Working Groups

Department of Biostatistics & Informatics

In addition to a weekly department seminar, there are several working groups that meet monthly to discuss specific biostatistical methods or problems. The goal of the working groups is to bring together faculty, research staff, and students to discuss these specific topics in a more focused environment. Working group presentations may be journal clubs, work-in-progress updates, or opportunities for students to practice their conference presentations.‚Äč

To find the latest information about working group meetings and how to join, please visit our event calendar.

Network Science Working Group


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The Network Science Working Group is a group of students, faculty, and staff from different departments and universities interested in methodologies pertaining to Network Science. At every meeting, one researcher presents his/her work based on network-related topics followed by a thought-provoking discussion section.

Statistical Genomics Working Group


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The Statistical Genomics Working Group is a monthly seminar with a friendly setting for discussions related to various topics in statistical genomics. Talks given during our meetings can cover anything related to statistical genomics, including personal research or a journal club. We have also had participants give panel discussions or discuss highlights from a recent conference they attended.

Imaging Analysis Working Group


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The Imaging Analysis Working Group is a group of students, faculty, and staff interested in methodologies pertaining to medical imaging. We are based out of the University of Colorado at the Anschutz Medical Campus, with collaboration from Johns Hopkins University. Our meetings consist of a presentation followed by a discussion. The presentation can be anything related to medical imaging including personal research or a journal club. We also encourage work-in-progress talks. 

Machine Learning Working Group


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The Machine Learning Working Group brings together everyone interested in the theory and applications of machine learning. We focus on new and state of the art research and aim. Some discussions are purely explanatory and others aim to find a working example of the method/theory in question relevant to our fields. Anyone can nominate a paper or their own research and lead a discussion around it. There are no restrictions on the discussion topic, as long as it is machine learning related.