Putting It All Together

What Does the Classroom of the Future Look Like? Educators worldwide are looking at diverse models for the future classroom, ranging from the most innovative college and university spaces, to models of industry and dot-com startups. Perhaps more important than what the future learning space looks like, however, is what students are being asked to do to demonstrate their learning. In this talk, catch a glimpse of what a future classroom might look like, in both its curriculum and its physical space. Come explore the DIY “Maker” movement, and what it might mean for the future. Take a look at how companies like Google foster creativity and innovation, and see what kids’ visions of a “learning center” might look like. Finally, we’ll look at an original way to create stronger community in the classroom using the popular game Minecraft as a virtual learning environment.

Short intro PowerPoint to Putting It All Together.

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Keynote Talk presented at the 1st Annual BiVi Community Annual Meeting (17 December 2014) http://bivi.co/page/bivi-annual-meeting-16-17th-december-2014 Visualization Approaches for Biomedical Omics Data: Putting It All Together The rapid proliferation of high quality, low cost genome-wide measurement technologies such as whole-genome and transcriptome sequencing, as well as advances in epigenomics and proteomics, are enabling researchers to perform studies that generate heterogeneous datasets for cohorts of thousands of individuals. A common feature of these studies is that a collection of genome-wide, molecular data types and phenotypic or clinical characterizations are available for each individual. These data can be used to identify the molecular basis of diseases and to characterize and describe the variations that are relevant for improved diagnosis, prognosis and targeted treatment of patients. An example for a study in which this approach has been successfully applied is The Cancer Genome Atlas project (http://cancergenome.nih.gov). In my talk I will discuss how visualization approaches can be applied to enable exploration and support analysis of data generated by such studies. Specifically, I will review techniques and tools for visual exploration of individual omics data types, their ability to scale to large numbers of individuals or samples, and emerging techniques that integrate multiple omics data types for interactive visual analysis. I will also examine technical and legal challenges that developers of such visualization tools are facing. To conclude my talk, I will outline research opportunities for the biological data visualization community that address major challenges in this domain.

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