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Artificial Intelligence in Health Event Banner

Artificial Intelligence in Health: Research and Applications in Toronto

November 28, 2022

Speakers:

Muhammad Mamdani

While the application of artificial intelligence (AI) has transformed numerous sectors including retail, transportation, and hospitality, its appli

New Advances in Genetic Risk Prediction: Model Fine-Tuning, Benchmarking, and Portability

November 17, 2022

Speakers:

Qiongshi Lu, University of Wisconsin-Madison

Genetic risk prediction is a major focus in human genetics research.

Quantitative Modeling of Brain-Behavior Relationships

December 5, 2022

Speakers:

Amy Kuceyeski

The brain is arguably the most complex object in the known universe, with ~100 billion neurons and ~100 trillion connections, from which arise huma

Using Interpretable Machine Learning and Network Systems Approaches to Uncover Mechanisms Underlying Pathophysiology of Immune Disorders

November 28, 2022

Speakers:

Jishnu Das

Modern multi-omic technologies can generate deep multi-scale profiles.

Looks Okay to Me: A Study of Best Practice in Data Analysis Code Review

November 14, 2022

Speakers:

Kelly Bodwin

Education in statistical computing requires that we train students not only in programming skills and principles, but also in good data science hab

Continuous-Time Incentives in Hierarchies

December 8, 2022

Speakers:

Emma Hubert, Princeton University

In this talk, we will study a model of continuous–time optimal contracting in a hierarchy, which generalizes the one-period framework of Sung (2015

Convolution-Type Smoothing Approach for Quantile Regression

November 24, 2022

Speakers:

Kean Ming Tan, University of Michigan

Quantile regression is a powerful tool for learning the relationship between a response variable and a multivariate predictor while exploring heter

Modelling Non-Gaussian Spatio-Temporal Processes

November 3, 2022

Speakers:

Alexandra Schmidt, McGill University

In the analysis of most spatiotemporal processes in environmental studies, observations present distributions that are not normal.

Outcome-Adaptive LASSO for Confounder Selection With Time-Varying Treatments

October 27, 2022

Speakers:

Mireille Schnitzer, University of Montreal

Data sparsity is a common problem when conducting causal inference with time-varying binary treatments, especially when treatment can change over m

High Dimensional Latent Panel Quantile Regression with an Application to Asset Pricing

September 8, 2022

Speakers:

Oscar Madrid Padilla, University of California

We propose a generalization of the linear panel quantile regression model to accommodate both sparse and dense parts: sparse means that while the n

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