About Me

I am a first-year doctoral student in the Operations Research Center (ORC) at the Massachusetts Institute of Technology (MIT). I previously received my Bachelors and Master’s degrees in Computer Science at the Georgia Institute of Technology (GT).

Research Themes

My interest is at the intersection of machine learning, statistics, and optimization. In recent years, data-driven models and algorithmic decision-making procedures of increasingly sophistication have become deeply embedded in engineering, science, and society. At the same time, we have become more aware of how these systems both shape — and are shaped by — the environments in which they operate.

For example, in social settings, decisions — human or algorithmic — often function as closed-loop controllers, continuously reshaping the environments they inhabit and generating both positive and negative externalities. In scientific discovery — across biological, physical, and social systems — progress depends on models and experimental procedures that yield robust, and ideally causal, insights.

To effectively address algorithmic decision-making and statistical inference in today’s data- and model-rich settings, I believe we must pursue foundational conceptual, algorithmic, and mathematical advances in order to (1) build models that reflect a deep, principled understanding of complex systems, and (2) design decision-making procedures that ensure trustworthiness, reliability, and fairness — and that ultimately align with long-term societal values and generate positive social impact. While operations researchers and statisticians have long studied these questions in simpler, more stylized settings, the modern data-rich era presents new challenges and opportunities, defined by high-dimensional, high-volume data often collected in dynamic environments influenced by the decision-making process itself. To this end, my work focuses on developing accessible, computationally efficient, statistically powerful, and reliable algorithms for inference and decision-making with theoretical guarantees.

Fields of Interest

Technical: Machine learning, statistics, mathematical optimization, causal inference, data-driven decision making, learning in spatial-temporal and sequential settings

Application Areas: Service Operations, Biostatistics and Bioinformatics.

News

• May 2025, talk at POMS 2025
• April 2025, poster at ICLR 2025

Professional Associations

• IEEE Student Member [Computer Society] (2021-)
• INFORMS Student Member (2022-)
• ACM Student Member (2021-)
• SIAM Student Member (2022-)