Jordan Singer

Research Assistant

I received my B.S. in Applied Mathematics from the Ohio State University in 2016. I’m interested in the applications of machine learning, notably in the realms of decision making and time-series forecasting.

In my time as an undergraduate, I worked with Dr. Ulrich Heinz in Ohio State’s High Energy Nuclear Theory group. My research focused on establishing the theoretical initial conditions of nuclear collisions happening at the LHC and RHIC. This model proved robust when tested against empirical data, and is now the de facto simulation tool for the field. I credit my passion for problem solving via computation to my time working with Dr. Heinz.

In the Hughey Lab, my goal is to bring and apply as many modern techniques as possible, in order to solve critical research problems in a field rich with questions begging to be asked.


Simphony: simulating large-scale, rhythmic data, Singer et al., bioRxiv

LimoRhyde: a flexible approach for differential analysis of rhythmic transcriptome data, Singer and Hughey, J Biol Rhythms 2018