报告题目：Optimization for Big Data Analytics and its Applications inHealthcare Improvement（大数据分析与医疗优化）
Vast amounts of healthcaredata are generated and collected daily. To improve the patient outcomesand health care service efficiency, optimization can play a big role fordecision-making from diseases prevention, diagnosis and treatment, to hospitalmanagement. In this talk, two data analytics approaches will be introduced:data-driven optimization models and algorithms for learning from uncertain ornoisy data (e.g., images in electronic health records), and automated knowledgediscovery based on phase-type distributions from complex data with covariates.As case studies, novel classification methods have been applied to evaluate thepancreatic carcinoma through the use of quantitative histopathologicalsignatures of nuclear images, and phase-type distribution is utilized to studyreadmission rates and discharge planning by analyzing the length-of-stay ofpatients. Other potential applications of proposed methodologies will be alsodiscussed.
Dr. NengFan is an assistant professor at Systems and Industrial Engineering Department,University of Arizona (UA). He obtained his bachelor degree in mathematics fromWuhan University, in 2004, and master degree in applied mathematics from NankaiUniversity in 2007. In 2009 and 2011, he obtained his master and PhD degreesfrom Industrial and Systems Engineering Department at University of Florida,respectively. Before joining UA, he worked in Los Alamos and Sandia NationalLaboratories from 2010 to 2012.
Hisresearch interests include optimization methodology, applied operationsresearch, data mining and machine learning. He has published more than 40papers in the journals such as Production and Operations Management, Journal ofApplied Clinical Medical Physics, IEEE Transaction on Power Systems, Journal ofGlobal Optimization, Annals of Operations Research, Computational ManagementScience, etc.