March  2, 2015


Thematic Program on Statistical Inference, Learning, and Models for Big Data
January to June, 2015
Organizing Committee
Nancy Reid (Toronto)
Yoshua Bengio (Montréal)
Hugh Chipman (Acadia)
Sallie Keller (Virginia Tech)
Lisa Lix (Manitoba)
Richard Lockhart (Simon Fraser)
Ruslan Salakhutdinov (Toronto)
Therese Stukel
International Advisory Committee
Constantine Gatsonis (Brown)
Susan Holmes (Stanford)
Snehelata Huzurbazar (Wyoming)
Nicolai Meinshausen (ETH Zurich)
Dale Schuurmans (Alberta)
Robert Tibshirani (Stanford)
Bin Yu (UC Berkeley)


This thematic program emphasizes both applied and theoretical aspects of statistical inference, learning and models in big data. The opening conference will serve as an introduction to the program, concentrating on overview lectures and background preparation. Workshops throughout the year will emphasize deep learning, statistical learning, visualization, networks, health and social policy, and physical sciences. A number of allied activities at PIMS, CRM and AARMS are also planned, and listed at the bottom of this page. This thematic program is taking place with the cooperation of the new Canadian Statistical Sciences Institute (CANSSI).
It is expected that all activities will be webcast using the FieldsLive system to permit wide participation.

Conferences and Workshops


Graduate Course on Large Scale Machine Learning
Monday, 11 a.m. -2 p.m, January 5 to March 30 ( no classes Feb 16-20), Stewart Library, Fields Institute
Instructor: Russ Salakhutdinov, Departments of Computer Science and Statistical Sciences, University of Toronto

    Description: Statistical machine learning is a very dynamic field that lies at the intersection of statistics and computational sciences. The goal of statistical machine learning is to develop algorithms that can "learn" from data using statistical and computational methods. Over the last decade, driven by rapid advances in numerous fields, such as computational biology, neuroscience, data mining, signal processing, and finance, applications that involve large amounts of high-dimensional data are not that uncommon.
    The goal of this course is to introduce core concepts of large-scale machine learning and discuss scalable techniques for analyzing large amounts of data. Both theoretical and practical aspects will be discussed.

Graduate Course on Topics in Inference for Big Data
For more detail see
Friday, 1 p.m. -4 p.m, January 9 to March 27 ( no classes Feb 16-20), Stewart Library, Fields Institute
Instructors: Nancy Reid, Department of Statistical Sciences, University of Toronto; Mu Zhu, Department of Statistics and Actuarial Science, University of Waterloo

    Description: This course will introduce students to the topics under discussion during the thematic program on Statistical Inference in Big Data, with a mix of background lectures and guest lectures. The goal is to prepare students, postdoctoral fellows, and other interested participants to benefit from upcoming workshops in the thematic program, and to provide a venue for further discussion of keynote presentations after the workshops.

There are opportunities for extended visits of senior (all but degree) graduate students. Please apply through the Application for Participant Support link.

These courses will be streamed using FieldsLive, and students are welcome to attend online. Students interested in obtaining credit for these courses need to arrange with their home department to have them approved as reading or research courses. We will make available the timetable and requirements for the course at the first lecture in January, 2015.

Allied Activity