In the MALLET lab I work on a project that looks to improve automated algorithm selection. Traditionally, automated algorithm selection models are trained on the problem instance
feature values and performance data from algorithm runs. The project I work on showed training these automated algorithm selection
models on the feature values of the algorithms in addition to problem instance features improves overall performance.
This allows the evaluation of sets of problem instances in less time and with less memory by choosing the most optimal algorithm more frequently.
An extended abstract on my work submitted to AAAI the Undergraduate Consortium. Abstract.pdf
3-minute lighting talk presented at the 2020 Wyoming Research Scholars Symposium
I've been an avid runner for nearly a decade now and my brother currently runs for a D1 cross-country and track program. One day we were discussing the NCAA national meet when
I realized the way cross-country meets are scored means the closer a runner is to the median of the field the same percentage improvement in their time has a
greater positive impact on a teams score. Check out the graphic below I made using ggplot to get a visual sense for why this is the case.
I set out to model and visualize this relationship in R. I ultimately created a script that will show a team which runners in their program
can help optimize their place in major competitions. See the pdf below for the full write up on this project.