Po-Chen Kuo (郭柏辰)

PhD Candidate

Univ of Washington, Neurosceince PhD Program

About Me

I am Po-Chen Kuo, a 4th-year Ph.D. Candidate in computational neuroscience at University of Washington and a visiting scientist at the Allen Institue for Neural Dynamics. I am fortunate to have Professor Edgar Y. Walker as my advisor, and work closely with the UW Computational Neuroscience Center. Previously I received my M.D. and B.Sc. in Physics at National Taiwan University in 2020.

I am interested in how neural circuits, dynamics, and computation support the complex phenomena of cognition, behavior, and intelligence in organmisms. I study how biological and artificial intelligent systems adapt under uncertainty, with a focus on reinforcement learning, Bayesian inference, and meta-learning. Please visit my research page for more details!

Aside from research, I enjoy reading, cooking, and baseball.

Contact

Email: pckuo [at] uw [dot] edu
Office: Magnuson Health Sciences Building, 1705 NE Pacific Street, Seattle, WA 98195

Publications

Peer-reviewed Conference Proceedings

Learning Bayes-Optimal Representation in Partially Observable Environments via Meta-Reinforcement Learning with Predictive Coding. Kuo, P,-C., Hou, H., Dabney, W., & Walker, E. Y. (2024). The First Workshop on NeuroAI @ NeurIPS2024.
Tractable Dendritic RNNs for Reconstructing Nonlinear Dynamical Systems. Brenner, M., Hess, F., Mikhaeil, J. M., Bereska, L. F., Monfared, Z., Kuo, P.-C., & Durstewitz, D. Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2292-2320, 2022.
Self-similarity student for partial label histopathology image segmentation. Cheng, H.-T., Yeh, C.-F., Kuo, P.-C., Wei, A., Liu, K.-C., Ko, M.-C., … & Liu, T.-L. European Conference on Computer Vision (pp. 117-132). Springer, Cham. 2020.

Peer-reviewed Journal Articles

Assessment of Expert-Level Automated Detection of Plasmodium falciparum in Digitized Thin Blood Smear Images. Kuo, P.-C., Cheng, H.-Y., Chen, P.-F., Liu, Y.-L., Kang, M., Kuo, M.-C., … & Chuang, J.-H. JAMA network open, 3(2), e200206-e200206. 2020.

Preprints

A cascaded learning strategy for robust covid-19 pneumonia chest x-ray screening. Yeh, C.-F., Cheng, H.-T., Wei, A., Chen, H.-M., Kuo, P.-C., Liu, K.-C., … & Liu, T.-L. arXiv preprint arXiv:2004.12786. 2020.

Book Chapters

Prize-winning Works of National Taiwan University Student Laureate for Philosophical Treatise, Vol. 3. Lee H.-C. National Taiwan University Press. 2016.

Conference Presentations

[Upcoming, Mar 2025] Computational and Systems Neuroscience (COSYNE) 2025 (Poster, Abstract) Task Structures Shape Underlying Dynamical Systems That Implement Computation. Kuo, P,-C., Walker, E. Y., & Driscoll, L. (2025).
[Dec 2024] The First Workshop on NeuroAI @ NeurIPS2024 (Poster, Paper) Learning Bayes-Optimal Representation in Partially Observable Environments via Meta-Reinforcement Learning with Predictive Coding. Kuo, P,-C., Hou, H., Dabney, W., & Walker, E. Y. (2024).
[Aug 2024] Analytical Connectionism Summer School 2024 (Poster, Abstract) “Uncovering the Computation of Dynamic Foraging with Actor-critic Recurrent Neural Networks” Kuo, P,-C., Driscoll, L., & Walker, E. Y.
[Aug 2024] Cognitive Computational Neuroscience 2024 (Poster, Paper) “Adaptive Learning Under Uncertainty With Variational Belief Deep Reinforcement Learning” Kuo, P.-C., Hou, H., & Walker, E. Y.
[Jun 2024] AREADNE 2024, Research in Encoding And Decoding of Neural Ensembles (Poster, Abstract) “An information-theoretical approach to optimize task design for distinguishing probabilistic codes in neural populations” Kuo, P.-C. and Walker, E. Y.
[May 2024] CoNectome 2024 Symposium (Poster, Abstract) “Bayesian reinforcement learning for the computational basis of dynamic foraging” Kuo, P.-C. and Walker, E. Y.
[Mar 2024] Hendrickson Trainee Symposium, University of Washington School of Medicine (Poster, Abstract) “Bayesian reinforcement learning for the computational basis of dynamic foraging” Kuo, P.-C. and Walker, E. Y.
[Feb 2024] Janelia Conference, Bridging Diverse Perspectives on the Mechanistic Basis of Foraging (Poster, Abstract) “Bayesian reinforcement learning as a mechanistic model for dynamic foraging behavior” Kuo, P.-C. and Walker, E. Y.

Invited Talks

[Jul 2024] TReND-CaMinA: Computational Neuroscience and Machine Learning in Africa. “From Neural Variability to Population Coding”
[Feb 2024] University of Washington, NEUSCI 403 Lecture (Computational Models For Cognitive Neuroscience). “Adaptive learning under uncertainty: learning to reinforcement learn with actor-critic recurrent neural networks”
[Aug 2023] Allen Institute for Brain Science, Summer Workshop on the Dynamic Brain. “What gives rise to neural variability and dynamics?”