Fatma Güney is an Assistant Professor at the Dept. of Computer Engineering at Koç University in Istanbul. Her research focuses on computer vision problems related to autonomous driving. In the last few years, she published papers on monocular depth estimation, unsupervised object segmentation, and future prediction in various representation spaces. She is leading a small team with amazing students at the KUIS AI center, working on a range of topics including but not limited to object-centric representation learning, end-to-end learning of driving, and future prediction.

Felix Heide is a professor of Computer Science at Princeton, where he leads the Princeton Computational Imaging Lab, and he is the Head of AI at Torc Robotics which builds full autonomy stacks for self-driving trucks. He previously founded the startup Algolux which was acquired by Torc and Daimler Trucks. His group at Princeton explores imaging and computer vision approaches that allow computers to see and understand what seems invisible today — enabling super-human capabilities for the cameras in his vehicles, personal devices, microscopes, telescopes, and the instrumentation he use for fundamental research in physics. This includes today's capture and vision challenges, including harsh environmental conditions, e.g., imaging under ultra-low or high illumination or computer vision through dense fog, rain, and snow, imaging at ultra-fast or slow time scales, freezing light in motion, imaging at extreme scene scales, from super-resolution microscopy to kilometer-scale depth sensing, and imaging via proxies using nearby object surfaces as sensors instead. Researching vision systems end-to-end, his work lies at the intersection of optics, machine learning, optimization, computer graphics, and computer vision. He received his Ph.D. from the University of British Columbia, and he was a postdoc at Stanford University. His doctoral dissertation won the Alain Fournier Ph.D. Dissertation Award and the SIGGRAPH outstanding doctoral dissertation award. He was recently named SIGGRAPH Significant New Researcher, Sloan Research Fellow and Packard Fellow.

Jitendra Malik was born in Mathura, India in 1960. He received the B.Tech degree in Electrical Engineering from Indian Institute of Technology, Kanpur in 1980 and the PhD degree in Computer Science from Stanford University in 1985. In January 1986, he joined the university of California at Berkeley, where he is currently the Arthur J. Chick Professor in the Department of Electrical Engineering and Computer Sciences. He is also on the faculty of the department of Bioengineering, and the Cognitive Science and Vision Science groups. During 2002-2004 he served as the Chair of the Computer Science Division, and as the Department Chair of EECS during 2004-2006 as well as 2016-2017. In 2018 and 2019, he served as Research Director and Site Lead of Facebook AI Research in Menlo Park, and he continues to work part-time at FAIR, Meta Inc.

Jitendra Malik's research group works on computer vision, robotics and machine learning. His group has also contributed to computational modeling of human vision, computer graphics and the analysis of biological images. Several well-known concepts and algorithms arose in this research, such as anisotropic diffusion, normalized cuts, high dynamic range imaging, shape contexts and R-CNN. His publications have received eleven best paper awards, including six test of time awards - the Longuet-Higgins Prize for papers published at CVPR (three times) and the Helmholtz Prize for papers published at ICCV (three times) He has mentored more than 80 PhD students and postdoctoral fellows, many of whom have gone on to become leading researchers at places like MIT, Berkeley, CMU, Caltech, Cornell, UIUC, UPenn, Michigan, UT Austin, Google and Meta.

Jitendra was one of the top ten students in the Indian School Certificate Exam, and received the gold medal for the best graduating student in Electrical Engineering from IIT Kanpur. He received a Presidential Young Investigator Award in 1989. He received the Distinguished Alumnus Award from IIT Kanpur in 2008. He received the 2013 IEEE PAMI-TC Distinguished Researcher in Computer Vision Award, the 2014 K.S. Fu Prize from the International Association of Pattern Recognition, the 2016 ACM-AAAI Allen Newell Award, the 2018 IJCAI Award for Research Excellence in AI, and the 2019 IEEE Computer Society Computer Pioneer Award. He is a fellow of the IEEE and the ACM. He is a member of the National Academy of Engineering and the National Academy of Sciences, and a fellow of the American Academy of Arts and Sciences.

