Directory at the HPC²
Biography
Wenmeng ‘Meg’ Tian received her Ph.D. degree in Industrial and Systems Engineering from Virginia Tech in 2017. She also holds an M.S. degree in Statistics from Virginia Tech, an M.S. and a B.S. degree in Industrial Engineering from Tianjin University, China.
Meg’s research efforts are focused on system informatics in data-rich environments. She is particularly interested in advanced sensing and analytics for system modeling, monitoring, and diagnosis. Applications of her research include manufacturing and healthcare systems.
Her publications have appeared in journals such as IISE Transactions, Journal of Manufacturing Systems, International Journal for Quality in Health Care, and several conference proceedings. She is an active member of Institute of Industrial and Systems Engineers (IISE) and Institute of Operations Research and the Management Sciences (INFORMS). Her teaching interests include engineering statistics, statistical quality control, production control systems, and systems simulation.
Meg’s research efforts are focused on system informatics in data-rich environments. She is particularly interested in advanced sensing and analytics for system modeling, monitoring, and diagnosis. Applications of her research include manufacturing and healthcare systems.
Her publications have appeared in journals such as IISE Transactions, Journal of Manufacturing Systems, International Journal for Quality in Health Care, and several conference proceedings. She is an active member of Institute of Industrial and Systems Engineers (IISE) and Institute of Operations Research and the Management Sciences (INFORMS). Her teaching interests include engineering statistics, statistical quality control, production control systems, and systems simulation.
Research Interest
Sensing and Analytics for Manufacturing Systems
Heterogeneous Data Fusion for Process Improvement
Statistical Quality Control
Variation Management for Complex Systems
Heterogeneous Data Fusion for Process Improvement
Statistical Quality Control
Variation Management for Complex Systems
Selected Publications
Total Publications: 8
Senanayaka, A., Tian, W., Falls, T. C., & Bian, L. (2023). Understanding the Effects of Process Conditions on Thermal-Defect Relationship: A Transfer Machine Learning Approach. Journal of Manufacturing Science and Engineering. American Society of Mechanical Engineers. 145(7), 071010. DOI:https://doi.org/10.1115/1.4057052. [Document Site]
Mamun, A. A., Bappy, M. M., Senanayaka, A., Li, J., Jiang, Z., Tian, Z., Fuller, S., Falls, T. C., Bian, L., & Tian, W. (2023). Multi-channel Sensor Fusion for Real-time Bearing Fault Diagnosis by Frequency-domain Multilinear Principal Component Analysis. The International Journal of Advanced Manufacturing Technology. Springer London. 124(3), 1321-1334. DOI:https://doi.org/10.1007/s00170-022-10525-4.
Senanayaka, A., Mamun, A. A., Bond, G., Tian, W., Wang, H., Fuller, S., Falls, T. C., Rahimi, S., & Bian, L. (2022). Similarity-based Multi-source Transfer Learning Approach for Time Series Classification. International Journal of Prognostics and Health Management. 13(2), 1-25. DOI:https://doi.org/10.36001/ijphm.2022.v13i2.3267. [Document Site]
Shelly, Z., Burch V, R. F., Tian, W., Strawderman, L., Piroli, A., & Bichey, C. (2020). Using K-means Clustering to Create Training Groups for Elite American Football Student-athletes Based on Game Demands. International Journal of Kinesiology & Sports Science. Australian International Academic Centre. 8(2), 47-63. DOI:10.7575//aiac.ijkss.v.8n.2p.47. [Abstract] [Document]
Seifi, S. H., Tian, W., Doude, H., Tschopp, M. A., & Bian, L. (2019). Layer-Wise Modeling and Anomaly Detection for Laser-Based Additive Manufacturing. Journal of Manufacturing Science and Engineering. ASME. 141(8), 12. [Document Site]
Mamun, A. A., Bappy, M. M., Senanayaka, A., Li, J., Jiang, Z., Tian, Z., Fuller, S., Falls, T. C., Bian, L., & Tian, W. (2023). Multi-channel Sensor Fusion for Real-time Bearing Fault Diagnosis by Frequency-domain Multilinear Principal Component Analysis. The International Journal of Advanced Manufacturing Technology. Springer London. 124(3), 1321-1334. DOI:https://doi.org/10.1007/s00170-022-10525-4.
Senanayaka, A., Mamun, A. A., Bond, G., Tian, W., Wang, H., Fuller, S., Falls, T. C., Rahimi, S., & Bian, L. (2022). Similarity-based Multi-source Transfer Learning Approach for Time Series Classification. International Journal of Prognostics and Health Management. 13(2), 1-25. DOI:https://doi.org/10.36001/ijphm.2022.v13i2.3267. [Document Site]
Shelly, Z., Burch V, R. F., Tian, W., Strawderman, L., Piroli, A., & Bichey, C. (2020). Using K-means Clustering to Create Training Groups for Elite American Football Student-athletes Based on Game Demands. International Journal of Kinesiology & Sports Science. Australian International Academic Centre. 8(2), 47-63. DOI:10.7575//aiac.ijkss.v.8n.2p.47. [Abstract] [Document]
Seifi, S. H., Tian, W., Doude, H., Tschopp, M. A., & Bian, L. (2019). Layer-Wise Modeling and Anomaly Detection for Laser-Based Additive Manufacturing. Journal of Manufacturing Science and Engineering. ASME. 141(8), 12. [Document Site]
