my first schematic ever. teensy-based soil sampling system. photosensors are used to determine the frequency of light shone through a soil sample. science team used these measurements to detect presence of materials in the sample. on board drilling, pump, and vibration controls.
high precision analog microphone for picking up heart sounds through vehicle upholestry. amp is tuned to pick up frequencies at the known heart rate range. differential amp blocks out any vehicle noise.
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ground up electrical design of an EV for FSAE michigan. this got scrappy. lot of late nights getting things to blink and evading solder fumes.
industry sponsored capstone. i still think about this project. em and circuit desgin colliding into the final boss of layout is such a fun challenge. this is a transcondutance filter for blocking noise from the mechanical "bounce" of the switch (from our pov the switch just closes but it actually turns on an off a few times before settling).
found diode's repo and wanted to try building in layout. they definitely have this its just not open sourced. so far im thinking:
super high level. so much more nuance to this but i like the idea of starting by trace speed. would also love the input of this system to be the standards you're trying to comply to (can get trace width, clearance, and creepage from that). this is what i have so far.
good agentic use case because the telemetry data from mavlink is too big to fit into the context window. built an intent classifier to determine what context to feed it. i chunk the flight data into segments (takeoff, landing, etc) and had json for direct questions (max acceleration, max temp). this also made me realize how important prompt eval work is. and that json eats up a ton of tokens. repo
this was contract work so can't share any code. but classical ml problem which gave me a great fundamental understanding of neural networks. problem was to recommend prospects to wealth managers with tabular prospect data (age income bracket, net worth, etc). goal was to get to collaborative filtering so put out a simple if else to a/b test on. started to vectorize the data and built a scoring system for user interactions (how valuable is a profile visit vs email sent) to test how well the weights of our vectors were working. used pca to reduce dimensionality on the refined vectors.
another contract project. system for automating outreach to nursing home owners with potential vendors. vendor would fill out a profile we'd use to find potential matches. sent out emails and used llms to analyze sentiment of responses. got to work with some exciting companies. we brought one from pre-seed to series a.