Hi! I'm Ana Crisan
I am a Vanier Scholar and UBC Public Scholar in the final year of study in Computer Science at the University of British Columbia. Under the joint supervision of Drs. Tamara Munzner (Computer Science) and Jennifer Gardy (School of Population and Public Health) I study how large and heterogenous public health data, which includes health records, geographic data, contact network, and genomic data, can be integrated and visualized. In the decade that spans my research career, beginning with my Masters of Science in 2008, through to research experience outside of academia (2010 - 2015), and finally to my doctoral research (September 2015 – present), I have witnessed, and have been a part of, an extraordinary accelerated technological change that has altered the kind of data that can be collected and analyzed in near real-time. My broad and productive research career has equipped me with a unique wholistic perspective on the actionable role that data science can play in healthcare. It is this perspective that has allowed me to develop and integrate a wealth of methodological and technical knowledge from within machine learning, biostatistics, epidemiology, genomics, data visualization, and human computer interaction that is evident in my research record.
I've started a new position as a Research Scientist with the Tableau Research Team !
We used a multi-phase mixed methods research design to study how public health experts make decisions with routine clinical data and data derived from tuberculosis whole genomes. Using our findings we implemented a clinical report that has been adopted by international public health agencies.
Adjutant supports literature reviews by obtaining, analyzing, summarizing, and visualizing research articles from a PubMed search by performing a fast and unsupervised topic clustering. It is distributed as an R package and can be used through a Shiny-based GUI or as a series of commands within and R script.
We created a human-in-the-loop method that used text and image analysis to conduct a systematic review of data visualizations from a corpus of research articles. We applied our method to the genomic epidemiology scientific literature and produced a Genomic Epidemiology Visualization Typology (GEViT).