Research
Research
AI-driven operational intelligence for quality and manufacturing systems in deep-tech hardware startups.
Doctoral research
PHD CANDIDATE · INFORMATION SYSTEMS ENGINEERING AND MANAGEMENT · HARRISBURG UNIVERSITY OF SCIENCE AND TECHNOLOGY
My doctoral research investigates how deep-tech hardware startups — companies developing genuinely novel physical products without an established manufacturing history to draw on — can build AI-driven operational intelligence into their quality and manufacturing systems. Legacy industries like automotive or aerospace inherit decades of process capability data, failure libraries, and supplier quality history. Deep-tech hardware startups, by contrast, are often building the failure library in real time, alongside the product itself.
This work extends the same question I've been sitting inside of professionally: how emerging hardware companies can build data-driven quality operations without the manufacturing maturity that legacy industries take for granted.
Operational intelligence
Deep-tech hardware
Quality systems
Applied AI
Information systems
Research interests
Beyond the dissertation, my broader research interest sits at the boundary between classical reliability engineering and emerging tooling — where methods like Weibull analysis, HALT/HASS, and DOE meet AI-assisted diagnosis, log parsing, and pattern detection in hardware quality data. Two areas I'm following closely:
- IC design and VLSI — as a longer-term learning direction, deepening the connection between quality engineering and the semiconductor layer underneath the systems I test.
- Applied scripting for QE workflows — using bash and lightweight tooling for log parsing, hardware diagnostics, and network debugging, in service of faster root-cause work.
Status: Dissertation research underway. Methodology, chapter progress, and findings will be posted here as the program advances.