Our AML research falls under the general category of industrial data science or engineering system informatics.  Two focus areas of engineering applications of our lab are: wind energy and material & manufacturing. Industrial data science research tackles engineering system problems with a full mind of the rapidly growing capability for data collection, the same capability creating the era of big data.  There is a pressing need to develop sophisticated statistical and computational methodologies that enable and assist

1. the handling of the rich data streams communicated by complex engineering systems,
2. the extraction of pertinent knowledge about the environmental and operational dynamics driving these systems, and
3. the exploitation of the acquired knowledge for enhanced design, analysis, and control.

Unlike purely data-driven approaches, our research emphasizes the integration of engineering system modeling and data science methods, thereby enhancing the capability of engineering decision making.  Recently, people also refer to such research approach as physics-informed machine learning (PIML) or scientific machine learning (SciML).

Additional Materials:

1. Aziz Ezzat explained our wind energy research https://youtu.be/rKQCIq2qsm4

2. Articles about data science for wind energy:  2018 , May 2021, August 2021.

3. Articles about data science for manufacturing and materials research: 2020, 2022.

4. A book about data science for wind energy https://aml.engr.tamu.edu/book-dswe/.

5. A book about nano image analysis https://aml.engr.tamu.edu/book-dsnia/