SCIENTIFIC PROGRAMS AND ACTIVITIES

Overview

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

• January 12 – 23, 2015
  Opening Conference and Boot Camp
  Organizing committee: Nancy Reid (Chair), Sallie Keller, Lisa Lix, Bin Yu
  
  
• January 26 – 30, 2015
  Workshop on Big Data and Statistical Machine Learning
  Organizing committee: Ruslan Salakhutdinov (Chair), Dale Schuurmans, Yoshua Bengio, Hugh Chipman, Bin Yu
  
  
• February 9 – 11 , 2015
  Workshop on Optimization and Matrix Methods in Big Data
  Organizing Committee: Stephen Vavasis Chair; Anima Anandkumar, Petros Drineas, Michael Friedlander, Nancy Reid, Martin Wainwright.
  
  
• February 23 – 27, 2015
  Workshop on Visualization for Big Data: Strategies and Principles
  Organizing Committee: Nancy Reid (Chair), Susan Holmes, Snehelata Huzurbazar, Hadley Wickham, Leland Wilkinson
  
  
• March 23-27, 2015
  Workshop on Big Data in Health Policy
  Organizing Committee: Lisa Lix (Chair), Constantine Gatsonis, Sharon-Lise Normand, Therese Stukel
  
  
• April 13 – 16, 2015
  Workshop on Big Data for Social Policy
  Organizing Committee: Sallie Keller (chair), Robert Groves, Mary Thompson
  
  
• June 12– 13, 2015
  Closing Conference
  Organizing Committee: Nancy Reid (Chair), Sallie Keller, Lisa Lix, Hugh Chipman, Rus Salakhutdinov, Yoshua Bengio, Richard Lockhart
  to be held at AARMS of Dalhousie University,
  Held in conjunction with the Annual Meeting of the Canadian Statistical Sciences Institute, in the two days preceding the Annual Meeting of the Statistical Society of Canada. Overview lectures by members of the organizing committee will highlight the research generated by the thematic program.
  

Training

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 http://fields2015bigdata2inference.weebly.com/
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


Description:

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

January - April, 2015 Joint Big Data Program-Statistics Department Colloquia July 21 – August 15, 2014 Summer School: Statistical Learning in Big Data Instructors: Hugh Chipman, Acadia; Sunny Wang, St. Francis Xavier held at AARMS April 7-9, 2015 Coxeter Lecture Series Michael Jordan (University of California, Berkeley) Room 230, Fields Institute April 9-10, 2015 Distinguished Lecture Series in Statistical Science Terry Speed (Walter and Eliza Hall Institute for Medical Research, Melbourne) Room 230, Fields Institute April 20– 24, 2015 Workshop Statistical Inference for Large Scale Data with Richard Lockhart (Chair), Nicolai Meinshausen held at PIMS, Simon Fraser April 23-24, 2015 Distinguished Lecture Series in Statistical Science Bin Yu, University of California, Berkeley Room 230, Fields Institute

April 21-24, 2015 CANSSI Workshop on Complex spatio-temporal data structures: Methods and applications held at the Fields Institutue April 29-30, 2015 Big Data in Commercial and Retail Banking with Mark Reesor, (Western); Matt Davison, (Western); Adam Metzler, (Wilfrid Laurier ) held at the Fields Institutue May 4 – 8, 2015 Workshop and Short Course on Statistical and computational challenges in networks, web mining and cybersecurity: with Hugh Chipman (Chair), François Théberge (U Ottawa) held at CRM, Montreal May 11–15, 2015 Workshop on Big Data in Environmental Science with Richard Lockhart (Chair), James Zidek (UBC) held at PIMS, University of British Columbia July 31- August 9, 2015 Deep Learning Summer School https://sites.google.com/site/deeplearningsummerschool/ Organizing Committee: Yoshua Bengio, Chair held at CRM, Montreal

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