Rodney Brooks came to the US from Australia in 1977. His PhD (1981) work at the Stanford Artificial Intelligence Lab was in model based computer vision in the "hand-eye group". After post-docs at CMU and MIT he joined the faculty at Stanford for one year, then joined the MIT faculty in 1984. There he formed a robotics research group that developed mobile and humanoid robots, many of which were vision-based. In 1987 he and Takeo Kanade founded the International Journal of Computer Vision. He became director of the MIT Artificial Intelligence Lab in 1997 and in 2003 he became the founding director of MIT CSAIL (Computer Science and Artificial Intelligence Lab). Along the way he has founded six startups, including iRobot, Rethink Robotics, and now Robust AI, which is developing a vision based collaborative mobile robot for existing cluttered warehouses.

Linda Shapiro, Professor of Computer Science and Engineering, Professor of Electrical and Computer Engineering, and Adjunct Professor of Biomedical and Informatics and Medical Education, earned a bachelor's degree in mathematics from the University of Illinois in 1970 and master's and Ph.D degrees in computer science from the University of Iowa in 1972 and 1974, respectively. She was a faculty member in Computer Science at Kansas State University from 1974 to 1978 and at Virginia Polytechnic Institute and State University from 1979 to 1984. She then spent two years as Director of Intelligent Systems at Machine Vision International in Ann Arbor, Michigan. She joined the University of Washington Electrical Engineering (now ECE) Department Department in 1986 and the Computer Science and Engineering Department in 1990. Professor Shapiro's research is in computer vision with related interests in image and multimedia database systems, artificial intelligence (search, reasoning, knowledge representation, learning), and applications in medicine and robotics. She has worked heavily in knowledge-based 3D object recognition and has contributed to both the theory of object matching and to the development of experimental machine vision systems. Her current work includes robot vision, cancer biopsy analysis, brain image analysis, and semantic segmentation. Professor Shapiro was the editor-in-chief of Computer Vision, Graphics, and Image Processing for 10 years. She was the 1993-95 chair of the IEEE Computer Society Technical Committee on Pattern Analysis and Machine Intelligence, conference chair of the 1986 IEEE Conference on Computer Vision and Pattern Recognition, co-program chairman of the 1994 conference, and co-chair of the 2008 conference. She was also the co-chair of the Biomedical and Multimedia Applications Track of the International Conference on Pattern Recognition in 2002. She has co-authored a textbook on data structures, a two-volume graduate text on computer and robot vision, and an undergraduate computer vision text. She is a Fellow of the IEEE and of the IAPR.

Jamie Shotton is a leader in AI research and development, with a track record of incubating transformative new technologies and experiences from early stage research to shipping product. He is Chief Scientist at Wayve, building foundation models for embodied intelligence to enable safe and adaptable autonomous vehicles. Prior to this he was Partner Director of Science at Microsoft and head of the Mixed Reality & AI Labs where he shipped foundational features including body tracking for Kinect and the hand- and eye-tracking that enable HoloLens 2’s instinctual interaction model. He has explored applications of AI in autonomous driving, mixed reality, virtual presence, human-computer interaction, gaming, robotics, and healthcare. He has received multiple Best Paper and Best Demo awards at top-tier academic conferences, and the Longuet-Higgins Prize test-of-time award at CVPR 2021. His work on Kinect was awarded the Royal Academy of Engineering’s gold medal MacRobert Award in 2011, and he shares Microsoft’s Outstanding Technical Achievement Award for 2012 with the Kinect engineering team. In 2014 he received the PAMI Young Researcher Award, and in 2015 the MIT Technology Review Innovator Under 35 Award. He was awarded the Royal Academy of Engineering’s Silver Medal in 2020. He was elected a Fellow of the Royal Academy of Engineering in 2021.

