, genomic datasets. Currently most of my research is connected to mutations.
Tianfeng Chai is an Associate Research Scientist at CICS-MD and the Department of Atmospheric & Oceanic Science, University of Maryland, College Park, Maryland, USA. He got his master and bachelor degrees from Tsinghua University in Beijing, majoring in Fluid Mechanics, Engineering Mechanics, and Environmental Engineering. He earned his Ph.D. at the University of Iowa, with his dissertation of "Four-Dimensional Variational Data Assimilation Using Lidar Data" focusing on atmospheric boundary flow. He then worked with Dr. Greg Carmichael to develop chemical transport model adjoints and computational framework for data assimilation applications before moving to working on the NOAA National Air Quality Forecast Capability (NAQFC) project in 2007. He currently works on the inverse modeling problems using HYSPLIT (Hybrid Single Particle Lagrangian Integrated Trajectory Model) to support several projects at NOAA Air Resources Laboratory.
Assistant Professor of Biostatistics, Mayo Clinic. Ph.D., University of Pennsylvania. My research concerns the development and application of powerful and robust statistical methods for high-dimensional "omics" data, arising from modern high-throughput technologies such as microarray and next-generation sequencing. I am particularly interested in methods for microbiome sequencing data. Much of this effort is motivated by ongoing collaborations in projects that study the role of the human microbiome in disease pathogenesis using metagenomic sequencing.
Research interests include statistical genetics, genomics and metagenomics; and high-dimensional statistics.
Gerstner Family Career Development Award, Mayo Clinic Center for Individualized Medicine, 2014
Saul Winegrad Award for Outstanding Dissertation, University of Pennsylvania, 2012
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.