麻豆传媒

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.

bigdata@cs.dal.ca

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.

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

We are a lab at 麻豆传媒 whose research interests are primarily in the fields of Computational Neuroscience and Machine Learning.

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.

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.

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:

• University of S茫o Paulo (Universidade de S茫o Paulo), Sao Carlos, Brazil (Professors , , )

• Universidade Federal de S茫o Paulo (UNIFESP) (Prof. )

• National University of the South (Universidad Nacional del Sur) & CONICET, Bahia Blanca, Argentina (Drs and )

• Universidad T茅cnica Federico Santa Mar铆a, Santiago, Chile ()

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

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 麻豆传媒.

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.