My work bridges mathematical neuroscience and machine learning. Leveraging dynamical systems theory, stochastic analysis, and physics-informed machine learning, I develop data-driven models of neurons and large-scale brain activity to better understand pathological dynamics in disorders such as epilepsy and Parkinson’s disease. Drawing inspiration from neural computation, I design neuroscience-inspired machine-learning algorithms that deepen our fundamental insight into brain function while powering new algorithms, neurotechnologies, and medical applications.
Research projects
- Dynamical systems theory
- Stochastic analysis
- Statistical physics
- Mathematical and computational neuroscience
- Data-driven methods for neuronal and brain dynamics
- Mathematical and data-driven modelling of epilepsy and Parkinson’s disease
- Neuroscience-inspired machine learning (e.g., liquid-state machines)
- Physics-informed machine learning and constrained optimisation
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My work bridges mathematical neuroscience and machine learning. Leveraging dynamical systems theory, stochastic analysis, and physics-informed machine learning, I develop data-driven models of neurons and large-scale brain activity to better understand pathological dynamics in disorders such as epilepsy and Parkinson’s disease. Drawing inspiration from neural computation, I design neuroscience-inspired machine-learning algorithms that deepen our fundamental insight into brain function while powering new algorithms, neurotechnologies, and medical applications.
Research projects
Related Research Fields:
Contact: