Dr. Denovellis is an experienced researcher in computational neuroscience. Skilled in Signal Processing, Python, Statistics, Probabilistic Machine Learning, Brain-Computer Interfaces and Neuroscience.

He is a computational research scientist in Dr. Loren Frank's lab at UCSF. His work focuses on developing scalable, interpretable algorithms and tools to categorize, decode and visualize neural representations. This work extends and applies marked point process switching state space models He developed during his postdoc with Dr. Uri T. Eden. He works closely with experimental collaborators to ensure these algorithms and tools are usable on large scale data.

Previously, he completed my Ph.D. in computational neuroscience at Boston University with Drs. Daniel H. Bullock and Earl K. Miller. There he developed computational tools and models to understand how prefrontal cortex supports the underlying neural comptuations necessary to switch between contexts.

Specifically, he showed how synchronous network oscillations in the prefrontal cortex provide a mechanism to flexibly coordinate context representations between groups of neurons during task switching. He used generalized additive models to show that anterior cingulate neurons can represent context, but do not play a significant role in switching between contexts. Finally, he developed a set of web-enabled interactive visualization tools designed to provide a multi-dimensional integrated view of electrophysiological datasets.