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About / Mission

Mission

The Gryphon Center for A.I. and Theoretical Sciences is a purpose-led organisation that is devoted to the study of foundational topics in artificial intelligence and applications of related mathematical methods in theoretical sciences. We are also dedicated towards democratizing A.I. education for our younger generation.

Our research goals can be broadly classified under the themes of artificial intelligence and mathematical methods in theoretical sciences. For the field of A.I., we endeavor to understand the underlying principles and explore novel mechanisms by which neural networks and other A.I. models work. Beyond its black box nature, we seek to comprehend the deeper reasons behind its effectiveness, and potential ways by which we can endow A.I. modeling with statistical robustness. In particular, we examine these topics within the context of medical physics and more broadly, biomedical sciences such as radiology and applications in radiation oncology. A secondary research focus of the Center lies in exploring how mathematical methods such as differential geometry, theory of differential equations etc. can be harnessed to construct informative models in natural and applied sciences. Ultimately, these techniques can be useful towards constructing neural network models that are inspired by physical and neurobiological principles, as well as those that complement conventional methods in solving problems in the physical sciences.  

Another important mission of the Center is to furnish educational materials and learning sessions with A.I. as the theme. In particular, we endeavor to make our educational programs accessible at no cost for children coming from underprivileged backgrounds, and in this manner, contribute towards democratizing A.I. education for our younger generation. If our vision resonates with you, please feel free to drop us an email to explore possibilities for collaboration or to be part of our team !

About the founder

Hai Siong Tan (PhD in Physics, UC Berkeley) works actively on various topics in artificial intelligence and physics. His first paper in the field of artificial intelligence was dated back in 2006, where he proposed a novel neural network model for aircraft engine fault diagnostics during his time with RSAF. Since then, his interdisciplinary work has gathered around 30 publications spanning AI, biomedical sciences, and physics. Previously, he has also worked as a research scientist at Nanyang Technological University where he was awarded a Faculty Teaching Excellence Award in 2019. In addition to his research work, he has been invited to be a reviewer for several journals in computer science and physics, such as Physics in Medicine and Biology and Cluster Computing. His most recent collaboration with medical physicists at Penn Radiation Oncology (University of Pennsylvania) is a novel uncertainty-aware A.I. model that can perform organ segmentation in MRI images.