Yizi Zhang
Hello! I am a PhD student at Columbia University in the Zuckerman Mind Brain Behavior Institute and the Department of Statistics. I was fortunate to be advised by Prof. Liam Paninski.
I develop scalable, data-driven models for neural encoding and decoding. Recently, I have been working on building foundation models to advance brain–computer interface (BCI) applications. My research aims to use computational neuroscience tools to help individuals with paralysis communicate and interact with the world through AI-assisted neuroprosthetics.
Prior to Columbia, I completed my master’s in Statistical Science at Duke University, where I had the privilege of working with Prof. David Dunson. Before that, I did my undergrad at UC Davis with a double major in Statistics and Neurobiology, Physiology and Behavior.
I am serving as the Vice President of Research for Columbia’s BCI Club: Neurotech X Columbia. We welcome all members of the Columbia community to join us!
I enjoy working with people who are passionate about neuroscience and BCI. If you would like to get involved or learn more about my work, feel free to email me anytime.
Recent News
- 10/2025:
- 🎉 Excited to share that our team (BIT) earned 1st place on both the Brain-to-Text 2024 and Brain-to-Text 2025 benchmarks!
- 09/2025:
- 🎉 Our paper was accepted at NeurIPS 2025: “Inpainting the Neural Picture: Inferring Unrecorded Brain Area Dynamics from Multi-Animal Datasets”.
- 🎉 My first-authored paper was accepted by the Neuron journal: “Exploiting correlations across trials and behavioral sessions to improve neural decoding”.
- 05/2025 - 09/2025:
- Worked on a summer internship at CTRL-Labs at Meta on EMG-based neural interfaces.
- Gave a talk at The Final Frontier of AI Workshop hosted by Flatiron Institute in New York about “Building towards a brain-wide foundation model at single-cell, single-spike resolution”.
- 04/2025:
- 🎉 My first-authored paper was accepted at ICML 2025 as a spotlight: “Neural Encoding and Decoding at Scale.”
- 03/2025:
- Gave a talk at the Building a Foundation Model for the Brain Workshop at COSYNE in Montreal, Canada on “Building towards a brain-wide foundation model at single-cell, single-spike resolution.”
- 11/2024:
- Gave a talk at the Computational Machinery of Cognition Journal Club at the University of Tübingen, Tübingen, Germany on “Neural Encoding and Decoding at Scale.”
- 10/2024:
- Gave a talk at the Computational Neuroscience Journal Club at Princeton University on “Towards a universal translator for neural dynamics at single-cell, single-spike resolution.”
- 09/2024:
- 🎉 My first-authored paper was accepted at NeurIPS 2024: “Towards a ‘universal translator’ for neural dynamics at single-cell, single-spike resolution”.
Publications
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Decoding inner speech with an end-to-end brain-to-text neural interface
Y. Zhang, L. He, C. Fan, T. Liu, H. Yu, T. Le, J. Li, S. Linderman, L. Duncker, F. Willett, N. Mesgaragni, L. Paninski
Preprint submitted to ICLR 2026 -
Self-supervised pretraining of vision transformers for animal behavioral analysis and neural encoding
Y. Wang, H. Yu, A. Blau, Y. Zhang, The International Brain Laboratory, L. Paninski, C. Hurwitz, M. Whiteway
Preprint submitted to ICLR 2026 [PDF] -
Inpainting the Neural Picture: Inferring Unrecorded Brain Area Dynamics from Multi-Animal Datasets
J. Xia, Y. Zhang, S. Wang, G. Allen, L. Pansinki, C. Hurwitz, K. Miller
NeurIPS 2025 [PDF] -
Neural Encoding and Decoding at Scale
Y. Zhang, Y. Wang, M. Azabou, A. Andre, Z. Wang, H. Lyu, The International Brain Laboratory, E. Dyer, L. Paninski, C. Hurwitz
ICML 2025 (Spotlight) [PDF] -
TimeInf: Time series data contribution with influence functions
Y. Zhang, J. Shen, X. Xiong*, Y. Kwon
ICLR 2025 [PDF] -
Towards a universal translator for neural dynamics at single-cell, single-spike resolution
Y. Zhang, Y. Wang, D. Jiménez Benetó, Z. Wang, M. Azabou, B. Richards, O. Winter, The International Brain Laboratory, E. Dyer, L. Paninski, C. Hurwitz
NeurIPS 2024 [PDF] -
Exploiting correlations across trials and behavioral sessions to improve neural decoding
Y. Zhang, H. Lyu, C. Hurwitz, S. Wang, C. Findling, F. Hubert, A. Pouget, International Brain Laboratory, E. Varol, L. Paninski
Neuron (In Press), 2024 [PDF] -
Reproducibility of in-vivo electrophysiological measurements in mice
International Brain Laboratory, K. Banga, J. Benson, J. Bhagat, D. Biderman, D. Birman, Y. Zhang, et al.
eLife 2024 [PDF] -
Rhesus infant nervous temperament predicts peri-adolescent central amygdala metabolism & behavioral inhibition
D. Holley, L. Campos, C. Drzewiecki, Y. Zhang, J. Capitanio, A. Fox
Nature Translational Psychiatry 2024 [PDF] -
Motion-invariant variational auto-encoding of brain structural connectomes
Y. Zhang, M. Liu, Z. Zhang, D. Dunson
Imaging Neuroscience 2024 [PDF] -
Bypassing spike sorting: Density-based decoding using spike localization from dense multielectrode probes
Y. Zhang, T. He, J. Boussard, C. Windolf, O. Winter, E. Trautmann, N. Roth, H. Barrell, M. Churchland, N. Steinmetz, E. Varol, C. Hurwitz, L. Paninski
NeurIPS 2023 (Spotlight) [PDF] -
Brain-Wide Representations of Prior Information in Mouse Decision-Making
C. Findling, F. Hubert, International Brain Laboratory, L. Acerbi, B. Benson, J. Benson, Y. Zhang, et al.
Nature 2023 [PDF] -
Predicting rare outcomes in abdominal wall reconstruction using image-based deep learning models
S. A. Ayuso, S. A. Elhage, Y. Zhang, B. Aladegbami, K. Gersin, J. Fischer, V. Augenstein, P. Colavita, B. Heniford
Surgery (Elsevier) 2023 [PDF]