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Grand Challenge 7

Grand Challenge 7

How do we advance new computational tools to unlock the mysteries of the brain?

Recent technological advances enable us to measure, model, and manipulate neural activity in living brains with unprecedented precision and scale. These capabilities promise to reveal the distributed brain-wide neural computations that underlie cognition, flexible behavior, and biological intelligence.

Allen School researchers are developing innovative theory, modeling, and machine learning techniques to realize this vision, including new computational theories of brain function, computational tools for complex data analysis and experimental design, and AI-enabled brain co-processors for next-generation brain-computer interfaces. This research could open up new avenues for diagnosing and treating neurological and psychiatric conditions that affect millions of people in Washington state and billions of people worldwide. And as technologies for neural data collection and neural interfaces become more powerful, we’re committed to ensuring they are developed and deployed in ways that are secure, privacy-preserving, fair and broadly accessible.


Selected Projects

Akari Asai, Jacqueline He, Rulin Shao, Weijia Shi, Amanpreet Singh, Joseph Chee Chang, Kyle Lo, Luca Soldaini, Sergey Feldman, Mike D’arcy, David Wadden, Matt Latzke, Minyang Tian, Pan Ji, Shengyan Liu, Hao Tong, Bohao Wu, Yanyu Xiong, Luke Zettlemoyer, Graham Neubig, Dan Weld, Doug Downey, Wen-tau Yih, Pang Wei Koh, and Hannaneh Hajishirzi
Nature 2026 (to appear)

Rulin Shao, Akari Asai, Shannon Zejiang Shen, Hamish Ivison, Varsha Kishore, Jingming Zhuo, Xinran Zhao, Molly Park, Samuel Finlayson, David Sontag, Tyler Murray, Sewon Min, Pradeep Dasigi, Luca Soldaini, Faeze Brahman, Wen-tau Yih, Tongshuang Wu, Luke Zettlemoyer, Yoon Kim, Hannaneh Hajishirzi, and Pang Wei Koh
arXiv 2025

Belle Liu, Jacob Sacks, Matthew D. Golub
International Conference on Machine Learning (ICML), 2025.

Matthew J Bryan, Felix Schwock, Azadeh Yazdan-Shahmorad and Rajesh P N Rao
Journal of Neural Engineering, 2025

Jonathan Mishler, Richy Yun, Steve Perlmutter, Rajesh P N Rao, Eberhard Fetz
Journal of Neural Engineering, 2025

Matthew J Bryan, Felix Schwock, Azadeh Yazdan-Shahmorad, Rajesh P N Rao
IEEE Engineering in Medicine and Biology, 2025

Rajesh P. N. Rao, Vishwas Sathish, Linxing Preston Jiang, Matthew Bryan, Prashant Rangarajan
arXiv preprint arXiv:2512.22568, 2025

Divyansh Pareek, Sewoong Oh, Simon Shaolei Du
Advances in Neural Information Processing Systems (NeurIPS), 2025

Anshul Nasery, Jonathan Hayase, Pang Wei Koh, Sewoong Oh
Conference on Computer Vision and Pattern Recognition (CVPR), 2025

Andrew Wagenmaker, Mitsuhiko Nakamoto, Yunchu Zhang, Seohong Park, Waleed Yagoub, Anusha Nagabandi, Abhishek Gupta, Sergey Levine
arXiv, 2025

Jacob Berg, Chuning Zhu, Yanda Bao, Ishan Durugkar, Abhishek Gupta
arXiv, 2025

Andrew Wagenmaker, Kevin Huang, Liyiming Ke, Kevin Jamieson, Abhishek Gupta
Advances in Neural Information Processing Systems (NeurIPS), 2024

A. Wagenmaker, L. Mi, M. Rozsa, M.S. Bull, K. Svoboda, K. Daie, M.D. Golub, and K. Jamieson
Advances in Neural Information Processing (NeurIPS), 2024

