I am an Assistant Professor at Michigan State University in the College of Natural Science and the College of Engineering. I have a joint appointment between the Department of Computational Mathematics, Science and Engineering and the Department of Mathematics.

My research is in **pure, applied, and computational harmonic analysis**. I develop mathematically provable machine learning algorithms for the analysis of high dimensional data and to circumvent prohibitively costly computations in scientific computing, thereby opening new avenues for scientific breakthroughs. My primary interests are:

- Wavelet theory and deep learning
- Diffusion based manifold learning
- Smooth extensions of Whitney type
- Quantum chemistry and many body problems
- Hyperspectral imagery analysis

Before arriving at Michigan State University, I was a Postdoctoral Researcher working in the Département d’Informatique at the École normale supérieure in Paris, France, where I was part of Stéphane Mallat’s Data Team. Prior to that appointment I was a Postdoctoral Associate working with Ronald Coifman in the Department of Mathematics at Yale University. I spent the two months between those appointments running an NSF Research Experience for Undergraduates (REU) in the Department of Mathematics at Cornell University on high dimensional data analysis.

I received my PhD in Mathematics from the University of Maryland. My advisors were John Benedetto and Kasso Okoudjou. I was also part of the Norbert Weiner Center for Harmonic Analysis and Applications while at Maryland. I obtained my BA in Mathematics from Cornell University, under the supervision of Robert Strichartz.