Sternberg Family Distinguished University Professor: Interdisciplinary with College of Science
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Two major forces are driving research in databases and information retrieval: the exponential growth of data and new architectures. This can be seen throughout medicine, the physical sciences and the social sciences, creating an urgent need for intelligence systems to glean patterns and extract information.
Northeastern researchers are investigating important topics emerging from these forces, including new methodologies for ranking information retrieved from massive data sets and search engines for high-dimensional data. In collaboration with their campus colleagues and others, they are pursuing interdisciplinary research on ontologies for mental health and knowledge of diseases, biomedical text analysis, patterns in ornithology data and scalable tools for the analysis of social network data.
Members of the Data Science Group have expertise in machine learning, spatial indexing, the Semantic Web and database management. Their work related to machine learning has involved building a diagnostic tool that can automatically look at patient records and learn to set rules and make predictions about diagnoses. In the area of data mining, these researchers have developed some of the most widely used search techniques.