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publications
Phase Retrieval from Random One-Bit Measurements
Published in University of Houston Libraries, 2020
Ph.D. dissertation providing thorough look at one-bit phase retrieval.
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Phase Retrieval by Binary Questions: Which Complementary Subspace is Closer?
Published in Constructive Approximation, 2022
Proving pointwise and uniform error bounds for phase retrieval from one-bit, quantized measurements.
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Iteratively Consistent One-Bit Phase Retrieval
Published in Analysis and Applications (accepted, to appear), 2022
Introduce ICQPhase algorithm for iteratively consistent phase retrieval. Prove simple error bounds and provide experimental results for rate of convergence.
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talks
Phase Retrieval from Proximity to Subspaces
Published:
An analysis of algorithms for one-bit, quantized phase retrieval
Published:
Presented at section meeting for the Quantum Computing research group.
teaching
MATH 2311: Introduction to Probability and Statistics
Undergraduate course, University of Houston, Department of Mathematics, 2019
Instructor of record in last year of my Ph.D.
MATH 1301: Accelerated Calculus II
Undergraduate course, Vanderbilt University, Department of Mathematics, 2020
Hybrid teaching, half of class online and half in-person.
MATH 3330/5330: Advanced Linear Algebra
Undergraduate/graduate course, Vanderbilt University, Department of Mathematics, 2021
Hybrid teaching, half of class online and half in-person.
MATH 1200: Calculus I
Undergraduate course, Vanderbilt University, Department of Mathematics, 2021
Online summer course.
MATH 2810: Probability and Statistics for Engineering
Undergraduate course, Vanderbilt University, Department of Mathematics, 2021
MATH 3670/5670: Mathematical Data Science
Undergraduate/graduate course, Vanderbilt University, Department of Mathematics, 2022
Course focuses on the mathematics of various data science and machine learning concepts including: K-nearest neighbors, linear regression, ridge regression, LASSO, best subset selection, linear discriminant analysis, support vector machines, K-means clustering, neural networks, and principal component analysis.