I am Adham Enaya. I am from Palestine and trained as a software engineer at the undergraduate level, subsequently spending several years working with NGOs on humanitarian digital systems. This experience grounded my technical skills in real-world social challenges, particularly in fragile and resource-limited contexts. After completing my master’s degree at the University of Warwick, my interests increasingly shifted toward research at the intersection of technology, cities, and social systems. I became particularly interested in how data-driven and AI-based methods can empower communities and institutions that are often excluded from advanced technological tools.
My background allows me to navigate both applied and theoretical spaces, with a strong motivation to produce research that is methodologically rigorous while remaining socially meaningful and accessible.
What are you working on?
I am currently balancing part-time research with the AI4CI Smart City Design team alongside my PhD research at UCL. My work explores how AI methods, particularly transfer learning and graph-based technologies, can be used to democratise access to advanced analytical tools. The core aim is to enable policy stakeholders to benefit from sophisticated methods by facilitating knowledge transfer across space, time, and data-scarce contexts. This includes supporting other research projects within the AI4CI team, applying software engineering practices to support NLP solutions for housing challenges in London.
What excites you most about your field of research?
What excites me most is the interdisciplinary nature of my field. The combination of software engineering, social science, and spatial analysis allows me to see the tangible impact of new technologies in real-world applications. Rather than developing methods in isolation, I am motivated by applying them to complex, lived contexts where outcomes genuinely matter. Equally important is working with people from diverse backgrounds. These interactions continuously challenge my assumptions, introduce new perspectives, and spark ideas that require deeper critical thinking and experiential understanding beyond purely technical access or abstract theory.
Who has influenced your academic journey?
I have been inspired by many people, from my current and previous supervisors, colleagues, and collaborators, from whom I am grateful to have learned and grown. Beyond my immediate circle, I am inspired by individuals and organizations that successfully integrate business, technology, and research. For example, Google teams led by figures such as Sundar Pichai, Demis Hassabis, and Jeff Dean have shaped how I think about impactful, scalable research. I also find Prof. Hasso Plattner’s journey especially inspiring, as it demonstrates that it is possible to combine entrepreneurial success with sustained academic contribution.
Can you recommend a paper you think we should read?
I strongly recommend the book “The New Science of Cities” by Prof. Michael Batty. Although not a single paper, it is foundational for anyone working at the intersection of urban systems, complexity science, and data-driven modelling. Batty reframes cities as complex adaptive systems, moving beyond static representations toward dynamic, network-based thinking. The book provides both conceptual depth and methodological insight, linking theory with computational and empirical approaches. It has been particularly influential in shaping how urban phenomena can be analysed using scalable models, making it highly relevant for researchers working with AI, spatial interaction models, and urban policy analysis.
What are your ‘Top Tips’ for early career researchers?
I believe the future belongs to generalist thinkers who take a horizontal approach to knowledge. I often remind myself to “take a flower from every garden”; this mindset fosters cross-disciplinary thinking and allows ideas to mature and evolve. Another key principle I follow is adopting an agile approach to research. Applying concepts from agile project management helps balance structure with creative exploration. By combining methodological rigor with openness to uncertainty, we create a productive environment that encourages innovation and leads to more meaningful research outcomes.