Prof. Dr. Tzu-Chien Wang | Machine Learning | Best Researcher Award

Prof. Dr. Tzu-Chien Wang | Machine Learning | Best Researcher Award

Prof. Dr. Tzu-Chien Wang | Machine Learning – Assistant Professor at Soochow University, Taiwan

Tzu-Chien Wang is an accomplished academic and researcher specializing in data science, artificial intelligence, and decision support systems. Currently serving as an assistant professor in the Department of Computer Science & Information Management at Soochow University, Taiwan, he holds a Ph.D. from National Taiwan University. Wang’s research revolves around leveraging advanced data mining techniques, machine learning algorithms, and natural language processing to develop innovative solutions for real-world applications. His expertise spans across industries, including healthcare, finance, and manufacturing, showcasing his ability to transform complex data into actionable insights.

Profile:

Orcid

Google Scholar

Education:


Tzu-Chien Wang earned his Ph.D. in Business Administration from National Taiwan University, where he focused on the integration of data analytics into strategic decision-making. His academic journey reflects a strong foundation in both theoretical frameworks and practical applications, equipping him with the skills necessary to excel in the rapidly evolving fields of data science and artificial intelligence.

Experience:


With over a decade of professional experience, Wang has held key academic and industry positions. He currently serves as an assistant professor at Soochow University, where he mentors graduate students and leads research projects. Previously, he worked as a manager in the Data Development Department at VISUALSOFT INFORMATION SYSTEM CO., LTD., and served as a senior data analyst at Fubon Life Insurance Co., Ltd. His roles have involved extensive project planning, data model construction, and collaboration with multidisciplinary teams to drive data-driven innovations.

Research Interests:


Wang’s research interests are diverse, focusing on data mining, machine learning, decision support systems, and process improvement techniques. He employs methodologies such as clustering, classification, natural language processing (NLP), optimization, heuristics, and predictive model building. His work aims to enhance operational efficiency, support strategic decision-making, and develop proof-of-concept models that address sector-specific challenges.

Awards:

  • High-Performance Health Smart Medical Alliance (2025-2028) – National Science and Technology Council, Taiwan 🏆

  • AI+BI Agile Development Data Platform Construction Project (2022) – Department of Industrial Technology, Ministry of Economic Affairs, Taiwan 🏅

  • Consumer Data-Driven Precision R&D Manufacturing (2021) – Bureau of Energy, Ministry of Economic Affairs, Taiwan 🎖️

Publications:

  1. Multi-Stage Data-Driven Framework for Customer Journey Optimization (2025) 📊
  2. Deep Learning-Based Prediction and Revenue Optimization for Online Platform User Journeys (2024) 📈
  3. Method for Determining Requirements of Customers (2024) 🧠
  4. Integrating Latent Dirichlet Allocation and Gradient Boosting Tree Methodology for Insurance Product Development Recommendation (2024) 📊
  5. An Integrated Data-Driven Procedure for Product Specification Recommendation Optimization (2023) 🔍
  6. Integrated Approach for Product Development Using Latent Dirichlet Allocation and Gradient Boosting Decision Tree Methods (2023) 🚀
  7. Data Mining Methods to Support C2M Product-Service Systems Design (2022) 🖥️

Conclusion:


Tzu-Chien Wang’s remarkable contributions to data science and artificial intelligence, combined with his extensive academic and professional experience, make him a strong candidate for the Best Researcher Award. His innovative research, leadership in data-driven projects, and dedication to advancing technology reflect his commitment to excellence. Wang’s ability to bridge the gap between theoretical research and practical applications has significantly impacted various industries, making him a distinguished scholar and an inspiring figure in the academic community. Recognizing his achievements with this prestigious award would not only honor his past contributions but also encourage continued advancements in the field of data science and artificial intelligence.

