Graph-focused Algorithm Engineer and Applied Researcher at the intersection of mathematics, optimization, and intelligent systems.
I design graph-based models for reasoning, scheduling, and decision-making in complex environments, translating structure into reliable, real-world software.

About Me

I come from a deeply theoretical background in mathematics, philosophy, and scientific modeling, with formal training spanning pure mathematics, applied mathematics, and astrophysics. Early in my career, I focused on discrete mathematics and combinatorics, with particular emphasis on graph theory, optimization, and structural reasoning, both in theory and applied research settings.

During graduate school and my time as a research assistant, I worked extensively with scientific and optimization software as part of applied research projects. This included implementing algorithms in Python and C++, formulating and solving optimization models using GAMS and CPLEX, and working with simulation, data-driven decision-making models, and time-indexed systems. My research contributions during this period culminated in published work that combined mathematical modeling with real operational constraints, grounding my theoretical training in practical computational problem-solving.

I then spent many years teaching and mentoring students while maintaining an active research mindset in mathematics and scientific modeling. Teaching was intellectually rewarding but highly time-intensive, constraining opportunities for sustained publication rather than interest or engagement with research itself. During the COVID era, a sudden pause in teaching created the space to fully re-engage with software and computational systems, allowing me to expand my existing programming and optimization background into modern data science, algorithmic engineering, and AI-driven workflows. This period represented an acceleration of long-standing interests into contemporary computational practice.

Since then, my work has centered on building and analyzing algorithmic systems that operate under real-world constraints. This includes graph-based modeling, optimization and scheduling problems, simulation and probabilistic reasoning, and the translation of mathematical structure into maintainable, production-oriented software. I am particularly drawn to problems where correctness, interpretability, and system-level thinking matter as much as raw performance.

Today, my interests span graph analytics, AI-driven decision systems, and emerging computational paradigms, including graph-based and probabilistic systems. I value clarity, rigor, and thoughtful abstraction, and I enjoy working at the boundary between theory and practice, where deep reasoning informs reliable engineering.

Research Interests

My work centers on graph theory, discrete optimization, and algorithmic reasoning, with interests in scheduling, mathematical programming, and building structured representations that support intelligent systems. I also draw from related areas including finite geometry, algebra, coding theory, and applied modeling.