Swinburne University of Technology Sarawak Campus - Malaysia
Author Profile
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Early academic pursuits 🎓
Lee Sue Han's academic journey began with a Bachelor of Engineering (Honours) in Electronics Engineering from Multimedia University, Malaysia. Her passion for technology and innovation propelled her to pursue a Master of Engineering in Electrical and Electronics at Shinshu University, Japan. Under the guidance of Professor Kiyoshi Tanaka, she focused on computer vision and machine learning, laying a solid foundation for her future research. Her quest for knowledge culminated in a Doctor of Philosophy in Computer Science & IT at University Malaya, where she further specialized in computer vision and deep learning under the mentorship of Professor Ir. Dr. Chan Chee Seng.
Professional endeavors 🏢
Since February 2020, Lee has served as a Lecturer at Swinburne University of Technology, Sarawak Campus, where she has taken on significant responsibilities, including Head of the Department of Information and Communication Technologies. Her role as an Interdisciplinary AI Research Driver emphasizes her commitment to advancing AI applications across various fields. Prior to this, she gained valuable experience as a Postdoctoral Fellow at INRAE in Montpellier, France, specializing in computational botany, which showcases her diverse expertise and adaptability in research.
Contributions and research focus 🔍
Lee's research is primarily centered on computer vision and deep learning, with a strong emphasis on machine learning applications. She has made significant strides in developing algorithms and techniques that enhance the understanding and analysis of visual data. Her contributions to computational botany highlight the Computational Botany intersection of technology and environmental science, where she explores innovative solutions for plant research through advanced computational methods. Her interdisciplinary approach not only enriches her field but also opens new avenues for research and application.
Accolades and recognition 🏅
Lee Sue Han's contributions to the field have been recognized through her active involvement in professional organizations. As a member of IEEE Young Professionals since 2019, she has engaged with a global community of engineers and technologists. Recently, she was designated a Graduate Technologist by the Malaysian Board Computational Botany of Technologists (MBOT) in 2023, acknowledging her expertise and commitment to the engineering profession. Her achievements reflect her dedication to excellence and her impactful role in the academic and professional landscape.
Impact and influence 🌍
Lee's work significantly influences the fields of computer vision, machine learning, and computational botany. Her research not only advances theoretical knowledge but also has practical implications for various industries, including agriculture and environmental science. By bridging the gap between technology and real-world Computational Botany applications, she contributes to solving pressing global challenges. Her mentorship and leadership at Swinburne University inspire the next generation of engineers and researchers to explore innovative solutions.
Legacy and future contributions đź”®
As Lee Sue Han continues her academic and research endeavors, her legacy is defined by her commitment to interdisciplinary collaboration and technological advancement. Her future contributions are expected to push the boundaries of knowledge in computer vision and AI, particularly in addressing environmental issues and enhancing agricultural practices. Lee's vision for the future is one where technology plays a pivotal role in creating sustainable solutions, and her ongoing work promises to have a lasting impact on both academia and industry.
Notable PublicationsÂ
How deep learning extracts and learns leaf features for plant classification 2017
Deep-plant: Plant identification with convolutional neural networks 2015
New perspectives on plant disease characterization based on deep learning 2020
Multi-organ plant classification based on convolutional and recurrent neural networks 2018
Attention-based recurrent neural network for plant disease classification 2020