Research

Interests

My research interests are driven by making analysis and data science (ML/AI) more accessible to nonexperts. I enjoy debugging multidisciplinary problems through the use of intuitive and thoughtful visualization design. The types of problems I address relate to:

• Decision-making, automatic question-answering, knowledge transfer
• Communication and collaboration between data, humans, and machines
• Sensemaking with messy, complex, high-dimensional data
• Increasing ML/AI model understanding and adoption

Projects

More research projects accessible from my Github or Observable notebook profile.

Interactive notebook to visualize regression model performance to subject matter experts & stakeholders

Clustering & decluttering of force-directed graph layouts via topological feature manipulation

Source code and examples on Github

Knowledge graph-assisted visual analytic systems for automatic insight discovery & data exploration

Demo available online (project in-progress)

Artifacts

Here are a few artifacts I've put together that might help those dipping their toes into research and/or grad school.

• My statement of purpose (i.e., written application to the PhD program). Not claiming it's any good, but may help those applying to grad school.
• Quick starter-guide on analyzing qualitative / interview data (e.g., developing a codebook, performing thematic analysis) with citations.
• A data scientist's check-list when beginning collaborations with SMEs that I developed at Novartis.