Primary research interests:
- Mathematical Foundations of Deep Learning. I
develop a rigorous, theorem-driven understanding of neural
networks using tools from geometry, topology and dynamical
systems. My work analyzes how structural features of
neural network parameterizations shape the behavior,
expressivity, and training dynamics of the functions they
represent. Three complementary themes motivate this
research:
- ReLU neural networks and piecewise-linear geometry.
Fully-connected
multilayer perceptrons with ReLU activations coincide
with the class of piecewise linear functions and are the
most "vanilla" type of neural network. I study these
networks through the geometry of their associated
polyhedral complexes, analyzing how the arrangement of
linear regions is determined by the network's
architecture and parameters.
- Parameter space symmetries and functional
redundancy. Modern deep learning models are
highly redundant, meaning that many distinct parameter
settings yield the same realized function. I analyze the
fibers, symmetries, and quotient geometry of the
non-injective realization map from parameter space to
function space, and study how these structures influence
training dynamics, implicit bias, generalization, and
model selection.
- Topological expressivity. We do not have
a good understanding of which functions can be realized
by which neural network architectures. I
investigate how the architecture constrains the
topological properties of the functions a network can
represent.
Prior to transitioning to the mathematics of deep learning, I
worked in dynamical systems. Although the mathematical
objects I study have shifted, my approach remains the
same. In both areas, I combine geometry and dynamical
perspectives with precise mathematical reasoning to illuminate
deep structural phenomena about parameterized families of
functions.
- Dynamical Systems.
My work on dynamical systems explores topological entropy
and Thurston sets in real and complex one-dimensional
dynamics, with an emphasis on how combinatorial models of
interval maps and polynomials reflect and encode dynamical
complexity (kneading theory). I have also worked in
holomorphic dynamics and billiards.
Research advisees:
Current:
Past:
- Laura
Seaberg, Ph.D., 2025
- Ethan
Farber, Ph.D., 2023
- Henry Bayly, senior thesis, 2022
- Alex Benanti, senior thesis, 2022
- Jieqi Di, scholar of the college thesis (2nd reader), 2022
- Hong Cai, geophysics MS student (2nd reader) 2022
Publications:
- Regularization Implies Balancedness in the Deep Linear
Network (with G.
Menon)
- Empirical NTK tracks task complexity (with E.
Grigsby).
- On Functional dimension and persistent peudodimension
(with E.
Grigsby)
- Hidden symmetries of ReLU neural networks (with E.
Grigsby, D. Rolnick)
- Proceedings of the 40th International Conference on
Machine Learning, PMLR 202:11734-11760
- Bicritical rational maps with a common
iterate (with S.
Koch, T. Sharland)
- Functional dimension and moduli spaces
of ReLU neural networks (with E.
Grigsby, R.
Meyerhoff, C. Wu)
- Local and global topological complexity measures of
generic, transversal ReLU neural network functions (with E.
Grigsby, M. Masden).
- Existence of maximum likelihood estimates in exponential
random graph models (with H. Bayly, A. Khanna).
- Master Teapots and entropy algorithms for the Mandelbrot
set (with G. Tiozzo,
C. Wu).
- On transversality of bent hyperplane arrangements and the
topological expressiveness of ReLU neural networks (with E.
Grigsby)
- A characterization of Thurston's Master Teapot (with C. Wu).
- Degree-d-invariant laminations (with W. Thurston, H. Baik,
Gao Yan, J. Hubbard, Tan Lei, D. Thurston).
- The Shape of Thurston's Master Teapot (with H. Bray,
D. Davis, C. Wu).
- Fekete polynomials and shapes of Julia sets (with M.
Younsi).
- Convex shapes and harmonic caps (with L.
DeMarco).
- Horocycle
flow
orbits and lattice surface characterizations (with
J. Chaika).
- Counting invariant components of
hyperelliptic translation surfaces.
- Shapes of polynomial Julia sets.
- A Game of Life on Penrose tilings
(with D.
Bailey).
- Flat surface models of ergodic systems
(with R.
Trevino).
- Measurable Sensitivity (with J.
James, T.
Koberda, C.
Silva, P. Speh).
- On ergodic transformations that are
both weakly mixing and uniformly rigid (with J.
James, T.
Koberda, C.
Silva, P. Speh).
- Families of dynamical systems
associated to translation surfaces.
- Ph.D. dissertation, Cornell
University, 2014.
- Descriptive dynamics of Borel
endomorphisms and group actions.
- Honors thesis in mathematics,
Williams College, 2007.
Grants
and Fellowships:
- NSF Award #2133822: Collaborative Research,
Probabilistic, Geometric and Topological Analysis of
Neural Networks, from Theory to Applications, 2022
- NSF Award #1901247: Shapes of Julia sets, Thurston sets,
and Neural Networks, 2019
- "Women in STEM," Major Grant, Institute for Liberal
Arts, Boston College, 2018
- Research Incentive Grant, Boston College, 2018
- NSF Award #1401133: NSF Mathematical Sciences
Postdoctoral Research Fellowship, 2014
- NSF Graduate Research Fellowship, 2009
- DoD National Defense Science and Engineering Graduate
Fellowship, 2009
- U.S. State Dept. Critical Languages Scholarship
(Chinese), 2008
- Cornell University Graduate Fellowship, 2007