David
Ogunmola.
Deep Learning · Reinforcement Learning · Embodied AI
Building the next generation of intelligent machines — systems that perceive, reason, and act in the physical world.
“I build systems that perceive, reason, and act in the physical world.”
I'm a Computer Engineering student at Covenant University, fascinated by the space where deep learning meets the physical world — robotics, neural rendering, autonomous agents. My work sits between research and execution: rigorous enough to publish, pragmatic enough to ship.
I value collaborative work and a research-first mindset — careful experiments, clear writing, reproducible code. Currently exploring 3D Gaussian Splatting, imitation learning, and on-device inference on FPGAs.
Open to research internships and full-time roles.
A research stack with execution muscle.
Four domains, one continuum — from training loops to register-transfer on silicon.
Things I've built that matter.
Research, competitions, and systems — not just side projects.
Research
Papers and ongoing investigations at the intersection of 3D vision, neural rendering, and hardware acceleration.
Multi-view Generative Augmented 3D Reconstruction Pipeline
Integrating large vision models with 3D Gaussian Splatting to solve geometric collapse in sparse-view, fault-tolerant photogrammetry — improving PSNR by 12.39 dB.
Hardware-Accelerated Adaptive Filtering for Real-Time Biomedical Signal Denoising
Ogunmola, D. (2026). Adaptive LMS FIR filters on Intel DE10-Lite FPGA: ECG denoising with sub-500ms convergence and 60Hz noise elimination.