Empowering Collaborative Innovation
The SJTU-Warwick Joint Seed Fund 2025 is now powering a fresh series of collaborative research initiatives, driving innovation and global impact. Over the next 12 months, this seed funding will fuel a vibrant mix of interdisciplinary projects that tap into the unique strengths of both institutions, driving fresh thinking, original research, and real-world impact.
Explore the projects below to see how curiosity, creativity, and collaboration are shaping the future of research...
Mapping Language Loss: How Network Analysis Is Helping Us Understand Aphasia Link opens in a new window
Aphasia is a language disorder that often follows a stroke, making it difficult for people to find and use words. It’s not just about forgetting vocabulary, it disrupts the entire structure of how words are stored and connected in the brain.
Traditionally, researchers have looked at word problems in isolation. But this new project, led by teams at Fudan University and the University of Warwick, takes a fresh approach by treating language like a network of interconnected ideas.
Rethinking Global History in a Changing WorldLink opens in a new window
Global history has been a major focus at the University of Warwick for nearly two decades, thanks to the pioneering work of scholars like Professor Maxine Berg and Professor Giorgio Riello. But the world and the academic landscape have changed. Movements like Black Lives Matter, the push to decolonise education, and growing awareness of global crises have challenged historians to take a fresh look at how we study the past.
The proposed project will focus on historical connections between Central Asia and China, and between China and Southeast Asia, offering fresh insights into global interactions and cultural exchange. By rethinking how global history is studied and shared, this collaboration aims to shape a more representative and relevant understanding of our shared past.
Why Storytelling is the Future of Science Communication
Science can be complex, but storytelling is helping make it more relatable and easier to understand. Unlike traditional methods of sharing information, storytelling turns research into engaging narratives that capture people’s attention and improve how science is communicated.
This collaboration aims to deepen our understanding of science communication and support its growth as a field, making science more accessible, engaging, and impactful for everyone.
Building Climate-Resilient Cities Through Nature-Based Solutions
Led by Dr Feng Mao from the School for Cross-Faculty Studies, University of Warwick and Prof Junxiang Li from the School of Design, Shanghai Jiao Tong University, this research project addresses one of the most urgent challenges of our time: how to make megacities more resilient to climate change.
Nature-based solutions (NbS) use ecosystems such as green spaces, wetlands, and urban forests, to reduce climate risks like flooding, heatwaves, and poor air quality. But implementing these solutions in dense, fast-changing cities is complex. This project takes Shanghai and London as comparative case studies and combines design thinking, environmental modelling, and spatial analytics to create data-driven strategies tailored to each city’s unique context. It tackles key technical challenges, including predicting long-term impacts, comparing strategies across different urban environments, and turning scientific evidence into actionable plans.
Uncovering Hidden Patterns in Complex Systems
Led by Prof Anastasia Papavasileiou from the Department of Statistics, University of Warwick and Prof Shan Luo from the Department of Statistics, Shanghai Jiao Tong University, this research project is tackling one of the most complex challenges in modern data science: understanding how interconnected systems evolve over time.
These systems, such as biological networks or medical data, are vast, dynamic, and often messy, with information coming in many forms, from numbers to categories to rankings. Traditional methods struggle to make sense of this complexity, especially when the data doesn’t follow neat patterns.