I'm a PhD student in computational neuroscience at Columbia. I'm interested in understanding how high-dimensional neural representations support complex behaviors and how these capabilities are learned. I graduated from Stanford in 2019 with an undergraduate major in math and a master's in computer science. At Stanford, I worked with Shaul Druckmann on modeling neural dynamics in mouse anterior lateral motor cortex under optogenetic perturbations, and with Surya Ganguli on developing models of representations in the retina and visual cortex. I've also spent time in Jay McClelland's lab at Stanford, using deep learning models to study memory and visual attention, and at Cerebras Systems, a machine learning hardware startup.


F. Li, J. Lindsey, E. Marin, N. Otto, M. Dreher, G. Dempsey, I. Stark, A. S. Bates, M. W. Pleijzier, P. Schlegel, A. Nern, S. Takemura, T. Yang, A. Francis, A. Braun, R. Parekh, M. Costa, L. Scheffer, Y. Aso, G. S. X. E. Jefferis, L. F. Abbott, A. Litwin-Kumar, S. Waddell & G. M. Rubin (2020). The connectome of the adult Drosophila mushroom body: implications for function. bioRxiv: 2020.08.29.273276. Link

J. Lindsey & A. Litwin-Kumar. Learning to Learn with Feedback and Local Plasticity (2020). arXiv: 2006.09549. Link

J. Lindsey, S. Ocko, S. Ganguli, & S. Deny (2019). A Unified Theory of Early Visual Representations from Retina to Cortex through Anatomically Constrained Deep CNNs. International Conference on Learning Representations (oral presentation). Link

S. Ocko, J. Lindsey, S. Ganguli, & S. Deny (2018). The Emergence of Multiple Retinal Cell types through Efficient Coding of Natural Movies. Neural Informational Processing Systems. Link

M. Jain & J. Lindsey (2018). A Neural Network Model of Complementary Learning Systems. CogSci 2018 proceedings (oral presentation). Link

M. Jain & J. Lindsey (2018). Semiparametric Reinforcement Learning. ICLR, Workshop Track. Link

J. Lindsey (2017). Pre-Training Attention Mechanisms. NeurIPS 2017 Workshop on Cognitively Informed Artificial Intelligence. Link

Deep Learning Implementations

Fun Projects from Long Ago