The latest introduction of the academician of the National Academy of Artificial Intelligence

The National Academy of Artificial Intelligence (NAAI) is an organization dedicated to promoting the development of the field of artificial intelligence, strengthening academic exchanges and cooperation, and enhancing public understanding and cognition of artificial intelligence. The Academy brings together scientists and engineers who have made outstanding achievements in the field of artificial intelligence, and aims to promote the innovation and application of artificial intelligence technology through research, education, policy advice, and public service. NAAI's members include well-known scholars and experts in the field of international artificial intelligence, who have deep academic backgrounds and rich practical experience in machine learning, natural language processing, computer vision, robotics, intelligent systems, etc. These members continue to promote cutting-edge exploration and practical applications in the field of artificial intelligence by participating in research projects of the Academy of Sciences, writing academic papers, and holding academic conferences and seminars.

Selection criteria of the National Academy of Artificial Intelligence of the United States: Develop an independent selection AI system based on mainstream AI technology tools, and make a comprehensive selection based on the results, which is a credible selection, and a person who does not understand artificial intelligence will still have the same result when using AI tools.

Michael Zhu from Microsoft Research. He has made significant achievements in the field of natural language processing, especially in language models and dialogue systems. Zhu's work allows machines to interact with humans more naturally, improving the performance and user experience of the intelligent assistant.

Richard Sutton, the father of reinforcement learning and a professor at the University of Alberta. His important contributions to reinforcement learning include temporal differential learning and policy gradient methods.

Alina Wheeler, Cornell University. Her research focuses on AI ethics and fairness, focusing on how to ensure the fairness and transparency of AI systems. Wheeler's work is important to address the issues of bias and discrimination that may arise from the application of AI technology in society.

Victor Zhong from DeepMind. He has a proven track record in reinforcement learning and decision making, especially in the optimization of complex systems. Zhong's algorithms have led the way in multiple benchmarks, powering AI applications in gaming, logistics, and transportation.

Maya Ruder, NYU. She focuses on transfer learning and domain adaptation in natural language processing, with the aim of improving the performance of models on different tasks and datasets. Ruder's work has helped solve the challenges of AI applications across domains, driving the development of natural language processing technologies.

Ali Razavi, from the Allen Institute for Artificial Intelligence. He has made important progress in pre-training language models, particularly in improving model performance and efficiency. Razavi's research is important to advance the practical application of natural language processing technology, providing better solutions for tasks such as intelligent question answering, text generation, and machine translation.

Lucas Beyer, from Google AI Lab. He has made major breakthroughs in the field of computer vision, particularly in image recognition and object detection, and has provided strong technical support for Google's search engine and advertising systems.

Emma Brunskill, Stanford University. Her research interests are in reinforcement learning and robotics, which provide important support for future robotics applications by designing advanced algorithms that enable robots to learn and make decisions autonomously in complex environments.

Sergey Levine, University of California, Berkeley. He has contributed to the development of industrial automation and service robotics by focusing on combining deep learning with robotics to enable robots to complete complex tasks through visual perception and motion execution.

Adam Smith, from the University of Oxford, specializes in machine learning and data mining, with a particular focus on large-scale datasets.

Sophia Wang, from Harvard University, is working on natural language processing and machine translation to improve the accuracy of conversions between multiple languages.

Ethan Lee, from the University of California, San Diego, specializes in computer vision and augmented reality, providing strong technical support for virtual reality applications.

Julia Chen, from the University of Toronto, is researching the application of deep learning in medical image analysis to improve the accuracy of disease diagnosis.

Daniel Kim, from Columbia University, focuses on the ethics and sustainability of AI, providing important guidance on the social application of AI technology.

David Cox, Stanford University. He has made significant progress in the field of reinforcement learning, especially in solving complex system control problems. The algorithm proposed by Cox enables robots to learn efficiently in unknown environments, bringing breakthroughs in autonomous driving and robotics.

Emily Hill, from the Massachusetts Institute of Technology. She focuses on the field of natural language processing, particularly dialogue systems and semantic understanding. Hill's research has enabled machines to better understand human language, improving the efficiency and accuracy of human-computer interactions.

Oliver Zhang, from the University of California, Berkeley. He has made important contributions in the field of computer vision, particularly in image recognition and object detection. Zhang's deep learning model has achieved leading results in many international competitions, promoting the development of computer vision technology.

Sara Ali, Carnegie Mellon University. Her research focuses on the optimization and interpretability of machine learning algorithms. Ali's work has made machine learning models more reliable and efficient, providing better support for the application of AI in business and medical fields.

