Jona Ballé

Jona Ballé is an Associate Professor in the Department of Electrical and Computer Engineering at New York University. His research focuses on visual media compression, with an emphasis on developing efficient representations of visual media using machine learning and end-to-end optimization. Ballé has contributed to the development of the JPEG AI standard and has served as a reviewer for top-tier publications in machine learning and image processing. He has also been actively involved in organizing the annual Challenge on Learned Image Compression (CLIC) since 2018 and has participated in the program committee of the Data Compression Conference (DCC) since 2022.

Jona Ballé is an Associate Professor at New York University, specializing in image and video coding, data compression, visual quality assessment, and machine learning. His research focuses on developing advanced techniques for efficient data compression using variational methods, end-to-end optimization, and perceptual quality assessment. Ballé's work explores the integration of machine learning with traditional compression algorithms to enhance visual fidelity and computational efficiency. His publications address challenges in nonlinear transform coding, perceptual image quality assessment, and scalable model compression. His research has significant implications for applications in multimedia systems and signal processing.