Prasenjit Mitra is the Associate Dean for Research and Professor in the College of Information Sciences and Technology.  His current research interests are in the areas of artificial intelligence, health informatics, big data analytics, applied machine learning, and visual analytics. In the past, he has contributed to the areas of data interoperation, data cleaning, and digital libraries especially in tabular data extraction, and citation recommendation.

       Mitra received his Ph.D. from Stanford University in 2004, his M.S. from the University of Texas at Austin in 1994, and a B.Tech.(Tons.) from the Indian Institute of Technology, Kharagpur in 1993.  At Penn State, he has pursued research on a broad range of topics ranging from data mining on the web and social media, scalable data cleaning, political text mining, chemical formula and name extraction from documents, and the extraction of data and metadata from figures and tables in digital documents.

       He was the principal investigator of the DOES project funded by the NSF CAREER Award. He has also been the co-principal investigator of the CiteSeerX, ChemXSeer, and ArchSeer digital library projects, the Regional Visualization and Analytics Center (NEVAC), and the GeoCAM visual analytics projects. Mitra serves as the director of the Cancer Informatics Initiative at Penn State. His research has been supported by the NSF, Microsoft Corporation, DoD, DHS, DoE, NGA, and DTRA.  Mitra has co-authored approximately 180 articles at top conferences and journals. He has supervised over 15 Ph.D. students; and several M.S. students

       In this talk, Professor Mitra will talk about an application and adaptations of natural language processing technologies for disaster relief and emergency response.  Information flows in a real-time fashion on microblogs such as Twitter.  Identifying useful information from such microblogs in real-time is a challenging problem. Professor Mitra will discuss how actionable tweets are identified using supervised machine learning with high accuracy while adjusting for topic drift. Professor Mitra show how information from tweets in one language can be used to train to extract information from tweets in another language with cognates.  Finally, Professor Mitra will talk about their work on extracting summaries automatically from the classified tweets and generate customized reports from the categorized information. Professor Mitra will conclude with thoughts on automatic information extraction, summarization, and automated authoring.