Location:
Ames, IA
Date:
When considering graduate research, now-DOE CSGF recipient Ashlynn Crisp leaned toward foundational projects that could be meaningful in many fields. “Variable selection is such a wide-reaching problem. Anyone working in data science, machine learning or statistics runs into it.” Now a Portland State University doctoral student, Crisp works with her advisor Daniel Taylor-Rodriguez to develop a new approach that uses a Bayesian framework designed to preserve uncertainty quantification while avoiding Markov chain Monte Carlo's long, unpredictable run times.