I am interested in NeuroAI, biomedical machine learning, and biologically inspired computational systems. I have a background in biomedical sciences and I enjoy building models inspired by biological systems, implementing them with cognitive simulations, analysing neural activity, and blending neuroscience with computational creativity.

Artificial intelligence can mimic - or learn from - biological intelligence. My goal is to gather data, insights, and eventually develop systems that support neurological function, or help scaffold cognitive deficit caused by any sort of neuropathology.

• EEG signal processing and seizure detection
• Neural dynamics and biologically inspired models
• Artificial memory and cognitive systems
• Machine learning for biomedical data

• EEG Seizure Detection – ML pipeline for seizure classification from EEG ⚡EEG_seizure_detection Work-in-progress pipeline for identifying seizure activity.
• EEG Analyser – signal preprocessing and feature extraction 🧪EEG_analyser Tools for exploring, filtering, and visualising EEG data
• Neuron vs Perceptron – comparison of biological neurons and ML models 🧠 neuron vs perceptron comparing the activity between a biological neuron with an artificial neuron
• Artificial Memory Systems – Hopfield networks and working memory

• 🧬cognitive_path_finder early exploration of computational paths and cognitive modelling.
• 🧩memory_insilico Experiments with memory mechanisms and neural-inspired dynamics.
• 🌟Harleone (Fibonacci Spiral) Personal artistic project blending sacred geometry with computational creativity

• Python
• NumPy, SciPy, scikit-learn
• Signal processing
• Machine learning
• Computational neuroscience

I build projects as learning tools, documenting both successes and limitations to develop deep understanding. I am self-taught, and constructive criticism is always welcomed, if it helps me grow.