Prof. Raed Abu Zitar | Machine Learning in Tracking | Best Researcher Award

Prof. Raed Abu Zitar | Machine Learning in Tracking | Best Researcher Award

Prof. Raed Abu Zitar, Sorbonne University, United Arab Emirates

Prof. Zitar is a distinguished academic with a Ph.D. in Computer Engineering focused on Artificial Intelligence and Neural Networks from Wayne State University. With a robust background that includes a Master’s in Genetic Algorithms and a Bachelor’s in Electrical Engineering, he has had a notable career as a Senior Research Scientist and Chair of Excellence at the Sorbonne Center of Artificial Intelligence, Sorbonne University, Abu Dhabi. His research, which includes advanced work on drone detection and tracking, spans AI, machine learning, and robotics. Notable for his contributions to metaheuristic optimization and machine learning, Prof. Zitar has received prestigious awards such as the ASAI and UNESCO Fellowships, and has been recognized for his leadership and innovative work in the field.

Professional Profile:

Scopus
Orcid
Google Scholar

Suitability for the Research for Best Researcher Award

Prof. Raed Abu Zitar is a highly suitable candidate for the Research for Best Researcher Award due to his extensive expertise and significant contributions to the fields of Artificial Intelligence, Machine Learning, Robotics, and Computer Vision. His educational background includes a Ph.D. in Computer Engineering with a focus on Artificial Intelligence and Neural Networks from Wayne State University, complemented by a Master’s in Computer Engineering and a Bachelor’s in Electrical Engineering. This robust academic foundation underpins his diverse research interests and accomplishments.

Dean of Faculty of Computing and Engineering, Liwa College 🎓
Prof. Raed Abu Zitar is the Dean of the Faculty of Computing and Engineering at Liwa College, Abu Dhabi. He began this role in September 2024, leading the faculty in advancing computing and engineering education.

Academic Background 📚

Prof. Zitar holds a Ph.D. in Computer Engineering with a focus on Artificial Intelligence and Neural Networks from Wayne State University, where he explored machine learning with rule extraction. He also earned a Master’s in Computer Engineering with a specialization in Genetic Algorithms from North Carolina A&T State University and a Bachelor’s in Electrical Engineering from the University of Jordan.

Professional Experience 💼

Prof. Zitar has a distinguished career as a Senior Research Scientist and Chair of Excellence at the Sorbonne Center of Artificial Intelligence, Sorbonne University, Abu Dhabi, from February 2021 to September 2024. His work there focused on drone detection and tracking using advanced machine learning techniques. He was also the Founding Coordinator of the Master of Artificial Intelligence Program at Ajman University and managed the Teaching and Learning Center.

Research Interests and Contributions 🔬

His research spans various areas, including artificial intelligence, machine learning, robotics, computer networks modeling, and computer vision. He has published significant papers on the JAYA algorithm and renewable energy optimization techniques, demonstrating his expertise in metaheuristic optimization and advanced machine learning applications.

Awards and Recognitions 🏆

Prof. Zitar has received several prestigious awards, including the ASAI and UNESCO Fellowships. He was honored for supervising the Best Graduation Projects at Ajman University and received an Appreciation Award from CUCA University for his contributions to the Smart Learning Conference.

Innovations and Impact 🚀

Prof. Zitar’s extensive research and leadership in AI and machine learning have made a notable impact on the field. His work continues to influence advancements in energy optimization and computational methods, reflecting his commitment to pushing the boundaries of technology and education.

Publication Top Notes:

  1. Title: Wind, Solar, and Photovoltaic Renewable Energy Systems with and Without Energy Storage Optimization: A Survey of Advanced Machine Learning and Deep Learning Techniques
    • Citations: 104
    • Year: 2022
  2. Title: Gene Selection for Microarray Data Classification Based on Gray Wolf Optimizer Enhanced with TRIZ-Inspired Operators
    • Citations: 95
    • Year: 2021
  3. Title: Development of an Efficient Neural-Based Segmentation Technique for Arabic Handwriting Recognition
    • Citations: 88
    • Year: 2010
  4. Title: Multiclass Feature Selection with Metaheuristic Optimization Algorithms: A Review
    • Citations: 86
    • Year: 2022