Hi! I'm Ana Crisan
I am a Lead Research Scientist at Tableau Research. I am part of the Office of the CTO, where I design and develop new visual modalities for humans and ML/AI systems to interact in data work.
Research Interests: Interactive Machine Learning, Human Centered ML/AI, Data Science, Data Visualization, Bioinformatics
I conduct interdisciplinary research that integrates techniques and methods from machine learning, human computer interaction, and data visualization. I analyze data, build tools, and conduct evaluative studies. My research focuses on the intersection of Data Science and Data Visualization. I am especially interested in the way humans can collaboratively work together with ML/AI systems through visual interfaces.
I completed my PhD in Computer Science at the University of British Columbia, where I was jointly advised by Tamara Munzner and Jennifer Gardy. Prior to my PhD, I was a research scientist at the British Columbia Centre for Disease Control and Decipher Biosciences, where I conducted research machine learning and data visualization research toward applications in infectious disease and cancer genomics. My research has appeared in publications of the ACM (CHI), IEEE (TVCG, CG&A), Oxford Bioinformatics, and Nature, and has over 3,7000 citations.
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).