Statistical methods for systems neuroscience and machine learning

Previously, I was a computational neuroscientist developing statistical tools to analyze neural and behavioral data. Until July 2024, I was a Staff Scientist at the Grossman Center for Quantiative Biology and Human Behavior at the University of Chicago. I use a variety of statistical, time series, and machine learning tools. My experience has included applications in modeling sensory processing, decision making, and learning.

Previously I was a postdoc in Dave Freedman's lab at the University of Chicago. My work primarily focused on characterizing spike trains recorded in the lateral intraparietal (LIP) cortex and frontal eye fields (FEF) during categorization tasks. I am interesting in understanding how categorical representations in these areas depend on training history.

Example project applying tensor-regression models to characterize population encoding and single-trial dynamics during a categorization task can be found on github: latimerk/GMLM_dmc

Before moving to Chicago, I was a postdoc in Adrienne Fairhall's lab where I studied models of adaptation at the single-neuron level. I completed my PhD in Neurscience in 2015 at the University of Texas at Austin with Jonathan Pillow and Alex Huk. My dissertation work focused on hypothesis testing for linking decision-making models to single-neuron spike trains recorded in area LIP. Additionally, I worked on models of retinal ganglion cell responses.

Example project bridging biophysical dynamics and statistical modeling for retinal ganglion cells on github: pillowlab/CBEM Example project applying state space methods for Bayesian model comparison of parietal neuron dynamics during decision-making: step-ramp