Deep generative models

Ahmad Sait

M.S. Student, Computer Science

Computer Vision machine learning Deep generative models

Ahmad Sait is a Master's student in the Computer Science program at KAUST. Before joining KAUST, Ahmad obtained his Bachelor's degree in Computer Science from King Abdulaziz University, Saudi Arabia. Ahmad is also a KAUST Academy graduate from the AI track, having been under the supervision of Prof. Naeemullah Khan. The Academy provided him with seven opportunities to assist in teaching students enrolled in subsequent cohorts.

Generative Models for Neural Fields

Ivan Skorokhodov, Ph.D. Student, Computer Science

Feb 15, 20:10 - 22:00

B1 L2 R2202

Deep generative models

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.

Victor Escorcia

Ph.D., Electrical and Computer Engineering

Computer Vision artificial intelligence Video Understanding Deep generative models Mobile AI

Research Scientist committed to innovating AI agents to empower creativity and increase productivity. Passionate about developing cutting-edge AI applications involving video understanding and creative storytelling. I have a wide range of research and engineering interests from on-device AI to General AI. Reach out!

Haozhe Liu

Ph.D. Student, Computer Science

artificial intelligence Neural Networks Deep generative models

Haozhe Liu is a PhD student at AI Initiative, King Abdullah University of Science and Technology (KAUST) under the supervision of Prof.Jürgen Schmidhuber.