Chelsea Finn is an Assistant Professor in Computer Science and Electrical Engineering at Stanford University. Her research interests lie in the capability of robots and other agents to develop broadly intelligent behavior through learning and interaction. To this end, her work has pioneered end-to-end deep learning methods for vision-based robotic manipulation, meta-learning algorithms for few-shot learning, and approaches for scaling robot learning to broad datasets. Her research has been recognized by awards such as the Sloan Fellowship, the IEEE RAS Early Academic Career Award, and the ACM doctoral dissertation award, and has been covered by various media outlets including the New York Times, Wired, and Bloomberg. Prior to Stanford, she received her Bachelor's degree in Electrical Engineering and Computer Science at MIT and her PhD in Computer Science at UC Berkeley.

Daniel Huttenlocher is the inaugural dean of the MIT Schwarzman College of Computing and is the Henry Ellis Warren (1894) Professor of Electrical Engineering and Computer Science. Previously he helped found Cornell Tech, the digital technology-oriented graduate school created by Cornell University in New York City, and served as its first Dean and Vice Provost. His research and teaching have been recognized by a number of awards including ACM Fellow and CASE Professor of the Year. He has a mix of academic and industry background, having been a Computer Science faculty member at Cornell, researcher and manager at the Xerox Palo Alto Research Center (PARC), and CTO of a fintech startup. Huttenlocher is an internationally recognized researcher in computer vision and the analysis of social media. His book, “The Age of AI: And Our Human Future,” co-authored with Henry Kissinger and Eric Schmidt, was published by Little, Brown in November 2021. He served as a member and as the chair of the board of the John D. and Catherine T. MacArthur Foundation, and currently serves as a member of the boards of Corning Inc. and Amazon.com. He received his bachelor’s degree from the University of Michigan, and master’s and doctorate from MIT.

Yejin Choi is Brett Helsel professor at the Paul G. Allen School of Computer Science & Engineering at the University of Washington and also a senior research director at AI2 overseeing the project Mosaic. Her research investigates a wide variety of problems across NLP and AI including commonsense knowledge and reasoning, neural language (de-)generation, language grounding with vision and experience, and AI for social good. She is a MacArthur Fellow and a co-recipient of the NAACL Best Paper Award in 2022, the ICML Outstanding Paper Award in 2022, the ACL Test of Time award in 2021, the CVPR Longuet-Higgins Prize (test of time award) in 2021, the NeurIPS Outstanding Paper Award in 2021, the AAAI Outstanding Paper Award in 2020, the Borg Early Career Award (BECA) in 2018, the inaugural Alexa Prize Challenge in 2017, IEEE AI's 10 to Watch in 2016, and the ICCV Marr Prize (best paper award) in 2013. She received her Ph.D. in Computer Science at Cornell University and BS in Computer Science and Engineering at Seoul National University in Korea.

Devi Parikh is a Research Director in the Generative AI organization at Meta, and an Associate Professor in the School of Interactive Computing at Georgia Tech. From 2013 to 2016, she was an Assistant Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech. From 2009 to 2012, she was a Research Assistant Professor at Toyota Technological Institute at Chicago (TTIC), an academic computer science institute affiliated with University of Chicago. She has held visiting positions at Cornell University, University of Texas at Austin, Microsoft Research, MIT, Carnegie Mellon University, and Facebook AI Research. She received her M.S. and Ph.D. degrees from the Electrical and Computer Engineering department at Carnegie Mellon University in 2007 and 2009 respectively. She received her B.S. in Electrical and Computer Engineering from Rowan University in 2005. Her research interests are in computer vision, natural language processing, embodied AI, human-AI collaboration, and AI for creativity. She is a recipient of an NSF CAREER award, an IJCAI Computers and Thought award, a Sloan Research Fellowship, an Office of Naval Research (ONR) Young Investigator Program (YIP) award, an Army Research Office (ARO) Young Investigator Program (YIP) award, a Sigma Xi Young Faculty Award at Georgia Tech, an Allen Distinguished Investigator Award in Artificial Intelligence from the Paul G. Allen Family Foundation, four Google Faculty Research Awards, an Amazon Academic Research Award, a Lockheed Martin Inspirational Young Faculty Award at Georgia Tech, an Outstanding New Assistant Professor award from the College of Engineering at Virginia Tech, a Rowan University Medal of Excellence for Alumni Achievement, Rowan University’s 40 under 40 recognition, a Forbes’ list of 20 “Incredible Women Advancing A.I. Research” recognition, and a Marr Best Paper Prize awarded at the International Conference on Computer Vision (ICCV).

