Academic role and professional focus

Dr Shahid Mahmood Awan is a Lecturer in Data Analytics at the University of the West of Scotland (UWS), Programme Leader for the BSc (Hons) Data Science and Artificial Intelligence, and Data Science Track Lead for the MSc Information Technology programme. He is a Fellow of the Higher Education Academy and holds a PhD in Computer Science, specialising in Artificial Intelligence.

He brings over 20 years of combined academic and industry experience across artificial intelligence, data science, software engineering, research, teaching and technology-led innovation. His work is united by a focus on developing responsible and practically useful AI systems that address real-world challenges in agriculture, health and wellbeing, energy, education, industrial automation and sustainability.

Research expertise and current directions

His principal areas of expertise include artificial intelligence, generative AI, machine learning, deep learning, computer vision, natural language processing, large language models, retrieval-augmented generation, intelligent agents, the Internet of Things, edge intelligence, data analytics and time-series forecasting.

His research increasingly explores multimodal AI, bringing together images, sensor measurements, text and contextual information to create intelligent systems capable of operating in real-world environments.

His main application areas include:

• Smart and precision agriculture

• Digital health and responsible health AI

• Industrial automation and enterprise generative AI

• AI-supported education and adaptive learning

• Energy and environmental forecasting

• Circular-economy systems

• Climate-resilient infrastructure and smart buildings

• Responsible, explainable and human-centred AI

In smart agriculture, his work covers AI-supported crop monitoring, plant disease detection, multispectral imaging, environmental sensing, edge AI and multisensor data fusion. Recent research includes deep-learning methods for potato disease detection, the SPADE AI-integrated monitoring system for soft-fruit production and IoT-enabled outdoor environmental monitoring.

His interdisciplinary health research includes AI-assisted diagnosis, conversational mental-health technologies, women’s health, dementia and Alzheimer’s research, and the ethical and responsible use of AI in health-related settings.

Research leadership and interdisciplinary collaboration

Dr Awan contributes to research and innovation through the UWS Digital Connectivity and Innovation Centre, where he established a Smart Farming Laboratory focused on monitoring Scottish crops, including potatoes and soft fruits. His work also connects with the DeLTA Research Group and the Institute of Artificial Intelligence, Virtual Communications and Networks through collaborative research, doctoral supervision and research dissemination.

He was selected for the UWS Crucible 2025 research leadership programme. Through Crucible, he contributed to an interdisciplinary project exploring how AI, community education and local support networks could help migrant women access educational opportunities. The relationships developed through this programme also supported continuing collaboration with Health Sciences colleagues, including a collaborative Vice-Chancellor-funded doctoral studentship and two dementia and Alzheimer’s data-challenges activity.

He presented collaborative work on AI and IoT-enabled smart farming and the circular economy at the AVCN Institute Conference in 2025. At the 2026 AVCN Institute Conference, he served as a session chair and supported a doctoral researcher and colleague to present their collaborative work.

Teaching, curriculum and educational scholarship

As Programme Leader for the BSc (Hons) Data Science and Artificial Intelligence, Dr Awan led the development of the programme specification and curriculum and supported its internal and external validation and approval in 2025. He continues to support implementation at the London Campus and preparations for delivery at Nepal.

As Data Science Track Lead within the MSc Information Technology programme, he contributes to curriculum development, module coordination and alignment of teaching with developments in data science and artificial intelligence. His recent teaching and module leadership include: Data Mining and Business Intelligence, Data Analysis and Visualisation, and Database Design and Implementation.

His teaching practice is shaped by experience of managing large and academically diverse cohorts. During 2025–26, he coordinated module delivery involving more than 1,300 students across the Paisley and London campuses.

He introduced flexible code-based and no-code learning pathways using Python and Orange, enabling students from varied technical and academic backgrounds to participate meaningfully in data analytics and machine learning. He also coordinates multi-campus teaching teams and contributes to learning resources, assessment design, marking guidance, moderation, VLE organisation and student communication.

Dr Awan was selected as a SICSA Learning & Teaching Scholar for the 2025 cohort. Working with colleagues from the University of Glasgow and Heriot-Watt University, he co-led the project “Student Engagement in Graduate Apprenticeship Projects”. The project uses an adapted survey framework to examine cognitive, behavioural, social and emotional dimensions of student engagement. 

