I am a postdoc in the OPTIM lab at EPFL, working with Nicolas Boumal.

I graduated from Georgia Tech in May 2022 with a Ph.D. in Electrical and Computer Engineering, advised by Mark Davenport.

My doctoral research was in learning theory and high-dimensional statistics.
My postdoc work has shifted focus toward the specific optimization problems that arise from such problems.
Most of my work falls under the broad umbrella of understanding how *problem structure* affects

- how many measurements/samples we need to make useful predictions or inferences,
- how much error/corruption we can expect due to noise or other factors, and
- how difficult the associated optimization problems are to solve.

Here is a non-comprehensive list of topics I've worked on recently:

- Nonconvex optimization landscapes arising from statistics and machine learning problems
- Low-rank (noisy) matrix completion via convex optimization
- Convex optimization for nonlinear recovery via lifting
- Reproducing kernel Hilbert space (RKHS) methods
- (Sparse) phase retrieval
- (Sparse) principal component analysis (PCA)

- Interpolation with noise (a.k.a. "Benign overfitting" or "Harmless interpolation")
- Classification theory (and how it differs from regression)
- Regression on a manifold domain