Markus Gross is the Chief Scientist of the Walt Disney Studios and a professor of Computer Science at ETH Zürich. He is one of the leading authorities in visual computing, computer animation, digital humans, virtual reality, and machine learning. In his role at Disney he leads the Studio segment’s research and innovation unit, where he and his team are pushing the forefront of technology innovation in service of the filmmaking process. Gross has published over 500 scientific papers and holds over 100 patents. His work and achievements have been recognized widely, including two Academy Awards and the ACM SIGGRAPH Steven Anson Coons Award. Gross is member of multiple academies of science and of the Academy of Motion Picture Arts and Sciences.
Ravi Ramamoorthi is the Ronald L. Graham Professor of Computer Science at UCSD and founding director of the UC San Diego Center for Visual Computing. He earlier held tenured faculty positions at UC Berkeley and Columbia University, in all of which he played a key leadership role in building multi-faculty research groups recognized as leaders in computer vision and graphics. He has authored more than 200 refereed publications in computer graphics and vision, including 90+ ACM SIGGRAPH/TOG papers. Prof. Ramamoorthi has introduced widely used theoretical representations and computational models for problems in vision and graphics, such as spherical harmonic lighting and neural radiance fields, and widely adopted methods in industry such as Monte Carlo denoising. He has consulted with Pixar and startups in computational imaging, and currently holds a part-time appointment as a Distinguished Research Scientist at NVIDIA. Prof. Ramamoorthi has received about twenty major honors for his research including the ACM SIGGRAPH Significant New Researcher Award for his work in computer graphics, and the Presidential Early Career Award for Scientists and Engineers for his work on physics-based computer vision. He is a fellow of IEEE, ACM and the SIGGRAPH Academy, recently received an inaugural Frontiers of Science Award, and has twice been honored with the edX Prize certificate for exceptional contributions in online teaching and learning. He has graduated more than 30 postdoctoral and Ph.D. students, whose theses have been recognized by the ACM Dissertation Award honorable mention, the ACM SIGGRAPH outstanding dissertation award and the UCSD Chancellor’s Dissertation Medal.
Tali Dekel is an Assistant Professor at the Mathematics and Computer Science Department at the Weizmann Institute, Israel. She is also a Staff Research Scientist at Google, developing algorithms at the intersection of computer vision, computer graphics, and machine learning. Before Google, she was a Postdoctoral Associate at the Computer Science and Artificial Intelligence Lab (CSAIL) at MIT. Tali completed her Ph.D. studies at the school of electrical engineering, Tel-Aviv University, Israel. Her research interests include computational photography, image/video synthesis, geometry, and 3D reconstruction. Her awards and honors include the National Postdoctoral Award for Advancing Women in Science (2014), the Rothschild Postdoctoral Fellowship (2015), the SAMSON – Prime Minister’s Researcher Recruitment Prize (2019), Best Paper Honorable Mention in CVPR 2019, and Best Paper Award (Marr Prize) in ICCV 2019. She often serves as program committee member and area chair of major vision and graphics conferences More information in: https://www.weizmann.ac.il/math/dekel/home
Leonidas Guibas is the Paul Pigott Professor of Computer Science (and by courtesy), Electrical Engineering at Stanford University, where he heads the Geometric Computation group, and also a Principal Scientist at Google. Prof. Guibas obtained his Ph.D. from Stanford University under the supervision of Donald Knuth. His main subsequent employers were Xerox PARC, DEC/SRC, MIT, and Stanford, including stays at Meta, Google, and Autodesk. He has worked in numerous areas of computer science, such as geometric algorithms, computer vision, computer graphics, robotics, machine learning, discrete mathematics, and biocomputation. At Stanford he is a member and past acting director of the Artificial Intelligence Laboratory and a member of the Computer Graphics Laboratory, the Institute for Computational and Mathematical Engineering (iCME), and the Bio-X program. Dr. Guibas has been elected to the US National Academy of Engineering, the US National Academy of Sciences, and the American Academy of Arts and Sciences and is an ACM Fellow, an IEEE Fellow, and winner of the ACM Allen Newell Award, the ICCV Helmholtz prize, and Siggraph’s Test-of-Time paper award.