On Friday, July 13, 2018, Dr. Andrew McArthur will be hosting a guest speaker, Ana Crisan, from the Department of Computer Science at the University of British Columbia. The seminar will take place from 10:00am to 11:00am in MUMC-HSC 4E20, with light refreshments served beforehand.
Ana is a CIHR Vanier Scholar and PhD candidate in her final year, and is studying how heterogenous types of public health data can be integrated and visualized under the joint supervision of Dr. Tamara Munzner (Computer Science) and Dr. Jennifer Gardy (School of Population and Public Health). Prior to her PhD studies, Ana worked with the British Columbia Centre for Disease Control supporting research in genomic epidemiology, and had also previously worked on prostate cancer biomarker development with a Vancouver based start-up. She holds a BSc and MSc in Computer Science, specializing in bioinformatics, from Queen’s University and the University of British Columbia, respectively. A summary of her present and past work can be found on her website: www.cs.ubc.ca/~acrisan.
Data visualization is an important tool for exploring and communicating findings from genomic and health datasets. Yet, without a systematic way of understanding the design space of data visualizations, researchers do not have a clear sense of what kind of visualizations are possible, or how to distinguish between good and bad options. We have devised an approach using both literature mining and human-in-the-loop analysis to construct a visualization design space from corpus of scientific research papers. We ascertain why and what visualizations were created, and how they are constructed. We applied our approach to derive a Genomic Epidemiology Visualization Typology (GEViT) and operationalized our results to produce an explorable gallery of the visualization design space containing hundreds of categorized visualizations. We are the first to take such a systematic approach to visualization analysis, which can be applied by future visualization tool developers to areas that extend beyond genomic epidemiology.