Muhammad Umar Farooq

Hi, I’m glad you’re here!

I’m a Ph.D. Candidate in Mechanical Engineering at the University of Michigan, where I work in the Resourceful Manufacturing and Design (ReMaDe) Lab under the guidance of Prof. Daniel Cooper (University of Michigan), Prof. Chenhui Shao (University of Illinois Urbana Champaign), Prof. Jeffery Abell (General Motors), Prof. Parth Vaishnav (University of Michigan), and Ming Xu (Tsinghua University).

My research broadly contributes to sustainable manufacturing, industrial decarbonization, and AI-enabled engineering decision support, with a particular focus on closing the gap between data-limited industrial systems and trustworthy, uncertainty-aware modeling tools. I am especially interested in physics-informed and data-driven modeling, multi-fidelity learning, uncertainty quantification, optimization under uncertainty, life cycle assessment, and supply-chain decarbonization.

My current work develops reproducible and interpretable methods for product carbon accounting, sustainability assessment, and process optimization in manufacturing industries. Across these efforts, I aim to build practical AI tools that support engineering decisions with traceability, uncertainty awareness, and domain knowledge integration.

Before joining Michigan, I completed the Erasmus Mundus Joint M.Sc. in Tribology of Surfaces and Interfaces through the TRIBOS+ Consortium across Europe, and previously earned both my M.Sc. in Engineering Management and B.Sc. in Industrial and Manufacturing Engineering from the University of Engineering and Technology Lahore.

I enjoy solving manufacturing problems with model-aware and data-efficient methods. Please feel free to browse around and reach out if anything interests you.

Research Interests

1. Sustainable manufacturing and industrial decarbonization: life cycle assessment, product carbon footprinting, material efficiency, and supply-chain decarbonization.
2. Physics-integrated AI for manufacturing: physics-informed learning, explainable machine learning, multi-fidelity modeling, and intelligent manufacturing systems.
3. Uncertainty-aware engineering decision support: uncertainty quantification, Bayesian and data-driven modeling, and optimization under uncertainty.
4. Manufacturing processes and systems: aluminum extrusion, machining and EDM, tribology, diagnostics, and reliability-oriented process improvement.