The program hosts a variety of seminar series and other events throughout the year including the Correlated and High-Dimensional Data Seminar, the Quantitative Issues in Cancer Research Working Seminar, the Environmental Statistics Seminar, the Program in Quantiative Genomics Seminar, and the Public Health Surveillance Working Group.
PO1 investors take a lead in organizing the following seminar series to promote statistical research in cancer and the dissemination of the PO1 findings:
Correlated and High-Dimensional Data Seminar This working seminar focuses on statistical and quantitative methods for analyzing correlated and high-dimensional data. High dimensional data arise from a wide range of studies in health science research, such as micro-array gene expression studies, proteomics, array CGH studies, genome-wide association studies. We discuss recent developments in statistical and quantitative methodology for analyzing high-dimensional data and interesting biomedical applications with high-dimensional data that could motivate new methodological research. The goal of this seminar is to stimulate more research in this challenging and important area and to promote interface of statistics and other quantitative disciplines in biomedical research.
Quantitative Issues in Cancer Research Working Seminar There are more than one million new cancer cases every year in the United States. An additional 5-8 million people are living with cancer. Research on cancer has greatly influenced the development of statistical methods in the past two decades and is likely to continue to do so in the future. This working seminar will be a forum for the discussion of current methodologic developments as well as cancer research having a strong quantitative basis. The working seminars will include expository reviews of special topics as well as the presentation of new research. All students and faculty are invited to attend and participate.
Environmental Statistics Seminar This seminar focuses on statistical issues related to assessing environmental effects on human health and analyzing environmental data in general. Specific areas of interest include air pollution epidemiology, exposure assessment, teratology, fertility and reproduction, respiratory studies, and community-based research as well as general topics such as errors-in-variables models, missing data methods, hierarchical modeling, smoothing, and methods for correlated data such as longitudinal and spatial data analysis. The seminars are generally pitched at a level that encourages student participation. Students interested in receiving credit for attending the seminars may sign up with individual faculty members for some guided readings on a special topic. Please see Chris Paciorek for details.
Program in Quantitiatve Genomics Seminar The aim of the PQG Seminar Series is to encourage the exchanging of ideas, promote interaction, collaboration, and research in quantitative genomics. It also aims to promote the mission of the PQG which is to improve health through an interdisciplinary study of genetics, behavior, environment and medicine. The seminar series looks to include the development and application of quantitative methods, especially for high-dimensional data, as well as a focus on the training of quantitative genomic scientists.
Public Health Surveillance Working Group This working group will focus on statistical issues related to public health surveillance. There will be special emphasis placed on biosurveillance and monitoring for bioterrorist attacks. Talks will be accessible to students, as well as researchers and other officials outside of the biostatistics department.
PQG short courses The PO1 project works with the PQG group to offer short courses on a variety of topics. These courses can be found on the PQG website.
The Evolution of Statistical Methods Used to Assess the Health Effects of Air Pollution Francesca Dominici - Sunday, May 1, 2011, 8:30-11:30am Building from HEI’s Perspective on "Airborne Particles and Health" published in 2001, this interactive workshop will provide an overview of how statistical methods have evolved to address key questions raised when characterizing the health effects of air pollution, including:
- How do we know if it’s the air pollution and not something else?
- How confident can we be about the size of an effect?
- How do we know what pollutants people are exposed to, and how can we determine which exposures matter?
- What are the continuing limitations in our approaches?
Building R Packages David Diez - Dec 17, 2010 A one hour short course for basic strategies and principles in building R packages. The source for the course slides (zip file with LaTeX) is available to facilitate replication of the course, or a standalone PDF may be downloaded. For licensing information, see the Software page.
Graphical Models: A Gentle Introduction Mathis Thoma - Oct 20, 2008
An Introduction to Causal Inference in Graphical Models Ilya Shpitser - Oct 27, 2008