## My talks

My talks, workshops, or other events with a time, date, and place.

Written by Byron C Jaeger

# Accelerated oblique random survival forests

The oblique random survival forest (RSF) is an ensemble supervised learning method for right-censored outcomes. Oblique RSF ensembles often have higher prediction accuracy than standard RSF ensembles. However, computational overhead and lack of interpretability are pervasive limitations of the oblique RSF, In this talk, I introduce a method to increase computational efficiency of the oblique RSF and a method to estimate importance of individual predictor variables with the oblique RSF. Both methods are available in the aorsf R package.

November 18, 2022

Virtual Biostatistics, Epidemiology, and Research Design (BERD) Methods Conference / Webex

By Byron C Jaeger in Talk

# Making Machine Learning Fast and Interpretable

Risk prediction can help direct treatment and interventions to patients who are most likely to benefit. The oblique random survival forest, a machine learning algorithm for risk prediction, has been used to develop a risk prediction algorithm for heart failure and to identify specific factors for adults who are black or white that drive predicted heart failure risk, with adverse social determinants of health being a major driver for adults who are black. This talk covers these topics and also introduces methods to increase the computational efficiency and interpretability of the oblique random survival forest.

September 7, 2022

Wake Forest University School of Medicine Grand Rounds, September 2022 / Winston-Salem, NC

By Byron C Jaeger in Seminar

# Disseminating Prediction Methods

Statisticians are trained to develop novel statistical techniques that can be used to engage with complex problems. However, we are less likely to receive training in software development. Without efficiently coded algorithms, intuitive documentation, and friendly APIs, the methods we ‘share’ in our R packages may cause frustration and turn potential users away (possibly to a less valid method!). In this talk, I focus on efficiently writing the core algorithms in R packages using Rcpp. I share my experience developing R packages with statistical methods and present four ideas that have made a positive impact on my work.

August 4, 2022

Joint Statistical Meetings, 2022 / Washington, DC

By Byron C Jaeger in Seminar

# Accelerated and interpretable oblique random survival forests

The oblique random survival forest (RSF) is an ensemble supervised learning method for right-censored outcomes. Oblique RSF ensembles often have higher prediction accuracy than standard RSF ensembles. However, computational overhead and lack of interpretability are pervasive limitations of the oblique RSF, In this talk, I introduce a method to increase computational efficiency of the oblique RSF and a method to estimate importance of individual predictor variables with the oblique RSF. Both methods are available in the aorsf R package.

June 21, 2022

International Chinese Statistical Association's Applied Statistics Symposium / Gainesville, FL

By Byron C Jaeger in Talk

# Happier version control with Git and GitHub

git and GitHub are fantastic tools for version control and collaboration. Data scientists have increasingly used GitHub as a platform for sharing their work and working together thanks to publicly available guides such as Jenny Bryan’s Happy git with R textbook. In this seminar, I walk through the basics of git and GitHub, beginning with the jargon of git and proceeding up through submitting pull requests on GitHub.

September 28, 2021

American Statistical Association's Section on Statistical Learning and Data Science / WebEx

By Byron C Jaeger in Seminar