CV

Education

• B.S. in Mathematics, Brigham Young University, 2020
• Ph.D in Computational and Applied Mathematics, University of Chicago, 2025 (expected)

Publications

Analysis of normal-form algorithms for solving systems of polynomial equations

ReLU Neural Networks with Linear Layers are Biased Towards Single- and Multi-Index Models

Depth Separation in Norm-Bounded Infinite-Width Neural Networks

Presentations

Finding Low-Rank Functions Using Linear Layers in Neural Networks

February 14, 2023

Talk at University of Chicago Computational and Applied Mathematics Student Seminar, Chicago, Illinois

Linear Layers Promote Learning Single-/Multiple-Index Models

May 17, 2023

Poster at Midwest Machine Learning Symposium, Chicago, Illinois

Depth Separation in Learning via Representation Costs

February 15, 2024

Talk at Brigham Young University Applied Math Seminar, Provo, Utah

Depth Separation in Learning via Representation Costs

July 01, 2024

Poster at 37th Annual Conference on Learning Theory (COLT), Edmonton, Canada

Service and leadership

• Chair of scientific content committee for the University of Chicago Computational Biology Outreach (UCCOMBO) group.
  • Led and developed materials for workshops with K-12 students