Michal Irani is a Professor at the Weizmann Institute of Science, Israel. She joined the Weizmann Institute in 1997, where she is currently the Dean of the Faculty of Mathematics and Computer-Science. Michal's research interests center around Computer-Vision, Image-Processing, Artificial-Intelligence, and Video information analysis. She also works on decoding visual information from Brain activity. Michal received a B.Sc. degree in Mathematics and Computer Science from the Hebrew University of Jerusalem, and M.Sc. and Ph.D. degrees in Computer Science from the same institution. During 1993-1996 she was a member of the Vision Technologies Laboratory at the Sarnoff Research Center (Princeton).

Michal's prizes and honors include the David Sarnoff Research Center Technical Achievement Award (1994), the Yigal Alon three-year Fellowship for Outstanding Young Scientists (1998), the Morris L. Levinson Prize in Mathematics (2003), the Maria Petrou Prize (awarded by the IAPR) for outstanding contributions to the fields of Computer Vision and Pattern Recognition (2016), the Landau Prize in Artificial Intelligence (2019), and the Rothschild Prize in Mathematics and Computer Science (2020). She received the ECCV Best Paper Award in 2000 and in 2002, and was awarded the Honorable Mention for the Marr Prize in 2001 and in 2005. In 2017 Michal received the Helmholtz Prize – the “Test of Time Award” (for the paper “Actions as space-time shapes”).

In 2023 Michal was elected member of the Israel Academy of Sciences and Humanities.

Aaron Hertzmann is a Principal Scientist at Adobe, and Affiliate Faculty at University of Washington. He received a BA in computer science and art/art history from Rice University in 1996, and a PhD in computer science from New York University in 2001. He was a Professor at University of Toronto for 10 years, and has also worked at Pixar Animation Studios and Microsoft Research. He has published over 100 papers in computer graphics, computer vision, machine learning, robotics, human-computer interaction, and art. He is an ACM Fellow and an IEEE Fellow.

A pioneer of software-based fine art, Jason Salavon works at the intersection of art, culture, and technology. Using self-authored code, he creates visually arresting artworks from culturally-loaded material: U.S. Census data, the IKEA catalog, episodes of The Simpsons, Wikipedia pages, the history of Western painting. Salavon's work embraces a tension between autonomous computational processes and more traditional creative forms. His work presaged, and engages with, the explosive rise of digital art in AI and on the blockchain. Born in Indiana, raised in Texas, and based in Chicago, Salavon earned his MFA at The School of the Art Institute of Chicago and his BA from The University of Texas at Austin. His work has been exhibited in museums and galleries around the world and been featured in publications such as The New York Times, Artforum, Art in America, and WIRED. Examples of his artwork are included in prominent public and private collections including the Museum of Modern Art, Metropolitan Museum of Art, the Whitney Museum of Art, and the Art Institute of Chicago among many others. He is currently associate professor in the Department of Visual Arts at the University of Chicago.

Larry Zitnick is a research director on the Fundamental AI Research team at Meta. He is currently focused on scientific applications of AI and machine learning, such as the discovery of new catalysts for renewable energy applications. Previously, his research in computer vision covered many areas such as the FastMRI project to speed up the acquisition of MRIs, and the COCO and VQA datasets to benchmark object detection and visual language tasks. He developed the PhotoDNA technology used by industry and various law enforcement agencies to combat illegal imagery on the web. Before joining FAIR, he was a principal researcher at Microsoft Research. He received the PhD degree in robotics from Carnegie Mellon University.

