About
I am a Senior Research Scientist at Google DeepMind in NYC and Adjunct Assistant Professor at the Center for Theoretical Neuroscience at Columbia University.
My research covers topics in Neuroscience and AI. On the Neuroscience side, I study how brains build models of the world that support memory and prediction. On the Machine Learning side, I work on implementing these cognitive functions in deep learning models. My work has been featured in The Atlantic, Quanta Magazine, Nautilus, and MIT Technology Review. In 2019, was named one of MIT Tech Review’s Innovators under 35.
I completed my PhD in Neuroscience at Princeton University where I was advised by Matthew Botvinick. Before that, I received my Bachelors from Tufts University in Mathematics (BA) and Chemical & Biological engineering (BS).
Outside of research, I like painting and drawing. I also enjoy backpacking and canyoneering with friends. I practiced Martial Arts for many years and have a blackbelt in Isshinryu Karate and another in Tae Kwon Do.
More information about Neuroscience at Google DeepMind can be found here.
My publications can be found on my Google Scholar page.
Research Interests
- Neuroscience of Learning and Memory
- Data-driven interpretable modeling of behavior
- Learned Simulation and Predictive Models
- Graph Neural Networks
- Reinforcement Learning
- Hippocampus
Education
- PhD in Quantitative & Computational Neuroscience, 2018
Princeton University - BS in Mathematics; Chemical & Biological Engineering, 2013
Tufts University
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NeuroKim 