Teaching
Applied Computational Genomics (2015 - Present)
In 2015, Aaron launched the Applied Computational Genomics course at the University of Utah. This course provides a comprehensive introduction to fundamental concepts and experimental approaches in analyzing and interpreting experimental genomics data, with the primary goal of empowering students to conduct independent genomic analyses.
Youtube channel
Additionally, I provide recorded lectures on my Youtube channel, which is used by learners worldwide.
Example lectures are provided below:
 |
 |
 |
DELPHI Biomedical Data Science Initiative
A core goal of the DELPHI Biomedical Data Science Initiative is to develop and sustain a world-class education program for health-related data science which will serve to train leading biomedical scientists for years to come. Supporting this mission, we are developing a series of data science workshops to complement existing, full-semester courses.
Salt Lake Learners of Biostats (SLLOBS)
Realizing the absence of a biostatistics curriculum for graduate students at the University of Utah, I launched the Salt Lake Learners of Biostatistics interest group with Tom Sasani in the summer of 2019. My goal was to bring together scientists interested in learning fundamental concepts in probability, statistical inference, and data analysis for biological research.
Click on one of the sample of video lectures below:
 |
 |
 |
Advanced Sequencing Technologies and Bioinformatics @ CSHL (2008-2022)
🔗 Course
We are committed to teaching core skills in computational genomics. Aaron was a lead instructor for the Advanced Sequencing Technologies & Bioinformatics Analysis course at Cold Spring Harbor Laboratories from 2008 through 2022. He taught nearly 500 students in this course, many of whom have gone on to launch their labs. A primary teaching focus was conveying uncertainty in genomics experiments, as most assays in genomics involve counting molecules. We equipped them with the concepts and tools to extract biological signals from extensive random and technical noise.