Recent Posts

Using service utilization data of 4,067 residents of Vancouver Island with sever alcohol addiction we demonstrate the cross-continuum …

I am a data scientist with background in quantitative methods and interest in data-driven models of human aging. I received my Ph.D. in …

Skills

Where I spend my time

R for Data Science

90%

Graph making

100%

Reproducible Research

100%

Statistical Learning

80%

Experience

 
 
 
 
 

Assistant Professor

University of Central Florida

Dec 2018 – Present Orlando, Florida
Research and teaching at the department of Health Management and Informatics
 
 
 
 
 

Health System Impact Fellow

Observatory for Population and Public Health (UBC)

Aug 2017 – Dec 2018 Vancouver, BC, Canada
Designing reproducible workflows for suppressing small cells before public release
 
 
 
 
 

Postdoctoral Fellow

University of Victoria

Aug 2014 – Aug 2017 Victoria, BC, Canada
Harmonization of longitudinal studies of aging with the IALSA network. Developing analytic workflows for longitudinal modeling.

Projects

Talks

Recent and Upcoming Events

Visualising results of statistical modeling is a key component of data science workflow. Statistical graphs are often the best means to explain and promote research findings. However, in order to find that one graph that tells the story worth sharing, we sometimes have to try out and sift through many data visualizations. How should we approach such a task? What can we do to make it easier from both production and evaluation perspectives?

Abstract While computational notebooks offer scientists and engineers many helpful features, the limitations of this medium make it but a starting point in creating software - the practical goal of data science. Where do we go from computational notebooks if our projects require multiple interconnected scripts and dynamic documents? How do we ensure reproducibility amidst growing complexity of analyses and operations? I will use a concrete analytical example to demonstrate how constructing workflows for reproducible analyses can serve as the next step from computational notebooks towards creating an analytical software.

The lecture introduces reproducible research and demonstrates digital self-publishing with RStudio and Git (Hub). The skills described and emphasized in this workflow include data manipulation, graph production, statistical modeling, and dynamic reporting. A series of four talks discusses each skill and gives examples of possible implementations in R.

Recent Publications

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Demonstrates the methods of suppressing small counts in a provincial surveillance system in preparation of data for public release.

Demonstrates how cross-continuum terrain of health services can be described in with flexible classification scheme.

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