- When: Monday, September 25, 2017 from 11:00 AM to 12:00 PM
- Speakers: Aidong Zhang, State University of New York at Buffalo and National Science Foundation
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With the growth of world wide web and large-scale digitization of documents, we are overwhelmed with massive information, formally through publication of various scientific journals or informally through internet. As an example, consider MEDLINE, a premier bibliographic database in life sciences, with currently more than 23 million references from approximately 5,600 worldwide journals. As a consequence, Literature Based Discovery (LBD) has become a sub-field of Text Mining that leverages these published articles to formulate hypotheses. In this talk, I will discuss how a self-learning based framework for knowledge discovery can be designed to mine hidden associations between non-interacting scientific concepts by rationally connecting independent nuggets of published literature. The self-learning process can model the evolutionary behavior of concepts to uncover latent associations between text concepts, which allows us to learn the evolutionary trajectories of text terms and detect informative terms in a completely unsupervised manner. Hence, meaningful hypotheses can be efficiently generated without prior knowledge. I will also discuss how this self-learning framework can be extended to include social media and Internet forums. With the capability to discern reliable information from various sources, this self-learning framework provides a platform for combining heterogeneous sources and intelligently learning new knowledge with no user intervention.
Dr. Aidong Zhang is a SUNY Distinguished Professor of Computer Science and Engineering at the State University of New York (SUNY) at Buffalo where she served as Department Chair from 2009 to 2015. She is currently on leave and serving as Program Director in the Information & Intelligent Systems Division of the Directorate for Computer & Information Science & Engineering, National Science Foundation. Her research interests include data analytics/data science, bioinformatics, and health informatics, and she has authored over 300 research publications in these areas. Dr. Zhang currently serves as the Editor-in-Chief of the IEEE Transactions on Computational Biology and Bioinformatics (TCBB). She served as the founding Chair of ACM Special Interest Group on Bioinformatics, Computational Biology and Biomedical Informatics during 2011-2015 and is currently Chair of its advisory board. She is also the founding and steering chair of ACM international conference on Bioinformatics, Computational Biology and Health Informatics. She has served as editor for several other journal editorial boards, and has also chaired or served on numerous program committees of international conferences and workshops. Dr. Zhang is an IEEE Fellow.
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