Data engineer, IoT builder, and founder of Sensoul — a privacy-first elderly care system powered by edge AI and sensor fusion.
I'm a data engineer and systems builder with a passion for turning hardware, data pipelines, and machine learning into real-world impact. I work at the intersection of embedded systems, edge computing, and AI.
Currently building Sensoul — a privacy-first elderly distress detection system that uses sensor fusion and edge ML to keep people safe without cameras or microphones.
A ceiling-mounted distress detection device that uses 5-sensor fusion to protect elderly individuals — with no camera, no microphone, and no cloud. All ML inference runs on-device on a Raspberry Pi 5.
When danger is detected, a weighted danger score triggers a 30-second cancel window before automatically alerting emergency services.
Production-grade 1-year continuous sensor logging pipeline. Parquet format, date partitioning, Great Expectations validation, and nightly Prefect orchestration with automated email reporting.
Live dashboard running on a 7" IPS display showing polar thermal radar visualization, multi-sensor status, and a weighted danger score bar — all updating at 2Hz from 5 simultaneous sensors.
Training a multi-sensor fall detection classifier using tsfresh for automatic feature extraction and scikit-learn Random Forest — running entirely on-device with ONNX Runtime inference.
Zero-config global remote access for the Sensoul device using Tailscale VPN + Samba file sharing. Accessible from anywhere in the world including mobile data via iPhone Files app.
Open to data engineering roles, IoT consulting, and conversations about Sensoul. Reach out anytime.