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. My research draws from and integrates techniques from within machine learning, epidemiology, biostatistics, information visualization, and human computer interaction. Prior to my doctoral studies, I worked with the British Columbia Centre for Disease Control conducting research in genomic epidemiology, and had also previously worked on prostate cancer biomarker development with a Vancouver based startup. I hold a BSc and MSc in Computer Science, specializing in bioinformatics, from Queen's University and the University of British Columbia, respectively.
I'm on the job market this year, more details to come soon! CV
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).