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