Mahathir Bappy

mmbappy@lsu.edu
Office: 3290H PFT

(225) 578-2466

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Mahathir Mohammad Bappy

Assistant Professor, Industrial Engineering

PhD, Mississippi State University (2024)

Expertise

AI/ML & Advanced Sensing • Digital Twins & Cyber-Physical-Social Systems • Smart Manufacturing • Structural Integrity & Prognostics • Systems 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, his MS in Industrial and Production Engineering from the Bangladesh University of Engineering and Technology, and his BS from Shahjalal University of Science and Technology. He has about five years of industry experience. Dr. Bappy’s research focuses on system informatics in data-rich environments, with particular emphasis on AI for cyber-physical systems, process monitoring, digital twin–based modeling and diagnosis, decision-focused optimization, and sustainable system resilience. His research advances the understanding of complex engineered systems through advanced sensing, data analytics, and intelligent monitoring frameworks. His research applications span system informatics, advanced manufacturing, quality control, data privacy, sustainability and resilience, and predictive maintenance. He has also contributed to emerging areas such as 3D bioprinting, energy analytics, and water systems optimization. His scholarly contributions have been published in prestigious journals, and he has received several accolades, including the Outstanding Graduate Student Researcher Award from Mississippi State University and multiple Best Poster Awards. Dr. Bappy is a member of the Institute of Industrial and Systems Engineers (IISE), the Society of Manufacturing Engineers (SME), and the Institute for Operations Research and the Management Sciences (INFORMS). In teaching, Dr. Bappy is passionate about advancing workforce development through courses such as advanced engineering statistics, quality control, and six sigma, machine learning for industrial engineering applications, AI in manufacturing, and production control systems. He is committed to integrating research insights into his teaching to prepare students for future challenges in industrial, manufacturing, and systems engineering.

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