BIO
Michael I. Jordan (Aberdeen, Maryland, United States, 1956) holds a master’s degree in Mathematics (Statistics) from Arizona State University (1980) and a PhD in Cognitive Science from the University of California, San Diego (1985). His career has largely been spent at the Massachusetts Institute of Technology (1988-1998), where he was a professor in the Department of Brain and Cognitive Sciences, and, latterly, at the University of California, Berkeley, where he has been Pehong Chen Distinguished Professor in the Department of Electrical Engineering and Computer Science and in the Department of Statistics and is currently Emeritus Professor. He is also a Senior Researcher at the National Institute for Research in Digital Science and Technology (INRIA) in Paris (France), where he heads the Markets and Machine Learning chair at the INRIA Foundation. Author of over 230 publications in scientific journals, he has served as president of the International Society for Bayesian Analysis and on the editorial boards of journals including Statistics and Computing and Stochastic Analysis and Applications.
CONTRIBUTION
Michael I. Jordan has developed mathematical and computational techniques that underpin a wealth of AI applications, from restaurant recommendation systems to generative language models like ChatGPT. He was at the forefront of the development of so-called variational inference models, able to approximate the solution to a mathematical problem that is not solvable with available computational resources. This technique is a core component of deep learning applications like the generative AI of ChatGPT and other language models.
In the 2000s, Jordan devised algorithms to run machine learning programs in hundreds or thousands of computers instead of just one, leading to the creation of the platform Ray which is now the basis for ChatGPT, numerous e-commerce firms and many other deep learning applications. Among the awardee’s more recent interests has been the application of machine learning to economics, building recommender systems that reflect people’s preferences while allowing them to collaborate within the same system (for instance, choosing alternative routes in a GPS app to avoid traffic hold-ups on the most favorable road).
