Syazwan Aizat Ismail is a distinguished researcher who holds a Ph.D. in Mechanical Engineering with environmental health background, focusing on the development of predictive modeling through the integration of artificial intelligence and machine learning algorithms. His groundbreaking research centers around the implementation of IoT equipment with sensor technology for measuring indoor air pollutants and the utilization of IoT devices to collect real-time data. By harnessing the power of artificial intelligence and machine learning, he has achieved remarkable results, including predictive analytics for health occurrence, such as Sick Building Syndrome (SBS) and Building Related Illness (BRI). Syazwan's expertise in micro-controllers, micro-computers, Python programming, and web development has enabled him to create advanced and user-friendly technological solutions that have revolutionized virtual industrial hygiene monitoring through IoT applications.

In addition to his remarkable research achievements to the field of indoor air quality since 2010 with successful completion of private grant research projects in 2012 have demonstrated his commitment to improving and integrating environmental health along with occupational health and safety through cutting-edge methodologies. With his Ph.D. it brings a vast knowledge base to tackle a variety of scientific challenges, coupled with his extensive experience in machine learning and artificial intelligence. Syazwan Aizat Ismail's exceptional research background, publication record, and dedication to innovation would further bolster the university's reputation as a leader in technological advancements and education.