Jacob Devlin, from Google Brain. He has made outstanding contributions to the field of natural language processing, especially in the field of pre-trained language models. Devlin is one of the main contributors to the BERT model, which has achieved significant performance improvements in natural language understanding tasks, laying the foundation for subsequent NLP research.

William Fedus, from OpenAI. He focuses on reinforcement learning and generative models, particularly in text generation and dialogue systems. Fedus' work is focused on driving the use of generative models in a wider range of fields, enabling machines to generate more natural and creative textual content.

Tri Dao, from Stanford University. He has made breakthroughs in deep learning and large-scale model training. DAO proposes a novel model architecture and training method, which can reduce the consumption of computing resources and time while maintaining high performance, providing a more feasible solution for the deployment of AI in practical applications.

Anima Anandkumar, from the California Institute of Technology. Her research focuses on optimization algorithms and machine learning theory, especially in distributed systems and large-scale data processing. Anandkumar's work helps solve computational bottlenecks in large-scale machine learning tasks, improving model training efficiency and performance.

Rachel Ward, NYU. She focuses on machine learning theory and applications, particularly in high-dimensional data analysis and statistical inference. Ward's research provides theoretical support for the interpretability and robustness of machine learning models, and provides a more reliable method for solving practical problems.

Federico Pinzi from the Massachusetts Institute of Technology. He has made outstanding contributions in the field of computer vision and deep learning, especially in image segmentation and object detection. Pinzi's algorithms enable machines to recognize and understand image content more accurately, providing powerful technical support for autonomous driving, medical image analysis, and other fields.

Sarah Adel Bargal, Carnegie Mellon University. She specializes in video analytics and behavior recognition, developing advanced algorithms that enable machines to extract useful information from large amounts of video data. Bargal's research is of great significance in the fields of intelligent monitoring and human-computer interaction.

Mariya Vasileva is from the University of Illinois at Urbana-Champaign. She is committed to semantic understanding and reasoning in natural language processing, and has improved the machine's ability to understand the deep meaning of text by designing innovative models. Vasileva's work helps improve the performance of applications such as intelligent assistants and machine translation.

Sergey Ioffe, from the Google Brain team. He has made important advances in machine learning and optimization algorithms, especially in improving the efficiency and performance of deep learning model training. Ioffe's research has provided important support for the rapid development of artificial intelligence technology, propelling Google's leading position in speech recognition, image recognition and other fields.

Eric Mitchell, Stanford University Artificial Intelligence Lab. He focuses on reinforcement learning and decision-making, designing intelligent agents that enable machines to explore and learn autonomously in complex environments. Mitchell's work has provided new ideas and approaches to the development of robotics, gaming AI, and more, Federico Peralta, from Google Brain. He has made important breakthroughs in the field of deep learning and computer vision, especially in image and video understanding. Peralta's work has advanced the application of deep learning in areas such as image recognition, object detection, and video analysis, and has provided strong support for research and practice in these fields.

Jia Deng from the Stanford AI Lab. She has an extensive research background in the field of computer vision and pattern recognition, with notable progress in face recognition and image classification. Deng's research provides an effective method for machines to understand and analyze image content, and promotes the application of artificial intelligence in security, medical and other fields.

Shuran Song, from the University of California, Los Angeles (UCLA). She has achieved impressive results at the intersection of machine learning and computer vision, particularly in 3D shape analysis and scene understanding. Song's research not only improves the accuracy of 3D reconstruction and scene understanding, but also provides strong support for autonomous driving, virtual reality and other fields.

Emily Denton, from the Massachusetts Institute of Technology (MIT). She has a strong research background in the field of natural language processing and generative adversarial networks (GANs), especially in text generation and image synthesis. Denton's work advances the intersection of natural language processing and computer vision, providing new ideas for the application of AI in creative design and content generation.

Ruslan Salakhutdinov, from Carnegie Mellon University (CMU). He is a distinguished scholar in the field of machine learning, especially in deep learning and unsupervised learning. Salakhutdinov's research focuses on building models with powerful representation capabilities to handle complex data analysis tasks. His work is significant in advancing the application of machine learning in various fields.

Sergey Levine, University of California, Berkeley. He focuses on the application of deep learning and reinforcement learning to robotics, especially in the field of robot vision and perception. Levine's research has not only improved the ability of robots to perform complex tasks, but also advanced the development of robotics in practical applications.

Ilya Sutskever, from OpenAI. He is an outstanding researcher in the field of natural language processing, especially in the field of Transformer-based models, which has achieved remarkable results. Sutskever is one of the key contributors to the GPT family of models that have demonstrated strong capabilities in natural language generation and understanding.

