Expertise

Scientific Director

Raymond T Ng

Professor, Computer Science
Canada Research Chair in Data Science and Analytics
Chief Informatics Officer, PROOF (Prevention of Organ Failure) Centre

Raymond’s main research area for the past two decades is on data mining, with a specific focus on health informatics and text mining. He has published over 180 peer-reviewed publications on data clustering, outlier detection, OLAP processing, health informatics and text mining. He is the recipient of two best paper awards – from the 2001 ACM SIGKDD conference, the premier data mining conference in the world, and the 2005 ACM SIGMOD conference, one of the top database conferences worldwide. For the past decade, he has co-led several large-scale genomic projects funded by Genome Canada, Genome BC and industrial collaborators. Since the inception of the PROOF Centre of Excellence, which focuses on biomarker development for end-stage organ failures, he has held the position of the Chief Informatics Officer of the Centre. From 2009 to 2014, Dr. Ng was the associate director of the NSERC-funded strategic network on business intelligence.

More about Raymond Ng

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Affiliated Faculty Members

Alexandre Bouchard-Cote

Associate Professor, Statistics

Alexandre’s main field of research is in statistical machine learning. He is interested in the mathematical side of the subject as well as in applications in linguistics and biology. On the methodology side, he is interested in Monte Carlo methods, graphical models, non-parametric Bayesian statistics, randomized algorithms and variational inference. His favorite applications, both in linguistics and biology, are related to phylogenetics. Some examples of recent studies include: automated reconstruction of proto-languages; cancer phylogenetics; population genetics; and pedigrees, tree and alignment inference.

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Jenny Bryan

Associate Professor, Statistics

Jenny's is an applied statistician who focuses on data analysis and computing, especially in the R programming environment (one of the top programming languages used in data science). Her most recent work, in collaboration with colleagues in the Michael Smith Labs at UBC, produced an assay and analytical method, which allow for the detection of specific mutations in colorectal cancers – a development that will be key in guiding patient treatment (DOI: 10.1016/j.jmoldx.2015.09.003).

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Giuseppe Carenini

Associate Professor, Computer Science

Giuseppe has broad interdisciplinary interests. His research focuses on how natural language processing and information visualization can be effectively combined to support data analysis and decision making. More specifically, he has been working on mining and summarization of conversational data (emails, meetings, blogs); discourse parsing; the generation and summarization of evaluative text; and visual text analytic techniques for opinions and conversations.

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Gabriela Cohen-Freue

Assistant Professor, Statistics

Gabriela works in statistical problems that usually appear in the analysis of proteomics and genomics data. In these applications, some variables may be measured with error; the number of features is much larger than the number of observations; the observations are not always independent; and outliers are present in the data. Gabriela develops statistical and computational methods to address some of these common issues in statistical genomics. In particular, she is interested in the development of robust estimation methods for sparse linear models.

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Michael Friedlander

Professor, Computer Science and Mathematics

Michael’s research focuses on solving large-scale optimization problems, and spans algorithm design, analysis, and software implementation. His work includes applications in signal processing and machine learning.

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Sara Mostafavi

Assistant Professor, Statistics

Sara develops and applies machine learning and statistical methods to study the genomics of complex diseases. In particular, she develops computational methods for combining multiple types of genomic data, such as gene expression and genotype data, and modeling prior biological pathways and networks for disentangling spurious from meaningful correlations.

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Tamara Munzner

Professor, Computer Science

Tamara's research interests include the development, evaluation, and characterization of information visualization systems and techniques. She has worked in a broad range of application domains, including genomics, evolutionary biology, geometric topology, computational linguistics, large-scale system administration, web log analysis, and journalism.

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Sarah Otto

Professor, Zoology, Biodiversity Research Centre

Understanding how evolution has led to the remarkable diversity of life is the key motivating force behind Sarah’s research. She combines mathematical models, experimental data analysis, and comparative phylogenetic studies to determine which evolutionary transitions are plausible, which are probable, and which are inaccessible.

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Yaniv Plan

Assistant Professor, Mathematics

Yaniv studies the mathematics of information, with a focus on compressed sensing and low-rank matrix recovery. He has a recent interest in deep learning. Much of his work studies the role of randomness in the analysis of high-dimensional data.

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Loren Rieseberg

Professor, Botany

Loren’s lab employs a combination of ecological, genomic, and bioinformatics approaches to study the origin and evolution of new species, exploits the genetic diversity of wild extremophile species for crop improvement, and combats invasive weeds, focusing on members of the sunflower family.

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Mark Schmidt

Assistant Professor, Computer Science

Mark works in the area of machine learning, which focuses on automatic discovery of patterns in large datasets. His theoretical focus is on the algorithms underlying these methods. He tries to make them scale up to huge datasets and work on developing generalizations that can model very complicated patterns. He has also worked on various practical applications, including several works on medical imaging.

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Ozgur Yilmaz

Professor, Mathematics

Ozgur’s work is on the theory of compressed sensing and related fields studying mathematics of information as well as the application of theory and computation in practical problems. The areas of applications he focuses on include seismic data analysis, audio signal processing, and analog-to-information conversion.

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