Educational Background

Ph.D., Chemical Engineering, Nanyang Technological University (NTU), Singapore, 2013
Dissertation: Experimental Study of Optimizing Control of Continuous Chromatographic Separation Process

B.Sc., Chemical Engineering, Bangladesh University of Engineering and Technology (BUET), Bangladesh, 2008
Graduated with University Honors

Professional Experience

Assistant Professor, Chemical Engineering
Florida Institute of Technology, Melbourne, Florida
2026–Present

Assistant Professor, Chemical and Biological Engineering
South Dakota School of Mines and Technology, Rapid City, South Dakota
2023–2026

Assistant Research Scientist, Energy Institute
Texas A&M University, College Station, Texas
2022–2023

Postdoctoral Fellow, Chemical Engineering
Qatar University, Doha, Qatar
2013–2022

Project Officer / Instructor, Chemical and Biomedical Engineering
Nanyang Technological University, Singapore
2009–2013

Earlier academic experience includes serving as a Lecturer at Green University of Bangladesh.

Current Courses

CHE 5110 – Equilibrium Thermodynamics
Graduate-level thermodynamics covering fundamental and applied concepts in phase and chemical equilibrium, equations of state, fugacity, activity, and computational thermodynamics.

Additional courses in process systems engineering, process control, applied engineering mathematics, optimization, and computational methods may be offered in future semesters.

Selected Publications

Kazi, M.-K., Varghese, S., Sarker, N., Aich, N., & Gadhamshetty, V. (2025). Advancing PFAS remediation through physics-based modeling of 2D materials: Recent progress, challenges, and opportunities. Industrial & Engineering Chemistry Research, 64(4), 1894–1906.

Mohammed, S., Eljack, F., Kazi, M.-K., & Atilhan, M. (2024). Development of a deep learning-based group contribution framework for targeted design of ionic liquids. Computers & Chemical Engineering, 186, 108715.

Kazi, M.-K., & Hasan, M. M. F. (2024). Optimal and secure peer-to-peer carbon emission trading: A game theory informed framework on blockchain. Computers & Chemical Engineering, 180, 108478.

Kazi, M.-K., & Mahdi, E. (2024). Crashworthiness optimization of composite hexagonal ring system using random forest classification and artificial neural network. Composites Part C, 13, 100440.

Hasan, M. M. F., Zantye, M. S., & Kazi, M.-K. (2022). Challenges and opportunities in carbon capture, utilization and storage: A process systems engineering perspective. Computers & Chemical Engineering, 166, 107925.

Kazi, M.-K., & Eljack, F. (2022). Practicality of green H₂ economy for industry and maritime sector decarbonization through multiobjective optimization and RNN-LSTM model analysis. Industrial & Engineering Chemistry Research, 61(18), 6173–6189.

Kazi, M.-K., Eljack, F., & Mahdi, E. (2020). Data-driven modeling to predict the load vs. displacement curves of targeted composite materials for Industry 4.0 and smart manufacturing. Composite Structures, 258, 113207.

Kazi, M.-K., Eljack, F., AlNouss, A., & Kazantzi, V. (2018). A process design approach to manage the uncertainty of industrial flaring during abnormal operations. Computers & Chemical Engineering, 117, 191–208.

Recognition & Awards

Outstanding Student Organization Advisor, South Dakota Mines Employee Award, 2026

Helen and Pat Goth Fellowship, awarded to PRODIGI Research Group Ph.D. student, South Dakota Mines, 2025

Best Postdoctoral Researcher, Department of Chemical Engineering, Qatar University, 2017

Best Paper Presentation, AIChE Annual Meeting, Integrated Process Engineering and Economics Analysis Session, 2015

Best Poster Award, “My Gateway to Research,” Qatar University, 2015

Graphical System Design Achievement Award, National Instruments ASEAN, 2011

Research Scholarship, Nanyang Technological University, Singapore, 2009–2012

Dean’s List and Technical Scholarship, Bangladesh University of Engineering and Technology, 2003–2007

Research

PRODIGI Research Group

The PRODIGI Research Group investigates how chemical and manufacturing systems can become more intelligent, reliable, sustainable, and autonomous by combining first-principles engineering models with data, artificial intelligence, optimization, and decision science.

Our research philosophy is:

Physics + Data + AI → Better Engineering Decisions

Major research directions include:

Physics-Informed and Hybrid Artificial Intelligence
Mechanistic/data-driven modeling, physics-informed machine learning, inverse problems, model-form assessment, uncertainty quantification, and digital twins for chemical and manufacturing systems.

Large Language Models and Agentic AI for Engineering
LLM-enabled engineering assistants, retrieval-augmented generation, autonomous AI agents, tool-using systems, knowledge-grounded decision support, and safe orchestration of engineering workflows.

Process Systems Engineering, Optimization, and Control
Process design, dynamic modeling, model predictive control, multi-objective optimization, uncertainty-aware decision-making, reinforcement learning, and autonomous process systems.

Sustainable Energy and Decarbonization
Hydrogen systems, carbon capture, utilization and storage, process electrification, methane pyrolysis, biomass processing, flare management, and integrated energy systems.

Smart Manufacturing and Advanced Materials
Physics-guided machine learning, digital twins, surrogate modeling, inverse design, optimization, process monitoring, and intelligent manufacturing.

Resilient and Autonomous Supply Chains
Blockchain, game theory, AI agents, sustainability assessment, logistics optimization, and trustworthy decentralized decision-making.

The group is particularly interested in research that connects multiple scales:

molecule → material → unit operation → process → plant → enterprise → supply chain

PRODIGI welcomes collaborations with experimental researchers, computational scientists, national laboratories, industry, manufacturing organizations, and students interested in combining domain knowledge with advanced computational methods.