Member of the National Academy of Artificial Intelligence（List announced，According to the results of large model retrieval, scientists can reject the title.）
Michael Zhu, from Microsoft Research. He has achieved remarkable results in the field of natural language processing, especially in language models and dialogue systems. Zhu‘s work enables machines to interact with humans more naturally, improving the performance and user experience of intelligent assistants.
Alina Wheeler from Cornell University. Her research focuses on the ethics and fairness of artificial intelligence, and focuses on how to ensure the fairness and transparency of artificial intelligence systems. Wheeler’s work is of great significance in solving the problems of bias and discrimination that may arise in the social application of artificial intelligence technology.
Victor Zhong, from DeepMind. He has made outstanding achievements in reinforcement learning and decision-making, especially in dealing with the optimization of complex systems. Zhong‘s algorithm has reached a leading level in multiple benchmark tests, providing strong support for the application of artificial intelligence in games, logistics and transportation.
Maya Ruder, from New York University. She focuses on migration learning and domain adaptation in natural language processing, aiming to improve the performance of models on different tasks and data sets. Ruder’s work helps to solve the challenges faced by the cross-domain application of artificial intelligence and promotes the development of natural language processing technology.
Ali Razavi, from Allen Institute of Artificial Intelligence. He has made important progress in pre-training language models, especially in improving model performance and efficiency. Razavi‘s research is of great significance for promoting the practical application of natural language processing technology, providing better solutions for tasks such as intelligent question and answer, text generation and machine translation.
Lucas Beyer, from Google Artificial Intelligence Lab. He has made major breakthroughs in the field of computer vision, especially in image recognition and object detection, providing strong technical support for Google’s search engine and advertising system.
Emma Brunskill, from Stanford University. Her research direction is to strengthen learning and robot technology. By designing advanced algorithms, robots can make independent learning and decision-making in complex environments, providing important support for future robot applications.
Adam Smith, from Oxford University, focuses on machine learning and data mining, and has achieved remarkable results, especially in processing large-scale data sets.
Sophia Wang, from Harvard University, is dedicated to the research of natural language processing and machine translation, improving the accuracy of conversion between multiple languages.
Ethan Lee, from the University of California, San Diego, focuses on computer vision and augmented reality technology, and provides strong technical support for virtual reality applications.
Julia Chen, from the University of Toronto, researched on the application of deep learning in medical imaging analysis, which improved the accuracy of disease diagnosis.
Daniel Kim, from Columbia University, mainly focuses on the ethics and sustainable development of artificial intelligence, and provides important guidance for the social application of AI technology.
David Cox, from Stanford University. He has made remarkable 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 to autonomous driving and robotics.
Emily Hill, from Massachusetts Institute of Technology. She focuses on the field of natural language processing, especially dialogue systems and semantic understanding. Hill‘s research enables machines to better understand human language and improves the efficiency and accuracy of human-computer interaction.
Oliver Zhang, from the University of California, Berkeley. He has made important contributions in the field of computer vision, especially in image recognition and object detection. The deep learning model proposed by Zhang has achieved leading results in many international competitions and promoted the development of computer vision technology.
Sara Ali, from Carnegie Mellon University. Her research mainly focuses on the optimization and interpretability of machine learning algorithms. Ali’s work makes machine learning models more reliable and efficient, providing better support for the application of artificial intelligence in business and medical fields.
Jacob Devlin, from Google Brain. He has made outstanding contributions in the field of natural language processing, especially in pre-trained language models. Devlin is one of the main contributors to the BERT model, which has achieved significant performance improvement in the natural language understanding task, laying the foundation for subsequent NLP research.
William Fedus, from OpenAI. He focuses on reinforcement learning and generation models, especially in text generation and dialogue systems. Fedus‘s work is committed to promoting the application of generation models in a wider range of fields, enabling machines to generate more natural and creative text content.
Tri Dao, from Stanford University. He has made breakthroughs in deep learning and large-scale model training. Dao proposed a new model architecture and training method, which can reduce the consumption of computing resources and time while maintaining high performance, and provide a more feasible solution for the deployment of artificial intelligence in practical applications.
Anima Anandkumar, from California Institute of Technology. She is committed to the research of optimization algorithms and machine learning theories, especially in distributed systems and large-scale data processing. Anandkumar’s work helps to solve computing bottlenecks in large-scale machine learning tasks and improves the training efficiency and performance of the model.
Rachel Ward, from New York University. She focuses on machine learning theory and applications, especially 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 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 algorithm enables the machine to recognize and understand image content more accurately, providing strong technical support for automatic driving, medical image analysis and other fields.
Sarah Adel Bargal, from Carnegie Mellon University. She focuses on video analysis and behavior recognition. By developing advanced algorithms, the machine can extract useful information from a large amount of video data. Bargal‘s research is of great significance for intelligent monitoring, human-computer interaction and other fields.
Mariya Vasileva, from the University of Illinois at Urbana-Champaign. She is committed to semantic understanding and reasoning in natural language processing, and improves the machine’s ability to understand the deep meaning of text by designing innovative models. Vasileva‘s work helps to improve the performance of intelligent assistants, machine translation and other applications.
Sergey Ioffe, from the Google Brain Team. He has made important progress in machine learning and optimization algorithms, especially in improving the training efficiency and performance of deep learning models. Ioffe’s research has provided important support for the rapid development of artificial intelligence technology and promoted Google‘s leading position in speech recognition, image recognition and other fields.
