Dr. Bian has a diverse multi-disciplinary background in data integration, semantic web and ontology, machine learning, natural language processing, social media analysis, network science, data privacy, and software engineering. Nevertheless, his expertise and background serve an overarching theme: data science with heterogeneous data, information, and knowledge resources. His research areas can be divided into three logical sections under this overarching theme: (1) data-driven medicine—applications of informatics techniques, including artificial intelligence and machine learning methods in medicine, to solving big and heterogeneous data problems; (2) mining the Internet, including the social web, to provide insights into health-related behavior and health outcomes of various populations and finding ways to develop interventions that promote public and consumer health; and (3) development of novel informatics methods, tools, and systems to support clinical and clinical research activities such as tools for data integration, clinical trial design, and surveillance.

Our current NCI-funded research project leverages a large repository of linked electronic health record (EHR) and administrative claims data to understand contemporary use of lung cancer screening and the associated health care outcomes and costs in real-world settings. The significance is two-fold: (1) understanding the benefits and harms of cancer screening in real-world settings, and (2) the utility of using real-world data such as EHRs and claims to create a longitudinal cohort by virtue of their routine care.