Summary
Mohammed Mahmoud is a Senior Lecturer in AI & Data Science with 14 years of experience translating biomechanics and sensor fusion research into industry-facing solutions for automotive, defense and mining partners. He leads multidisciplinary teams to build deep learning and inverse reinforcement learning models that fuse inertial, marker-based/markerless motion capture, eye-gaze and physiological signals (EMG/EEG/EOG) to assess human intent, driver competency and visual fatigue in VR/AR. His work spans end-to-end systems from OpenSim/AnyBody musculoskeletal modelling to ROS-enabled robotic/vehicle integrations and GPU-accelerated deep learning pipelines. He has attracted over AU$6.6M in funding and partnered with organisations including Ford, General Motors, SWICK Mining and Lockheed Martin to evaluate assistive devices and exoskeleton efficacy. Notably, he augments traditional biomechanics by incorporating ocular motility and muscle control signals to predict vulnerable road-user trajectories in dense urban environments.
14 years of coding experience
10 years of employment as a software developer
PhD, Computer Science, PhD, Computer Science at Deakin University
BSc, Computer Science, Ranked 1st on Computer Science Department (93%), BSc, Computer Science, Ranked 1st on Computer Science Department (93%) at Cairo University
English, Arabic