Our active research institute & groups:

Institute for Big Data Analytics

The Institute for Big Data Analytics is a first of its kind in Canada. It has a mission create knowledge and expertise in the field of Big Data Analytics by facilitating fundamental, interdisciplinary and collaborative research, advanced applications, advanced training and partnerships with industry.


Director: Dr. Stan Matwin, Ph.D. CRC

Following his Ph.D., Stan was an Assistant Professor in the Department of Mathematics and Computer Science, Warsaw University. He joined University of Guelph in 1977, and Acadia University in 1980. Since 1981 at the University of Ottawa, as of 2011 a Distinguished University Professor (on leave). For many years in charge of graduate studies in Computer Science at the University of Ottawa, and a founding father of the Graduate Certificate in Electronic Commerce at University of Ottawa in 1999. Also affiliated with the Institute for Computer Science of the Polish Academy of Sciences as a Professor, Stan has worked at universities in the U.S, Europe, and Latin America. Recognized internationally for his work in text mining, applications of Machine Learning, and data privacy, author and co-author of more than 250 research paper. Former president of the Canadian Artificial Intelligence Association (CAIAC) and of the IFIP Working Group 12.2 (Machine Learning). Stan has significant experience and interest in innovation and technology transfer. One of the founders of Distil Interactive Inc. and Devera Logic Inc.

Dalhousie Natural Language Processing (DLNP) group

The Dalhousie Natural Language Processing Group (DNLP) provides information about NLP-related research conducted at the Dalhousie University, and it is a forum for discussion, collaboration, and interaction between researchers interested in the philosophies, theories, and applications related to NLP.

Group Website: http://dnlp.ca
Contact Information: Dr. Vlado Keselj
Phone: 1-902-494-2893
Fax: 1-902-492-1517 (att. Vlado Keselj)
Research Areas and
  • Language modeling, syntactic and semantic analysis, n-grams
  • Information extraction, information retrieval, question answering
  • Text data mining, text categorization, document clustering
  • Speech recognition, automatic translation
  • Computational linguistics, sylabification, multi-word expressions
  • Text messages normalization
  • Sentiment analysis in micro-blogs
  • Computational musicology, music structure analysis
Faculty Members:
Other Members:
  • Dr. Axel Soto

Hierarchical Anticipatory Learning (HAL) Lab

We are a lab at Dalhousie University whose research interests are primarily in the fields of Computational Neuroscience and Machine Learning.

Group Website: http://projects.cs.dal.ca/hallab
Contact Information: Thomas Trappenberg at: tt@cs.dal.ca
Research Areas and
  • Machine learning
  • Computational Neuroscience
  • Cognitive Robotics
Funding: NSERC, CIHR
Faculty Members:
Graduate Students and
Research Assistants:
  • Patrick Connor
  • Paul Hollenen
  • Warren Connors
Academic Collaborators:
  • Dr. Mae Seto (Engineering, DRDC)
  • Dr. Doug Munoz (Queens Univ.)
  • Dr. Brian Coe (Queens Univ.)
  • Dr. Pitoyo Hartono (Chukyo Univ.)
Industry Partners: DRDC
Seminar Series: Hallab chats Wednesdays 10am-11:30
Related Conferences and

Image Pattern Analysis and Machine Intelligence (IPAMI)

The research interests of the group include human vision perception and perceptual organization, computation models of perception and perceptify technology, statistical and structural image pattern analysis, perceptual pattern learning, generic image segmentation, perceptual feature classification and grouping, vision applications on surveillance (motion), content based image retrieval, medical imaging, and robot vision, etc.

Group Website: http://projects.cs.dal.ca/ipami/
Contact Information: Dr. Qigang Gao
Phone: 1-902-494-3356
Email: q.gao@dal.ca
Research Areas and
  • Vision Perceptify
    • Vision perceptify language: perceptual partition and grouping
    • Vision based computer-user interface
  • Image Segmentation
    • Perceptify token-based generic region segmentation
    • Medical imaging: retina vessel map extraction
  • Content-based Image Retrieval
    • Content-based image retrieval using perceptual shape features
    • Content-based image retrieval using extended autocorrelogram
  • Surveillance and Motion Analysis
    • Motion object analysis: motion tracking and license recognition
    • Gesture analysis for video game control
  • Robot Vision
    • Web-based robot control, Multi-sensor data fusion
    • Autonomous underwater hexapod robot
  • Case-based reasoning and expert systems
  • Visual attention and computational neuroscience
Funding: NSERC, MITACS & Industrial partners
Faculty Members:
Other Members:
  • Dr. Jason Gu, jason.gu@dal.ca, Department of Electrical and Computer Engineering

kNowledge Intensive Computing for Healthcare Enterprises (NICHE)

The NICHE (kNowledge Intensive Computing for Healthcare Enterprises) Research Group conducts research in advancing knowledge technologies and developing innovative knowledge-intensive solutions, in particular for healthcare enterprises.

The NICHE group both promotes and pursues inter-disciplinary research whereby the group's investigations span from the abstract epistemological orientations of knowledge to the capture and representation of knowledge to practical operationalization of knowledge via intelligent systems.

Group Website: http://niche.cs.dal.ca/
Contact Information: niche@cs.dal.ca
Tel: 1-902-494-2129
Fax: 1-902-492-1517
Research Areas and

The wide spectrum of activities conducted in NICHE group falls in the realm of four inter-related research areas:
1. Knowledge Management and Semantic Web
2. Health Informatics
3. Intelligent Information and Services Personalization
4. Health Data Mining

NICHE researchers work across a cross-section of the above-mentioned themes, developing both novel knowledge-centric methods and applying these methods in knowledge-intensive tools. Currently, the researchers are working towards the development of a knowledge creation, morphing and sharing framework for capturing and operationalizing heterogeneous knowledge modalities present within an enterprise; the development and application of intelligent techniques to customize web-based services and information content as per a user-model; the formal computerization of healthcare knowledge artifacts to offer point-of-care decision support services; and the application of knowledge and data-driven intelligent systems to provide innovative healthcare services for both practitioners and patients. Details of individual projects can be found at the individual researcher's websites. Our research projects are largely funded by government agencies, private organizations and industry.

