In the world of sports, there is a Michael Jordan in basketball known to everyone, and in the field of machine learning, there is also a Michael Jordan.
Introduction to Michael Jordan
Michael Irwin Jordan (born February 25, 1956) is an American scientist, professor at the University of California, Berkeley, and research expert in machine learning, statistics, and artificial intelligence. Due to his contributions to the foundations and applications of machine learning, Jordan was elected as a member of the National Academy of Engineering in 2010.
Michael Jordan is one of the leading figures in machine learning. In 2016, Science magazine reported that he was the world's most influential computer scientist.
In 2022, Michael Jordan received the inaugural WLA Prize in Computer Science or Mathematics, "for his fundamental contributions to the foundations of machine learning and its applications".
Education
Michael Jordan received a Bachelor of Science degree in psychology with honors from Louisiana State University in 1978, a Master of Science degree in mathematics from Arizona State University in 1980, and a PhD in cognitive science from the University of California, San Diego in 1985. At the University of California, San Diego, Jordan was a student of David Rumelhart and was a member of the parallel distributed processing (PDP) group in the 1980s.
Work and Research
Michael Jordan is a Distinguished Professor at the University of California, Berkeley, where he mainly teaches EECS and statistics. From 1988 to 1998, he served as a professor in the Department of Brain and Cognitive Sciences at MIT. His research interests span computational, statistical, cognitive, biological, and social sciences.
In the 1980s, Jordan began developing recurrent neural networks as cognitive models. In recent years, his work has been driven less from a cognitive perspective and more from a traditional statistical background.
Jordanpopularized Bayesian networks in the machine learning communityand is known forpointing out the connection between machine learning and statistics. He has also made outstanding contributions to the formalization of variational methods for approximate inference and the popularization of the expectation-maximization algorithm in machine learning.
Awards and Honors
- 2004, Medallion Lecturer of the Institute of Mathematical Statistics
- 2009, ACM/AAAI Allen Newell Award (Association for Computing Machinery ACM, Association for the Advancement of Artificial Intelligence AAAI)
- 2010, Member of the National Academy of Sciences
- 2010, Member of the National Academy of Engineering
- 2011, Member of the American Academy of Arts and Sciences
- 2015, Rumelhart Prize (Cognitive Science Society CSS)
- 2016, International Joint Conference on Artificial Intelligence Award for Research Excellence (IJCAI)
- 2020, John von Neumann Award (Institute of Electrical and Electronics Engineers IEEE)
- 2021, Mitchell Prize (International Society for Bayesian Analysis, ISBA)
- 2021, Ulf Grenander Prize in Stochastic Theory and Modeling (American Mathematical Society, AMS)
- 2022, Inaugural Grace Wahba Lecturer of the Institute of Mathematical Statistics
- 2022, World Laureates Association Prize in Intelligent Science or Mathematics