I am an assistant professor in Department of Industrial and Manufacturing Systems Engineering (IMSE) at the University of Michigan-Dearborn. Prior to joining UM-Dearborn, I was a research assistant professor and postdoctoral research scholar at Vanderbilt University. My research areas of interest are uncertainty quantification, Bayesian data analytics, big data analytics, machine learning, optimization under uncertainty, and applications of data analytics and machine learning in aerospace, mechanical and manufacturing systems, and material science. The goal of my research is to develop novel computational methods to design sustainable and reliable engineering systems by leveraging the rich information contained in the high-fidelity computational simulation models, experimental data, and big operational data and historical data.
Dr. Jin Lu is an Assistant Professor of Computer and Information Science at the University of Michigan, Dearborn.
His major research interests include machine learning, data mining, optimization, matrix analysis, biomedical informatics, and health informatics. Two main directions are being pursued:
(1) Large-scale machine learning problems with data heterogeneity. Data heterogeneity is common across many high-impact application domains, ranging from recommendation system to Computer Vision, Bioinformatics and Health-informatics. Such heterogeneity can be present in a variety of forms, including (a) sample heterogeneity, where multiple resources of data samples are available as side information; (b) task heterogeneity, where multiple related learning tasks can be jointly learned to improve the overall performance; (c) view heterogeneity, where complementary information is available from various sources. My research interests focus on building efficient machine learning methods from such data heterogeneity, aiming to improve the learning model by making the best use of all data resources.
(2) Machine learning methods with provable guarantees. Machine learning has been substantially developed and has demonstrated great success in various domains. Despite its practical success, many of the applications involve solving NP-hard problems based on heuristics. It is challenging to analyze whether a heuristic scheme has any theoretical guarantee. My research interest is to employ granular data structure, e.g. sample clusters or features describing an aspect of the sample, to design new theoretically-sound models and algorithms for machine learning problems.
My research interests are in the areas of machine learning, statistical network analysis, analysis of high-dimensional data, and their applications in health sciences, biology, finance and marketing.