Lulu (Luke) Gong

Email: lulu.gong@yale.edu

Address: 1121A, 100 College St, New Haven, CT, USA

I am a Swartz Postdoc Fellow of Theoretical and Computational Neuroscience working with Dr. Shreya Saxena at Wu Tsai Institute, Yale University. From 2022.02 to 2024.08, I was a Postdoc Research Associate working with Dr. ShiNung Ching at the Department of Electrical and Systems Engineering, Washington University in St. Louis. I received my PhD degree in System and Control in 2022 from the University of Groningen, the Netherlands, advised by Prof. Ming Cao. Before that, I obtained Bachelor’s and Master’s degrees in Mechanical and Electrical Engineering respectively in China.

My research interests lie in the emerging field of Neuro-AI. My research combines dynamical systems theory, computational modeling, and machine learning to understand how biological systems generate adaptive behavior. I develop mathematical models of interacting biological processes and statistical methods for discovering latent dynamics from data, with a particular focus on feedback, multiple timescales, and neural computation.

news

Nov 12, 2026	I will be traveling to NIH Bethesda to talk about our multi-timescale neural dynamics inference work at the minisymposium Models for Understanding Neural Computation on Nov. 13. Subsequently, I will attend the Society of Neuroscience Conference 2026 (Nov. 14-18) at Washington D.C.
Oct 01, 2026	I partipated the Data-driven modeling, simulation and inference for neurobiological problems at National Institute for Theory and Mathematics in Biology (NITMB) from Sep. 28 to Oct. 02.
Sep 25, 2026	A new paper titled “Inferring Multi-Timescale Neural Dynamics with Switching Linear Dynamical Systems” was submitted.
Aug 11, 2026	I attended the 2026 BRAIN Initiative Conference at NIH, Bethesda, where I presented a poster on our work on “Inferring Multi-time Neural Dynamics Using Swtiching Linear Dynamical Systems”.
May 18, 2026	I attended the Kavli Neuroscience Syposium at Kavli Institute for Brain Science at Columbia University, where I gave a talk on my work on “Principled Inference of Neural Population Dynamics across Modes and Timescales”.
Apr 09, 2026	Invited talk at the EBRAIN workshop on Multiscale Modeling of Brain Systems at SUNY Downstate, where I presented my work, “Principled Inference of Neural Population Dynamics across Modes and Timescales”.
Jan 26, 2026	Our paper “Learning Mixtures of Linear Dynamical Systems via Hybrid Tensor-EM Method” was accepted to ICLR 2026.
Jan 11, 2026	Co-authored paper “Multitimescale Computation by Astrocytes” was submitted for review. Check out the preprint.

selected publications

ArXiv

Inferring Multi-timescale Neural Population Dynamics Using Switching Linear Dynamical Systems

Lulu Gong, Yongxu Zhang, and Shreya Saxena

Arxiv preprint, 2026

Bib

@article{gong2026mtsslds,
  title = {Inferring Multi-timescale Neural Population Dynamics Using Switching Linear Dynamical Systems},
  author = {Gong, Lulu and Zhang, Yongxu and Saxena, Shreya},
  year = {2026},
  publisher = {Arxiv},
  url = {https://doi.org/10.48550/arXiv.2610.01786},
  journal = {Arxiv preprint}
}

ICLR

Learning Mixtures of Linear Dynamical Systems (MoLDS) via Hybrid Tensor-EM Method

Lulu Gong, and Shreya Saxena

The Fourteenth International Conference on Learning Representations, 2026

Bib

@article{gong2025molds,
  title = {Learning Mixtures of Linear Dynamical Systems (MoLDS) via Hybrid Tensor-EM Method},
  author = {Gong, Lulu and Saxena, Shreya},
  year = {2026},
  publisher = {The Fourteenth International Conference on Learning Representations},
  url = {https://openreview.net/forum?id=1GkzDABbME},
  journal = {The Fourteenth International Conference on Learning Representations}
}

PLoS-CB

Astrocytes as a mechanism for contextually-guided network dynamics and function

Lulu Gong, Fabio Pasqualetti, Thomas Papouin, and 1 more author

PLOS Computational Biology, 2024

Bib PDF

@article{gong2024astrocytes,
  title = {Astrocytes as a mechanism for contextually-guided network dynamics and function},
  author = {Gong, Lulu and Pasqualetti, Fabio and Papouin, Thomas and Ching, ShiNung},
  journal = {PLOS Computational Biology},
  volume = {20},
  number = {5},
  pages = {e1012186},
  year = {2024},
  publisher = {Public Library of Science San Francisco, CA USA},
  doi = {10.1371/journal.pcbi.1012186},
  url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012186},
}

Automatica

Limit cycles analysis and control of evolutionary game dynamics with environmental feedback

Lulu Gong, Weijia Yao, Jian Gao, and 1 more author

Automatica, 2022

Bib PDF

@article{gong2022limit,
  title = {Limit cycles analysis and control of evolutionary game dynamics with environmental feedback},
  author = {Gong, Lulu and Yao, Weijia and Gao, Jian and Cao, Ming},
  journal = {Automatica},
  volume = {145},
  pages = {110536},
  year = {2022},
  publisher = {Elsevier},
  doi = {doi.org/10.1016/j.automatica.2022.110536},
  url = {https://www.sciencedirect.com/science/article/pii/S0005109822003971},
}

IEEE-TNNLS

Strong anti-Hebbian plasticity alters the convexity of network attractor landscapes

Lulu Gong, Xudong Chen, and ShiNung Ching

IEEE Transactions on Neural Networks and Learning Systems, 2025

Bib

@article{gong2025strong,
  title = {Strong anti-Hebbian plasticity alters the convexity of network attractor landscapes},
  author = {Gong, Lulu and Chen, Xudong and Ching, ShiNung},
  year = {2025},
  journal = {IEEE Transactions on Neural Networks and Learning Systems},
  url = {https://ieeexplore.ieee.org/document/10981480}
}