My path into AI is tied closely to biomedical engineering and neurotechnology. I studied Systems and Biomedical Engineering at Cairo University, then moved into an MSc in Bionics Engineering on the Neural Engineering track at Scuola Superiore Sant’Anna and the University of Pisa.
That background changes how I think about software. In neurotechnology, the code is not only a product surface; it is often the bridge between signals, clinicians, researchers, and the person using the system.
For my graduation project, I worked on a brain-computer interface and virtual reality system for post-stroke rehabilitation. The system connected EEG processing, machine learning, LSL-based real-time feedback, and Unity-based VR experiences.
The rehabilitation design question was not only “can we classify a signal?” It was also “can the feedback feel embodied and engaging enough to support therapy?” That is where software engineering, game interaction, signal processing, and clinical imagination meet.
During Google Summer of Code, I developed an interactive TMS motor mapping visualization feature for InVesalius. The work involved Python, VTK, brain surface heatmaps, custom colormaps, and real-time Motor Evoked Potential exploration.
Visualization is a serious part of neurotechnology. Researchers and clinicians need interfaces that make spatial patterns readable without hiding the underlying uncertainty.
AI automation and neurotechnology may look like separate worlds, but the engineering instincts overlap: understand the domain, preserve context, design for failure, and build tools that make expert work easier.
That is the thread through my work, from RAG systems and voice agents to BCI, VR rehabilitation, and TMS visualization.