Sara Beery will join MIT as an assistant professor in their Faculty of Artificial Intelligence and Decision-Making in September 2023. She is currently a visiting researcher at Google, working on large-scale urban forest monitoring as part of the Auto Arborist project. Beery received her PhD in Computing and Mathematical Sciences at Caltech in 2022, where she was advised by Pietro Perona and awarded the Amori Doctoral Prize for her thesis. Her research focuses on building computer vision methods that enable global-scale environmental and biodiversity monitoring across data modalities, tackling real-world challenges including geospatial and temporal domain shift, learning from imperfect data, fine-grained categories, and long-tailed distributions. She partners with industry, nongovernmental organizations, and government agencies to deploy her methods in the wild worldwide. She works toward increasing the diversity and accessibility of academic research in artificial intelligence through interdisciplinary capacity building and education, and has founded the AI for Conservation slack community, serves as the Biodiversity Community Lead for Climate Change AI, and founded and directs the Summer Workshop on Computer Vision Methods for Ecology.

Dr. Elizabeth (Libby) Barnes is a Professor of Atmospheric Science at Colorado State University. She joined the CSU faculty in 2013 after obtaining dual B.S. degrees (Honors) in Physics and Mathematics from the University of Minnesota, obtaining her Ph.D. in Atmospheric Science from the University of Washington, and spending a year as a NOAA Climate & Global Change Fellow at the Lamont-Doherty Earth Observatory. Professor Barnes' research is largely focused on climate variability and change and the data analysis tools used to understand it. Topics of interest include earth system predictability, jet-stream dynamics, Arctic-midlatitude connections, subseasonal-to-decadal (S2D) prediction, and data science methods for earth system research (e.g. machine learning, causal discovery). She teaches graduate courses on fundamental atmospheric dynamics and data science and statistical analysis methods. Professor Barnes is involved in a number of research community activities. In addition to being a member of the National Academies's Committee on Earth Science and Applications from Space, on the National Academies' Board on Atmospheric Science and Climate, a funded member of the NSF AI Institute for Research on Trustworthy AI in Weather, Climate and Coastal Oceanography (AI2ES), and on the Steering Committee of the CSU Data Science Research Institute, she recently finished being the lead of the NOAA MAPP S2S Prediction Task Force (2016-2020). Dr. Barnes received the AGU Macelwane Medal and became a Fellow of the AGU in 2021, received the AGU Turco Lectureship for 2020, AMS Clarence Leroy Meisinger Award for 2020, and was awarded an NSF CAREER grant in 2018. She received the George T. Abell Outstanding Early-Career Faculty Award in 2016 and was recognized for her teaching and mentoring by being awarded an Honorable Mention for the CSU Graduate Advising and Mentorship Award in 2017 and being named the Outstanding Professor of the Year Award in 2016 and 2022 by the graduate students of the Department of Atmospheric Science. In 2014 she was the recipient of an AGU James R. Holton Junior Scientist Award.

Joshua Bloom is Chair of the Department of Astronomy at the University of California, Berkeley where, as Professor, he teaches radiative processes, high-energy astrophysics, and a graduate-level "Python for Data Science" course. He has published over 300 refereed articles on time-domain transients events, machine learning, and telescope/insight automation. He co-founded the Berkeley Institute for Data Science. Josh has been awarded the Data-Driven Discovery prize from the Gordon and Betty Moore Foundation and the Pierce Prize from the American Astronomical Society; he is also a former Sloan Fellow, Junior Fellow at the Harvard Society, and Hertz Foundation Fellow. He holds a PhD from Caltech and degrees from Harvard College and Cambridge University. He was co-founder and CTO of Wise.io, an AI application startup, acquired by GE Digital in 2016. His book on gamma-ray bursts, a technical introduction for physical scientists, was published by Princeton University Press.

Kyle Cranmer is the David R. Anderson Director of the UW-Madison Data Science Institute and a Professor of Physics with courtesy appointments in Statistics and Computer Science. He is also the Editor-in-Chief of the journal Machine Learning Science and Technology. Professor Cranmer obtained his Ph.D. in Physics from the University of Wisconsin-Madison in 2005. He was awarded the Presidential Early Career Award for Science and Engineering in 2007, the National Science Foundation's Career Award in 2009, and became a Fellow of the American Physical Society in 2021 for his work at the Large Hadron Collider. Professor Cranmer developed a framework that enables collaborative statistical modeling, which was used extensively for the discovery of the Higgs boson in 2012. His current interests are at the intersection of physics, statistics, and machine learning.