Carla Gomes is the Ronald C. and Antonia V. Nielsen Professor of Computing and Information Science, the director of the Institute for Computational Sustainability at Cornell University, and co-director of the Cornell University AI for Science Institute. Gomes received a Ph.D. in computer science in artificial intelligence from the University of Edinburgh. Her research area is Artificial Intelligence with a focus on large-scale constraint reasoning, optimization, and machine learning. Recently, Gomes has become deeply immersed in research on scientific discovery for a sustainable future and, more generally, in research in the new field of Computational Sustainability. Computational Sustainability aims to develop computational methods to help solve some of the key environmental, economic, and societal challenges to help put us on a path toward a sustainable future. Gomes was the lead PI of two NSF Expeditions in Computing awards. Gomes has (co-)authored over 200 publications, which have appeared in venues spanning Nature, Science, and a variety of conferences and journals in AI and Computer Science, including five best paper awards. Gomes was named the “most influential Cornell professor” by a Merrill Presidential Scholar (2020). Gomes was also the recipient of the Association for the Advancement of Artificial Intelligence (AAAI) Feigenbaum Prize (2021) for “high-impact contributions to the field of artificial intelligence, through innovations in constraint reasoning, optimization, the integration of reasoning and learning, and through founding the field of Computational Sustainability, with impactful applications in ecology, species conservation, environmental sustainability, and materials discovery for energy” and of the 2022 ACM/AAAI Allen Newell Award, for contributions bridging computer science and other disciplines. Gomes is a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), a Fellow of the Association for Computing Machinery (ACM), and a Fellow of the American Association for the Advancement of Science (AAAS).
Artificial Intelligence (AI) is a rapidly advancing field inspired by human intelligence. AI systems are now performing at human and even superhuman levels on various tasks, such as image identification and face and speech recognition. Can AI also dramatically accelerate scientific discovery and perhaps even win a Nobel prize in Science? Hiroaki Kitano first posed this question in 2016. We further discussed this question at a Turing Institute workshop in 2020, chaired by Gil, Kitano, and King, and formulated the AI Scientist Grand Challenge. In my own research, I am interested in accelerating scientific discovery for a sustainable future, particularly materials discovery for clean energy.
The tremendous AI progress that we have witnessed in the last decade has been largely driven by deep learning advances and heavily hinges on the availability of large annotated datasets to supervise model training. However, scientists generally only have access to small datasets and incomplete data: Scientists amplify a few data examples with human intuitions and detailed reasoning from first principles for discovery.
Our AI systems need to encapsulate the scientific process for scientific discovery: We need AI systems that combine learning with reasoning about scientific knowledge and find suitable problem representations for scalable solutions. Our AI systems need to be able to predict far outside the training distributions for scientific discovery, while current machine learning systems primarily perform data interpolation. Furthermore, our AI systems need to interpret results and understand causation beyond correlation to discover new scientific concepts and knowledge. Can we automate such a hybrid scientific discovery strategy? Full Bio
Eric Horvitz serves as Microsoft’s Chief Scientific Officer. He spearheads company-wide initiatives, navigating opportunities and challenges at the confluence of scientific frontiers, technology, and society, including strategic efforts in AI, medicine, and the biosciences.
Dr. Horvitz is known for his contributions to AI theory and practice, with a focus on principles and applications of AI amidst the complexities of the open world. His research endeavors have been direction-setting, including harnessing probability and utility in machine learning and reasoning, developing models of bounded rationality, constructing systems that perceive and act via interpreting multisensory streams of information, and pioneering principles and mechanisms for supporting human-AI collaboration and complementarity. His efforts and collaborations have led to fielded systems in healthcare, transportation, ecommerce, operating systems, and aerospace.
Beyond his scientific work, he has pursued programs, organizations, and studies on ethics, values, and safety with applications and influences of AI. He founded and chairs Microsoft’s Aether committee on AI, effects, and ethics in engineering and research. He established the One Hundred Year Study on AI at Stanford University and co-founded and serves as board chair of the Partnership on AI (PAI). He served as a Congressionally appointed commissioner on the National Security Commission on AI, where he chaired the line of effort on ethical and trustworthy AI.