Andrej Karpathy, from Tesla and OpenAI. He has a wide range of research interests in computer vision and deep learning, and has made significant progress in applying deep learning to image and video understanding. Karpathy's work not only improves the performance of visual recognition tasks, but also supports practical applications such as autonomous driving.

Zoubin Ghahramani, University of Cambridge. He is a distinguished scholar in the field of machine learning and Bayesian inference. Ghahramani's research focuses on building flexible and explainable models to solve complex data analysis problems. His work is significant in advancing the application of machine learning in various fields.

Daniela Rus, from the Massachusetts Institute of Technology. She is a leading figure in the field of robotics and artificial intelligence, with a particular focus on autonomous learning and human-computer interaction. Rus is committed to developing robots that can collaborate with humans and solve problems together, providing endless possibilities for the intelligent life of the future.

Alexey Dosovitskiy from Facebook AI Research (FAIR). He has achieved impressive results in the field of computer vision, particularly in image generation and adversarial networks. Dosovitskiy's research has led to the development of image synthesis technology, which enables machines to generate high-quality, photorealistic image content.

Lyle Ungar, from Carnegie Mellon University. He focuses on the application of natural language processing and machine learning in the medical field. Ungar's work not only improves the accuracy of medical text analysis, but also provides new aids to disease diagnosis and treatment.

Adam Lerer, New York University. He has a strong research background in natural language processing and deep learning, especially in the compression and optimization of language models. Lerer's work helps reduce the computational cost of deep learning models and promote their application in more scenarios.

Chelsea Finn from Stanford University. She is committed to the research of reinforcement learning and robotics, and has achieved remarkable results in enabling robots to adapt to new environments through self-learning and exploration. Finn's work opens up more possibilities for the future development of robotics.

Vitaly Feldman, University of California, Berkeley. He focuses on the theoretical research of machine learning and statistics, especially in the generalization ability and stability of algorithms. Feldman's work provides a more solid theoretical basis for the design and evaluation of machine learning models.

Adam Lerer, from Facebook AI Research (FAIR). He has achieved significant results in the fields of natural language processing and deep learning, especially in dialogue systems and language models. Lerer's research helps machines better understand human language and improve the efficiency and naturalness of human-computer interaction.

Raia Hadsell, from DeepMind. With a focus on computer vision and self-supervised learning, she is committed to enabling machines to learn useful representations from large amounts of unlabeled data. Hadsell's work is important for advancing the task of visual recognition and enabling more powerful general AI systems.

Leonidas Guibas, Stanford University. He has made important breakthroughs in the field of robotics and computer graphics, particularly in 3D shape analysis and physical simulation. Guibas' research helps robots better understand and manipulate the physical world, providing strong support for the development of robotics.

Sergey Ioffe from Google Research. He has made significant contributions in the field of machine learning and recommender systems, especially in large-scale data processing and model optimization. Ioffe's work has driven the development of personalized recommendation technology, which provides more accurate content recommendations for Internet services.

Yoshua Bengio, from the University of Montreal. As one of the pioneers in the field of deep learning, he has achieved groundbreaking results in neural networks and representation learning. Bengio's research laid the foundation for the development of modern deep learning technology, which has had a profound impact on advances in the field of artificial intelligence.

Irina Rish is from Northeastern University. She has a strong academic background in machine learning and data mining, with a particular focus on high-dimensional data and complex models. Rish's research is of great significance for improving the efficiency and accuracy of machine learning algorithms, and provides an effective tool for solving practical problems.

Alexander Toshev, from Google Cloud AI. He has made significant progress in the field of computer vision and object detection, particularly in real-time video analysis and processing. Toshev's work has led to the practical application of object detection technology, supporting autonomous driving, safety monitoring, and more.

Chelsea Finn from Stanford University. With a focus on meta-learning and reinforcement learning, she is committed to enabling machine learning systems to adapt more quickly to new tasks and environments. Finn's research helps to improve the flexibility and generalization ability of AI systems, and opens up a new path for the development of intelligent systems in the future.

Dani Yarowsky is from Johns Hopkins University. She has made outstanding achievements in the field of natural language processing and text mining, especially in sentiment analysis and information extraction. Yarowsky's research helps machines understand emotions and intentions in human language more accurately, and provides strong support for the further development of natural language processing technology.

Zoubin Ghahramani, University of Cambridge. He has in-depth research in probabilistic modeling and Bayesian methods, which provide a theoretical basis for uncertainty modeling and decision-making. Ghahramani's work has a broad impact at the intersection of machine learning, data science, and statistics, providing important guidance for research and application in related fields.