An international conference on "Statistical Learning and Data Mining" will be held June 4-7, 2012 on the Ann Arbor campus of the University of Michigan. The objective is to bring together researchers in statistical learning and data mining from academia, industry, and government in a relaxed and stimulating atmosphere focused on the development of statistical learning theory, methods and applications. The conference will feature three plenary talks by internationally prominent researchers whose work are cutting-edge in the field of statistical learning and data mining. Eighteen invited breakout sessions, each with three talks, will cover additional topics with great interest to the field. These include Computational Advertisement, Function Estimation, High-dimensional Methods, Structured Learning, Graphical Models, Learning Theory, Model Selection, Covariance Estimation, Network Analysis, Computational Biology, Signal and Image Processing and Data Mining Applications. There will also be seven contributed paper sessions and two contributed poster sessions where junior investigators and graduate students are expected to participate.
Statistical learning is a relatively new discipline, evolving from machine learning methods of artificial intelligence and multivariate statistics. The general goals of statistical learning are discovery, classification and prediction, often in very high, effectively infinite, dimensional contexts. The advent of powerful computers with accompanying massive data sets has brought the discipline to the forefront of statistical theory and practice. The major goal of the proposed conference is to present some of the most important recent advances in the field and to discuss future research directions. A major part of the conference focuses on bringing statistical research leaders together with students, postdoctoral fellows, and young academics in a stimulating environment. The funding from the NSF will mainly support graduate students and junior researchers in American universities to attend the conference and present either a talk or a poster. The conference is expected to accelerate interactions and collaborations among researchers in the important area of statistical learning and data mining, and thereby lead to the development of new and more effective methods of modeling and inference.
Statistical learning is a relatively new discipline, evolving from machine learning methods of artificial intelligence and multivariate statistics. The general goals of statistical learning are discovery, classification and prediction, often in very high, effectively infinite, dimensional contexts. The advent of powerful computers with accompanying massive data sets has brought the discipline to the forefront of statistical theory and practice. The goal of this conference on "Statistical Learning and Data Mining" was to present some of the most important recent advances in the field and to discuss future research directions. The conference on "Statistical Learning and Data Mining" was held June 5-7, 2012 at the University of Michigan. The conference was co-sponsored by the newly formed American Statistical Association's (ASA) Section on Statistical Learning and Data Mining, NSF, NSA, the University of Michigan, Google and SAS. The conference featured three plenary talks by internationally prominent researchers whose work are cutting-edge in the field of statistical learning and data mining. Twenty-one invited breakout sessions, each with three talks, covered additional topics with great interest to the field. These included Computational Advertisement, Function Estimation, High-dimensional Methods, Structured Learning, Graphical Models, Learning Theory, Model Selection, Covariance Estimation, Network Analysis, Computational Biology, Signal and Image Processing and Data Mining Applications. There was also a contributed poster session where junior investigators and graduate students presented their work. A major part of the conference focused on bringing statistical research leaders together with students, postdoctoral fellows, and young academics in a stimulating environment. The funding from the NSF mainly supported graduate students and junior researchers in American universities to attend the conference and present either a talk or a poster. The conference accelerated interactions and collaborations among researchers in the important area of statistical learning and data mining, and thereby lead to the development of new and more effective methods of modeling and inference.
