Research and expertise
As a Research Associate at Loughborough University, I am a data scientist with extensive experience in creating and applying machine learning to system-level analysis, particularly focusing on IoT networks and real-time prototypes for decision support systems. My expertise spans developing and deploying sensor networks, data fusion, and machine learning models using Python, along with various ML libraries including Pandas, Scikit-learn, Keras, and TensorFlow.
I have successfully led machine learning initiatives that increased user engagement by 25% through developing personalised control systems. I have hands-on experience with environmental sensor deployment and calibration, specifically working with Dantec temperature and relative humidity sensors for gathering novel datasets. I have also demonstrated success in bridging academia-industry gaps, notably by drafting agreements between Loughborough University and the Building Research Establishment (BRE) for cross-disciplinary research.
My technical portfolio includes developing various IoT-based systems, from environmental monitoring solutions to computer vision applications. Notable achievements include creating interactive Power BI dashboards for visualising multi-source sensor data and implementing real-time monitoring systems. I have proven expertise in large-scale data analysis, machine learning model development, sensor data processing, and database management, complemented by strong programming skills in Python, Java, and C++.
Current research activity
As a Research Associate at Loughborough University, I am leading research on developing novel machine learning approaches for environmental monitoring and control systems. My research focuses on:
- Design and implementation of environmental sensor networks
- Development of advanced machine learning models for real-time data analysis
- Creation of data fusion approaches to integrate diverse sensor inputs into a centralised database
- Implementation of interactive dashboards for real-time data visualisation and decision support
- Leading a coding club in The School of ABCE, enabling researchers to utilise state-of-the-art machine learning techniques
My research exemplifies interdisciplinary collaboration, successfully integrating expertise from Architecture, Building and Civil Engineering (ABCE), Computer Science (CompSci), and Sport Exercise and Health Science (SSEHS). This cross-disciplinary approach has enabled effective knowledge transfer between domains and fostered innovative solutions to complex challenges. The impact of this work is evidenced by publications in high-impact publishers and consecutive first-place wins in the American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE) PhD competitions in 2021 and 2022. These achievements demonstrate both the academic rigor and practical applicability of my research in environmental monitoring and control systems.
Recently completed research projects
- Exploring the viability of machine learning models to optimise thermal comfort for people with dementia and their family caregivers: towards designing dementia-friendly environments
- Improving Medical Intervention Systems with Smart IoT Technologies: designed and created a novel eye blink switch to enable people with upper limb disabilities to control the light using their eye blinks
- Assessing IoT security and privacy risks and challenges in drone-based systems for smart cities
Recent publications
- Aladawi, A, Roberts, BM, Hogervorst, E, Cook, M (2023) Indoor environmental quality studies. In Halsall, B, Riley, M, Hogervorst, E (ed) Design for Dementia, Routledge, pp.138-153, ISBN: 9781003306054. DOI: 10.1201/9781003306054-8
- Jain, M, Aladawi, A, Hogervorst, E (2023) The medical background of dementia. In Halsall, B, Riley, M, Hogervorst, E (ed) Design for Dementia, Routledge, pp.18-38, ISBN: 9781003306054. DOI: 10.1201/9781003306054-3
Teaching
I contribute to learning and teaching activities across different Schools’ programmes including:
Undergraduate
- Introduction to programming and databases (COA122)
- Web programming (COA123)
- Object-oriented programming (COA256)
- Databases (COA201)
- Profesional practice (CVZ002)
Postgraduate
- Programming for data science (COP504)
- Network monitoring and management (WSP019)
- Programming for Specialist Applications (COP501)
Enterprise
Current projects
- Creating a digital twin utilising different data science techniques
Recently completed projects
- Machine learning thermal comfort model for people with dementia and their family caregivers
- Novel eye-blink switch
- Designer and creator of ACTInG website (https://acting-research.lboro.ac.uk/)
Profile
As a Research Associate at Loughborough University, I specialise in data science and machine learning applications for environmental monitoring and control systems. My expertise encompasses developing IoT sensor networks, creating machine learning models, and implementing real-time data analysis solutions. I lead initiatives in bridging academia-industry partnerships, notably establishing collaboration between Loughborough University and the Building Research Establishment (BRE).
My academic background includes an MSc in Internet of Things (with Distinction) from Bournemouth University and a First-Class Honours in Computer Science from King Khalid University. As a Machine Learning Engineer at University Hospitals Dorset NHS, I designed an innovative eye-blink detection system that improved quality of life for over 50 patients with limited mobility, demonstrating my ability to translate complex technical solutions into practical applications.
My technical expertise is strengthened by specialised training in Linux Systems, Robotics Applications, and various programming languages. I currently lead a coding club in the ABCE school, enabling researchers to utilise state-of-the-art machine learning techniques. My work's impact is evidenced by publications and consecutive wins in ASHRAE competitions, demonstrating the successful application of advanced technologies to real-world challenges.
My research interests focus on developing adaptive machine-learning solutions for environmental systems and intelligent monitoring applications. I aim to bridge the gap between AI advancement and practical applications, particularly in creating sustainable, smart environments that address real-world challenges.
Professional affiliations
- Professional member at IEEE
- Professional member at BCS
- Member at CIBSE
- Member of the Applied Cognition, Technology and Interaction Group (ACTInG)
Awards
- Won first place, TWICE, in the American Society of Heating, Refrigerating, and Air-Conditioning
- Engineers (ASHRAE) competitions in 2021 and 2022
- Won an “Excellent Prize” for the PhD Video Challenge 2022 in the “Buildings and Cities” Journal
- Presenter of Bournemouth University in the NHS Environmental Research
- Best BSc graduation project, KKU
- Bronze medal in the best presenter competition in the KKU
Key collaborators
My research and enterprise activities are conducted with a range of academic and stakeholder partners, including:
- Building Research Establishment (BRE)
- NHS
- AbilityNet