CanQueue 2023

CanQueue 2023

Welcome to the website of the 23nd edition of the CanQueue workshop, which will be held on August 25-26, 2023 in Niagara-on-the-Lake, Ontario.




What is CanQueue?

CanQueue is one of the major queueing conferences in North America. The first workshop was organized by Dr. A. S. Alfa at the University of Manitoba in 1999, and the name CanQueue has been associated with these meetings since the 2000 edition in London, Ontario. The goal of the conference is to promote research and applications of queueing theory.

This annual conference provides an important platform for people, including leading Canadian and international queueing theorists, applied probabilists, scientists, researchers, engineers, executives, and students, to meet and share their new research findings, and to encourage collaboration on ongoing research initiatives.

Organizing Committee

Keynote Speaker

We are pleased to inform that Dr. Cynthia Rudin (https://users.cs.duke.edu/~cynthia/) from Duke University will be our keynote speaker this year.

Title:

How to Create Simple Risk Scores from Complex Models and Datasets

Abstract:

Consider predicting congestion at the intensive care unit (ICU) of a hospital, where overcrowding is life-threatening. While we would think this task is challenging due to heterogeneity of patients who have different diagnoses, complexities, and recovery times, as it turns out, simple linear machine learning models can predict congestion results of queueing models surprisingly well. This is true regardless of which queueing model is used for the ICU. In fact, simple models have been used across healthcare for decades. The vast majority of these models are scoring systems, which are sparse linear models with integer coefficients. Generally, such models are created without data, or are constructed by manual feature selection and rounding logistic regression coefficients, but these manual techniques sacrifice performance since humans are not naturally adept at highdimensional optimization. I will present the first practical algorithms for building optimized scoring systems from data. These methods have been used for several important applications to healthcare and criminal justice.

Bio:

Dr. Cynthia Rudin is the Earl D. McLean, Jr. Professor of Computer Science and Engineering at Duke University. She directs the Interpretable Machine Learning Lab, and her goal is to design predictive models that people can understand. Her lab applies machine learning in many areas, such as healthcare, criminal justice, and energy reliability. She holds degrees from the University at Buffalo and Princeton. She is the recipient of the 2022 Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity from the Association for the Advancement of Artificial Intelligence (the “Nobel Prize of AI”). She received a 2022 Guggenheim fellowship, and is a fellow of the American Statistical Association, the Institute of Mathematical Statistics, and the Association for the Advancement of Artificial Intelligence.