Antonio Loquercio is an Assistant Professor at University of Pennsylvania. He is broadly interested in the problem of physical intelligence, whose overarching goal is to empower robots with the capability to learn, understand, and interact with their surroundings in a way that is both reliable and adaptable. His main emphasis is agile autonomy: he builds robots that, with only on-board sensing and computation, can perform complex tasks with great speed, precision, and agility. To do so, he designes unified approaches to perception and action drawing from the fields of computer vision and artificial intelligence. He received his doctoral degree at the University of Zurich with the Robotics and Perception Group working with Prof. Davide Scaramuzza. After that, he was a PostDoc at BAIR with Prof. Jitendra Malik.

Vincent Vanhoucke is a Distinguished Engineer at Waymo. His work spans robotics, machine learning, audio and visual perception. He previously founded and led the robotics research team at Google for 8 years. He also co-created the Conference on Robot Learning and taught Deep Learning on Udacity. He grew up in France and now lives in San Francisco.

David Crandall is the Luddy Professor of Computer Science and inaugural Director of the Luddy Artificial Intelligence Center at Indiana University. He is a member of the Computer Science, Informatics, Cognitive Science, Neuroscience, and Data Science programs, and also founded and directed the Center for Machine Learning. He obtained the Ph.D. in Computer Science from Cornell University in 2008, and was a Postdoctoral Research Associate at Cornell from 2008-2010. He received the B.S. and M.S. degrees in Computer Science and Engineering from the Pennsylvania State University in 2001, and was a Senior Research Scientist at Eastman Kodak Company from 2001-2003. Since joining IU in 2010, he has been PI or Co-PI on over $24 million in research grants and contracts from the National Science Foundation, the Lilly Endowment, Yahoo, Google, Meta/Facebook, NVidia, the U.S. Intelligence Advanced Research Projects Activity (IARPA), the U.S. Navy, NASA, Toyota Research Institute, the IU Office of the Vice President for Research, the Defense Threat Reduction Agency, the Office of Naval Research, the Electronics and Telecommunications Research Institute, the Air Force Office of Scientific Research, the Indiana Innovation Institute (IN3), the U.S. Department of Defense, and Eastman Kodak Company. He has published over 200 technical articles in top international venues, and has received best paper awards or nominations in CVPR, WWW, CHI, ICCV, and ICDL. He has served as an Associate Editor of the \textit{IEEE Transactions on Pattern Analysis and Machine Intelligence} and the \textit{IEEE Transactions on Multimedia}, has been an Area Chair for CVPR, ICCV, ECCV, WACV, AAAI, ICML, NeurIPS, and IJCAI, and Program Chair for WACV 2023, CVPR 2024, and ICDL 2024. He has received an NSF CAREER award (2013), two Google Faculty Research Awards (2014 and 2020), an IU Trustees Teaching Award (2017), a Grant Thornton Fellowship (2019), a Luddy Professorship (2021), and Distinguished ACM Membership (2022).

Abhinav Gupta is an Associate Professor at the Robotics Institute, Carnegie Mellon University. His research focuses on scaling up learning by building self-supervised, lifelong and interactive learning systems. Specifically, he is interested in how self-supervised systems can effectively use data to learn visual representation, common sense and representation for actions in robots. Abhinav is a recipient of several awards including IAPR 2020 JK Aggarwal Prize, PAMI 2016 Young Researcher Award, ONR Young Investigator Award, Sloan Research Fellowship, Okawa Foundation Grant, Bosch Young Faculty Fellowship, YPO Fellowship, IJCAI Early Career Spotlight, ICRA Best Student Paper award, and the ECCV Best Paper Runner-up Award. His research has also been featured in Newsweek, BBC, Wall Street Journal, Wired and Slashdot.