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Research / Theme
The Gryphon Center for A.I. and Theoretical Sciences is a purpose-led organization devoted to the study of foundational topics in artificial intelligence and applications of related mathematical methods in scientific disciplines.
Our research work is devoted to various topics in the rapidly growing field of artificial intelligence, often inspired by a blend of foundational principles and real-life applications. The projects that we have completed in the past include incorporating mathematical techniques like Uniform-Manifold-Approximation-and-Projection (UMAP) in active learning methods, and a novel scientific machine learning framework that synthesizes fundamental principles of evidential deep learning and physics-informed neural networks. Uncertainty quantification frameworks in machine learning is also a major theme which characterises our research focus. Our work in A.I. has found applications within the field of biomedical sciences, in particular medical physics and the domain of medical imaging. The foundational techniques that we study carry a certain level of universal significance, and can be applied broadly.
A secondary focus of our research lies in mathematical methods that arise in the study of diverse scientific topics especially in connection with neural network-based modeling. Projects that we completed under this theme include : (a) using a set of coupled nonlinear differential equations in modelling biochemical reactions in cells that can potentially inform and guide our understanding of ultra-high-dose-rate radiotherapy (b) incorporating Bayesian statistics and Gaussian processes within physics-informed neural networks to infer cosmological parameters and their posterior distributions from astrophysical datasets and cosmological surveys.