Carleton University

Prediction and Detection of Anomalous Events

Prediction of anomalous or rare events, such as the automated identification of fraudulent financial transactions, the prediction of novel microRNA molecules within the human genome, or the correct diagnosis of rare cancerous biopsies, is both important and challenging. This lab focuses on the development and evaluation of statistical models and machine learning systems for mixed mode data, and on making predictions in the presence of class imbalance. Graduate training is critically needed in this area since many events of interest in the real world tend to be rare.

 

Our lab offers training to address the unique challenges associated with applying statistical machine learning for the prediction of rare events. The inherently low prevalence of rare events must be explicitly addressed during both the training and testing of such systems; otherwise systems either dramatically under- or over-predict rare events. Application and extension of software such as HDoutliers in R is part our research focus. Application areas include biomedical informatics, network security applications, sports analytics, and business informatics.

Lab Leaders

Industry Partners

James Green

Professor

Faculty of Engineering and Design

Department of Systems and Computer Engineering

Carleton University

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Shirley Mills

Associate Professor

Faculty of Science

School of Mathematics & Statistics

Carleton University

Lab Team

Postdocs and Graduate Trainees

Claire Austen

Environment & Climate Change Canada 

Wesley Burr

Queen's University

Song Chai

Carleton University 

Ana-Maria Cretu

Carleton University 

Paul McNicholas
McMaster University

Sreeraman Rajan

Carleton University 

Glen Takahara
Queen's University

Paul Villeneuve

Carleton University

Steven Wang
York University

Ben Burr
Master's Candidate, Carleton University

David Charles
Master's Candidate, Carleton University

Roy Chih Chung Wang
Postdoctoral Fellow, Carleton University

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