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Tien-En Chang

Tien-En Chang (張天恩)

PhD student in Institue of Industrial Engineering (GIIE) @ National Taiwan University

I'm Tien-En (Willy), a fourth-year PhD student at National Taiwan University (NTU) in Taipei, Taiwan. I am a member of Statistical and Data Mining Lab, supervised by Prof. Argon Chen.

My main research interest is variable selection method. I started my research from the relative importance analysis and I investigates why Johnson's Relative Weight works so well on approximating General Dominance index / Shapley Value. Currently, I focus on improving the Relative Weight, including building its theoretical foundation and invsetigating potential improvement. I am also interested in the weight initialization method in deep neural network.

Curriculum Vitae 

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Latest News

(09.27.2025) Our work, “Anatomically-Focused Patches for Lightweight and Explainable Knee OA Grading,” received the Best Paper Award in MICCAI ShapeMI 2025. 

(08.01.2025) One workshop paper accepted to ShapeMI 2025 in MICCAI 2025! See you at Daejeon! 

Anatomically-Focused Patches for Lightweight and Explainable Knee OA Grading, MICCAI ShapeMI 2025

Tien-En Chang, Hervé Lombaert

(02.10.2025-06.09.2025) Joined 2025 Polytechnique Montreal Winter Research Internship Program.

Research topic: Machine Learning and Interaction with Large-Scale Medical Imaging Datasets.

Under the supervition of Prof. Herve Lombaert in the PolyShape Lab. 

Internship Program Report: 蒙特婁理工學院實習心得

(05.31.2024) One abstract accepted to DATA 2024! See you at Dijon! 

Understanding the Orthonormality Transformation based Measures of Relative Importance, DATA 2024.

Tien-En Chang, Argon Chen

(11.25.2023) Celebrating our team's success: Awarded the Outstanding Award at the Intelligent Manufacturing and Big Data Analytics Contest 2023.

The finals of Project B: the image classification problem in the manufacturing. 

Team name: 真替你們感到難過; members: Huai-Wei Wang , Zih-An Yi,  Tien-En Chang; mentor:  Jakey Blue


(10.09.2023) One abstract accepted to IASC-ARS 2023! See you at Sydney! 

Using Relative Weight for Variable Importance Assessment - How Does It Work and Could It Work Better?,  IASC-ARS 2023.

Tien-En Chang, Argon Chen


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