Edward Banner
Applied Machine Learning Group
Eebanner@ccs.neu.edu
MS in Computer Science, University of Texas at Austin
BS in Computer Science & Mathematics, State University of New York at Potsdam
AS in Computer Information Systems, Hudson Valley Community College
Hometown: Ballston Lake, New York
Field of Study: Computer Science
PhD Advisor: Byron Wallace
Edward Banner is a PhD student in the Computer Science program at Northeastern University's Khoury College of Computer Sciences, advised by Professor Byron Wallace. A native of Ballston Lake, NY, Edward earned his bachelor's degree in computer science and mathematics from the State University of New York at Potsdam and his master's degree in computer science from the University of Texas at Austin.
Edward's research areas include artificial intelligence, data science, machine learning and natural language processing and information retrieval. He is also a member of the Applied Machine Learning Group. Edward hopes to make advancements in the field of natural language processing and artificial intelligence.
My graduate education thus far has consisted primarily on familiarizing myself with various application areas of artificial intelligence, such as natural language processing, computer vision, and robotics, as well as identifying research problems in these areas.
I have worked on several research projects with focuses ranging from crowd-sourcing to modeling human movement from motion-tracking data. I have focused much of my research efforts since my involvement with these projects on natural language processing due to personal interests and potential for improving everyday life.
Multi-document summarization. For example, having a system which is able to automatically determine the efficacy of a drug for a disease from all available evidence in the medical literature would be very useful for medical practitioners!
Since entering graduate school, I have been very excited by the field of artificial intelligence and the challenge of building intelligent computer systems. One counter-intuitive fact of artificial intelligence is that some of the hardest problems for computers to solve are very easily solved by humans, for instance understanding linguistic, visual, and audio data. The contrapositive is also true; many problems that many humans find hard are very easy for computers, such as board games like chess.
My research goals include contributing novel, efficient, and practical methods and models for natural language processing.