Dr. Horvitz received the Feigenbaum Prize and the Allen Newell Prize for his fundamental contributions to the science and practice of AI. He received the CHI Academy honor for his work at the intersection of AI and human-computer interaction. He has been elected fellow of the National Academy of Engineering (NAE), the Association of Computing Machinery (ACM), Association for the Advancement of Artificial Intelligence (AAAI), the American Association for the Advancement of Science (AAAS), the American Academy of Arts and Sciences, the American College of Medical Informatics, and the American Philosophical Society.
He currently serves on the President’s Council of Advisors on Science and Technology (PCAST) and advisory boards of the Allen Institute for AI and Stanford’s Institute for Human-Centered AI (HAI). He served as president of the AAAI, as a board member on the Computer Science and Telecommunications Board (CSTB), and on advisory committees for the National Science Foundation (NSF), National Institutes of Health (NIH), Defense Advanced Research Projects Agency (DARPA), and the Computing Community Consortium (CCC).
He received Ph.D. and M.D. degrees at Stanford University. Before moving into the role of Chief Scientific Officer, he served as director of Microsoft Research overseeing research labs in Redmond, Washington; Cambridge, Massachusetts; New York City, New York; Montreal, Canada; Cambridge, United Kingdom; and Bangalore, India. More information can be found on his home page. A selected list of publications can be found here.
Kimberly Sablon is currently the Principal Director for Trusted AI and Autonomy at the Undersecretary of Defense for Research and Engineering (USD(R&E)). In this role, she oversees AI/ML and autonomy research activities across the DOD enterprise. She also has responsibility for shaping the strategic direction for integrated AI and autonomy with an emphasis on trust and responsible decision making.
Kimberly Sablon received her PhD in applied physics with a focus on nanophotonics from the University of Arkansas in 2009. She has published more than 60 peer-reviewed papers and contributedto ten scientific and technical books. She performed a set of critical reviews to identify technologicalchallenges and research opportunities in the areas of reconfigurable multimodal sensing, communications, cognitive autonomous systems, AI-controlled networks and generative AI that could offer the greatest benefit to the Department of Defense while assessing the risks to national security. In her current role as the Principal Director for Trusted Artificial Intelligence and Autonomy at the Office of the Assistance Secretary of Defense for Critical Technologies, Dr. Kimberly Sablon leads and coordinates scientific and technological efforts to ensure DOD superiority in future cognitive autonomous systems and hierarchical networks placing much emphasis on testing, evaluation, verification, and validation of dynamic AI systems. To accelerate development of AI-enabled systems and critical enablers, Dr. Sablon has set a strategic vision that is centered around AI systems engineering taking into consideration complexities of the real-world environment. Her strategic vision reflects the need for an advanced intellectual and research base in critical areas, which includes multimodal and interactive trusted perception, Warfighter-in-the-loop design, development and training in live, virtual and constructive environments, autonomous cognition and prediction, distributed, hetero-hierarchical AI architectures to enable edge intelligence, autonomous networks of autonomous systems, and continuous adversarial testing and red-teaming to enhance resiliency of AI systems against adversarial manipulation and deception along with development of approaches for recognizing machine-generated deception. Considering AI operation in autonomous systems requires novel hardware, Dr. Sablon works closely with industry to ensure development of hardware with embedded intelligence to support continuous learning at the edge taking into consideration limited energy budgets and weight constraints. Furthermore, in this role, Dr. Sablon has put in place key initiatives to include a Center for Calibrated Trust Measurement and Evaluation (CaTE) that will serve to operationalize responsible AI for the DOD, AI hubs clustered around imaging processing, signal processing and decision making, and a Community of Action with focused integrated product teams to accelerate AI capabilities for the DOD.
In her previous position as Director of Army Science and Technology, Army Futures Command, Dr. Sablon developed a pipeline for innovation in areas such as AI-controllable networks, distributed AI with emphasis on decentralized architectures that can adapt to the changing electromagnetic environment, control data rates and spectrum requirements, neuromorphic cyber, and Soldier-AI system adaptation. To accelerate development of a strong AI base while ensuring security of these systems, Dr. Sablon led the development of Army S&T strategies across the Army priority research areas to include emerging cyber technologies. Her strategy emphasized development of dynamic, self-learning information systems capable of detecting and isolating threats to provide effective response to suppress sources of attacks, and to reason about deception in a way that would ensure secure operation of the of AI-agents while making our Warfighter less vulnerable and more lethal. Considering the rapid changing landscape of AI-based technologies and its potential to change the game for sensing, navigation, human-AI teaming and communications, Dr. Sablon continues to work with the broader research and development ecosystem to ensure the DOD is equipped with the right technologies to defend our nation.
