Research
What I study
I'm interested in how the brain holds a decision in mind and acts on it — and in the quieter problem underneath it: who are the neurons doing the computing? My work moves between large-scale recordings in behaving primates and the applied math/machine learning tools needed to make sense of them.
Decision-making
How prefrontal cortex commits to a choice
I train monkeys (Macaca mulatta) on perceptual and context-dependent decision-making tasks while recording from dorsolateral prefrontal cortex with laminar and Neuropixels probes. Across hundreds of sessions, I recorded extracellular spiking activity while the animal deliberates on evidence, holds it in working memory, and computes an action.
Rather than reading single cells one at a time, I treat the population as a dynamical system and ask how its computational geometry relates to how stimulus, memory, and motor signals are computed in neural state space, and how that arrangement supports flexible behavior. To test mechanism, I model the same tasks with low-rank recurrent neural networks and compare them to the data.
Population activity as trajectories through neural state space (illustrative).
Cell types
Reading cell types from a spike's shape
Extracellular electrophysiology is powerful in its scaling but is traditionally nearly blind to cell identity: every neuron is expressed as an array of spike times. With WaveMAP, I showed that nonlinear dimensionality reduction of action-potential waveforms recovers a rich diversity of putative cell types. This is structure that lines up with known cell type physiologies (eLife, 2021; STAR Protocols, 2023).
Since then I've pushed toward multimodal identification of molecular cell types — composing waveform, firing, and anatomical signals into a single graph-based view of cell-type space (Nature Communications, 2026) and contributed to efforts in learning cross-modal representations with contrastive methods (ICLR Spotlight, 2025). The goal: turn a spike train back into a labeled, interpretable population for cell-type-aware neural dynamics. On this topic I coordinated with Josh Siegle the first COSYNE workshop on cell-type identification from electrophysiology.
Waveforms embedded into putative cell-type clusters, WaveMAP-style (illustrative).
Methods & tools
Applied math and machine learning for neural dynamics
Threaded through everything is my approach of matching modern tools to scientific questions: graph-based methods and multimodal data integration to identify cell types; dimensionality reduction and low-rank RNNs to interpret neural computation.
I also care about making these usable by others — releasing protocols and open datasets has led to adoption of my tools for everything from brain organoids to human brains.
Low-rank recurrent networks as testable models of computation (illustrative).