Recent Posts

Graphing the trends of suicides in Florida from 2006 to 2017 among youth between 10 and 24 years of age.

Demonstrating how to 1) build interactive visualizationsusing plotly::ggplotly(), 2) compute relative timelines for each country and 3) …

A list of learning resources that I like having on speed dial

Graphing the trends of suicides in Florida from 2006 to 2017, exploring the differences in age, gender, and race among persons 10 years …

This blogposts shows how to extract population estimates data reported by the Florida Department of Health and prepare them for …

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.

Talks

Recent and Upcoming Events

The workshop introduces R and RStudio and makes the case for project-oriented workflows for applied data analysis. Using logistic regression on Titanic data as an example, the participants will learn to communicate statistical findings more effectively, and will evaluate the advantages of using computational notebooks in RStudio to disseminate the results

Visualising results of statistical modeling is a key component of data science workflow. Statistical graphs often is 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?

Describing the application of Clinical Context Coding Scheme to stratification of Mental Health and Substance Use services on Vancouver Island from 2007 to 2017

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.

Recent Publications

Quickly discover relevant content by filtering publications.

The paper tracks the response of US government to the unfolding pandemi of COVID-19

Demonstrates using reproducible data visualisations for augmenting redaction decisions during small cell supression and creating …

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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