scientific computing

Empowering Natural Intelligence with Artificial Intelligence: a Mathematician's Perspective

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B9, L2, R2322

A Dean’s Distinguished Lecture on natural intelligence, artificial intelligence and scientific machine learning.
Georgios Grekas

Georgios Grekas

Postdoctoral Research Fellow, Applied Mathematics and Computational Science
Georgios Grekas research focuses on mathematical modeling (biomechanics, solid mechanics), numerical analysis, scientific and parallel computing, and scientific machine learning.

Deep Learning and Scientific Computing

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B9 L2 R2322

In this talk, I will first give an elementary introduction to basic deep learning models and training algorithms from a scientific computing viewpoint. Using image classification as an example, I will try to give mathematical explanations of why and how some popular deep learning models such as convolutional neural network (CNN) work. Most of the talk will be assessable to an audience who have basic knowledge of calculus and matrix. Toward the end of the talk, I will touch upon some advanced topics to demonstrate the potential of new mathematical insights for helping understand and improve the efficiency of deep learning technologies.

Xiaotian Jin

Ph.D. Student, Statistics
Xiaotian's research focuses on developing computationally efficient non-Gaussian statistical frameworks and the theoretical analysis, with a particular focus on modeling complex spatio-temporal dependencies in high-dimensional environmental datasets.