I develop data analysis methods for understanding neural datasets based on machine learning and information theory. During my Ph.D. I worked on understanding inference in spiking neural network models and applied deep learning to a dataset of ECoG recordings collected from human performing a vocal articulation task to understand how the production of speech is organized in cortex. Recently, I’ve been working on linear dimensionality reduction methods for timeseries analysis and doing some theory to try and understand the impact of correlated variability in neural populations engaged in categorical tasks.
I started my life in science as a physicist and did research at the small (by LHC standards) particle accelerator at Cornell. In grad school at UC Berkeley I joined Mike DeWeese’s group to study computational neuroscience and machine learning.
