My research interests focus on developing efficient and scalable machine learning and representation learning algorithms for large-scale high-dimensional data with complex heterogeneous data structure to extract information or useful features for the purpose of data fusion for assessment of system performance, early detection of system anomalies, intelligent sampling and sensing for data collection and decision making to achieve optimal system performance. My research lies at the intersection of statistics, machine learning and industrial engineering can be categorized into the following areas:

More specifically, my main research includes

1. Real time modeling and analysis with large scale high dimensional data: Develop scalable and computational efficient algorithms for real time modeling and analysis of high dimensional data with complex structure (tensor structure, complex spatio-temporal structure, etc.)
2. Data fusion for modeling of complex systems: Develop data analysis and data fusion techniques to combine information from multiple sensors for process modeling, anomaly detection and quality improvement for complex systems.
3. Smart adaptive sampling strategy and data reconstruction: Develop smart and adaptive sampling for different systems to reduce the data collection time. Develop quality measurement and data reconstruction techniques using compressive sensing.

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