Interdisciplinary Research Themes
Big Data Analytics and Data-Driven Robust Optimization
Quantum Computing for Optimization and Deep Learning
Sustainability Analysis of Nanotechnology and Advanced Materials
Water-Energy Nexus
Integrated Materials and Process Design
Chemical Manufacturing from Shale Gas
Pharmaceutical Manufacturing: Process Modeling and Control
Multi-Scale battery systems engineering and energy storage
Sustainable Design and Synthesis of Energy Systems
Agent-Based Production Scheduling
Decision-making under Multi-scale Uncertainties
Integrated Scheduling and Control
Energy Supply Chain Design and Management
Network Analysis and Optimization of Product Systems
Multi-Scale Life Cycle Optimization from Process to Macroeconomics
Systems Analysis and Design for Resilience
Quantum Computing, Deep Learning, and Automatic Control
We are an interdisciplinary Systems Engineering and Data Science research group. Our research focuses on advanced computational models, optimization algorithms, statistical machine learning methods, and multi-scale systems and data analytics tools for materials informatics, smart manufacturing, digital agriculture, quantum computing, energy systems, and sustainability. The research work at Process-Energy-Environmental Systems Engineering (PEESE) lab provides a balance between theory, computation and real world applications.
