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Biography
Dr. Tan obtained his PhD from University of Nottingham, Malaysia and has a total of 15 years of work experience in both industry and academia. Experiences includes software engineer, teaching, academic administration, research, and research supervision for both undergraduate and post-graduate students. Prior to joining UNMC, he held the position of senior lecturer, School of Computing (2014-2021), at Asia Pacific University of Technology & Innovation (APU). Dr. Tan have been actively involved in various university-industry collaboration activities and entrepreneurship accelerator program. He is the academic advisor of Digital Internet Association Malaysia (iaM) and contributed to raise awareness and share the benefits of Digital & Internet technology through seminar, workshop, and hackathon. He also mentored students and won the award in Inotech, Asian Youth Innovation Awards, Science and Engineering Design Exhibition (SEDEX), Young International Innovation Exhibition (YIIX), CREST The Great Lab (TGL) Grand Design Challenge, "Hack for Good" IoT hackathon, Hilti IoT competition and more.
Teaching Summary
Current Teaching Modules:
COMP1044 Databases and Interfaces
COMP2039 Artificial Intelligence Methods
COMP2042 Developing Maintainable Software
Research Summary
I am primarily interested in applied research with strong commercial and business relevance, focusing on the development of practical and industry-driven intelligent solutions. My research interests… read more
Recent Publications
Current Research
I am primarily interested in applied research with strong commercial and business relevance, focusing on the development of practical and industry-driven intelligent solutions. My research interests span FinTech analytics for enhancing investment decision-making, quantum algorithms for improving computational efficiency, and AI-driven intelligent systems across manufacturing, healthcare, and finance. Through interdisciplinary research, I aim to bridge academic innovation with real-world industry needs by integrating advanced analytics, machine learning, and emerging computational technologies.
Financial Intelligence for Smart Investing
This research focuses on AI-driven investment, integrating financial reports, sentiment analysis, and market data to improve decision-making. It focuses on investor risk profiling, portfolio optimization, and asset price prediction across equities and cryptocurrencies, enabling adaptive and personalized investment strategies.
Quantum Algorithms
This research explores quantum computing to improve efficiency in solving complex computational problems. It focuses on translating classical algorithms into quantum frameworks, including Quantum Convolutional Neural Networks, for enhanced learning, optimization, and pattern recognition in high-dimensional data.
AI-Driven Intelligent Systems
This interdisciplinary research focuses on AI-powered intelligent applications across manufacturing, healthcare, and finance, integrating advanced analytics and machine learning. It aims to bridge academic innovation with industry needs by delivering practical, data-driven solutions that enhance efficiency, decision-making, and real-world operational performance.