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.