Anamaria Crisan

About

I am a multidisciplinary researcher that integrates techniques and methods from applied statistics, 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.

Selected Publications

A full list of my publications is available on my Google Scholar Profile

Data Visualization

•  Uncovering Data Landscapes through Data Reconnaissance and Task Wrangling
•  A systematic method for surveying data visualizations and a resulting genomic epidemiology visualization typology: GEViT
•  How to Evaluate an Evaluation Study? Comparing and Contrasting Practices in Vis with Those of Other Disciplines
•  On Regulatory and Organizational Constraints in Visualization Design and Evaluation

Data Presentation

•  Evidence-based design and evaluation of a whole genome sequencing clinical report for the reference microbiology laboratory

Data Science / Machine Learning

•  Adjutant: an R-based tool to support topic discovery for systematic and literature reviews
•  Combined value of validated clinical and genomic risk stratification tools for predicting prostate cancer mortality in a high-risk prostatectomy cohort
•  Discovery and validation of a prostate cancer genomic classifier that predicts early metastasis following radical prostatectomy
•  Mutation discovery in regions of segmental cancer genome amplifications with CoNAn-SNV: a mixture model for next generation sequencing of tumors