Cap serves as the principal scientific authority and independent researcher in the field of multi-sensor automatic target recognition and sensor fusion. He initiates, technically plans, coordinates, evaluates, and conducts research and development to advance the knowledge of interdisciplinary ATR and sensor fusion systems for all Air Force aircraft, missile and space systems. Rogers leads collaboration across AFRL in object detection, tracking, geo-location, identification and supporting technologies. He also conducts research and development activities in the broad area of ATR and sensor-fusion technology including phenomenology modeling, model-based and learning algorithms, evaluation and tracking. He also conducts research and development in image and signal processing, synthetic target and scene modeling, resource allocation and evidence accrual aimed at decreasing the cost and improving the performance of Air Force and Department of Defense systems. Full Bio
Tom Mitchell’s research lies in machine learning, artificial intelligence, and cognitive neuroscience. His current research includes developing machine learning approaches to natural language understanding by computers, as well as brain imaging studies of natural language understanding by humans. A pioneer in artificial intelligence and machine learning, Mitchell’s research focuses on statistical learning algorithms for discovering how the human brain represents information and for enabling computers to understand the meaning of what humans say and write. His work with colleagues in the Psychology Department produced the first computational model to predict brain activation patterns associated with virtually any concrete noun, work that has since been extended to other word types, word sequences and emotions. His Never Ending Language Learner is a computer program that searches through web pages 24/7 as it teaches itself to read. Full Bio
Tom Mitchell is an American computer scientist and the Founders University Professor at Carnegie Mellon University (CMU). He is a founder and former Chair of the Machine Learning Department at CMU. Mitchell is known for his contributions to the advancement of machine learning, artificial intelligence, and cognitive neuroscience and is the author of the textbook Machine Learning. He is a member of the United States National Academy of Engineering since 2010. He is also a Fellow of the American Academy of Arts and Sciences, the American Association for the Advancement of Science and a Fellow and past President of the Association for the Advancement of Artificial Intelligence. In October 2018, Mitchell was appointed as the Interim Dean of the School of Computer Science at Carnegie Mellon.
Mitchell received his bachelor of Science degree in Electrical Engineering from the Massachusetts Institute of Technology in 1973 and a Ph.D. from Stanford University under the direction of Bruce G. Buchanan in 1979. Mitchell began his teaching career at Rutgers University in 1978. During his tenure at Rutgers, he held the positions of Assistant and Associate Professor in the Department of Computer Science. In 1986 he left Rutgers and joined Carnegie Mellon University, Pittsburgh as a Professor. In 1999 he became the E. Fredkin Professor in the School of Computer Science. In 2006 Mitchell was appointed as the first Chair of the Machine Learning Department within the School of Computer Science. He became University Professor in 2009,[7] and served as Interim Dean of the Carnegie Mellon School of Computer Science during 2018-2019. Mitchell currently serves on the Scientific Advisory Board of the Allen Institute for AI and on the Science Board of the Santa Fe Institute. He was elected into the United States National Academy of Engineering in 2010 “for pioneering contributions and leadership in the methods and applications of machine learning.” He is also a Fellow of the American Association for the Advancement of Science (AAAS) since 2008 and a Fellow the Association for the Advancement of Artificial Intelligence (AAAI) since 1990. In 2016 he became a Fellow of the American Academy of Arts and Sciences. Mitchell was awarded an Honorary Doctor of Laws degree from Dalhousie University in 2015 for his contributions to machine learning and to cognitive neuroscience, and the President’s Medal from Stevens Institute of Technology in 2018. He is a recipient of the NSF Presidential Young Investigator Award in 1984.
Yann LeCun is VP and Chief AI Scientist at Meta and Silver Professor at NYU affiliated with the Courant Institute, the Center for Data Science, the Center for Neural Science and the Electrical and Computer Engineering Department. He was the founding Director of Facebook AI Research and of the NYU Center for Data Science.
