This event has reached capacity and registration is now closed. You may watch this event live through our streaming service FieldsLive. Registration for this event includes attendence to Data Science in Industry: at MARS with Vector Institute.

This is a retrospective workshop for the thematic program “Statistical Models, Learning and Inference for Big Data”, held from January to June 2015. The new title reflects the shift in emphasis from the size of the data to the science of collecting, storing, querying, modeling, drawing conclusions, visualizing and communicating the insights derived. 

The landscape is changing rapidly – there are now many new undergraduate and graduate programs in data science, there have been extensive developments in computing platforms for data carpentry, there is renewed emphasis on workflow for reproducible research, the Fields Institute has a workshop series on data science in its Commercial and Industrial Mathematics program, and there has been a substantial investment in deep learning through the Pan-Canadian artificial intelligence strategy announced by the federal government in March 2017. This workshop will provide an opportunity to assess how these and other changes are impacting the research areas discussed during the thematic program.

Schedule Overview

September 24: AM:  Environmental Science: -- attribution of climate extremes, multivariate extreme value modeling, policy implications for insurance 

September 24: PM: Visualization: -- paradigms for visualization, statistical methods, integration with the data science toolbox 

September 25: AM: Social Policy: -- urban analytics, methodology, privacy and algorithmic fairness 

September 25: PM: Health Policy: -- open data, linking diverse types of data, causality, and communicating with decision makers 

September 26: AM: Networks: -- social networks, randomization designs for network data; stochastic block models and extensions

September 26: PM: Inference: -- high-dimensional inference, model selection, model robustness 

September 27: AM:  Optimization: -- the boundary between optimization and machine learning; non-convex optimization; tensor methods

September 27: PM: Deep Learning and Statistical Modeling: -- understanding the behavior of deep learning algorithms

September 28: Data Science in Industry: at MARS with Vector Institute

Invited Speakers

Todd Kuffner; Washington University in St Louis

Jelena Bradic; University of California, San Diego

Veronika Rockova; Booth School, U Chicago

Greg Ridgeway; University of Pennsylvania 

Mark Fox; University of Toronto

Fanny Chevalier; University of Toronto

Isabel Meirelles; OCAD

Heike Hofmann; Iowa State University

Lisa Lix; University of Manitoba

George Paliouras; Institute of Informatics & Telecommunications, Athens

Laura Hatfield; Harvard Medical School

Ruth Etzioni; Fred Hutchinson Cancer Research Center

Francis Zwiers; University of Victoria

Debbie Dupuis; HEC Montreal

Raymond Ng; University of British Columbia

Edoardo Airoldi; Harvard University

Eric Kolaczyk; Boston University

Sofia Ohlede; University College London

Mark Schmidt; University of British Columbia

Yaoliang Yu; University of Waterloo

Rahul Mazumder; Massachusetts Institute of Technology Sloan School

Marzyeh Ghassemi; Massachusetts Institute of Technology

Jimmy Ba; CS and Vector, University of Toronto

Simon Lacoste-Julien; CS and OR, University of Montreal

Michael Correll, Tableau 

Ravi Shroff, NYU

Nathan Srebro, UChicago