Valentin Deschaintre, PhD
About Valentin Deschaintre, PhD
Valentin Deschaintre, PhD, is a Research Scientist at Adobe in London, where he has worked since 2021. His research specializes in computational photography, focusing on the acquisition of material properties for rendering through deep learning techniques.
Work at Adobe
Valentin Deschaintre has been employed at Adobe as a Research Scientist since 2021. His role involves conducting research and development in the field of computer graphics, particularly focusing on innovative methodologies and technologies. Based in London, he contributes to Adobe's initiatives aimed at enhancing user experiences through advanced imaging techniques.
Education and Expertise
Valentin Deschaintre holds a PhD in Computer Science, which he obtained from Inria between 2016 and 2019. His doctoral research concentrated on the acquisition of material properties for rendering, utilizing deep learning techniques. He also earned a Computer Science engineer degree from the University of Technology Belfort-Montbéliard, where he studied Imaging, Interaction, and Virtual Reality from 2009 to 2014.
Background
Valentin Deschaintre's professional background includes various roles in research and development. He began his career as an R&D Computer Science Engineer Intern at Dassault Systemes in 2014. He later worked as a Computer Science Engineer Intern at GumGum from 2012 to 2013 and served as an Associate Researcher at Imperial College London from 2019 to 2021.
Previous Experience
Before joining Adobe, Valentin Deschaintre worked at Optis SAS as an R&D Software Engineer from 2015 to 2019. His responsibilities included developing software solutions in the field of computer graphics. He also held a position as an R&D Computer Science Engineer Intern at Dassault Systemes for six months in 2014, gaining valuable experience in the industry.
Research Focus
Valentin Deschaintre's research interests lie in computational photography and vision. He aims to develop lightweight and convenient solutions in computer graphics, focusing on enhancing rendering techniques through deep learning. His work is significant in advancing the field and improving the efficiency of graphical representations.