Our active research labs & groups:

Learn about working in Algorithms

Researchers in our group explore the design, analysis and implementation of algorithms and data structures – understanding and developing algorithms to make today’s applications smarter and faster.  

Areas of focus include:

  • Graph algorithms
  • Computational geometry
  • Parallel algorithms
  • Algorithms for large data sets
Faculty Members: Dr. Meng He Dr. Norbert Zeh

Learn about working in Bioinformatics

Bioinformatics entails the development and application of statistical and algorithmic methods to biological data sets. New technologies are giving us unprecedented insights into the inner workings of living things, and reshaping our views of biodiversity. Driving this revolution are leaps in data-generating technologies such as DNA sequencing and protein structure analysis. While these technologies can transform our understanding of the living world, making sense of them is no trivial task, and we are continuously developing new methods that can be used to analyze ever-increasing data sets in increasingly precise ways.

Bioinformatics research in Computer Science encompasses the development of new algorithms and software, and application of tools to new types of data. Our labs combine trainees with backgrounds in disciplines including Computer Science, Biology, and Statistics, providing valuable cross-training experience and unique collaborative opportunities. We work closely with government agencies such as the Public Health Agency of Canada and the Department of Fisheries and Oceans to generate new insights in epidemiology and marine biodiversity from cutting-edge data sets. Our research areas include:

  • Algorithms to tackle new DNA-based approaches to biodiversity analysis;
  • Modeling and simulation of proteins with key roles in disease;
  • "Genomic epidemiology" tools to better track and analyze infectious-disease outbreaks;
  • New tools to investigate the structure, diversity, function, and changes in the human microbiome;
  • Algorithms that can scale up to tens of thousands of genomes;
  • Phylogenetic methods, and application of these methods to large "phylogenomic" data.

We work closely with our Algorithms colleagues to tackle data sets in new and innovative ways.

Faculty Members:


Who we are:

Rob Beiko

  • Bioinformatics
  • Comparative genomics and phylogenetics
  • Machine learning
  • Visualization of biological data
  • Human microbiome

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Christian Blouin

  • Protein evolution and biophysics
  • Algorithms / Statistical mechanics
  • Phylogenetics
  • High-performance computing
  • Molecular modeling

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Travis Gagie

  • Compact Data Structures
  • Data Compression
  • Pattern Matching
  • String Algorithms

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Nauzer Kalyaniwalla

  • Networked information spaces
  • Analysis of massive dynamic graphs
  • Graph and tree compression algorithms
  • Compressibilty as a measure of information content in graphs

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Meng He

  • Algorithms & data structures
  • Computational geometry

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About Dr. Whidden

  • Approximation and fixed-parameter algorithms
  • Computational biology
  • Evolutionary trees and networks
  • Graph theory
  • Hybridization and lateral genetic transfer
  • NP-hardness
  • Oceans data analytics

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Norbert Zeh

  • Algorithms and data structures
  • I/O-efficient and cache-oblivious algorithms
  • Parallel algorithms
  • Graph algorithms
  • Computational geometry

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