Joel Galang

Joel Galang is a Data Analyst at Nift. Previously, they worked as a Senior Data Analyst / Data Scientist II at UW Health from August 2016 to December 2021. In this role, they created Docker containers and Databricks notebooks for a cloud-based (Azure) natural language processing pipeline that processes HL7 message streams and returns predictive model scores via a web-based API. Joel also helped develop, implement, and maintain predictive models for the onset of severe sepsis and for risk of at-home falls. In addition, Joel led an initiative to store source code in GitHub and administered and analyzed a yearly survey of interest and skill level in data science.

Joel has applied Bayesian statistics in several projects and quadratic optimization in determining patient weighting in physician panel sizing. Joel has also created R and Python packages to facilitate accessing and storing data in departmental databases. Furthermore, Joel has supported operational and quality improvement initiatives by writing and validating SQL queries against the electronic medical record system and other large databases and providing appropriate tabular and graphical summaries. Joel has also advised clinical staff on the feasibility and appropriateness of potential performance metrics. Finally, Joel has automated recurring data requests using business intelligence software (Crystal Reports, SAP Business Objects, QlikView, and QlikSense).

Currently, Joel serves as an advisor in areas such as statistical process control, sampling plans, and statistical modeling. Joel also founded a monthly workgroup for sharing knowledge about R.

Joel Galang has a MS in Statistics from Texas A&M University, a MS in Chemistry from the University of Michigan, and a BA in Chemistry from Rice University.