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About

I study how neural circuits in the prefrontal cortex give rise to flexible decision-making. My lab combines large-scale electrophysiology, computational modeling, and machine learning to decode the population dynamics underlying cognitive control. Before joining Stanford, I completed postdoctoral training at MIT and earned my PhD at UC Berkeley. My work has been supported by the NIH, NSF, and the Simons Foundation.

Research areas

Computational neuroscienceNeural population dynamicsDecision-making circuitsPrefrontal cortexMachine learning for neuroscienceDimensionality reductionBrain-computer interfaces

Education

Ph.D., Neuroscience

University of California, Berkeley · 2016

M.S., Bioengineering

University of California, Berkeley · 2013

B.S., Biomedical Engineering

University of Michigan · 2011

Experience

Associate Professor

Stanford University, Department of Neurobiology · 2024 – present

Assistant Professor

Stanford University, Department of Neurobiology · 2019 – 2024

Postdoctoral Fellow

MIT, McGovern Institute for Brain Research · 2016 – 2019

Recent publications

Population-level dynamics in prefrontal cortex reflect hierarchical decision structure

Nature Neuroscience · 202512

A unified framework for latent factor models of neural population activity

Neuron · 202487

Recurrent neural network models reveal context-dependent population codes in PFC

Nature Communications · 202445

Real-time decoding of prefrontal ensemble dynamics during cognitive flexibility tasks

Proceedings of the National Academy of Sciences · 2023128

Deep learning approaches for spike sorting in high-density electrode arrays

NeurIPS · 202394

Prefrontal-hippocampal interactions during spatial working memory: evidence from simultaneous recordings

eLife · 2022156

View all 12 publications →

Grants and funding

Neural population dynamics of hierarchical decision-making in PFC

NIH R01 · $2,400,000 · PI

Computational models of prefrontal-hippocampal interaction during working memory

NSF BRAIN Initiative · $1,200,000 · PI

Machine learning tools for large-scale neural data analysis

Simons Foundation · $600,000 · PI

Awards

McKnight Scholar Award2024

Sloan Research Fellowship2022

NIH New Innovator Award (DP2)2021

CAREER Award2020