New Faculty Member, 2021-2022
Assistant Professor, Manufacturing engineering
Andi Wang believes that big data generated from engineering systems is “the 90% of the iceberg below the water level.”
Through his research at Arizona State University, Wang looks forward to focusing on the interaction between data science and advanced manufacturing to uncover correlations and patterns that could potentially advance quality and the efficiency of various industries to reduce cost and waste.
“Nowadays, the importance of data collection and inferences based on machine learning and artificial intelligence is widely recognized,” Wang says. “The integration of manufacturing systems with specific engineering areas is an essential component of advanced manufacturing initiatives nationally.”
To make an even bigger impact, he hopes to engage in interdisciplinary research opportunities with his colleagues at The Polytechnic School and the new manufacturing school — two of the seven schools in the Ira A. Fulton Schools of Engineering — who are specializing in advanced manufacturing areas like 3D printing and nanomanufacturing.
“This school has an active and solid engineering research program that I look forward to contributing to,” Wang says. “With my expertise in data-driven modeling and machine learning, we can complement each other’s work and develop original courses and research projects to benefit the field.”
More specifically, Wang develops advanced data-driven models and algorithms for heterogeneous sensor measurements obtained from interconnected systems like in steel rolling and semiconductor manufacturing. These methods are motivated by the system and data structures, integrate data from multiple sources to reveal the data association, detect erroneous measurements and identify events that cause excessive system variations.
Wang has received substantial recognition for his efforts in this field. From published articles to several best paper finalist awards, he hopes to continue on this active research trajectory.
In addition to research, Wang will bring his enthusiasm for advanced manufacturing into the classroom to generate interest around the broad potential for this field.
His expertise in data fusion for complex systems, system informatics, data-driven modeling for the internet-of-things and intelligent systems are areas he hopes to introduce to his students.