Big Ideas & Big Data - Faculty of Computer Science - Dalhousie ...

4 Computer ScienceQ & A with Stan MatwinWhat is your line of research?I work in Machine Learning and DataMining. Machine Learning is a researcharea in which a computer is givenexamples of something (e.g. what is andwhat isn’t an oil spill in a satellite imageof the sea) and, from these examples,it learns how to classify or predict newexamples of that ‘something’ (e.g. torecognize oil spills in new, unseen images).This is an old idea, dating back tothe 1950s, and it was part of the originalArtificial Intelligence manifesto. Everybodyagrees that learning is an inherentpart of intelligence, but I like to see itmore pragmatically. I am interested inthe use of learning programs to learnpractical things: to predict who in theemergency room will need hospitalization,to recognize oil spills, to categorizemedical articles or to catch emergingtrends in a political campaign or inpublic opinion.Of particular interest for me is learningfrom text data: papers, blogs, tweets,notes, etc. I believe that such data callsfor methods that take into accountits linguistic character – we will havestronger methods if they understand thelexical, syntactic and semantic characterof such data. This is the main topic ofmy Canada Research Chair here at Dal.Data Mining, for me, is MachineLearning in the large. First, one is dealingwith large data sets in millions ofrecords and terabytes of volume. Second– in data mining – it is recognized thatone spends most of their effort not in the“model building” phase, but instead inthe data cleaning and data preparationphase (e.g. doing “attribute engineering”).In order to do this, the data minermust learn the basics of the domainfrom which the data is coming: they willhave to create in their head fundamental“ontology” of that domain: what are themain entities and what are the relationshipsbetween those entities.I am also interested in data privacy.I work on methods that make it hard, orpractically impossible, to identify a givenperson in a dataset.How did you get interestedin that?Well, it all started many years ago when Iwas involved in one of the early projectsin Expert Systems (ES), a joint projectwith Cognos. At that time, we weretrying to build an ES that would process(or assist in processing) governmenttravel claims. I got to learn more thanI ever wished about that “fascinating”topic! A question which arose was, howdoes one acquire rules which form theknowledge base of an expert system?Somebody suggested that I look atMachine Learning – indeed, one of itsearly goals was to replace the classical“Knowledge Acquisition” approach withlearning the rules from examples. I wentto spend a sabbatical with one of the