About J.Zorraquin

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I am a future?computational neuroscientist?focused on predicting and optimizing treatments for mental health conditions using advanced data analysis techniques. My work lies at the intersection of?neuroscience, data science, and machine learning, and I'm driven by the potential of these fields to unlock new insights into brain function and health. My passion is to apply cutting-edge computational methods to real-world challenges in mental well-being.

My research journey in?Neuro AI?began during my Master's thesis, where I applied deep learning to analyze fMRI functional connectivity. This pivotal work was a collaborative effort, bridging the?Cognitive Neuroscience Lab with the Artificial Intelligence Lab of 新万博体育下载_万博体育app【投注官网】ical Imaging.

Building on that foundation, I've continuously advanced the diagnostic accuracy for neurodevelopmental disorders. My approach involves exploring a diverse range of advanced machine learning techniques, including XGBoost algorithms, Graph Neural Networks, MLP regressors, and state-of-the-art Transformer models.

My more advanced independent project was a?Transformer-based fMRI encoder. This model meticulously integrates multi-modal data (fMRI signals, age, and gender) to perform robust binary classification. Extensively trained on large datasets like ABIDE (for autism), ADHD-200, and the Pixar Movie fMRI dataset, my ultimate aim is to harness these advanced computational techniques to develop a clinically viable tool. This tool will provide probabilistic diagnostics, offering clinicians actionable insights for personalized patient treatment.

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https://github.com/lacomaofficial

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