Saeed Hassanpour, PhD

Associate Professor, Departments of Biomedical Data Science and Epidemiology, Geisel School of Medicine at Dartmouth College

Dr. Saeed Hassanpour is an Associate Professor in the Department of Biomedical Data Science at Geisel School of Medicine at Dartmouth, with adjunct appointments in the Computer Science and Epidemiology Departments. His research is mainly focused on developing computational methods to capture and organize unstructured information content in a structured format. He aims to capture and use the structured information from biomedical and clinical text and images in providing intelligent tools to help translational researchers better understand their data and develop and evaluate new hypotheses, as well as assist clinicians in medical diagnosis and practice. Before joining Dartmouth, Dr. Hassanpour completed his postdoctoral training in the Department of Radiology at Stanford University School of Medicine, where he developed novel computational methods to extract clinically significant information from radiology reports. His research resulted in radiology report-based information extraction and risk prediction methods, which are published in major radiology and biomedical informatics journals. Before Dr. Hassanpour’s postdoctoral position at Stanford, he worked as a Research Engineer in the Microsoft Search Query Understanding Group for more than two years. His research at Microsoft was focused on high-throughput semantic text analysis to extract user intents from Web search queries. Dr. Hassanpour’s work provided a framework for semantic and contextual query annotation. This research resulted in multiple patents and has been incorporated into the Bing search engine and is used on a large scale on the Web. He received his Ph.D. in Electrical Engineering with a minor in Biomedical Informatics from Stanford University. Dr. Hassanpour’s doctoral research aimed at formal knowledge extraction from full-text publications in the domain of autism phenotyping. This work received multiple awards, including the Best Paper and Best System Demonstration awards at leading computer science conferences.