I’m a dedicated early career researcher specializing in artificial intelligence, committed to harnessing technology for positive global impact. Explore how my work in advancing AI addresses pressing challenges and fosters meaningful change. Learn more about my research interests and background.
PhD interdisciplinary (Computer Science & Civil Engineering) | MSc in Computer Science | BSc in Software Engineering
I am a dedicated researcher with a unique interdisciplinary background, bridging Computer Science and Civil Engineering. My doctoral journey focused on leveraging Machine Learning to optimize Thermal Energy Storage Air Conditioning (TES-AC) systems in commercial buildings, ensuring energy efficiency without compromising occupant comfort—a critical area in tropical climates where sustainable alternatives to conventional AC systems are increasingly necessary due to global warming.
My academic path began with a foundation in engineering, where I was awarded a high achievers scholarship. I then pursued a Bachelor of Science in Software Engineering, graduating with first-class honors. My passion for research was ignited during my Master of Science in Computer Science, where I received a scholarship and began exploring the vast potential of the field. This passion led me to apply for a competitive, fully-funded PhD program, where I successfully secured an honorable scholarship after presenting my research proposal during the interview.
The journey of my PhD was filled with challenges, from securing commercial collaborators to mastering the intricacies of thermal energy storage chillers. These experiences tested my resilience and determination, but ultimately strengthened my resolve. After passing my MPhil, I continued with my PhD, successfully balancing the demands of academia and motherhood. I defended my thesis on November 17, 2023, and proudly graduated on March 9, 2024. Throughout this journey, I published in Q1 and Q2 journals, presented at international conferences, and authored a chapter in an academic book by Clemson University.
Beyond my doctoral research, I am deeply involved in several innovative projects, including autonomous vehicle security, early cancer detection, sustainable building practices, and AI-driven environmental mapping. My work has earned recognition through numerous awards, including IT Graduate of the Year (2nd place), semi-finalist in the poster presentation at the American Association for the Advancement of Sciences (AAAS), and Thesis in 5 Minutes (second place). I am also passionate about teaching, having served as a teaching assistant, where I developed lab materials, marked assignments, and completed postgraduate teacher training. My commitment to education is evident in my publications and my dedication to making complex concepts accessible to students.
As a global ambassador for the Women Tech Network, I have spoken at international conferences to inspire women to pursue their dreams. I also contributed to the technical committee of an international conference organized by the University of Nottingham, Malaysia.
Outside of academia, I maintain a healthy lifestyle, with a top finish in a 10km charity race in 2017. I am actively involved in charity organizations and enjoy activities such as CrossFit, chess, and Pilates. I am driven by a desire to contribute to society and appreciate the small things in life. My journey has been one of perseverance, passion, and a commitment to advancing knowledge and making a meaningful impact.
Ownership shares in companies, bought and sold in stock markets. Debt securities issued by governments or corporations, paying fixed interest.
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Basic accounts for depositing and managing money. Time-based savings accounts with fixed interest rates.
Driven by a long-standing interest in cancer research, I am currently focusing on computer vision techniques for early detection of breast cancer as part of the ACROBAT Challenge. My work aims to leverage advanced imaging and machine learning methods to enhance diagnostic accuracy and support medical professionals in identifying cancer at its earliest stages, potentially saving lives through early intervention. Current Collaboration: Dr. Iman Yi Liao; Mostafa Abdulrazic
My research in autonomous vehicle security centers on enhancing V2X communication, a crucial aspect of the evolving landscape of intelligent transportation. I investigate potential vulnerabilities and develop strategies to secure communications between vehicles, infrastructure, and other connected devices, contributing to safer, greener, and more efficient urban mobility systems. Current Collaboration: Dr. Vimal Rau; Dr. Tomas Maul; Mostafa Abdulrazic
I am committed to applying AI to create resilient and sustainable urban environments, focusing on mitigating urban heat island effects in densely populated cities. My research explores how AI-driven solutions can optimize urban design, energy use, and environmental management, contributing to healthier and more livable cities in the face of climate change. Current Collaboration: Dr. Jing Ying Wong; Dr. Chun-Chieh Yip; Mostafa Abdulrazic
In the pursuit of a cleaner environment, I utilize drone technology combined with GIS for detecting and mapping waste dump sites. This research aims to provide efficient and accurate data to aid in environmental monitoring, policy-making, and urban planning, addressing the pressing issue of waste management in growing urban areas. Current Collaboration: Dr. Shafi Mohammed Tareq; Mostafa Abdulrazic
My passion for enhancing educational experiences drives my research into technological advancements in teaching and learning. From exploring lecture-based and virtual reality game-based education to developing gamified virtual labs, my work aims to create interactive and low-risk learning environments in fields like biology, chemistry, pharmaceuticals, and BIM education. My goal is to harness technology to make learning more engaging, accessible, and effective for students across various disciplines.
This study explores the use of virtual reality (VR) games as educational tools, particularly when combined with traditional lectures. It aims to determine whether blending immersive VR games with lectures enhances learning, memory, and knowledge retention while adding entertainment value. The research compares three learning methods: traditional lectures, a VR game on oil rig exploration, and a combination of both. Results show that the combined approach significantly boosts learning and retention, suggesting it could be a valuable teaching tool. However, the study's findings are based on a single VR game, and varying participant groups may have slightly influenced the outcomes. The research provides evidence that integrating VR games into curricula could enhance educational effectiveness.