He also mentors colleagues undertaking professional development, including Fellowship and postgraduate academic-practice routes, and provides developmental feedback through teaching observation and collaborative review of module delivery.

Doctoral supervision and researcher development

Dr Awan has supervised eight doctoral researchers, serving as lead supervisor for two and supervisor for six. This includes three researchers supported through UWS Vice-Chancellor-funded studentships. He has supported doctoral completions, publication development, conference participation, teaching development and progression into academic and research careers.

His supervisory approach combines intellectual challenge with structured, supportive mentorship. He places particular emphasis on researcher confidence, wellbeing, independence and the ability to communicate a coherent research story. He also prepares candidates carefully for progression reviews, presentations and doctoral examinations.

This contribution was recognised through a student nomination for the SICSA Supervisor of the Year Award in 2026. He was subsequently recognised among the top six nominees from more than 250 Computing nominations.

He welcomes doctoral enquiries in:

• Artificial intelligence and generative AI

• Machine learning and deep learning

• Smart and precision agriculture

• Digital health and responsible health AI

• Internet of Things and edge intelligence

• Multimodal data analysis

• AI-supported education

• Responsible and explainable AI

• Industry-linked intelligent systems

Industry innovation and knowledge exchange

Dr Awan is a Co-Investigator on two active Innovate UK Knowledge Transfer Partnerships.

The SpaceandPeople KTP focuses on embedding business-process-management expertise to streamline and automate business-critical systems and support organisational growth. The Booth Welsh KTP applies artificial intelligence to industrial and engineering processes to improve productivity and organisational capability. 

Earlier in his career, Dr Awan served as an Innovate UK KTP Associate at the University of Derby, contributing to the development of an edge-machine-learning platform for monitoring, operational safety and decision-making.

Through a British Council Going Global Partnerships project, he supported UK-Pakistan collaboration in IoT-enabled environmental monitoring, smart infrastructure and climate resilience. The collaboration has contributed to an accepted and in-press journal article on real-time outdoor environmental monitoring. A related book chapter on generative AI for detecting climate-induced damage in heritage structures and smart buildings is in development.

His wider applied-research experience includes electricity-demand and solar-generation forecasting, dengue surveillance and forecasting, circular-economy systems, smart-meter analytics and AI-supported environmental monitoring.

External academic standing and policy engagement

Dr Awan serves as Lead External Examiner at both Northumbria University and the University of Huddersfield. He contributes to academic standards, curriculum quality, assessment design and enhancement of the student learning experience across undergraduate and postgraduate computing programmes. He also mentors and supports newly appointed external examiners.

He was an acknowledged contributor to UK Parliament POSTnote 735, “Energy security and AI”, published in December 2024. The briefing examined current and emerging applications of AI and machine learning within the energy system, implementation barriers, risks and policy considerations.

His wider academic service includes:

• Guest Associate Editor appointment with Frontiers in Communications and Networks

• Technical Programme Committee membership for SKIMA 2027

• Multiple Journals and conferences peer review

• Doctoral examination and viva participation

• Conference session chairing

• Invited talks at the AI in Education Forum and IEEE ICOSST

• Contributions to international academic-development and knowledge-exchange activities

Recognition and professional standing

Dr Awan’s professional recognition includes Fellowship of the Higher Education Academy, selection for the UWS Crucible 2025 research leadership programme, selection as a SICSA Learning & Teaching Scholar, and recognition among the top six nominees for the SICSA Supervisor of the Year Award 2026.

He was nominated through the UWS Students’ Union for the Teacher of the Year Award in 2025. Earlier in his career, he supervised the deep-learning project “Cricket Video Scene Description”, which received the Rector’s Award for Entrepreneurship and Innovation in 2018.

Future direction and collaboration

Dr Awan’s longer-term goal is to develop trustworthy AI systems that progress beyond experimental prototypes and produce measurable benefits for industry, communities and public services.

His current priorities include multimodal AI, affordable edge-AI tools for agriculture and environmental monitoring, responsible generative AI for health and education, secure enterprise AI architectures and human-centred evaluation of AI-supported services.

He welcomes collaboration with universities, agricultural and food-sector organisations, healthcare researchers, engineering companies, public-sector bodies, technology providers and third-sector organisations. He is particularly interested in joint funding applications, Knowledge Transfer Partnerships, doctoral research, AI feasibility studies, prototype development, professional training and interdisciplinary research with the potential to create practical economic or societal impact.