David Zoltowski 
research /

Featured publications are listed below. A full list is available at my Google Scholar .

Featured Publications
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    Parallelizing MCMC Across the Sequence Length
    David Zoltowski*, Skyler Wu*, Xavier Gonzalez, Leo Kozachkov, Scott W. Linderman
    Paper Code
    arXiv 2025; To appear in NeurIPS 2025

    We develop methods to parallelize Gibbs, MALA, and HMC sampling across the chain length using parallel Newton methods.

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    Predictability Enables Parallelization of Nonlinear State Space Models
    Xavier Gonzalez, Leo Kozachkov, David Zoltowski, Kenneth L. Clarkson, Scott W. Linderman
    Paper
    arXiv 2025; To appear in NeurIPS 2025

    This paper estabishes a theory of when parallel Newton methods (DEER) efficiently converge.

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    Competitive integration of time and reward explains value-sensitive foraging decisions and frontal cortex ramping dynamics
    Michael Bukwich, Malcolm G. Campbell, David Zoltowski, Lyle Kingsbury, Momchil S. Tomov, Joshua Stern, HyungGoo R. Kim, Jan Drugowitsch, Scott W. Linderman, and Naoshige Uchida
    Paper
    Neuron 2025

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    Modeling Latent Neural Dynamics with Gaussian Process Switching Linear Dynamical Systems
    Amber Hu, David Zoltowski, Aditya Nair, David Anderson, Lea Duncker, Scott Linderman
    Paper Code
    NeurIPS 2024

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    Slice Sampling Reparameterization Gradients
    David Zoltowski, Diana Cai, Ryan P Adams
    Paper Code
    NeurIPS 2021

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    A general recurrent state space framework for modeling neural dynamics during decision-making
    David Zoltowski, Jonathan W Pillow, Scott W Linderman
    Paper Code
    ICML 2020

    This paper unifies many models of dynamics during sensory-decision making in a recurrent switching state-space framework. It also proposes the variational Laplace EM algorithm for fitting SLDS models.

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    Discrete stepping and nonlinear ramping dynamics underlie spiking responses of LIP neurons during decision-making
    David Zoltowski, Kenneth W Latimer, Jacob L Yates, Alexander C Huk, Jonathan W Pillow
    Paper
    Neuron 2019

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    Scaling the Poisson GLM to massive neural datasets through polynomial approximations
    David Zoltowski, Jonathan W Pillow
    Paper Code
    NeurIPS 2018

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