Eric Denovellis
Research Scientist II at Howard Hughes Medical Institute
San Francisco, California, United States
Summary
👤
Senior🎓
Top SchoolEric Denovellis is a computational research scientist with 11 years of experience specializing in scalable, interpretable algorithms for neural data analysis and brain-computer interfaces. Based in San Francisco, he develops and applies marked point process switching state-space models to categorize, decode, and visualize large-scale electrophysiological datasets in the Loren Frank lab at UCSF. His work spans signal processing, probabilistic machine learning, and statistics, and emphasizes close collaboration with experimentalists to make tools practical for real data. During his PhD and postdoc he produced novel insights into prefrontal cortex dynamics—linking synchronous oscillations to flexible context coordination—and built web-enabled interactive visualizations that integrate multi-dimensional neural recordings. His background in mathematics and philosophy underpins a rigorous, concept-driven approach to modeling complex neural computation.
11 years of coding experience
2 years of employment as a software developer
B.S., Mathematics, B.S., Mathematics at University of California, Santa BarbaraDoctor of Philosophy - PhD, Computational Neuroscience, Doctor of Philosophy - PhD, Computational Neuroscience at Boston University
Github Skills (114)
motion-tracking10
computational-neuroscience10
spectrogram10
python10
topological-data-analysis10
connectivity10
bids10
replay10
frank10
neurons10
behavior10
electrophysiology10
kalman-filter10
nwb10
neuroscience10
Programming languages (7)
Github contributions (5)
edeno/Better-Science-Code
Jan 2017 - Jun 2017
Contributions:205 commits, 81 pushes, 2 branches in 4 months
edeno/release_pypi_conda
Oct 2017 - Jun 2021
Contributions:31 commits, 13 pushes, 2 branches in 3 years 8 months
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