Engineer. Researcher. Builder. Working at the intersection of edge AI hardware, machine learning, and product strategy — turning deep technical work into real-world impact.
I'm a graduate researcher at Prairie View A&M University pursuing my MSc in Electrical Engineering, where I'm prototyping early-exit neural networks on edge hardware to make real-time AI inference faster and cheaper.
Before grad school I spent years shipping products that mattered — from a women's health platform that scaled to five figures, to healthcare APIs that opened three new regional markets and generated $10M in year-one revenue.
What drives me is the full stack: from soldering a microcontroller to training a PyTorch model to launching a product that changes how people live. The most interesting problems live at the edges — edge hardware, edge cases, and the edges between disciplines.
Outside of work I mentor engineers, co-led XR strategy across 16+ African countries, and teach anyone willing to learn.
Edge AI, early-exit networks, spectrum sensing, SVM & deep learning models.
PCB design, embedded firmware, STM32 / ESP32, IoT systems, signal processing.
0→1 launches, B2B SaaS, AI automation, $10M revenue, 290K+ users.
Android mentor, XR Africa strategist, teaching assistant, team lead.
From cognitive radio networks to efficient neural networks on constrained hardware — research that bridges theory and deployment.
Designing adaptive computation strategies that allow neural networks to exit early on easy inputs, targeting ≥40% latency reduction and ≥30% energy savings while maintaining accuracy. Deployed on real edge hardware.
Engineered a full spectrum sensing pipeline combining hybrid feature extraction with SVM classifiers. Evaluated robustness under noisy RF conditions, achieving 91.7% accuracy and improving detection reliability by ~25% over prior baselines.
A research collaboration, an engineering or product internship, or just a conversation, I'm open to it.
hellojesutofunmi@gmail.com