Research Interests:
- Smart manufacturing
- Big data
- Data analytics
- Cancer informatics
- Modeling and control
Keywords:
- Machine Learning, Advanced Data Analytics, Monitoring, Model Predictive Control
Research interest: Interface of systems engineering and data analytics
Application Areas: Manufacturing; Biomedical (Speech disorder, Breast Cancer, Prostate Cancer); Healthcare (hospital Operations); Energy (Cellulosic Biofuels, Biogas Conversion); Agriculture (Smart Irrigation, Data-Driven Plant Breeding); Waste to Food, Energy and Water (W2FEW).
Central theme: Integrating systems engineering principles and approaches to data analytics – to address emerging challenges and to dramatically improve performance and effectiveness.
The central theme of the research program is the theoretical and systematic study of the integration of human learning (e.g., characterization of system unique features, exploitation of system fundamental differences, simplification of the governing chemical/physical principles, etc.) with computationally efficient machine learning and deep learning algorithms for developing hybrid and synergistic human and artificial intelligence based decision-making solutions for various applications.
Research:
- Smart Manufacturing
- Next-Generation Process Monitoring Techniques for Smart Manufacturing
- Additive Manufacturing
- In-Situ Metrology and Machine Learning Modeling for Additive Manufacturin
- DEEP
- DEEP: Data-Enabled Engineering Projects (DEEPs) for Data Science and Engineering Education
- Biomedical
- Disease (particularly cancer) Detection
- Cancer Informatics
- Agriculture
- Data-Enabled Climate-Smart Analytics for Irrigation Management
- Healthcare
- Sustainable Energy
