Doughnut enjoyer. I like statistics and optimization with geometry hidden underneath.
Welcome! I am currently pursuing a PhD in Applied Mathematics and Statistics under the joint supervision of Mauro Maggioni and Mateo Díaz at Johns Hopkins University.
Research Interests
I am interested in the theoretical and algorithmic foundations of Data Science, motivated by the impact of exploiting data for advancing scientific discovery. To exploit data, one must use fast/scalable algorithms while side-stepping the curse of dimensionality which negatively effects convergence and statistical rates.
To this end, I explore the interplay of optimization, geometry, and statistics through a variety of different mathematical lenses including Riemannian geometry, optimal transport, and high-dimensional probability.
Research Pipeline
Below are some of my ongoing and recent research projects.
- Neural Dynamic Portfolio Control with Provable Learning Guarantees (Revise and Resubmit at Management Science) SSRN
with , Rui Gao, Shuang Li, Luhao Zhang
Are there neural approaches to portfolio control that incorporate historical returns with provable end-to-end global guarantees? See more… »
- Nonsmooth Riemannian Optimization with Inexact Information (Submitted) arXiv
with Mateo Díaz and Benjamin Grimmer
Can nonsmooth convex Riemannian optimization admit nonasymptotic convergence rates using only subgradients, first-order retractions, and vector transports? See more… »
- Constrained Fréchet Means of Unknown Submanifolds of Non-Euclidean Spaces (In-Preparation)
with Mauro Maggioni
Given only a local sampler of a non-convex submanifold of some non-Euclidean space, i.e., Riemannian or Wasserstein, is there an algorithm to compute the empirical intrinsic mean up to arbitrary accuracy with provable guarantees?
For a full list of publications, see Publications.
Upcoming Talks
Below are some upcoming talks. If you will be at any of these, please say hi! I promise I’ll say hi back!
- MOPTA 2026 — August 18, 2026 · Lehigh University
Talk: Nonsmooth Riemannian Optimization with Inexact Manifold Primitives via Bundle Methods
- INFORMS Annual Meeting — November 1–4, 2026 · San Francisco
Talk: Constrained Fréchet Means of Unknown Submanifolds of Non-Euclidean Spaces
Co-organizing session Optimization in Data Science with Mateo Díaz - INFORMS Conference on Financial Engineering and Fintech — November 6-7, 2026 · New Jersey
Talk: Neural Dynamic Portfolio Control with Provable Learning Guarantees
Co-organizing session Optimization in Data Science with Mateo Díaz - SIAM Conference on Mathematics of Data Science (MDS26) — November 16–20, 2026
Talk: Constrained Fréchet Means of Unknown Submanifolds of Non-Euclidean Spaces
Co-organizing session Geometry, Dynamics, and Inference with Mauro Maggioni and George Kevrekidis
For a full list of talks and poster presentations, see Presentations.