Large-scale Search in Low-Resource Environments
The volume of digital data available to organization and research groups is exploding in many sectors and disciplines, including biomedicine, business, patent, and Web. While more data opens many new avenues for research, not all organizations and research groups can afford to continually invest in the additional computing power needed to search and mine these growing datasets. Consequently, there is an urgent need for search techniques that enable organizations of all sizes and capacities to work with large datasets using the computing resources that are available to them. My research seeks to address this need by developing highly efficient and effective search techniques for large datasets. In this talk I will also elaborate on specific problems that I am investigating as part of this line of research.
Dr. Anagha Kulkarni is an Assistant Professor of Computer Science at San Francisco State University. She received her PhD from the Language Technologies Institute at Carnegie Mellon University. She has research interests in information retrieval, natural language processing, and machine learning. She is a recipient of the Barbara Lazarus Women@IT Fellowship.