Prof. E-Wen HuangTaiwan
National Yang Ming Chiao Tung University
| 2026/2 to present | | Department Chair, Dept. of Materials Science and Engineering, National Yang Ming Chiao Tung University (NYCU) |
| 2025/11 to present | | Editor, Materials Letters: X |
| 2025/1 to present | | Editor, Materials Letters |
| 2025 - 2026 | | Deputy Director, Center for Institutional Research and Data Analytics (CIRDA), NYCU |
| 2023 - 2025 | | Group Leader, Division of Strategic Planning, Office of Research and Development, NYCU |
| 2025 - 2026 | | 中國工程師學會教育小組 |
| 2024/6 - 2024/8 | | Visiting Professor, Dept. of Materials Science and Engineering, Case Western Reserve University |
| 2024/1 - 2024/2 | | Visiting Scholar, Indian Institute of Technology Kanpur, Scheme for Promotion of Academic and Research Collaboration (SPARC) |
| 2018 - 2021 | | Adjunct Distinguished Research Fellow, Division of Advance Metallic & Composite Materials Research, Material and Chemical Research Laboratories, Industrial Technology Research Institute |
| 2018 - 2020 | | Specialist, Science & Technology Policy Advisory Office, Board of Science and Technology, Executive Yuan |
| 2017 - 2017 | | Chairman Elected of the National Synchrotron Radiation Research Center Users’ Executive Committee |
| 2025 | | Outstanding Alumni Award, National Dong Hwa University |
| 2022 | | Higher Education Academy Senior Fellowship, Advance HE |
| 2019 | | Distinguished Mentor Award, National Chiao Tung University |
Neutron diffraction, Synchrotron x-ray diffraction, Mechanical behavior, Integrated Computational Materials Engineering (ICME), Metals, High Entropy Alloys
E-Wen Huang is a Professor & Chair in the Department of Materials Science and Engineering at National Yang Ming Chiao Tung University (NYCU) in Taiwan. He earned his Ph.D. in Materials Science and Engineering from The University of Tennessee in 2009. His notable research focuses on high-entropy alloys and additive manufacturing. Additionally, Huang serves as the Deputy Director of the Center for Institutional Research and Data Analytics at NYCU. His contributions have earned him multiple honors, including the Ta-You Wu Memorial Award.
High-throughput synchrotron x-ray-based combinatorial stoichiometry & microstructure hardness mapping via machine learning-assisted prediction & validation of advanced alloys
TBA TBA
High-Entropy Materials/TBA
A supervised machine learning (ML) framework using different regression models was applied to predict localized hardness maps based on both composition and microstructure in the Cu15Ni35Ti25Hf12.5Zr12.5, and the predictions were evaluated against nanoindentation hardness map. Favorable element distribution of Ni-Hf-rich dendrite and Cu-Ti-Zr-rich interdendritic regions was obtained using x-ray fluorescence. The lattice characteristic distribution of specific orientations was determined using x-ray nanodiffraction. The nanoindentation hardness was performed and calibrated to correlate with predicted hardness distributions. Using a cluster-wise modeling strategy combined with the CatBoost model enables accurate prediction of mechanical properties in heterogeneous microstructure. The predictive accuracy can be enhanced by incorporating additional microstructural-related features in addition to the composition as input data. The ML-based hardness prediction shows great promise in elucidating the correlation between localized microstructure and hardness at the highly spatially resolved micrometer scale beyond stoichiometry. The methodological development enables scalable, high accuracy prediction of mechanical properties and facilitates future studies on processing-induced heterogeneous microstructures in multicomponent alloys.