Julia Nakhleh

jnakhleh@wisc.edu

I'm a PhD candidate at the University of Wisconsin–Madison, advised by Robert D. Nowak. My interests broadly lie in the foundations of machine learning, particularly in the mathematical properties of neural networks and deep learning. My recent and ongoing research projects have characterized:

Function-space structure and complexity associated with depth in norm-constrained networks,
Sparsity and functional structure of global minimizers of neural network training problems, and
Implicit bias in single- and multi-task neural network representations.

I am also interested in connections with applied and computational harmonic analysis, nonparametric regression and function estimation, approximation theory, and compressed sensing.

Publications

Julia B. Nakhleh and Robert D. Nowak. "Deep Neural Variation Spaces: A Unifying Perspective on Depth and Complexity." Preprint. [arXiv]

Julia B. Nakhleh and Robert D. Nowak. "Global Minimizers of $\ell^p$-Regularized Objectives Yield the Sparsest ReLU Networks." NeurIPS 2025. [proceedings] [arXiv]

Julia B. Nakhleh, Joseph Shenouda, and Robert D. Nowak. "A New Neural Kernel Regime: the Inductive Bias of Multi-Task Learning." NeurIPS 2024. [proceedings] [arXiv]

Julian J. Katz-Samuels*, Julia B. Nakhleh*, Robert D. Nowak, and Yixuan Li. "Training OOD Detectors in their Natural Habitats." ICML 2022. [proceedings] [arXiv] *equal contribution

M. Giselle Fernández-Godino, Michael J. Grosskopf, Julia B. Nakhleh, Brandon M. Wilson, John L. Kline, and Gowri Srinivasan. "Identifying Entangled Physics Relationships through Sparse Matrix Decomposition to Inform Plasma Fusion Design." IEEE Transactions on Plasma Science 2021. [IEEExplore] [arXiv]

Julia B. Nakhleh, M. Giselle Fernández-Godino, Michael J. Grosskopf, Brandon M. Wilson, John L. Kline, and Gowri Srinivasan. "Exploring Sensitivity of ICF Outputs to Design Parameters in Experiments using Machine Learning." IEEE Transactions on Plasma Science 2021. [IEEExplore] [arXiv]