Dr Wei Qin Chuah is an early-career academic with a passion for bridging fundamental and applied research in engineering. Specializing in computer vision, machine learning, deep learning, automation, robotics, and digital twins, Dr Chuah’s work aims to deliver real-world impact and solve complex engineering problems through cutting-edge technology.
Chuah recently joined the School of Mechanical, Mechatronics and Manufacturing Engineering at RMIT as a Lecturer, where he continues to advance research at the intersection of intelligent systems and industrial applications. His research seeks to translate foundational advances into practical solutions, helping shape the future of automation and digital transformation in industry.
Chuah has actively collaborated with domestic and international partners across the manufacturing, agriculture, and food processing industries. These multidisciplinary collaborations leverage computer vision, deep learning, automation, and digital twins to enhance productivity, drive innovation, and accelerate technology adoption throughout the industrial sector.
Having completed his PhD in 2023 under the guidance of Professor Alireza Bab-Hadiashar and Dr Ruwan Tennakoon, Chuah is committed to academic excellence, interdisciplinary collaboration, and the mentorship of emerging engineers. His academic journey has been marked by dynamic engagement across research and industry, with a strong dedication to delivering solutions that make a meaningful difference.
Dr Wei Qin Chuah’s research interests span the fields of computer vision, machine learning, deep learning, automation, robotics, and digital twins. He is especially focused on developing intelligent systems that bridge foundational scientific advances with practical engineering applications. His work addresses complex challenges in areas such as visual perception, object recognition, scene understanding, and autonomous decision-making, with an emphasis on robust solutions for industrial automation. Chuah is passionate about integrating cutting-edge algorithms and data-driven approaches to improve the efficiency, adaptability, and intelligence of next-generation manufacturing, agricultural, and food processing systems.
RMIT University acknowledges the people of the Woi wurrung and Boon wurrung language groups of the eastern Kulin Nation on whose unceded lands we conduct the business of the University. RMIT University respectfully acknowledges their Ancestors and Elders, past and present. RMIT also acknowledges the Traditional Custodians and their Ancestors of the lands and waters across Australia where we conduct our business - Artwork 'Sentient' by Hollie Johnson, Gunaikurnai and Monero Ngarigo.
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