Neil Rohit Mallinar

UC San Diego. Halıcıoğlu Data Science Institute. nmallina@ucsd.edu.

neil_elfin2.jpg

3rd floor

HDSI

La Jolla, CA 92093

I’m pursuing my PhD at UC San Diego, advised by Misha Belkin. I am also a PhD Research Fellow at The Eric and Wendy Schmidt Center of The Broad Institute where I was adivsed by Anthony Philippakis (now a General Partner at GV). For a full list of publications, see Google Scholar.

My research focuses on understanding deep learning, both theoretically and practically. In the past, I studied: benign overfitting in neural networks and kernel regression; calibration in neural networks as it relates to generalization; spectral properties of the de Bruijn graph Laplacian; high-dimensional covariate shifts; and emergent phenomena in non-neural models through grokking modular arithmetic.

I am currently researching topics on fundamentals of feature learning, foundation model training and evaluation (through the lens of single-cell data), scaling laws and feature alignment, and multi-class learning in kernels and neural networks.

news

Jun 10, 2024 Joined Google Research (Mountain View) as a PhD Research Intern
Jun 05, 2023 Joined Microsoft Research New England (MSR-NE) as a PhD Research Intern
Jul 06, 2022 Attended the Summer Cluster on Deep Learning Theory at Simons Institute for Theory of Computing from July - August, 2022.
Jan 01, 2022 Honored to be supported in my PhD through funding from the Eric and Wendy Schmidt Center at The Broad Institute of MIT & Harvard.
Sep 28, 2020 Started my PhD at UC San Diego
Aug 01, 2019 Joined Pryon Inc in Brooklyn as an AI Research Engineer
May 18, 2016 Got my B.S. from Johns Hopkins University in Computer Science and Mathematics

selected publications

  1. A Scalable Framework for Evaluating Health Language Models
    Neil Mallinar, A Ali Heydari, Xin Liu, and 10 more authors
    arXiv preprint, Mar 2025
  2. Emergence in non-neural models: grokking modular arithmetic via average gradient outer product
    Neil Mallinar, Daniel Beaglehole, Libin Zhu, and 3 more authors
    arXin preprint, Oct 2024
  3. Eigenvectors of the De Bruijn Graph Laplacian: A Natural Basis for the Cut and Cycle Space
    Anthony Philippakis, Neil Mallinar, Parthe Pandit, and 1 more author
    arXiv preprint, Oct 2024
  4. Minimum-Norm Interpolation Under Covariate Shift
    Neil Mallinar*, Austin Zane*, Spencer Frei, and 1 more author
    In Proceedings of the 41st International Conference on Machine Learning (ICML 2024), Mar 2024
  5. Benign, tempered, or catastrophic: A taxonomy of overfitting
    Neil Mallinar*, James B Simon*, Amirhesam Abedsoltan, and 3 more authors
    In 36th Conference on Neural Information Processing Systems (NeurIPS 2022), Jul 2022