Minhee Kim

I am an Assistant Professor in the Department of Industrial and Systems Engineering at the University of Florida.

My research interests are in the areas of quality engineering, machine learning, and statistics. I am especially interested in 

[Google Scholar] [DISIDE Lab] [CV] (last updated: August, 2024)

Education

University of Wisconsin–Madison

University of Wisconsin–Madison

Pohang Univ. of Sci. and Tech. (POSTECH), South Korea

Publications [Google Scholar]

Published or Accepted:

1.       Minhee Kim* (2024), “Iterative Durability Design of Products via Degradation-Informed Bayesian Optimization”, to be published in IEEE Transactions on Automation Science and Engineering

2.   Ye Kwon Huh, Minhee Kim*, Kaibo Liu, and Shiyu Zhou (2024), “An Integrated Uncertainty Quantification Model for Longitudinal and Time-to-event Data”, to be published in IEEE Transactions on Automation Science and Engineering

3.   Ye Kwon Huh, Minhee Kim*, Katie Olivas, Todd Allen and Kaibo Liu (2024), “Degradation Modeling using Bayesian Hierarchical Piecewise Linear Models: A case study to predict void swelling in irradiated materials”, to be published in Journal of Quality Technology

4.   Minhee Kim, Todd Allen and Kaibo Liu* (2023), “Covariate-dependent Sparse Data Analysis,” INFORMS Journal on Data Science, 2 (1), 81-98.

–         Selected for presentation in the Natrella invited session in 2021 Quality & Productivity Research Conference (QPRC)

5.   Elisa Ou, Minhee Kim, Todd Allen, and Kaibo Liu* (2022), “Automatic Recognition System for Document Digitization in Nuclear Power Plants”, Nuclear Engineering and Design, 398, 111975.

6.   Minhee Kim, Changyue Song, and Kaibo Liu* (2022), “Individualized Degradation Modeling and Prognostics in a Heterogeneous Group via Incorporating Static Covariate Information,” IEEE Transactions on Automation Science and Engineering, 19 (3), 2074-2094.

7.  Minhee Kim, Jing-Ru C. Cheng, and Kaibo Liu* (2021), “An Adaptive Sensor Selection Framework for Multisensor Prognostics,” Journal of Quality Technology, 53 (5), 566-585.

8.       Zhan Ma, Shu Wang, Minhee Kim, Kaibo Liu, Chun-Long Chen, and Wenxiao Pan* (2021), “Transfer learning of memory kernels for transferable coarse-graining of polymer dynamics,” Soft Matter, 17, 5864-5877.

–         Featured on the front cover of Soft Matter

9.   Minhee Kim and Kaibo Liu* (2020), “A Bayesian Deep Learning Framework for Interval Estimation of Remaining Useful Life in Complex Systems by Incorporating General Degradation Characteristics,” IISE Transactions, 53(3), 326-340.

–         Received the Best Student Poster Competition (Honorable mention) in the Quality, Statistics and Reliability Section of 2020 INFORMS Annual Meeting

–         Selected for presentation in the IISE Transactions invited session in 2021 INFORMS Annual Meeting

–         Selected as a feature article in ISE Magazine

10.   Minhee Kim, Elisa Ou, Po-Ling Loh, Todd Allen, Robert Agasie, and Kaibo Liu* (2020), “RNN-Based Online Anomaly Detection in Nuclear Reactors for Highly Imbalanced Datasets with Uncertainty,” Nuclear Engineering and Design, 364, 110699.

–         Received the Best Student Paper Award (2nd place) in the Energy Systems Section of 2021 Industrial and Systems Engineering Research Conference (ISERC)

11.   Minhee Kim, Changyue Song, and Kaibo Liu* (2019), “A Generic Health Index Approach for Multisensor Degradation Modeling and Sensor Selection,” IEEE Transactions on Automation Science and Engineering, 16(3), 1426-1437.

–         Selected for presentation in the IEEE T-ASE invited session in 2019 INFORMS Annual Meeting

12.   Chang Hyup Oh, Minhee Kim, Byung-In Kim, and Young Myoung Ko* (2019), “An Efficient Building Evacuation Algorithm in Congested Networks,” IEEE Access, 7, 169480-169494.

Under review or In preparation:

13.   Zihan Li, Akash Deep, Jaesung Lee and Minhee Kim*, “A New View of Neural Network-based Health Index: Connecting Prediction and Operational Decision-Making to Address Underspecification,” in preparation

Openings

We are looking for highly self-motivated Ph.D. students with interests in one or more of the following fields to join our research group:

We will focus on establishing novel data-driven methodologies for modeling, prediction, and control of complex systems in various applications including manufacturing and energy systems.

The positions are fully funded and begin in Spring 2025 / Fall 2025.

Ideal candidates should meet the following qualifications:

The positions will remain open until filled by qualified candidates. Candidates from all backgrounds, especially those historically underrepresented in STEM fields, are strongly encouraged to apply. If you are interested, please email your CV, (unofficial) transcripts, and research interests to mkim3@ufl.edu.