Shenghan Guo is an Assistant Professor in The School of Manufacturing Systems and Networks at Arizona State University. Her research interests include statistical process monitoring, data mining, interpretable machine learning, and their applications in smart manufacturing. She is expertised with real data from manufacturing applications that possess complex properties, e.g., in-situ thermal video, multi-sensory data streams. Her recent research projects focus on the development of data-driven solutions to quality improvement in laser-based additive manufacturing and resistance spot welding.
Ph.D. in Industrial and Systems Engineering, Rutgers, The State University of New Jersey, New Brunswich, NJ, U.S. (2021)
M.S. in Engineering Sciences and Applied Mathematics, Northwestern Unviersity, Evanston, IL, U.S. (2016)
M.S. in Financial Mathematics, The Johns Hopkins University, Baltimore, MD, U.S. (2014)
B.S. in Financial Engineering, Jilin University, Changchun, China (2013)
- Statistical quality control and process monitoring
- Data-driven decision-making and predictive analytics
- Interpretable machine learning and artificial intelligence
- Application of big data analytics in smart manufacturing and industrial informatics
- Shenghan Guo, Dali Wang, Zhili Feng, Jian Chen, and Weihong Guo, “Predicting Nugget Size of Resistance Spot Welds Using Infrared Thermal Videos with Image Segmentation and Convolutional Neural Network,” AMSE Journal of Manufacturing Science and Engineering. 144(2): 021009, DOI: 10.1115/1.4051829.
- Shenghan Guo, Dali Wang, Zhili Feng, and Weihong Guo (2021) “UIR-Net: Object Detection in Infrared Imaging of Thermomechanical Processes in Automotive Manufacturing,” IEEE Transactions on Automation Science and Engineering. doi: 10.1109/TASE.2021.3116040.
- Shenghan Guo, Weihong Guo, Amir Abolhassani, and Rajeev Kalamdani, (2021) “Nonparametric, Real-Time Detection of Process Deteriorations in Manufacturing with Parsimonious Smoothing,” IISE Transactions, 53(5), pp. 568-581, DOI: 10.1080/24725854.2020.1786195.
- Qi Tian, Shenghan Guo, Erika Melder, Linkan Bian, and Weihong “Grace” Guo (2021) “Deep Learning-based Data Fusion Method for In-Situ Porosity Detection in Laser-based Additive Manufacturing,” AMSE Journal of Manufacturing Science and Engineering. 143(4): 041011, DOI: 10.1115/1.4048957.
- Shenghan Guo, Mengfei Chen, Amir Abolhassani, Rajeev Kalamdani, and Weihong Guo (2021) “Identifying Manufacturing Operational Conditions by Unsupervised Feature Extraction and Ensemble Clustering,” Journal of Manufacturing Systems. 60, pp. 162-175, ISSN 0278-6125.
- • Shenghan Guo, Weihong “Grace” Guo and Linkan Bian (2020) “Hierarchical Spatial-Temporal Modeling and Monitoring of Melt Pool Evolution in Laser-Based Additive Manufacturing,” IISE Transactions, 52(9), pp. 1-21, ISSN 2472-5854.
- Shenghan Guo and Weihong “Grace” Guo, (2020) “Process Monitoring and Fault Prediction in Multivariate Time Series Using Bag-of-Words,” IEEE Transactions on Automation Science and Engineering, DOI: 10.1109/TASE.2020.3026065.
- Weihong “Grace” Guo, Qi Tian, Shenghan Guo, Yuebin Guo (2020) “A physics-driven deep learning model for process-porosity causal relationship and porosity prediction with interpretability in laser metal deposition,” CIRP Annals, (69)1, pp. 205-208, ISSN 0007-8506.
- Shenghan Guo., Weihong Guo, Abolhassani, A., Kalamdani, R., Puchala, S., Januszczak, A., and Jalluri, C. (2019). “Manufacturing Process Monitoring with Nonparametric Change-Point Detection in Automotive Industry,” ASME Journal of Manufacturing Science and Engineering, 141(7): 071013.
- Weihong Guo, Shenghan Guo, H. Wang, X. Yu, A. Januszczak, and S. Suriano, (2017) “A Data-Driven Diagnostic System Utilizing Manufacturing Data Mining and Analytics,” SAE International Journal of Materials and Manufacturing, 10 (3), pp. 282-292, DOI: 10.4271/2017-01-0233.
- Jim Kyung-Soo Liew, Shenghan Guo and Tongli Zhang, (2017) “Tweet Sentiments and Crowd-Sourced Earnings Estimates as Valuable Sources of Information Around Earnings Releases,” The Journal of Alternative Investments, 19(3), pp. 7-26, DOI: 10.3905/jai.2017.19.3.007.
- Shenghan Guo and James C. Spall, “Chapter 10. Relative Accuracy of Two Methods for Approximating Observed Fisher Information,” Data-Driven Modeling, Filtering and Control: Methods and Applications, (Control, Robotics & Sensors, 2019) DOI: IET Digital Library, https://digital-library.theiet.org/content/books/ce/pbce123e.
• Runner-up, Best Paper Competition - Applied Track, 16th INFORMS Hybrid Workshop on Data Mining and Decision Analytics, Oct. 23, 2021, virtual/in-person, Anaheim, CA. • NSF Student Support Award, 49th NAMRI/SME North American Manufacturing Research Conference (NAMRC 49) and the 2021 ASME International Manufacturing Science and Engineering Conference (MSEC 2021), June 21-25, virtual conference. • 2nd prize, Data Analytics and Information Sciences (DAIS) 1st Student Data Analytics Competition, the 2020 Institute of Industrial & Systems Engineers (IISE 2020), Nov. 1-3, virtual conference. • Winner, Quality Control and Reliability Engineering (QCRE) Data Challenge, the 2019 Institute of Industrial & Systems Engineers (IISE 2019), May 18-21, Orlando, FL • Finalist, Best Paper Competition, Quality, Statistics, and Reliability (QSR) Paper Competition, the 2018 Institute for Operations Research and the Management Sciences (INFORMS 2018), Nov. 4-7, Phoenix, AZ