AI empowered Sensors and Measuring Devices for IoT, Robotics, Drones and Mechatronics

Prof. Dr. Subhas Mukhopadhyay

FIEEE (USA), FIEE (UK), FIETE (India)

School of Engineering, Macquarie University, NSW 2109

Email: Subhas.Mukhopadhyay@mq.edu.au

Abstract:

The advancement of sensing technologies, embedded systems, wireless communication technologies, nano-materials, miniaturization, vision sensing and processing speed makes it possible to develop smart mechatronics and machine systems. This seminar will discuss recent research and developmental activities on different sensors and sensing system, measurement devices, incorporating machine learning and artificial intelligent towards implementation of IoT, Mechatronics, robotics and drones along with machine visions at Macquarie University as applicable to medical science, biology and environmental monitoring.

Biography:

Subhas holds a B.E.E. (gold medalist), M.E.E., Ph.D. (India) and Doctor of Engineering (Japan). Currently he is working as a Professor of Mechanical/Electronics Engineering, Macquarie University, Australia and is the Discipline Leader of the Mechatronics Engineering Degree Programme. His fields of interest include Smart Sensors and sensing technology, instrumentation techniques, wireless sensors and network (WSN), Internet of Things (IoT), Robotics, Mechatronics and Drones etc. He has supervised over 60 postgraduate students and over 200 Honours students. He has examined over 85 postgraduate theses.

He has been co-inventor of 14 patents and published over 450 papers in different international journals and conference proceedings, written ten books and sixty two book chapters and edited twenty five conference proceedings. He has also edited fifty books with Springer-Verlag and thirty five journal special issues. He has organized over 20 international conferences as either General Chairs/co-chairs or Technical Programme Chair. He has delivered 456 presentations including keynote, invited, tutorial and special lectures. As per Scholargoogle, his total citation is 27769 and h-index is 86.

He is a Fellow of IEEE (USA), a Fellow of IET (UK), a Fellow of IETE (India). He is a Topical Editor of IEEE Sensors journal, an associate editor of IEEE Transactions on Instrumentation and Measurements and IEEE Reviews in Biomedical Engineering (RBME). He is EiC of the International Journal on Smart Sensing and Intelligent Systems. He was a Distinguished Lecturer of the IEEE Sensors Council from 2017 to 2022. He chairs the IEEE Instrumentation and Measurement Society NSW chapter.

AI-Enhanced Multi-Modal Sensing for Precision Biomolecular Diagnostics to Clinical Application

Assoc Prof. Kwoh Chee-Keong

Nanyang Technological University (NTU), Singapore

Abstract:

Recent advances in artificial intelligence and multi-modal sensing are transforming the way we detect, analyse, and respond to complex biomedical challenges. This keynote will explore AI-enhanced biomolecular diagnostics, focusing on the integration of heterogeneous data from genomics, proteomics, and clinical sensors into unified, predictive models. Drawing on our work in protein interaction network analysis, drug–target prediction, and pandemic response, we will demonstrate how smart sensors, data fusion, and big data analytics can yield clinically actionable insights. Case studies in infectious disease surveillance, single-cell sequencing and precision medicine will illustrate the path from algorithm development to bedside application.

Biography:

Kwoh Chee-Keong is an Associate Professor at the College of Computing and Data Science (CCDS), Nanyang Technological University (NTU), Singapore.

He earned his Bachelor’s degree in Electrical Engineering (First Class Honours) from the National University of Singapore in 1987, followed by a Master’s degree in Industrial Systems Engineering in 1991. He completed his PhD at Imperial College London in 1995.

Assoc. Prof. Kwoh's research spans data analytics, machine learning, soft computing, and bioinformatics. His work addresses challenges in high-dimensional data, ensemble learning, and the interpretation of complex datasets across domains such as engineering, education, life sciences, medicine, and manufacturing. He has authored over 400 publications, including high-impact journal papers and international conference presentations. According to Google Scholar, his works have garnered extensive recognition, with over 12,000 citations and an h-index of 54.

He is a Senior Member of IEEE and an active contributor to professional societies in Singapore and internationally. His awards include the Public Service Medal (2008) for his contributions to Singapore, the Ministry of Education Long Service Medal (2016) for his contributions to the education, and the COVID-19 Service Medal (2023) for his contributions during the pandemic. He also serves on advisory panels and committees for academic and industrial research initiatives.

Assoc. Prof. Kwoh is actively involved in interdisciplinary research and has led multiple projects that apply data science and Explainable AI to real-world problems in medicine, engineering, and manufacturing. He is also a mentor to numerous PhD students and young researchers, fostering innovation and knowledge transfer.