Our research is on developing data-driven optimization and control theory and computational algorithms for uncertain decision-making systems in the age of Artificial Intelligence. The DOCU lab has been awarded several Best Paper Awards below.
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2020 O. Hugo Schuck Best Paper Award
Best Paper Award of 2022 International Conference on Industrial Articial Intelligence
Best Paper Award of 2022 IEEE International Electrical and Energy Conference
2019 AIChE Sustainable Engineering Forum Student Paper Award
Research Theme 1: Data-Driven Optimization Under Uncertainty
In the data-driven optimization paradigm, uncertainty model is formulated based on data, thus allowing uncertainty data “speak” for themselves in the optimization algorithm. In this way, rich information underlying uncertainty data can be harnessed in an automatic manner for smart and data-driven decision making.
Research Theme 2: Learning-Based Predictive Control of Uncertain Systems
With the ever-increasing availability of data in control systems, there is a growing trend to leverage the information embedded within data via machine learning to improve the performance of MPC.
Research Theme 3: Sustainable Electricity-Hydrogen-Chemical Processes (Application)
The hydrogen-based multimicrogrid has emerged as a game changer for sustainable energy transition. With the aid of hydrogen technologies, including electrolyzer and hydrogen storage facilities, networked microgrids within a certain area are transformed into a hydrogen-based multimicrogrid. It undoubtedly expedites the popularization and penetration of green hydrogen.
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