Dr. Yuxin Wen
Assistant Professor
Fowler School of Engineering; Electrical Engineering and Computer Science
Office Location:
Keck Center for Science and Engineering Swenson Hall N325
Email: yuwen@chapman.edu
- Education:
- Zhejiang University, Master of Science
The University of Texas At El Paso, Ph.D.
Biography
2016 - 2020
Ph.D. in Electrical and Computer Engineering, The University of Texas at El Paso, (Mentors: Prof. Bill Tseng and Prof. Jianguo Wu)
2011 - 2014
M.S. in Biomedical Engineering, Zhejiang University
2007 - 2011
B.S. in Medical Information Engineering, Sichuan University
RESEARCH/TEACHING INTERESTS
Dr. Wen’s research interests focus on big data analysis, machine learning and statistical modeling for quality improvement, prognostics in complex systems with the applications in manufacturing, aerospace, healthcare, and traffic, etc. Specific projects under investigation include data driven system fault detection, diagnosis and prognostics, severity assessment and prediction of COVID-19 infection using Artificial Intelligence.
Recent Creative, Scholarly Work and Publications
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Zhuang, Y., Rahman, M. F., Wen, Y., Pokojovy, M., McCaffrey, P., Vo, A., ... & Tseng, T. L. B. (2022). An interpretable multi-task system for clinically applicable COVID-19 diagnosis using CXR. Journal of X-Ray Science and Technology, (Preprint), 1-16.
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Rahman, M. F., Tseng, T. L. B., Wu, J., Wen, Y., & Lin, Y. (2022). A deep learning-based approach to extraction of filler morphology in SEM images with the application of automated quality inspection. AI EDAM, 36.
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Wen, Y., Rahman, M. F., Xu, H., & Tseng, T. L. B. (2022). Recent advances and trends of predictive maintenance from data-driven machine prognostics perspective. Measurement, 187, 110276.
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Wen, Y., Guo, X., Son, J., & Wu, J. (2022). A neural-network-based proportional hazard model for IoT signal fusion and failure prediction. IISE Transactions, 1-15.
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Solaiyappan, S., & Wen, Y. (2022). Machine learning based medical image deepfake detection: A comparative study. Machine Learning with Applications, 8, 100298.
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Soangra, R., Wen, Y., Yang, H., & Grant-Beuttler, M. (2022). Classifying Toe Walking Gait Patterns Among Children Diagnosed With Idiopathic Toe Walking Using Wearable Sensors and Machine Learning Algorithms. IEEE Access, 10, 77054-77067.
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Wen, Y., Rahman, M. F., Zhuang, Y., Pokojovy, M., Xu, H., McCaffrey, P., ... & Tseng, T. L. B. (2022). Time-to-event modeling for hospital length of stay prediction for COVID-19 patients. Machine Learning with Applications, 9, 100365.
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Chen, J., Deng, X., Wen, Y., Chen, W., Zeb, A., & Zhang, D. (2022). Weakly-supervised learning method for the recognition of potato leaf diseases. Artificial Intelligence Review, 1-18.
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Gao, Y., Wen, Y., & Wu, J. (2020). A neural network-based joint prognostic model for data fusion and remaining useful life prediction. IEEE transactions on neural networks and learning systems, 32(1), 117-127.
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Rahman, M. F., Wen, Y., Xu, H., Tseng, T. L. B., & Akundi, S. (2020). Data mining in telemedicine. Advances in Telemedicine for Health Monitoring: Technologies, Design and Applications, 103-131.
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Wen, Y., AlHakeem, D., Mandal, P., Chakraborty, S., Wu, Y. K., Senjyu, T., ... & Tseng, T. L. (2019). Performance evaluation of probabilistic methods based on bootstrap and quantile regression to quantify PV power point forecast uncertainty. IEEE transactions on neural networks and learning systems, 31(4), 1134-1144.
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Wen, Y., Wu, J., Das, D., & Tseng, T. L. B. (2018). Degradation modeling and RUL prediction using Wiener process subject to multiple change points and unit heterogeneity. Reliability Engineering & System Safety, 176, 113-124.
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Wen, Y., Wu, J., Zhou, Q., & Tseng, T. L. (2018). Multiple-change-point modeling and exact Bayesian inference of degradation signal for prognostic improvement. IEEE Transactions on Automation Science and Engineering, 16(2), 613-628.
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Y. Wen, J. Wu and Y. Yuan, "Multiple-Phase Modeling of Degradation Signal for Condition Monitoring and Remaining Useful Life Prediction," IEEE Transactions on Reliability, vol. 66, no. 3, pp. 924-938, Sept. 2017.