Boang Liu is a PhD candidate in the Department of Statistics at the University of Michigan. She has been working with Professor Ji Zhu and Professor Akbar Waljee on using statistical machine learning methods to solve health care problems in areas of inflammatory bowel disease and chronic hepatitis C, with the goal of assisting clinical decision making with predictive modeling.
What drew you to MiCHAMP? MiCHAMP has provided a unique collaborative and interactive environment where I can learn research projects done by scientists from various fields across the campus. In particular, it is a great opportunity to learn more about the healthcare field and get to see different perspectives offered by researchers with diverse scientific backgrounds.
What specific expertise do you bring to MiCHAMP? I am currently a Statistics PhD candidate. Being part of an interdisciplinary research team with physicians and researchers of diverse backgrounds, I use statistical machine learning methods to find underlying patterns in clinical data to provide automatic and accurate predictions for clinical outcomes, which can potentially assist physicians and health care institutions in therapeutic decision making.
What are your research interests and how do they tie into MiCHAMP? I am enthusiastic about using statistical and machine learning methods to solve health care problems, specifically in identification of disease status and prediction of future progression. These are interesting and important problems from both clinical and statistical points of view, and the goals are very well aligned with the health analytics and medical prediction perspectives of MiCHAMP.
Xuefei Zhang Biography
I am a Ph.D. candidate in the Statistics Department at the University of Michigan. My research focuses on statistical network analysis, statistical machine learning and its applications in healthcare and medicine. Before coming to Michigan, I obtained my bachelor’s degree in Statistics from Nanjing University, China.
What drew you to MiCHAMP? Professor Ji Zhu and Professor Akbar Waljee invited me to attend the MiCHAMP Friday seminar series about one year ago. I always appreciate the opportunity of communicating with people from diverse scientific backgrounds and I am excited to learn about research in interdisciplinary areas. As a student member of MiCHAMP, I have benefited from learning various healthcare related research topics. At the same time, collaborations with several members of MiCHAMP help me achieve a deeper understanding about how statistical methods could be applied to solve real-world medical and health problems.
What specific expertise do you bring to MiCHAMP? Data with complex structures frequently appear in healthcare studies. As a statistician, I am interested in developing statistical methodologies and applying appropriate statistical methods to process, model, and analyze such data. Currently I am focused on developing accurate statistical and machine learning predictive models from massive longitudinal data to predict Hepatitis C disease progression.
What are your research interests and how do they tie into MiCHAMP? My research interests lie in the areas of statistical network analysis, statistical machine learning and applications in healthcare and medicine. Those research topics would allow me to contribute to MiCHAMP from two aspects: first, it is crucial to build appropriate statistical models to make inference and predictions from the complex data; secondly, modern medical data are usually of large scale and require nontrivial computing resources. My research experience would allow me to develop appropriate and feasible statistical methods that can be applied to MiCHAMP projects.