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faculty
Md Habibor Rahman, PhD
Assistant Professor
Mechanical Engineering
Contact
508-999-8692
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Violette Research 219
Education
| 2024 | The University of Arizona | PhD |
| 2022 | The University of Arizona | MS |
Teaching
Courses
A team-based learning experience that gives students the opportunity to synthesize prerequisite course material and to conduct real-world analytics projects using large data sets of diverse types and sources. Students work in independent teams to design, implement, and evaluate an appropriate data integration, analysis, and display system. Oral and written reports and ethical aspects are highlighted.
Seminar discussions presented by faculty, graduate students, and outside speakers on topics of current research interests.
Analysis of engineering costs and capital investments. The course will cover analysis of economic merits of alternatives including interest and income tax considerations. Additional subjects will include break-even points and sensitivity analysis, replacement analysis, and risk and sensitivity exploration techniques. Use of mathematical programming and computers will be introduced for optimal economic decisions.
Project research in conjunction with industry under a faculty advisor. A formal report must be submitted to fulfill the course requirements.
Md Habibor Rahman is an assistant professor in the Department of Mechanical Engineering at the University of Massachusetts Dartmouth. He received his PhD in Systems and Industrial Engineering and his MS in Industrial Engineering from the University of Arizona in 2024 and 2022, respectively. He also earned his MS and a BS degree in Industrial and Production Engineering from Bangladesh University of Engineering and Technology (BUET) in 2017 and 2015, respectively. His research focuses on
- security-aware system design and operations and
- process monitoring, diagnosis, control, and optimization, with applications in discrete manufacturing and water treatment systems.
His research offers new methodological advances utilizing taxonomies, ontologies, graph theory, digital twins, game theory, statistical process control, and physics-informed Machine Learning. His scholarly work has been published in top journals, including the Journal of Manufacturing Systems, the Journal of Manufacturing Processes, and the ASME Journal of Computing and Information Science in Engineering. He has received several awards and recognitions for his research work, including winner of the American National Standards Institute (ANSI) Student Paper Competition, finalist in the Institute of Industrial and Systems Engineers (IISE) Manufacturing and Design Division Best Student Paper Competition, NSF travel award, and winner of the 2020 ASCEND Propel Pitch Competition sponsored by Lockheed Martin.