People: Edwin Bonilla, Wray Buntine, Tiberio Caetano, Stephen Gould, Fabio Ramos, Scott Sanner, Yang Wang, Justin Domke, Khoa Nguyen, Wei Liu
Human Computer Interaction and Cognitive Analytics
People: Fang Chen, Julien Epps, Ronnie Taib, Yang Wang
Large-scale Machine Learning
Description: The digital revolution is producing increasing amounts of data in virtually all areas of the human enterprise. The Internet, medicine, science, trading, telephony, advertisement, banking, and publishing now rely on vast and ever-increasing data repositories. We research how to extract relevant knowledge and meaning out of this ocean of data by designing efficient and scalable machine learning methods. We apply these methods across industry, government and university sectors in several domains, often in partnerships with domain experts.
People: Tiberio Caetano, Fabio Ramos, Christfried Webers
People: Mark Reid, Bob Williamson, Xinhua Zhang
Description: Many prediction problems are highly structured and entail huge output spaces which cannot be enumerated naively. Examples are protein structure prediction, text translation, image segmentation and ill-posed inversion problems. Since traditional methods are intractable for structured prediction, our research focuses on developing new methods while still delivering high prediction accuracy. We also develop models and algorithms tailored to specific types of structured prediction problems, such as network structure inference and automatic image tagging.
People: Edwin Bonilla, Tiberio Caetano, Simon O'Callaghan, Alistair Reid, Khoa Nguyen, Wei Liu
Text Analysis and Language Technology
Description: Text analysis and language technology involve techniques for the computational analysis of human language in the form of text documents, speech transcripts, and more recently, the language of social media, including blogs, forums, and tweets. Our approach includes supervised and unsupervised machine learning techniques for automated classification and information extraction, information retrieval, and deep parsing and semantic analysis. One particular focus of our work is in biomedicine and health, where technical language and concepts introduce new challenges. We are using the lens as a large scale testbed for our technology.
People: Tim Baldwin, Wray Buntine, Lawrence Cavedon, David Martinez, Scott Sanner, Hanna Suominen, James Bailey, Gabriela Ferraro
Description: We look at topic models for applications in text and document analysis and focus on their use in visual, exploratory interfaces for document collections. We work with non-parametric Bayesian methods such as hierarchical Pitman-Yor processes, and use natural language processing tools to further our research. Recent published work has investigated structured documents (sections, sentences, sequences), and word priors to favour more semantically coherent models.
People: Edwin Bonilla, Wray Buntine, Scott Sanner