Rajesh P. N. Rao
Nature Neuroscience, 2024

Linxing Preston Jiang, Rajesh P. N. Rao
PLOS Computational Biology, 2024

Romain Camilleri, Andrew Wagenmaker, Jamie Morgenstern, Lalit Jain, Kevin Jamieson
Uncertainty in Artificial Intelligence (UAI), 2024

Divyansh Pareek, Simon S. Du, Sewoong Oh
Advances in Neural Information Processing Systems (NeurIPS), 2024

Thao Nguyen, Matthew Wallingford, Sebastin Santy, Wei-Chiu Ma, Sewoong Oh, Ludwig Schmidt, Pang Wei Koh, Ranjay Krishna
Advances in Neural Information Processing Systems, 2024

Thao Nguyen, Matthew Wallingford, Sebastin Santy, Wei-Chiu Ma, Sewoong Oh, Ludwig Schmidt, Pang Wei Koh, Ranjay Krishna
Advances in Neural Information Processing Systems, 2024

Rajiv Movva, Pang Wei Koh and Emma Pierson
Nature Medicine, 2024

Rajesh P N Rao, Dimitrios C Gklezakos, and Vishwas Sathish
Neural Computation, 2023

Matthew J Bryan, Linxing Preston Jiang and Rajesh P N Rao
Journal of Neural Engineering, 2023

S.H. Singh, F. van Breugel, R.P.N. Rao, B.W. Brunton
Nature Machine Intelligence, 2023

Andrew Wagenmaker, Guanya Shi, Kevin G. Jamieson
Advances in Neural Information Processing Systems (NeurIPS), 2023

Chuning Zhu, Max Simchowitz, Siri Gadipudi, Abhishek Gupta
Advances in Neural Information Processing Systems 36 (NeurIPS 2023)

Romain Camilleri, Andrew Wagenmaker, Jamie H Morgenstern, Lalit Jain, Kevin G. Jamieson
Advances in Neural Information Processing Systems (NeurIPS), 2022

Steven M. Peterson, Zoe Steine-Hanson, Nathan Davis, Rajesh P. N. Rao and Bingni W. Brunton
Journal of Neural Engineering, 2021

Andrew .J Wagenmaker, Max Simchowitz, Kevin Jamieson
International Conference on Machine Learning (ICML), 2021

Saurabh Vyas, Matthew D. Golub, David Sussillo, and Krishna V. Shenoy
Annual Review of Neuroscience, 43:249-275, 2020

Andrew Wagenmaker, Kevin Jamieson
Conference on Learning Theory (CoLT), 2020

Shiori Sagawa, Aditi Raghunathan, Pang Wei Koh, Percy Liang
International Conference on Machine Learning, 2020

David J Caldwell, Jeffrey G Ojemann, Rajesh P. N. Rao
Frontiers in Neuroscience, 2019

L Jiang, A Stocco, DM Losey, JA Abernethy, CS Prat, RPN Rao
Scientific Reports, 2019

E.R. Oby, M.D. Golub, J.A. Hennig, A.D. Degenhart, E.C. Tyler-Kabara, B.M. Yu, S.M. Chase, and A.P. Batista
Proceedings of the National Academy of Sciences, 116(30):15210-15215, 2019

Matthew D. Golub, Patrick T. Sadtler,, Emily R. Oby, Kristin M. Quick, Stephen I. Ryu, Elizabeth C. Tyler-Kabara, Aaron P. Batista, Steven M. Chase, and Byron M. Yu
Nature Neuroscience, 21(4):607-616, 2018

M.D. Golub, S.M. Chase, A.P. Batista, and B.M. Yu
Current Opinion in Neurobiology, 37:53-58, 2016

M.D. Golub, B.M. Yu, and S.M. Chase
eLife, 4:e10015, 2015

P.T. Sadtler, K.M. Quick, M.D. Golub, S.M. Chase, S.I. Ryu, E.C. Tyler-Kabara, B.M. Yu, and A.P. Batista
Nature, 512:423-426, 2014

Rajesh P. N. Rao
Cambridge University Press. 2013