ISE welcomes new faculty member Pingfeng Wang

Madeleine Hubbard

Professor Pingfeng Wang
Professor Pingfeng Wang
From a young age, Professor Pingfeng Wang knew he wanted to teach, but not at the university level. He says, “When I was in middle school, I really admired teachers.” He envisioned himself sitting in an office, surrounded by piles of papers and fellow teachers.

When Wang was picking a major for college, he had abandoned the idea of being a teacher. He says, “I was very much interested in airplanes, and I thought that was what I wanted to do.” One of Wang’s teachers at the University of Science and Technology Beijing told him, “aerospace engineering is focused on aircraft, but if you do mechanical engineering, then you would not only be able to do aircraft, but also do cars and such.” From there, Wang decided to enter mechanical engineering.

After finishing his bachelor’s, Wang says, “I worked three years in industry, and then I felt like academe is where I wanted to be.”

Wang worked in industry for Dell as a quality engineer. It was his time at Dell that made him appreciate systems engineering and design. He felt he would benefit from more "systems thinking, instead of just knowing the structures.” This led him to earn his master’s in applied mathematics in 2006 at Tsinghua University in Beijing. 

For his PhD in mechanical engineering, Wang went to the University of Maryland. It was there Wang first got involved in research. Describing his time at Maryland, Wang says he was “was under very high pressure but still very productive. I really appreciated the guidance that my advisor provided.”

After earning his PhD in 2010, Wang taught at the Department of Industrial and Manufacturing Engineering at Wichita State University. Comparing his time in Illinois to Wichita State, Wang says, “Illinois has one of the best industrial engineering departments in the world… I think it’s very unique.” He says he’s “really looking forward to working together with the faculty and students here.”

Professor Wang is working with two ongoing research grants. Currently, he says his main research interests have “been focusing on systems design and trying to address system failures.” His research starts in the design stage of engineering systems, aiming to “understand their failure modes and effects, and then develop methodologies and design tools to prevent those failure modes. We also develop techniques to help monitor the system for potential failures at the operational stage.”

He says, “It’s a very exciting field. I’ve been working in the field for the past seven years and I am continuing to extend this to more applications such as energy systems, transportation systems, and more complex systems.”

His research has earned Wang several awards, including the National Science Foundation NSF CAREER Award in 2014. This award allowed him to continue his research on “Designing Engineered Systems for Resilience and Sustainability Considering Post-Design Retrofits.” Other awards include the ASME 2016 Design Automation Young Investigator Award, several best paper awards from ASME and IEEE, and five research and teaching awards from Wichita State University for his work there. 

Wang says the most difficult part of his research is “withstanding failures because when you’re working on something new… you’re going to try and fail and try and fail.”

As a teacher, Wang likes seeing the students “get so excited doing something that they’ve never done before.” He also enjoys seeing the students learn from his classes and go on to have prosperous careers. Wang says it can be “a very rewarding process to see students succeed.”

Fall of 2017, Wang is teaching SE 450, or Decision Analysis 1. His wife, Professor Yumeng Li joins ISE this fall as well. She is teaching SE 498, Numerical Methods in Engineering.

Selected Publications: 

Yodo, N.*, Wang, P., and Zhou, Z., "Predictive Resilience Analysis of Complex Systems Using Dynamic Bayesian Networks," IEEE Trans. On Reliability, DOI: 10.1109/TR.2017.2722471, 2017. 

Bai, G.*, and Wang, P., "Prognostics Using An Adaptive Self-Cognizant Dynamic System Approach," IEEE Trans. on Reliability, 65(3), 1427-1437, 2016.

Yodo, N.*, and Wang, P., "Engineering Resilience Quantification and System Design Implications: A Literature Survey," Journal of Mechanical Design, 138(11), 111408, 2016.

Bai, G *, and Wang, P., "An Internal State Variable Mapping Approach for Li-Plating Diagnosis," Power Sources, Vol. 323, pp.115-124, 2016.

Wang, Z. *, and Wang, P., "Accelerated Failure Identification Sampling for Probability Analysis of Rare Failure Events," Struct. Multidiscipl. Optim. 54(1), 137-149, 2016.

Yodo, N. *, and Wang, P., "Resilience Modeling and Quantification for Engineering Systems Using Bayesian Networks," Journal of Mechanical Design, 138(3), 031404, 2016. 

Almaktoom, A.T. *, Krishnan, K., and Wang, P., "Cost Efficient Robust Global Supply Chain System Design under Uncertainty," International Journal of Advanced Manufacturing Technology, 85(1), pp. 853-868, 2016.

Wang, P., Wang, Z. *, Youn, B., and Lee, S., "Reliability-based Robust Design of Smart Sensing Systems for Failure Diagnostics Using Piezoelectric Materials," Computers & Structures, vol.156, pp.110-121, 2015.

Bai, G. *, Wang, P., Hu, C., and Pecht, M., "A Generic Model-Free Approach for Lithium-ion Battery Health Management," Applied Energy, vol. 135, pp. 247-260, 2014.

Tamilselvan, P. *, and Wang, P., “Failure Diagnosis Using Deep Belief Learning Based Health State Classification,” Reliability Engineering and System Safety, Vol. 115, pp.124-135, 2013.

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