Python & Applied AI Developer | Developer Tools | Automation | TinyML
I turn ideas into testable software. I build Python applications and tools at the intersection of AI, CAD, slicers, automation, and resource-constrained computing.
MS Data Science — completed. My thesis explored semantic and context-aware radar gesture recognition for TinyML-enabled IoT devices, from compact CNN classification and quantization to context-dependent intent mapping.
| Project | What it does | Engineering evidence |
|---|---|---|
| PolyForge | Generate editable printable CAD from descriptions/dimensions | CLI, local GUI, CAD backends, tests |
| Cura Connect | Add mechanical connectors when cutting models in Cura | Mesh geometry, Cura/QML integration, tests |
| PrintTally | Estimate production/selling costs across slicers | Python domain logic, cross-slicer adapters, tests |
| GrantCompass | Discover and rank research funding opportunities | CLI, structured scoring, reports, tests |
Research: Radar I/Q signals → compact CNN gesture recognition → int8/MCU constraints → semantic intent mapped to the active application.
How I work: problem-first design, independently testable core logic, AI-assisted workflows with validation, and connecting code to physical systems.