Health Informatics Faculty Candidate Seminar

March 16, 2017 - 3:30 PM to 4:30 PM
WH 335
Lecture title: Applied Data Analytics in Neuroscience and Natural Language
Speaker: Dr. Forrest Bao

Dr. Forrest Bao is an assistant professor with Department of Electrical and Computer Engineering at University of Akron, where he joined in fall 2013. His research interests are on artificial intelligence (AI) and biomedical data analytics (computational biology and computational neuroscience). In AI, he works on combining natural language processing (NLP) and knowledge representation (KR). His current research is funded by National Science Foundation (NSF, PI), Federal Aviation Administration (FAA, PI) and Air Force Research Lab (AFRL, co-I). He received his PhD degree in Computer Science with a minor in Electrical Engineering from Texas Tech University in August 2012. From September 2012 to August 2013, he was a postdoc at Stony Brook University (SUNY Stony Brook) and Texas Tech University. His research has been covered by MIT Technology Review and Lancet Neurology.

Dr. Forrest Bao will present two projects that employ data analytics on neuroscience and Natural Language, respectively. On one hand, certain neuropsychiatric disorders, such as Alzheimer's disease or depression, are associated with morphological changes on human brain cortex. A key to effective diagnosis and treatment is to detect such changes that are too minor to be seen by doctors from MRI images directly. Dr. Bao's research in MRI image processing focuses on extracting anatomical landmarks on cortical surface.On the other hand, while online shopping is rapidly changing retailing and our life, it has generated a massive amount of data: the product reviews. However, there are often too many reviews and too much noise in the reviews. We need artificial intelligence (AI), especially natural language processing (NLP) and machine learning (ML), to help consumers find out helpful reviews for better online shopping experience. In this talk, Dr. Bao will discuss about his work on using semantic analysis and hierarchical topic modeling to measure the helpfulness of reviews.

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