Five Questions with Gerrit Schipper, Quartermaster of Convergence AI, Data, and Digitalisation

1. Can you provide an overview of some of the interdisciplinary projects undertaken under Convergence AI, Data and Digitalisation (Convergence AI)?

One of our notable projects is the Center for Energy Systems Intelligence. This centre aims to develop a toolbox of sophisticated energy-system models and methods that can help us understand the energy transition needs. Our research and subsequent co-development with industry partners will enable us to implement solutions that have a positive socio-economic impact.

Another great initiative is the Center for FinTech. This centre is dedicated to co-creating in various areas, such as generating synthetic and encrypted data sets for use in test cases, text mining for large language models in financial research, and econometrics. We are also building an IT infrastructure that will enable secure sharing of data and computations across partners, including external companies. Additionally, we are developing a governance framework to manage convergent FinTech research.

Lastly, we have the AI Port Center, which focuses on developing, implementing, and adopting AI technologies in the port environment. This project involves collaboration with regional knowledge institutes, commercial partners, governmental and societal organizations, and the Port of Rotterdam industrial cluster. The AI-Port will be a dedicated research centre with a physical presence in Rotterdam. Initially, the Erasmus Centre for Data Analytics (ECDA) at Erasmus University Rotterdam (EUR) will serve as the location for AI-Port, but we aim to develop it into its own location in the future.

These projects represent the kind of interdisciplinary collaboration we are striving for under the Convergence AI, Data and Digitalisation initiative. By bringing together experts from different fields, we can develop innovative solutions that have a meaningful impact on society.

2. How do Convergence AI projects differ from traditional AI research projects at EUR?

Regarding traditional AI research projects undertaken at EUR, the primary difference with Convergence projects lies in their focus and funding approach. While traditional projects may have a narrower focus, Convergence projects prioritize joint, interdisciplinary research aimed at solving real-world problems within a shorter timeframe.

Convergence provides substantial start-up funding for several years to ensure a successful launch. However, during the start-up phase, the project team needs to secure additional funding from external partners to turn the project into a structural program, which requires engaging with public and/or private organizations and working on structural partnerships.

Therefore, compared to traditional AI research projects, Convergence projects are more geared towards solving immediate problems and require more extensive engagement with external partners to secure additional funding and create sustainable programs.

3. Can you discuss any challenges or opportunities that arise when working on interdisciplinary AI projects?

There are definitely some challenges to be aware of when it comes to interdisciplinary AI projects. One of the biggest hurdles is convincing researchers to step out of their comfort zone and appreciate the benefits of collaboration across multiple fields. However, once they participate in such projects and discover the advantages of multi-faceted research, they can appreciate why Convergence was founded. The initiative brings together a wealth of knowledge and expertise from various fields, from business, law, and economics to social sciences, medical, ethical, and psychological research, and even hard-core science, such as machine learning and quantum computing.

Despite the challenges, the opportunities that arise from interdisciplinary AI projects are unparalleled. By bringing together the best of the best in these different fields, Convergence creates a space for innovation and discovery that would be impossible to achieve working in isolation. The benefits of working together in this way are enormous and have the potential to change the way we think about AI and its impact on society. So, while there may be challenges along the way, the rewards of interdisciplinary collaboration are well worth the effort.

4. How important is it for universities to collaborate with societal partners and stakeholders when conducting research in the field of AI?

The impact of AI on society dwarfs the impact of AI on technology. Therefore, it is impossible to conduct this type of research in isolation without the involvement of societal partners and stakeholders. We will become redundant if we think otherwise.

5. What is the uniqueness of how Convergence AI research approaches and addresses societal AI challenges?

The uniqueness of how Convergence AI or EUR research approaches and addresses societal AI challenges lies in its holistic approach. At the heart of this approach is ECDA, a flagship centre at EUR that enables and facilitates both applied research and active learning in the domain of AI, Digitalization, and Data. ECDA focuses on developing and applying data analytics methods to solve real-world problems.

Furthermore, AI-based initiatives of EUR, such as AiPact or MAGPIE, are rooted in ECDA and benefit from the centre's AI community. The Erasmus Data Collaboratory  serves as a blueprint for the campus development initiative within Convergence AI. It has been a model for the founding of Mondai within TU Delft. The successful 'Leadership Challenge with Data Analytics' program developed by ECDA is another fine example of a holistic approach to dealing with the societal impact of AI. The program has even been adopted by Surf for educating faculty and staff of WO, HO, and MBO institutions in the Netherlands, with faculty from TU Delft, Leiden, and ErasmusMC teaching.

Therefore, the unique approach of Convergence AI or EUR research in addressing societal AI challenges is in its holistic approach through ECDA and other initiatives that aim to develop and apply data analytics methods to solve real-world problems.

For more information contact: Convergence Office at

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