B.S. Applied Mathematics, and B.S. Neuroscience, University of Washington, 2025
Summary of Research
Broadly, I work on scientific machine learning (SciML) methods for neuroscience. Currently, I am working on using Shallow Recurrent Decoders (SHRED) combined with Sparse Identification of Nonlinear Dynamics (SINDy-SHRED) to learn interpretable, parametrized dynamical systems in low-dimensional latent spaces. My work focuses on extending these approaches to multi-scale, noisy datasets arising in neuroscience and related systems, with the goal of improving both interpretability and generalizability.
I am also interested in improving transparency and establishing fair benchmarking standards for scientific ML models. As part of this effort, I have contributed to a Common Task Framework (CTF) designed to evaluate and benchmark models across shared datasets and tasks.
Moving forward, I plan to continue developing SciML methods for neuroscience while creating open-source tools that make these approaches more accessible to the general public. My long-term goal is to build a toolkit of robust, well-documented methods that can be used in both research and educational settings.
Publications
Gao, M., Bao, Y., Rude, A. S., Shen X., & Kutz, J. N. (April 2026). UQ-SHRED: Uncertainty
Quantification of Shallow Recurrent Decoder Networks for Sparse Sensing via Engression. Uploaded
to Arxiv (Submitted to the Proceedings of the Royal Society B)
Yermakov, A., Zhao, Y., Denolle, M., Ni, Y., Wyder, P. M., Goldfeder, J. A., Riva, S., Williams, J.
P., Zoro, D., Rude, A. S., Tomasetto, M., Germany, J., Bakarji, J., Maierhofer, G., Cranmer, M., &
Kutz, J. N. (April 2026). The Seismic Wavefield Common Task Framework. ICLR 2026
Rude, A., & Kutz, J. N. (Accepted October 2025). Shallow Recurrent Decoders for Neural and Behavioral Dynamics. Proceedings of the Royal Society B: Biological Sciences. https://doi.org/10.1098/rstb.2024.0461 (Also presented at NeurIPS 2025 - Data Brain and Mind Workshop)
Wyder, P. M., Goldfeder, J. A., Yermakov, A., Zhao, Y., Riva, S., Williams, J. P., Zoro, D., Rude, A. S., Tomasetto, M., Germany, J., Bakarji, J., Maierhofer, G., Cranmer, M., & Kutz, J. N. (December 2025). Common Task Framework for a Critical Evaluation of Scientific Machine Learning Algorithms. NeurIPS 2025 Datasets and Benchmarks Track.
Adler-Wachter, M., Schweitzer, B., Rude, A., Sumanth, S., McDonough, A., Chen, Y.J., Richards,
T., Lee, D., Wulff, H., Weinstein, J.R. (November 2024). Effects of the KCa3.1 inhibitor senicapoc in acute ischemic stroke. Society for Neuroscience, Chicago, IL.
Weinstein, J., Lee, R., Chen, Y., Singh, L., Adler-Wachter, M., Schweitzer, B., Rude, A., McDonough,
A., Wulff, H. (November 2023). Repurposing the KCa3.1 Inhibitor Senicapoc for Treatment of Acute
Ischemic Stroke. Society for Neuroscience, Washington D.C.
Awards
Computational Science Graduate Fellowship, DOE (2026)
Summa cum laude, University of Washington (2025)
Phi Beta Kappa, University of Washington (2025)
Carl S. Pearson Award for Excellence, University of Washington (2024)
Grosswirth-Salny Scholarship, Mensa Foundation (2021)