He received the Electrical Engineer Diploma from Ecole Supérieure d’Ingénieurs en Electrotechnique et Electronique (ESIEE), Paris in 1983, and a PhD in Computer Science from Sorbonne Université (then Université Pierre et Marie Curie, Paris) in 1987. After a postdoc at the University of Toronto in Geoffrey Hinton’s group, he joined AT&T Bell Laboratories in Holmdel, NJ in 1988. He became head of the Image Processing Research Department at AT&T Labs-Research in 1996, and joined NYU as a professor in 2003, following a brief period as a Fellow of the NEC Research Institute in Princeton. From 2012 to 2014 he directed NYU’s initiative in data science and became the founding director of the NYU Center for Data Science. He was named Director of AI Research at Facebook in late 2013 and retains a part-time position on the NYU faculty. He held a visiting professor chair at Collège de France in 2015-2016.
His current interests include AI, machine learning, computer perception, mobile robotics, and computational neuroscience. He has published over 190 technical papers and book chapters on these topics as well as on neural networks, handwriting recognition, image processing and compression, and on dedicated circuits and architectures for computer perception. The character recognition technology he developed at Bell Labs is used by several banks around the world to read checks and was reading between 10 and 20% of all the checks in the US in the early 2000s. His image compression technology, called DjVu, is used by hundreds of web sites and publishers and millions of users to access scanned documents on the Web. Since the mid 1980’s he has been working on deep learning methods, particularly the convolutional network model, which is the basis of many products and services deployed by companies such as Facebook, Google, Microsoft, Baidu, IBM, NEC, AT&T and others for image and video understanding, document recognition, human-computer interaction, and speech recognition.
LeCun has been on the editorial board of IJCV, IEEE PAMI, and IEEE Trans. Neural Networks, was program chair of CVPR’06, and is chair of ICLR 2013 and 2014. He is co-chair of the program Learning in Machines and Brains of the Canadian Institute for Advanced Research.
He is on the science advisory board of Institute for Pure and Applied Mathematics since 2008, and the board of trustees of ICERM. He has advised many large and small companies about machine learning technology, and co-founded startups Elements Inc. and Museami. He was the lead faculty at NYU for the Moore-Sloan Data Science Environment, a $36M initiative in collaboration with UC Berkeley and University of Washington to develop data-driven methods in the sciences.
He is on the New Jersey Inventor Hall of Fame. He is a member of the US National Academy of Sciences, the National Academy of Engineering, and the French Acad ‘emie des Sciences. He is a Chevalier de la Légion d’Honneur, a Fellow of AAAI and AAAS, the recipient of the 2022 Princess of Asturias Award, the 2014 IEEE Neural Network Pioneer Award, the 2015 IEEE PAMI Distinguished Researcher Award, the 2016 Lovie Lifetime Achievement Award from the International Academy of Digital Arts and Science, the recipient of the 2018 Pender Award from the University of Pennsylvania, and Doctorates Honoris Causa from Instituto Politéchnico Nacional de Mexico, from Ecole Polytechnique Fédérale de Lausanne, and from Université Côte d’Azur. He was selected in Wired Magazine’s “100 List of global influencers, 2016” and “Next List 2016 of 25 geniuses who are creating the future of business.”
He is the recipient of the 2018 ACM Turing Award (with Geoffrey Hinton and Yoshua Bengio) for “conceptual and engineering breakthroughs that have made deep neural networks a critical component of computing.” Full bio
Doug Riecken is a trained concert pianist with a B.A. from the Manhattan School of Music and studies at the Julliard School of Music. He spent many years performing classical, jazz, and rock styles on international concert tours with world-renowned artists before he switched to a career in cognitive and computing science. He received his PhD from Rutgers University under thesis advisor Dr. Marvin Minsky from MIT; a founding father of artificial intelligence. Riecken and Minsky spent 30+ years in friendship researching learning and the mind. Riecken is a thought leader in the areas of learning, big data analytics, machine learning, intelligent software agents, human-computer interaction/design, and knowledge discovery and data mining, He joined the Air Force Office of Scientific Research as a program officer in 2014 and is a senior member of the AFRL ACT3 team. During prior decades he was a department head/PI in Area 11 Bell Labs Research, IBM Watson Research and Columbia University. Full Bio
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