November 26, 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)
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.

    Short Course on Latent Tree graphical models

    April 27, 2015 at 10:00 a.m. - 12:00 p.m.
    April 28, 2015 at 10:00 a.m. - 12:00 p.m.
    April 29, 2015 at 10:00 a.m. - 11:00 a.m.

    Stewart Library, The Fields Institute
    Instructor: Piotr Zwiernik, University of Genoa


      1. Trees, tree metrics and the space of trees.
      I will introduce basic graph-theoretic tree concepts, tree metrics andother tree spaces that arise naturally in the study of latent treegraphical models.

      2. Latent tree graphical models.
      I will define the model and discuss the basic links to Bayesian networks and undirected graphical models on trees. I will present somebasic results concerning identifiability and moment structure.

      3. Inference.
      In many application the main interest is in learning the underlying tree. I will give an overview of some methods of learning the tree and show how the idea of tree metrics provides a natural estimator.

      4. Parameter estimation.
      I will introduce the structural EM algorithm for the MLE estimation and discuss some other approximate methods.

      5. Special submodels: Hidden Markov model, symmetric models and models
      used in phylogenetics
      Many popular models arise as special cases of latent tree Graphicalmodels. In this lecture I discuss these examples.

Postdoctoral Fellows and Program Visitors

The Thematic Program on Statistical Inference, Learning, and Models for Big Data is pleased to welcome the following Postdoctoral Fellows to the Program

      Postdoctoral Fellows
      Fuqi Chen
      PhD, University of Windsor

      Armin Hatefi
      PhD, University of Manitoba
      Fields-Ontario Postdoctoral Fellow

      Einat Gil
      PhD, University of Haifa
      Roger Grosse
      PhD, Massachusetts Institute of Technology
      Alexander Schwing
      PhD, ETH Zurich
      Cathal Smyth
      PhD, University of Toronto



Allied Activity