Expertise

Advanced sensing and data analytics • system informatics • smart manufacturing • privacy-preserving modeling • AI for cyber-physical security • supply chain resilience.

Biographical Sketch

Dr. Mahathir Mohammad Bappy is an Assistant Professor of Industrial Engineering in the Department of Mechanical and Industrial Engineering at Louisiana State University. He achieved his PhD in Industrial and Systems Engineering from Mississippi State University. He received his MS from the Bangladesh University of Engineering and Technology and a BS from Shahjalal University of Science and Technology in Industrial and Production Engineering. He has about five years of experience in the manufacturing industry. His research efforts are focused on system informatics in data-rich environments. He is particularly interested in advanced sensing and data analytics to understand complex systems through modeling, monitoring, and diagnosis. His research applications include advanced manufacturing, cyber-physical systems security, and supply chain resilience. His research has also been applied to other areas, including predictive maintenance and bioprinting for bone tissue engineering. His publications have appeared in some prestigious journals. Dr. Bappy received the Outstanding Graduate Student Researcher Award from MSU and multiple Best Poster Awards. He is a member of the Institute of Industrial and Systems Engineers (IISE) and the Institute of Operations Research and the Management Sciences (INFORMS). His teaching interests include advanced engineering statistics, statistical quality control, machine learning with IE applications, manufacturing processes, and production control systems.

Key Publications

Fullington, D., Yangue, E, Bappy, M. M., Liu, C., & Tian, W. (2024). Leveraging small-scale datasets for additive manufacturing process modeling and part certification: Current practice and remaining gaps. Journal of Manufacturing Systems, 75,306-321. https://doi.org/10.1016/j.jmsy.2024.04.021
Bappy, M. M., Fullington, D., Bian, L., & Tian, W. (2023). Evaluation of Design Information Disclosure through Thermal Feature Extraction in Metal-based Additive Manufacturing. Manufacturing Letters, 36, 86-90. https://doi.org/10.1016/j.mfglet.2023.03.004.
Al Mamun, A., Bappy, M. M., Mudiyanselage, A. S., 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, 124(3-4), 1321-1334. https://doi.org/10.1007/s00170-022-10525-4.
Al Mamun, A., Bappy, M. M., Bian, L., & Tian, W. (2023). Missing Signal Imputation for Multi-channel Sensing Signals on Rotary Machinery by Tensor Factorization. Manufacturing Letters, 35(2023), 1109-1118. https://doi.org/10.1016/j.mfglet.2023.08.097.
Bappy, M. M., Liu, C., Bian, L., & Tian, W. (2022). Morphological Dynamics-based Anomaly Detection towards In-situ Layer-wise Certification for Directed Energy Deposition Processes. Journal of Manufacturing Science and Engineering, 144(11), 111007. https://doi.org/10.1115/1.4054805
Esfahani, M. N., Bappy, M. M., Bian, L., & Tian, W. (2021). In-situ Layer-wise Certification for Direct Laser Deposition Processes based on Thermal Image Series Analysis. Journal of Manufacturing Processes, 75, 895-902. https://doi.org/10.1016/j.jmapro.2021.12.041
Bappy, M. M., Key, J., Hossain, N. U. I., & Jaradat, R.(2022). Assessing the Social Impacts of Additive Manufacturing Using Hierarchical Evidential Reasoning Approach. Global Journal of Flexible Systems Management, 23(2), 201-220. https://doi.org/10.1007/s40171-021-00295-5
Rahman, S., Hossain, N. U. I., Govindan, K., Nur, F., Bappy, M. M. (2021). Assessing Cyber Resilience of Additive Manufacturing Supply Chain Leveraging Data Fusion Technique: A Model to Generate Cyber Resilience Index of a Supply Chain. CIRP journal of manufacturing science and technology, 35(911-928). https://doi.org/10.1016/j.cirpj.2021.09.008
Bappy, M. M., Ali, S. M., Kabir, G., Paul, S. K. (2019). Supply Chain Sustainability Assessment with Dempster-Shafer Evidence Theory: Implications in Cleaner Production. Journal of Cleaner Production, 237, 117771. https://doi.org/10.1016/j.jclepro.2019.117771
Chilukoti, S. V., Hossen, M. D., Shan, L., Tida, V. S., Bappy, M. M., Tian, W., Hei, X. (2024) Dp-Sgd-Global-Adapt-V2-S: Triad Improvements of Privacy, Accuracy and Fairness Via Step Decay Noise Multiplier and Step Decay Upper Clipping Threshold. Available at SSRN: https://ssrn.com/abstract=4906113 or http://dx.doi.org/10.2139/ssrn.4906113