Interdisciplinary & Innovation-forward
The Interdisciplinary & Innovation-forward initiative at City St George’s, University of London, School of Science & Technology, Department of Computer Science, promotes collaboration between students, staff, and partner institutions to address real-world, interdisciplinary challenges.
It supports the application of data science, machine learning, and artificial intelligence in practice-oriented settings through activities such as hackathons, visualisation challenges, knowledge-sharing and spark sessions, and incubator projects, enabling participants to develop and evaluate data-driven solutions to complex problems.
From AI Experimentation to Scalable Business Value: An Interdisciplinary Industry Engagement Session
On 29 October 2026, MSc Data Science students took part in an interactive session exploring how organisations can move beyond AI experimentation to deliver scalable, sustainable business value. Led and organised by Alex Galkin and James Lupton, Chief Technology Officer at Cynozure, the session combined a collaborative card game with group discussion to connect technical AI capabilities with organisational strategy, business priorities, and real-world implementation challenges.
The session provided a practical setting for students to explore the interdisciplinary nature of AI adoption. Working in groups, participants used AI capability and use-case cards to identify opportunities, assess potential value, and prioritise initiatives within three categories: Now, Next and Future.
Through collaborative decision-making, students considered how technical capabilities must align with business objectives, organisational readiness, governance, risk management, and ways of working. They explored the dependencies and trade-offs involved in moving AI projects from initial experimentation to implementation at scale, recognising that successful AI delivery requires more than technical expertise alone. It also depends on strategic thinking, cross-functional collaboration, and an understanding of the organisational contexts in which data-driven solutions are developed and deployed.
The session concluded with a group playback, bringing together the priorities, insights, and observations generated by the teams. This encouraged participants to compare perspectives, reflect on different approaches to AI delivery, and consider how interdisciplinary collaboration can support more informed and effective decision-making.
By combining practical problem-solving with collaborative learning, the session exemplified the initiative’s commitment to connecting data science, artificial intelligence, and machine learning with real-world challenges. It also strengthened engagement between academic learning and industry practice, giving students an opportunity to explore how data and AI can be translated into meaningful organisational outcomes.
Data Science & Entrepreneurship Mentoring Event
18 November 2025 | Bayes Business School
On 18 November 2025, MSc Data Science students joined forces with MSc Entrepreneurship students at Bayes Business School for an interdisciplinary Innovation Challenge delivered as part of the Mentoring programme. Led and organised by Alex Galkin and Stefania Zerbinati, the event provided a dynamic environment for collaboration across disciplines, focused on addressing real-world challenges in areas including health, social impact, agriculture, and finance.
This initiative created a unique opportunity for MSc Data Science students to apply their technical expertise within an entrepreneurial context. Working in interdisciplinary teams, students contributed to the development and evaluation of innovative business ideas, ensuring that proposed solutions were grounded in robust data-driven thinking.
MSc Data Science participants played an important role in shaping team outcomes by:
- Understanding business problems and identifying the data required to address key questions
- Assessing data availability, quality, and usability, and proposing strategies to access or generate relevant data
- Evaluating technical feasibility, including appropriate analytical methods, potential challenges, and resource requirements
- Advising on the realism of proposed solutions from a data-driven perspective, including the viability of predictions, insights, and automation
Impact on Learning and Student Experience
The event demonstrated the value of interdisciplinary collaboration in enhancing both technical and professional skills. Students worked intensively in teams to bridge the gap between data science and entrepreneurship, culminating in presentations that showcased innovative, data-informed solutions.
MSc Data Science students made a significant impact by bringing analytical rigour to early-stage business concepts. Their contributions helped teams better understand what could realistically be achieved through data, strengthening the overall quality and feasibility of proposed ideas.
The experience reinforced key learning outcomes, including problem formulation, critical evaluation of data, and the application of analytical thinking in complex, real-world scenarios. It also highlighted the importance of communication and collaboration across disciplines.
