EPA SBIR Phase 1 — Residential Energy Optimization Algorithms

Overview

Won a $66,890 EPA SBIR grant to develop adaptive energy management algorithms using sensors and machine learning for residential HVAC optimization — an early smart thermostat system built before Nest existed.

This was part of the student-era SBIR portfolio — winning competitive federal research funding as undergraduates through the Interdisciplinary Design Collaborative at Missouri S&T. The Phase 1 work combined sensor networks with machine learning to adapt residential heating and cooling to how a home was actually being used, years before “smart thermostat” was a product category.

Beyond the technology, the durable lesson was the proposal system itself: federal grants reward the ability to translate an ambitious technical idea into a rigorously scoped, reviewable plan — a skill that transfers directly to venture design, enterprise proposals, and every engagement where someone else’s confidence has to be earned on paper before anything gets built.