About Me
I am an educator and interdisciplinary researcher with a background in financial engineering, statistics, and data science. My work explores how interpretable and reproducible computational methods can transform complex educational and financial data into meaningful evidence for research, teaching, and decision-making.
My recent projects examine AI-assisted curriculum–industry alignment, interpretable machine learning for teacher workforce research, and large language model applications in financial news analysis. Across these projects, I am particularly interested in combining quantitative methods with domain knowledge to produce findings that are transparent, practical, and grounded in real-world contexts.
Beyond research, I enjoy developing open-source research tools, building reproducible data science workflows, and helping students engage with mathematics, finance, programming, and artificial intelligence through applied learning.
Academic Journey
My academic background combines financial engineering, statistics, and artificial intelligence. Throughout my studies and research, I have been interested in how quantitative methods can bridge theory and practice by solving meaningful real-world problems.
Over time, my interests naturally expanded from financial modeling toward educational data science and trustworthy AI. Today, my research brings together statistics, machine learning, and domain knowledge to build transparent and evidence-based intelligent systems for both education and quantitative applications.
Current Work
Currently, I teach Special Education Geometry at Lyndhurst High School and serve as an adjunct faculty member in the Department of Accounting and Finance at Kean University, where I teach undergraduate courses in derivatives and machine learning. Alongside my teaching, I pursue interdisciplinary research at the intersection of educational analytics, large language models, and quantitative finance. My recent work includes AI-assisted curriculum–industry alignment, interpretable machine learning for teacher workforce research, and large language model applications in financial news analysis.
Research Interests
My research interests include Artificial Intelligence, Machine Learning, Statistics, Educational Data Mining, Large Language Models, Learning Analytics, Financial Data Science, and Decision Intelligence.
“I believe the most valuable AI systems are those that not only produce accurate predictions, but also help people understand, trust, and act on data.”