Michael I. Jordan
Biography
Considered one of the most influential computer scientists in the world and a leader in advancing the field of machine learning, Michael I. Jordan helped develop unsupervised learning into a powerful algorithmic tool for solving real-world challenges in many areas including natural language processing, computational biology, and signal processing. A potent blend of computer science, statistics, and applied mathematics, machine learning involves the use of algorithms and statistical models that enable computers to carry out specific tasks without explicit instructions and to continually improve. Jordan helped transform unsupervised machine learning, which can find structure in data without preexisting labels, from a collection of unrelated algorithms to an intellectually coherent field that solves real-world problems. His pioneering work on latent Dirichlet allocation (or topic models) demonstrated how statistical modeling ideas can be used to learn, in an unsupervised manner, models of nontraditional data sets (such as documents) as compositions of different parts (such as topics), where the representations of the parts themselves are also learned simultaneously.
In his work on topic models and beyond, Jordan augmented the classical analytical distributions of Bayesian statistics with computational entities having graphical, combinatorial, temporal, and spectral structure, and he then used ideas from convex analysis, optimization, and statistical physics to develop new approximation algorithms, referred to as variational inference algorithms, that exploited these structures. Variational methods became a major area of machine learning and the principal engine behind scalable unsupervised learning. Today, they transcend subdisciplines of machine learning and play an important role in both deep learning and probabilistic machine learning.
An IEEE Fellow and member of the U.S. National Academy of Sciences and the U. S. National Academy of Engineering, Jordan is the Pehong Chen Distinguished Professor in the Department of Electrical Engineering and Computer Science and the Department of Statistics at the University of California, Berkeley, CA, USA.
