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2022 CANSSI Ontario Research Day

September 29, 2022

The CANSSI Ontario Research Day will showcase the work and discoveries by the data sciences and statistics community in Ontario.

Random Structures and Patterns in Spatio-Temporal Data: Probabilistic Modelling and Statistical Inference

May 24, 2022

Speakers:

Radu Stoica

The useful information carried by spatio-temporal data is often outlined by geometric structures and patterns.

Statistics at Its Best

May 5, 2022

This workshop will celebrate the outstanding contributions of

Bayesian, Fiducial &amp; Frequentist (BFF7) Conference poster

Bayesian, Fiducial and Frequentist (BFF7) Conference

May 2, 2022 to May 4, 2022

Statistics is a young discipline with several different and competing paths in its approaches and foundations.

How are the Causes of Complex Disease Distributed in the Human Genome

February 4, 2022

Speakers:

David Balding

The advent of very large, richly-phenotyped and high-quality human genomics datasets, together with the development of models that allow joint anal

Spatial Skills in STEM with a Focus on Computing and Diversity

January 24, 2022

Speakers:

Michael Liut

Numerous correlational studies have demonstrated the link between spatial skills and success in STEM.

Employing Social Media to Improve Mental Health: Pitfalls, Lessons Learned, and the Next Frontier

November 15, 2021

Speakers:

Munmun De Choudhury

Social media data is being increasingly used to computationally learn about and infer the mental health states of individuals and populations.

Information Geometry: Geometrization of Statistical Inference

July 15, 2019

Speakers:

Jun Zhang, University of Michigan

Information Geometry is the differential geometric study of the ma

Leveraging genetic ancestry for new insights into complex traits

June 19, 2019

Speakers:

Timothy Thornton (University of Washington)

Genetic studies in multi-ethnic cohorts offer great potential to elucidate the genetic factors influencing complex traits.

Towards the finite-sample variance bounds for unbiased estimators

June 5, 2019

Speakers:

Ioannis Kosmidis (University of Warwick)

The inverse of the Fisher information matrix in a likelihood problem is i) the variance-covariance matrix of the asymptotic distributio

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