Front of Olmsted Hall

Dr. Yishu Bai

Assistant Professor of Electrical Engineering

Room 262, Koch Center for Engineering and Science
812-488-2994
yb32@evansville.edu

Dr. Yishu Bai joined the University of Evansville in 2025. She earned her Ph.D. in Electrical and Computer Engineering from the University of Connecticut in 2024. Her work focuses on smart manufacturing, operations research, and energy eƯiciency, with the goal of developing intelligent systems that improve industrial productivity while reducing environmental impact. She enjoys bridging theory and practice—whether that means building digital twins of factory floors, designing real-time control algorithms, or applying machine learning to energy systems.

At UE, Dr. Bai is building an independent research program that blends industrial operations with sustainable energy systems, and she brings that same focus into her teaching. She is passionate about helping students connect classroom concepts to realworld applications, fostering both technical skills and independent thinking.

Education

  • Doctor of Philosophy, University of Connecticut, 2024
  • MS, University of Illinois at Chicago, 2018
  • BS, Northeastern University at Qinhuangdao (NEU), China, 2017

Research Interests

  • Production systems modeling, analyses, and optimization: performance metrics prediction and real-time optimization
  • Data-driven analytics and decision-making: stochastic processes, machine learning, and diagnostics
  • Energy-efficiency technologies and clean energy solutions: building and electrical systems, industrial processes, energy efficiency principles, data analysis and measurement, sustainability practices, and renewable energy

Selected Publications

Journal Papers:

  • Bai, J. Tu, M. Yang, L. Zhang, and P. Denno (2021), “A New Aggregation Algorithm for Performance Metric Calculation in Serial Production Lines with Exponential Machines: Design, Accuracy and Robustness”, in International Journal of Production Research, 59:13, 4072-4089, DOI: 10.1080/00207543.2020.1757777.
  • Bai and L. Zhang (2023), “Recursive Decomposition/Aggregation Algorithms for Performance Metrics Calculation in Multi-level Assembly/Disassembly Production Systems with Exponential Reliability Machines”, in International Journal of Production Research, 61:23, 8133-8158, DOI:10.1080/00207543.2023.2166622.
  • Tu, Y. Bai*, M. Yang, L. Zhang and P. Denno, “Real-Time Bottleneck in Serial Production Lines With Bernoulli Machines: Theory and Case Study,” in IEEE Transactions on Automation Science and Engineering, vol. 18, no. 4, pp. 1822-1834, Oct. 2021, DOI: 10.1109/TASE.2020.3021346.

Conference Papers:

  • Bai, T. Zhu and L. Zhang, ”Performance Evaluation of Two-Machine Bernoulli Lines with Conveyor Buffers,” 2024 IEEE 20th International Conference on Automation Science and Engineering (CASE), Bari, Italy, 2024, pp. 3466-3471, doi: 10.1109/CASE59546.2024.10711513.
  • Bai and L. Zhang, “Performance Metrics Calculation for Assembly Systems with Exponential Reliability Machines,” 2021 IEEE International Conference on Robotics and Automation (ICRA), Xi’an, China, 2021, pp. 896-901, DOI: 10.1109/ICRA48506.2021.9561947.
  • Bai, J. Tu, M. Yang, L. Zhang and P. Denno, “An Accurate and Robust Algorithm for Performance Evaluation of Exponential Serial Lines with Finite Buffers,” 2019 IEEE 15th International Conference on Automation Science and  Engineering (CASE), Vancouver, BC, Canada, 2019, pp. 42-47, DOI: 10.1109/COASE.2019.8842906. Best Student Paper Finalist
  • Tu, Y. Bai, M. Yang, L. Zhang and P. Denno, “Dynamic Bottleneck in Serial Production Lines with Bernoulli Machines,” 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE), Vancouver, BC, Canada, 2019, pp. 79-84, DOI: 10.1109/COASE.2019.8842924.
  • Tu, T. Zhu, Y. Bai and L. Zhang, “Estimation of Machine Parameters in Exponential Serial Lines using Feedforward Neural Networks,” 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE), Hong Kong, China, 2020, pp. 816-821, DOI: 10.1109/CASE48305.2020.9217000.