Medical AI
To improve diagnostic and prognosis accuracy and provide faster diagnosis and treatment for patients using AI in areas such as Cervical Cancer, Cardiovascular Calcification, Colorectal Cancer, and Epilepsy.
Our research spans applied and theoretical machine learning with strong collaborations in healthcare, renewable energy, and scientific AI.
To improve diagnostic and prognosis accuracy and provide faster diagnosis and treatment for patients using AI in areas such as Cervical Cancer, Cardiovascular Calcification, Colorectal Cancer, and Epilepsy.
To develop surrogate AI models to speed up traditional numerics for fusion energy and meta lenses, including inverse models using Physics-Informed Neural Networks (PINNs).
Developing theoretical and practical machine unlearning methods for selective forgetting, complemented by research on federated learning, bias mitigation, and robustness.