Journal of Applied Research in Higher Education
DOI: https://doi.org/10.1108/JARHE-09-2020-0302
This study investigates the use of gamified virtual labs in higher education, focusing on their effectiveness as educational tools. It explores whether these immersive labs can enhance student engagement and facilitate low-risk active learning. The research includes two gamified virtual labs with nine essential biology and chemistry experiments, integrated into a science foundation course. A post-participation survey assessed the impact of gamification. Results show that gamified labs boost student involvement and knowledge development, making them promising for interactive learning. However, the study's limitation is the absence of a comparison with non-gamified simulations.
Journal of Applied Research in Higher Education
DOI: https://doi.org/10.1108/JARHE-09-2022-0281
Thermal-Energy-Storage Air-Conditioning (TES-AC) is a sustainable cooling method that stores chilled water at night when energy demand is low and uses it to cool buildings during the day. However, external weather conditions might impact the stored thermal energy and water consumption in TES-AC systems. Understanding the relationship between weather and water usage is crucial for applying computational intelligence, like machine learning, to predict maintenance needs in TES-AC systems. Warmer weather may lead to increased water consumption due to evaporation, affecting the system’s efficiency. This research explores how external weather data influences water consumption in TES-AC and emphasizes its importance in predictive maintenance models.
Energy Nexus
DOI: https://doi.org/10.1016/j.nexus.2023.100187
This research aims to develop a machine learning model to predict the optimal water volume to be chilled in Thermal-Energy-Storage Air-Conditioning (TES-AC) systems. TES-AC stores chilled water during off-peak hours, reducing chiller use, lowering costs, and cutting carbon emissions. Predicting the correct amount of water to chill daily can be difficult. The proposed model uses inputs like weather, day of the week, and occupancy data to predict the needed water volume. A Multilayer Perceptron model was used, achieving 93.4% accuracy. The model offers facility managers specific water ranges to minimize errors and can be retrained for different TES-AC systems, making it a flexible and sustainable solution for greener buildings.
Journal of Building Engineering
DOI: https://doi.org/10.1016/j.jobe.2023.107016
Deep learning has significant potential in Facility Management (FM), especially for optimizing Heating, Ventilation, and Air Conditioning (HVAC) systems. Despite promising research, its application in FM remains limited. HVAC failures can lead to substantial financial losses, making predictive maintenance vital. This review covers 100 studies on neural networks in FM, emphasizing deep learning's role in predicting issues, reducing energy use, and improving maintenance efficiency. It also highlights the need for public datasets to enhance model effectiveness. Applying deep learning to Thermal-Storage Air-Conditioning (TS-AC) systems could boost sustainability and cost-efficiency in buildings.
Automation in Construction
DOI: https://doi.org/10.1016/j.autcon.2022.104445
In my commitment to continuous learning and professional development, I successfully completed certification in postgraduate teacher training for the University of Nottingham, adhering to the university's stringent protocols.
I received the Nottingham Advantage Award, which recognizes commitment to employability by providing valuable skills development, expert guidance, and notable achievements included on my degree transcript and graduation ceremony. This award stands for excellence in leveraging university experiences to enhance career prospects. Additionally, I earned two nanodegrees for Artificial Intelligence and Machine Learning from AWS x Udacity, which I attained through two highly competitive international scholarships. My dedication to excellence was further recognized at the prestigious WomenTech Conference, where I was honored with the IT Graduate of the Year award in November 2022. View my Linkedin Profile for more information.
As an early career researcher, I have had the privilege of contributing to and advancing knowledge in my field through various research projects and teaching roles. My experience ranges from conducting research to assisting in student education, all of which have enriched my academic journey and fueled my passion for innovation and learning.
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Tomas Maul
Jing Ying Wong
Chun Chieh Yip
My research primarily focuses on artificial intelligence for societal good, sustainable building technologies, and autonomous vehicle security. I am particularly interested in early cancer detection using computer vision, securing V2X communication for safer intelligent transportation, and optimizing urban environments to combat climate change. I intend to explore AI's potential in national security and stability. Driven by a passion for environmental sustainability, I am researching drone technology and GIS for waste management and advance education through interactive, tech-driven learning environments.
My most significant contributions include applying deep learning in facility management to optimize HVAC systems, leading to improved predictive maintenance, energy efficiency, and reduced operational costs. I have developed a machine learning model with a 93.4% accuracy rate for predicting water load in Thermal-Energy-Storage Air-Conditioning systems, enhancing sustainability in building operations. Additionally, my research in educational technology has demonstrated that combining virtual reality games with traditional lectures can significantly boost learning outcomes, and I have pioneered the use of gamified virtual labs for low-risk, interactive learning in science education.
You can find my published work in various journals and conference proceedings, including Automation in Construction, Energy Nexus, Journal of Building Engineering (Elsevier), Simulation and Gaming (SAGE), and the Journal of Applied Research in Higher Education (Emerald Insights). I have also presented at IEEE and MDPI conferences. A complete list of my publications is available on my Google Scholar profile, ResearchGate profile, and LinkedIn profile.
Yes, I am open to research collaborations that align with my expertise in artificial intelligence, sustainable building technologies, autonomous vehicle security, and early cancer detection. I am also interested in exploring research in national security, particularly in the areas of cybersecurity and stability in the context of cyberweapons. If you have a project in mind, please feel free to contact me through my LinkedIn profile.
I am certified to teach and have received certification in postgraduate teacher training. I have experience teaching and mentoring students at both undergraduate and postgraduate levels, serving as a Graduate Teaching Assistant and lab instructor, focusing on software engineering, civil engineering, and computer science. My roles have included grading, tutoring, preparing lab materials, assessing coursework, and providing academic mentorship to help students deepen their understanding of complex concepts.
Yes, I offer consulting services in artificial intelligence and its applications. You can reach me via email or Linkedin. I typically respond within 48 hours.
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