Michigan State University
I studied Electrical and Electronics Engineering at Michigan State University from 2016 to 2020. The curriculum moved from circuit analysis and semiconductor electronics through signal processing, radio-frequency electronics, digital logic, and power electronics.
That education trained me to reason from physical components and measured signals toward the behavior of a complete system. Inputs have provenance. Interfaces carry assumptions. Failures leave evidence. Those ideas still shape the way I design software.
Electrical and Electronics Engineering
Michigan State University · Rocketry · Tau Beta Pi Engineering Honor Society
Teaching reinforced the fundamentals
From August 2018 through March 2020, I worked as a tutor in the Michigan State University College of Engineering walk-in tutoring center. I helped students work through physics and calculus, with the goal of improving both understanding and confidence.
Explaining technical material exposed a useful engineering truth: knowing the answer is different from being able to make the reasoning visible to someone else. That distinction later became central to presenting investigations, documenting production tools, and building interfaces for operators.
Computer science became the deeper path
At Michigan State, computer science coursework introduced object-oriented design, algorithms, data structures, and discrete mathematics and logic. Software offered a faster laboratory for the systems thinking I already enjoyed: a model could be changed, an interface observed, and an idea tested without waiting for another physical prototype.
While I love electronics, my passion is truly software.
Syracuse University
While working full time at ASML, I continued that path through the Master of Science in Computer Science program at Syracuse University from January 2021 through December 2023.
The graduate work expanded the software foundation into modeling, machine learning, and research-oriented experimentation. One substantial project became a face-recognition research pipeline with dataset handling, face detection, recognition models, experiment orchestration, performance reporting, and a documented literature review spanning classical methods and deep-learning approaches.
Computer Science
Syracuse University · completed while progressing through full-time engineering roles at ASML
One engineering practice
I do not see the electrical and software sides as competing identities. Electrical engineering provides respect for the physical environment, measurement, interfaces, and constraints. Computer science provides the vocabulary for representing state, designing reusable behavior, evaluating algorithms, and managing complexity.
The portfolio repeatedly combines those perspectives: embedded sensors behind a web service, industrial protocol plugins, real-time transit feeds, production-tool software, computer-vision experiments, local inference on Apple Silicon, and simulations with deterministic cores.