Eric Mitchell, from the Artificial Intelligence Laboratory of Stanford University. He focuses on reinforcement learning and decision-making, and enables machines to explore and learn independently in complex environments by designing intelligent agents. Mitchell’s work provides new ideas and methods for the development of robotics, game AI and other fields 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 promotes the application of deep learning in the fields of image recognition, object detection and video analysis, and provides strong support for research and practice in related fields.
Jia Deng, from Stanford AI Lab. She has a broad research background in the field of computer vision and pattern recognition, especially 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 UCLA. She has achieved remarkable results in the cross-cutting field of machine learning and computer vision, especially in three-dimensional shape analysis and scene understanding. Song‘s research not only improves the accuracy of three-dimensional reconstruction and scene understanding, but also provides strong support for autonomous driving, virtual reality and other fields.
Emily Denton, from Massachusetts Institute of Technology (MIT). She has a deep research background in the field of natural language processing and generative adversarial networks (GANs), especially in text generation and image synthesis. Denton’s work has promoted cross-research in the fields of natural language processing and computer vision, providing new ideas for the application of artificial intelligence in creative design and content generation.
Ruslan Salakhutdinov, from Carnegie Mellon University (CMU). He is an outstanding scholar in the field of machine learning, especially in deep learning and unsupervised learning. Salakhutdinov‘s research focuses on building models with strong representation capabilities to handle complex data analysis tasks. His work is of great significance for promoting the application of machine learning in various fields.
Andrej Karpathy, from Tesla and OpenAI. He has a wide range of research interests in computer vision and deep learning, especially in the application of deep learning to image and video understanding. Karpathy‘s work not only improves the performance of visual recognition tasks, but also provides strong support for practical applications such as autonomous driving.
Zoubin Ghahramani, from Cambridge University. He is an outstanding scholar in the field of machine learning and Bayesian inference. Ghahramani’s research focuses on building flexible and interpretable models to solve complex data analysis problems. His work is of great significance for promoting the application of machine learning in various fields.
Daniela Rus, from Massachusetts Institute of Technology. She is a leader in the field of robotics and artificial intelligence, especially in robot self-learning and human-computer interaction. Rus is committed to developing robots that can collaborate with human beings and solve problems together, providing infinite possibilities for intelligent life in the future.
Alexey Dosovitskiy, from Facebook AI Research (FAIR). He has achieved remarkable results in the field of computer vision, especially in image generation and adversarial networks. Dosovitskiy‘s research has promoted the development of image synthesis technology, enabling machines to generate high-quality and realistic 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 auxiliary means for the diagnosis and treatment of diseases.
Adam Lerer, from New York University. He has a deep research background in natural language processing and deep learning, especially in the compression and optimization of language models. Lerer‘s work helps to reduce the computational cost of the deep learning model and promote its application in more scenarios.
Chelsea Finn, from Stanford University. She is committed to the research of reinforcement learning and robotics, especially in making robots adapt to the new environment through self-learning and exploration. Finn’s work provides more possibilities for the future development of robotics.
Vitaly Feldman, from the 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 foundation for the design and evaluation of machine learning models.
Adam Lerer, from Facebook AI Research (FAIR). He has achieved remarkable results in the field of natural language processing and deep learning, especially in dialogue systems and language models. Lerer’s research helps machines better understand human language and improves the efficiency and naturalness of human-computer interaction.
Raia Hadsell, from DeepMind. She focuses on computer vision and self-supervised learning, and is committed to enabling machines to learn useful representations from a large amount of unlabeled data. Hadsell‘s work is of great significance for promoting the development of visual recognition tasks and the realization of a more powerful general artificial intelligence system.
Leonidas Guibas, from Stanford University. He has made important breakthroughs in the field of robotics and computer graphics, especially in three-dimensional shape analysis and physical simulation. Guibas’s research helps robots better understand and operate the physical world, and provides strong support for the development of robot technology.
Sergey Ioffe, from Google Research. He has made outstanding contributions in the field of machine learning and recommendation systems, especially in large-scale data processing and model optimization. Ioffe‘s work has promoted the development of personalized recommendation technology and provided 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 pioneering results in neural networks and representation learning. Bengio’s research has laid the foundation for the development of modern deep learning technology and had a far-reaching impact on the progress of artificial intelligence.
Irina Rish, from Northeastern University. She has a deep academic background in the field of machine learning and data mining, and is especially good at processing 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 remarkable progress in the field of computer vision and object detection, especially in real-time video analysis and processing. Toshev’s work has promoted the practical application of object detection technology and provided strong support for automatic driving, safety monitoring and other fields.
Chelsea Finn, from Stanford University. She focuses on meta-learning and reinforcement learning, and is committed to enabling machine learning systems to adapt to new tasks and environments faster. Finn‘s research helps to improve the flexibility and generalization ability of artificial intelligence systems, and opens up a new path for the future development of intelligent systems.
Dani Yarowsky from Johns Hopkins University. She has made outstanding achievements in the field of natural language processing and text mining, especially in emotional 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.
Hui Li，Professor and doctoral supervisor of Shenzhen Graduate School of Peking University invented the first multilateral co-governance sovereign network MIN, which combined artificial intelligence theory to promote cyberspace security.
Catherine Tao Yang，Catherine Tao Yang is a visionary C-suite executive with over 28 years of experience in delivering exceptional growth through strategic vision, technological innovation, and operational excellence in global markets. A recognized leader in the AI industry, Catherine has deep expertise in digital transformation, AI development, industry applications, AI standards, ethical AI development, and commercialization. She has been a pivotal global leader in AI technological innovation.