2024年开学第一课——香港理工大学黄坚教授：人工智能中的统计学及其相互作用
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报告摘要
In this talk, we will discuss the role of probabilistic and statistical thinking in the development of AI. We will discuss the importance of statistics in many of the current generative AI models, including large image and language models. Additionally, we will examine the role of AI in the future development of statistics and data science. Through various examples, we will illustrate how statistical analysis can leverage large models to address problems that are challenging for traditional methods.
嘉宾简介
Jian Huang is a Chair Professor of Data Science and Analytics in the Departments of Data Science and AI, and Applied Mathematics at The Hong Kong Polytechnic University. He obtained his Ph.D. in Statistics from the University of Washington in Seattle. His current research interests include deep generative models, statistical inference in deep learning, representation learning, and statistical analysis leveraging pretrained large models. He has published extensively in the fields of Statistics, Biostatistics, Machine Learning, Bioinformatics, and Econometrics. From 2015 to 2019, he was designated a Highly Cited Researcher in the field of Mathematics by the Web of Science Group at Clarivate. He was also included in the list of the top 2% of the world's most cited scientists by Elsevier BV and Stanford University in 2022 and 2023. He serves on the editorial boards of the Journal of the American Statistical Association and the Journal of the Royal Statistical Society (Series B). Professor Huang is a Fellow of the American Statistical Association and a Fellow of the Institute of Mathematical Statistics.
直播分享时间：2024年9月12日
In this talk, we will discuss the role of probabilistic and statistical thinking in the development of AI. We will discuss the importance of statistics in many of the current generative AI models, including large image and language models. Additionally, we will examine the role of AI in the future development of statistics and data science. Through various examples, we will illustrate how statistical analysis can leverage large models to address problems that are challenging for traditional methods.
嘉宾简介
Jian Huang is a Chair Professor of Data Science and Analytics in the Departments of Data Science and AI, and Applied Mathematics at The Hong Kong Polytechnic University. He obtained his Ph.D. in Statistics from the University of Washington in Seattle. His current research interests include deep generative models, statistical inference in deep learning, representation learning, and statistical analysis leveraging pretrained large models. He has published extensively in the fields of Statistics, Biostatistics, Machine Learning, Bioinformatics, and Econometrics. From 2015 to 2019, he was designated a Highly Cited Researcher in the field of Mathematics by the Web of Science Group at Clarivate. He was also included in the list of the top 2% of the world's most cited scientists by Elsevier BV and Stanford University in 2022 and 2023. He serves on the editorial boards of the Journal of the American Statistical Association and the Journal of the Royal Statistical Society (Series B). Professor Huang is a Fellow of the American Statistical Association and a Fellow of the Institute of Mathematical Statistics.
直播分享时间：2024年9月12日
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