Assist. Prof. Dr. Tzu-Chien Wang | AI | Best Researcher Award

Tzu-Chien Wang at Department of Computer Science and Information Management Soochow University, Taiwan

Dr. Tzu-Chien Wang is an Assistant Professor in the Department of Computer Science and Information Management at Soochow University. He specializes in artificial intelligence, data mining, decision support systems, and process improvement techniques. With a strong background in machine learning, natural language processing, and predictive modeling, he has contributed significantly to both academia and industry by developing proof-of-concept models for operational processes.

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Education Background

Dr. Tzu-Chien Wang earned his Ph.D. in Business Administration from National Taiwan University, where he specialized in data-driven decision-making, artificial intelligence applications, and business intelligence. His doctoral research focused on leveraging machine learning, data mining, and optimization techniques to enhance decision support systems and operational efficiency. His academic training has provided him with a strong foundation in predictive modeling, natural language processing, and process improvement methodologies, which he has effectively applied in both research and industry settings.

Professional Development

Dr. Wang has a diverse professional background, spanning academia, industry, and research institutions. Before joining Soochow University in 2025, he served as an Assistant Professor at Mackay Junior College of Medicine, Nursing, and Management. He also held managerial roles in data development at VisualSoft Information System Co., Ltd. and worked as a Senior Data Analyst at Fubon Life Insurance Co., Ltd. Additionally, he contributed as an Assistant Research Fellow at the Commerce Development Research Institute, focusing on international digital commerce.

Research Focus

His research interests include artificial intelligence, data mining, decision support systems, natural language processing, optimization, clustering, classification, and predictive model building. He is particularly engaged in developing AI-driven solutions for business intelligence, healthcare applications, and digital transformation.

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Publication Top Notes

1. Deep Learning-Based Prediction and Revenue Optimization for Online Platform User Journeys

• Author: T.C. Wang
• Journal: Quantitative Finance and Economics (2024)
• Type: Research Article
• Citations: 6
• Summary: This study utilizes deep learning techniques to predict user behavior and optimize revenue generation on online platforms, improving personalized recommendations and business strategies.

2. An Integrated Data-Driven Procedure for Product Specification Recommendation Optimization with LDA-LightGBM and QFD

• Authors: T.C. Wang, R.S. Guo, C. Chen
• Journal: Sustainability (2023)
• Type: Research Article
• Citations: 5
• Summary: This research presents a hybrid framework combining Latent Dirichlet Allocation (LDA), LightGBM, and Quality Function Deployment (QFD) to optimize product specification recommendations, improving efficiency in sustainable manufacturing.

3. Integrating Latent Dirichlet Allocation and Gradient Boosting Tree Methodology for Insurance Product Development Recommendation

• Authors: W.Y. Chen, T.C. Wang, R.S. Guo, C. Chen
• Conference: Proceedings of the 9th International Conference on Big Data Analytics (ICBDA) (2024)
• Type: Conference Paper
• Citations: 1
• Summary: This paper integrates LDA and Gradient Boosting Trees to refine insurance product development recommendations, offering a data-driven approach for personalized insurance solutions.

4. Data Mining Methods to Support C2M Product-Service Systems Design and Recommendation System Based on User Value

• Authors: T.C. Wang, R.S. Guo, C. Chen
• Conference: 2022 Portland International Conference on Management of Engineering and Technology (PICMET)
• Type: Conference Paper
• Citations: 1
• Summary: This study explores data mining techniques to enhance Consumer-to-Manufacturer (C2M) product-service system design, optimizing recommendation systems based on user value analysis.

5. Customer Demand Evaluation Method

• Author: T.C. Wang
• Patent: TW Patent TW202,414,306 A (2024)
• Type: Patent
• Summary: This patent presents a novel method for evaluating customer demand using AI-driven analytics, enhancing precision in product development and market segmentation.

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