I am a computational research scientist in Dr. Loren Frank's lab at UCSF. My work focuses on developing scalable, interpretable algorithms and tools to decode, categorize and visualize neural representations. This work extends and applies marked point process switching state space models I developed during my postdoc with Dr. Uri T. Eden. I work closely with experimental collaborators to ensure these algorithms and tools are usable on large scale data.
I also build open source software packages for neural data analysis. These include:
- replay_trajectory_classification a Python package for decoding spatial position represented by neural activity and categorizing the type of trajectory.
- Spyglass is a data analysis framework that facilitates the storage, analysis, visualization, and sharing of neuroscience data to support reproducible research. It is designed to be interoperable with the NWB format (a data standard for neurophysiology) and integrates open-source tools such as SpikeInterface and DeepLabCut into a coherent framework.
- spectral_connectivity is a Python software package that computes multitaper spectral estimates and frequency-domain brain connectivity measures such as coherence, spectral granger causality, and the phase lag index using the multitaper Fourier transform.
Previously, I completed my PhD in computational neuroscience at Boston University with Drs. Daniel H. Bullock and Earl K. Miller. There I developed computational tools and models to understand how prefrontal cortex supports the underlying neural computations necessary to switch between contexts. Specifically:
- I showed how synchronous network oscillations in the prefrontal cortex provide a mechanism to flexibly coordinate context representations between groups of neurons during task switching
- I 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, I developed a set of web-enabled interactive visualization tools designed to provide a multi-dimensional integrated view of electrophysiological datasets.
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