Edmond Chow, Professor and Associate Chair, School of Computational Science and Engineering, Georgia Institute of Technology
Tuesday, June 06, 2023, 16:00
- 17:00
Building 2, Level 5, Room 5220
Coffee Time: 15:30 - 16:00. Kernel matrices can be found in computational physics, chemistry, statistics, and machine learning. Fast algorithms for matrix-vector multiplication for kernel matrices have been developed, and is a subject of continuing interest, including here at KAUST. One also often needs fast algorithms to solve systems of equations involving large kernel matrices. Fast direct methods can sometimes be used, for example, when the physical problem is 2-dimensional. In this talk, we address preconditioning for the iterative solution of kernel matrix systems. The spectrum of a kernel matrix significantly depends on the parameters of the kernel function used to define the kernel matrix, e.g., a length scale.
The 2nd SAAI Factory Hackathon Kickoff Symposium 2023
Tuesday, May 02, 2023, 09:00
- 17:00
Building 20, Auditorium
Contact Person

We are pleased to invite you to the second SAAI (Super Artistic AI) Factory Hackathon 2023, a program chaire

Wednesday, February 15, 2023, 20:10
- 22:00
Building 1, Level 2, Room 2202; https://kaust.zoom.us/j/96924769576
In computer vision, generative AI models are typically built for images, videos, and 3D objects. Recently, there has emerged a paradigm of neural fields, which unifies the representations of such types of data by parametrizing them via neural networks. In this thesis, we develop generative models for images, videos, and 3D scenes which treat the underlying data in such a form and explore the benefits which such a perspective provides.
Monday, May 16, 2022, 12:00
- 13:00
Building 9, Room 2322, Hall 1
Datasets that capture the connection between vision, language, and affection are limited, causing a lack of understanding of the emotional aspect of human intelligence. As a step in this direction, the ArtEmis dataset was recently introduced as a large-scale dataset of emotional reactions to images along with language explanations of these chosen emotions.
Monday, March 08, 2021, 12:00
- 13:00
We present a novel large-scale dataset and accompanying machine learning models aimed at providing a detailed understanding of the interplay between visual content, its emotional effect, and explanations for the latter in language. In contrast to most existing annotation datasets in computer vision, we focus on the affective experience triggered by visual artworks and ask the annotators to indicate the dominant emotion they feel for a given image and, crucially, to also provide a grounded verbal explanation for their emotion choice.