Who Am I?

Efrain G. Garza

My name is Efrain G. Garza, and I am an undergraduate student at the University of Texas at San Antonio, enrolled in the Carlos Alvarez College of Business. I am pursuing dual degrees in Business Analytics and Actuarial Science, with a minor in Statistics.

On the PhD Path

I am actively pursuing doctoral training, with a focused interest in Quantitative Finance and Empirical Financial Research. In June 2025, I was selected to present my research at the National Conference on Undergraduate Research (NCUR) in Pittsburgh, Pennsylvania—an experience that marked a significant milestone in my preparation for PhD-level study.

My primary research project, titled “Optimizing Mentor–Mentee Pairing: A Linear Programming Approach for Diverse Business Majors,” centers on mentor–mentee optimization using mathematical programming and data-driven similarity measures. The study applied linear and integer programming techniques to improve mentorship alignment across 15 undergraduate business degree programs, demonstrating how structured optimization can enhance institutional decision-making. This work was conducted through the Pre-Ph.D. Pathway Program at the Alvarez College of Business, which provides intensive research training for students preparing for research-oriented doctoral programs.

In parallel with this work, I have engaged in applied financial research using empirical methods. Most notably, I conducted an event-study analysis examining market reactions surrounding the Enron scandal, replicating and extending prior findings on abnormal returns and reputational spillovers. This project strengthened my interest in asset pricing, inference under uncertainty, and the sensitivity of empirical results to modeling assumptions.

Collectively, these research experiences—spanning optimization, empirical finance, and applied analytics—have solidified my commitment to pursuing a Finance PhD. For additional context on my research trajectory and national conference presentation, see the UT San Antonio feature highlighting my work:
https://business.utsa.edu/news/2025/06/efrain-garza-student-research.html

Personal Synopsis

My academic and professional background reflects a strong interest in decision-making under uncertainty, risk analysis, and applied analytics. I am particularly motivated by problems where quantitative modeling, optimization, and data-driven inference support real-world decisions. My current academic focus is preparing for doctoral-level research in finance, analytics, and related quantitative fields.

Academic Background

Honors and Programs:

Professional Background

Prior to returning to full-time academic study, I completed a 24-year career in the United States Air Force, serving in leadership roles across operational, analytical, and information systems environments. These roles involved managing teams, coordinating complex operations, and supporting decision-making under resource constraints and uncertainty.

Experience with R and Data Mining

I use R for data cleaning, exploratory data analysis, visualization, and statistical modeling. My exposure to data mining includes classification, regression, clustering concepts, and text-based feature engineering. I am continuing to strengthen my skills in applying these methods to structured and unstructured data within academic research projects.

Experience with Other Analytic Software

In addition to R, I have experience with:

I have also worked with optimization solvers and GPU-accelerated tools in applied research settings. In particular, I have used Gurobi as a primary optimization solver for linear and integer programming models arising in assignment and matching problems, where solution quality, computational efficiency, and sensitivity to constraint formulation are central to the analysis. For data preprocessing and large matrix operations, I have experience with GPU-accelerated workflows using libraries such as Polars and cuDF, enabling scalable manipulation of structured and similarity-based data. I have also explored TensorFlow at an introductory level for matrix-oriented computation and prototyping, viewing machine learning frameworks as complementary tools that support feature construction and numerical efficiency rather than substitutes for principled optimization or empirical modeling.