【Title】Optimal Expert Knowledge Elicitation for Bayesian Network Structure Identification

【Presenter】Shuai Huang, Ph.D., Assistant Professor, Department of Industrial and Systems Engineering, University of Washington

【Host】 Dr. Kaibo Wang

【Date】 May 31, 2016, 10:30-11:30 (Tuesday)

【Venue】Room 510, Shunde Building

【Language】English

【Abstract】Bayesian network (BN) has been a popular tool for gaining mechanistic understanding of variables by revealing how the variables influence each other. It has been found very effective in a few studies in quality control and process monitoring. However, for complex problems where the structure of a BN is unknown, a common approach is to learn the BN structure from observational data. A fundamental bottleneck of this approach is that observational data can only be used to discover part of the influential relationships among variables. To overcome this problem, we propose to combine observational data and expert knowledge. To the best of our knowledge, our approach is the first of its kind that can automate the expert elicitation process and collect the most informative expert knowledge, optimally matched to the observational data, to learn the BN structure.

【Bio】Shuai is a Statistician and also a System Engineer. He enjoys working with healthcare professionals to formulate complex healthcare problems and pursue data-driven solutions for effective management of these problems. With theoretical training in his undergraduate study for Mathematics & Statistics from the School of Gifted Young at the University of Science and Technology of China and Ph.D. training in the Industrial Engineering program at the Arizona State University, his academic training prepares him well for developing holistic methodologies for real-world problems by seamless combination of theory, computation, and practice. He develops methodologies for modeling, monitoring, diagnosis, and prognosis of complex networked systems where the stochasticity of the system entities are interdependent, such as the brain connectivity networks, social networks, manufacturing processes, and disease progression process of Type 1 diabetes and other progressive diseases that have multiple stages and pathways. He also develops novel statistical and data mining models to integrate the massive and heterogeneous datasets such as neuroimaging, genomics, proteomics, laboratory tests, demographics, and clinical variables, for facilitating scientific discoveries in biomedical research and better decision-makings in clinical practices. More information can be found in https://sites.google.com/site/shuaihuang28/. His research has been funded by the National Science Foundation, Juvenile Diabetes Research Foundation, and several biomedical research institutes.

All interested are welcome!