Funding: The NICHE projects have been funded by CANARIE, National Sciences and Engineering Research Council of Canada (NSERC), Canadian Foundation of Innovation (CFI), Nova Scotia Health Research Foundation (NSHRF), Green Shield Foundation Canada and Agfa Healthcare Canada.
Faculty Members: Dr. Raza Abidi



Combining textual content and link information for describing, classifying, clustering and visualizing networked information spaces formed by large collections of documents are the focal tasks of the MALNIS lab.  The core methodology involves the application of machine learning, graph theory and natural language processing to problems in networked information spaces, i.e. large document collections which have the form of a graph, where nodes are occupied by documents and links represent relations between documents (hyperlinks or citations). Specific research problems addressed include similarity and clustering based on both content and link information, low-dimensional representations of special text corpora based on knowledge resources such as Wikipedia and word, term and document embeddings, document summarization, and interactive visualization of document corpora to support high-recall interactive information retrieval and sense-making tasks for the subject matter expert. Networked information spaces of particular interest include the scientific and medical research literature, list servers of communities of experts, social media data streams, and grey literature. To address the computational requirements of these tasks, we have been using cloud computing resources provided by Compute Canada.

The MALNIS lab has active collaborations with the following universities:

Students interested in doing a thesis with the MALNIS lab are encouraged to take as many of the following courses as possible as part of their course requirement towards their degree:

  • CSCI 4152/6509: Natural Language Processing
  • CSCI 6515: Machine Learning for Big Data
  • CSCI 4155/6505: Machine Learning
  • CSCI 6612: Visual Analytics
  • CSCI 4146: Process of Data Science
  • CSCI 4xxx: Deep Learning
  • CSCI 4163/6610: Human-Computer Interaction
  • CSCI 4166/6406: Visualization
Group Website: https://projects.cs.dal.ca/malnis/
Contact Information: Dr. Evangelos Milios
Email: eem@cs.dal.ca
Phone: 902-494-7111
Research Areas and

Publications of the lab that provide details on current and recent research projects can be found on Google Scholar. Highlighted areas include:

  • Interactive clustering of documents
  • Statistical learning for OCR error correction
  • Information extraction from medical reports
  • Vector space representations of concepts using Wikipedia graph structure
  • Rumour detection and spread visualization in social media

Funded MALNIS projects

The following projects all have openings with funding for eligible Master's and PhD students. You are encouraged to make a Skype appointment by email or simply drop in to discuss potential thesis topics. Our industrial partners offer employment opportunities to graduates of the MALNIS group.

  • Exploiting Semantic Analysis of Documents (NSERC Discovery Grant)

  • Visual Text Analytics for Total Recall Information Retrieval in Large Noisy Text Datasets (NSERC and the Boeing Company)

  • Beyond Structured Administrative Data (BEST DATA) (CIHR)

  • Deep Sense: A Platform for Academic and Industry Collaboration of Applied R&D in Analytics and the Ocean Economy

Faculty Members:

Ocean Data Analytics (ODA) Lab

The Ocean Data Analytics (ODA) Lab is a research group lead by Luis Torgo that is part of the Institute for Big Data Analytics at Dalhousie University.

The mission of this Lab is to use the power of Data Science to benefit the economic, environmental, regulatory, and social aspects of the Ocean.

Group Website: https://web.cs.dal.ca/~ltorgo/ODA/
Contact Information: Dr. Luis Torgo
Research Areas
  • Spatiotemporal Analytics
  • Rare Events and Forecasting

We study these topics using a multitude of Machine Learning and Aritificial Intelligence methods

Research Projects
  • Nitrolimit: Life at the Edge - Define the Boundaries of the Nitrogen Cycle in the Extreme Antarctic Environments
  • DeepSense: A solution for data analytics in the ocean economy
  • MARINEYE: A Prototype for a Multitrophic Ocean Monitoring
  • Mussels: Analyzing the role of mussels as biosentinels in aquaculture farms
  • Coral: Sustainable Ocean Exploitation, Tools and Sensors
  • MORWAQ: Monitoring and Predicting Water Quality Parameters
  • MODAL: Models for Predicting Algae Blooms in River Douro


Who we are:

Raza Abidi

  • Semantic web
  • Health informatics
  • Knowledge management
  • Information personalization & intelligent systems

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Vlado Keselj

  • Text processing
  • Natural language processing
  • Data mining
  • Computational number theory
  • Programming languages

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Stan Matwin

  • Artificial intelligence
  • Machine learning
  • Data mining / Text mining and text analytics
  • Big data
  • Data privacy

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Evangelos Milios

  • Networked information spaces
  • Machine learning
  • Social network mining
  • Digital libraries

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Sageev Oore

  • Machine learning
  • Deep learning
  • Computational creativity
  • Deep learning models and tools for music, art & text generation

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Thomas Trappenberg

  • Computational neuroscience
  • Machine learning

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Dirk Arnold

Dirk Arnold
  • Evolutionary computation
  • Optimization

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Janarthanan Rajendran

Janarthanan Rajendran
  • Reinforcement Learning
  • Deep Learning
  • Natural Language Processing
  • Machine Learning
  • Artificial Intelligence

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Frank Rudzicz

New Faculty AEM  - 4
  • Machine learning
  • Natural language processing
  • Health + AI 
  • Fairness and Ethics of AI

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