Adeyemi Ademola

Conducted and contributed novel research as part of a prestigious EU FET PROACT-funded project involving European consortium partners such as DFKI, Aalto, Grassroots Arts, and VivitNet. Implemented a patch-based Shape-from-Shading technique with precomputed Moore–Penrose inverse matrices enabling fine-scale facial geometry reconstruction at interactive frame rates. Applied texturing and material diffusion modelling techniques to animated avatars and 3D props using OpenGL and Unity game engine for realistic rendering in virtual interactive applications. Spearheaded development and optimization of data analysis pipelines and deep learning architectures, improving motion prediction accuracy by 45%. Developed a time-series forecasting pipeline for motion tracking and prediction, achieving 30% improvement in long-term prediction accuracy. Conducted research on classification and regression tasks using motion captured data from stereo cameras to enhance predictive performance of VR-based h

Developed and managed administrative information systems to evaluate banking software. Programmed stepper motors and ultrasonic sensors for automation and control. Tested, analyzed, and performed performance evaluation of banking ATM software solutions ensuring compatibility, efficiency, and alignment with organizational objectives. Achieved 65% reduction in system bugs by implementing testing protocols. Programmed IoT devices including Arduino Micro Controller and XM1000.

Devised and developed a zoom camera system capable of moving horizontally and vertically to accurately evaluate skincare products. Used MAYA to model a creative 3D camera zoom system.