Herbert Wertheim College of Engineering
Minhee Kim Lab
Led by Minhee Kim · Industrial and Systems Engineering
What we study
Minhee Kim develops engineering-informed data analytics and machine learning to monitor, model and predict how industrial and engineering systems wear out, including uncertainty quantification and digital twins. She co-directs the DISIDE Laboratory.
What working here is like
- Computational
- Coding required
- Papers
- 5
Recent papers, explained
- Manifold-informed meta-learning for out-of-distribution detection and classification in small-scale industrial datasets
2026 · Journal of Intelligent Manufacturing
Creates a meta-learning framework to detect and classify defects in small industrial datasets.
- Scalable and robust bayesian transfer learning for in-situ qualification in laser powder bed fusion additive manufacturing
2026 · Journal of Quality Technology
Develops a method to transfer knowledge between different additive manufacturing settings while managing uncertainty.
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- Examining and Mitigating Underspecification in Neural Network-based Wafer Defect Pattern Classification
2026 · Journal of Intelligent Manufacturing
Tests whether deep learning models for wafer defect classification perform consistently across different settings.
- Multistable structures for large-scale applications
2026 · Smart Materials and Structures
Reviews challenges in designing large-scale systems that can switch between stable states.
- Federated Conformal Approach for Trustworthy Uncertainty Quantification in Distributed Fleet Prognostics
2026 · Technometrics
Creates a method to predict system failures while keeping data private and uncertainty reliable.
Plain-language summaries are AI-written and reviewed by us; check the paper for details.
How to reach out
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