Why we need the UvA Data Science Centre

Accelerating science through data

As scientists and researchers, we are already immersed in what Turing Award winner Jim Gray termed the 4th paradigm: the use of data-intensive techniques to attack the hardest problems in research. This can clearly be seen in how researchers have tackled the many, sometimes unexpected, problems that have arisen from the COVID-19 pandemic. For example, what is the real-time economic impact of COVID-19? Through the use of novel data sources and data science techniques, Raj Chetty and his team at Harvard have been answering that question. During the Amsterdam Data Science meetup series on COVID-19, we saw how data science is being used to answer diverse research questions, such as understanding the virus’s impact on our transportation system or building better predictive models for the disease. At the University of Amsterdam (UvA), rarely a week goes by where I don’t see my colleagues in a variety of disciplines engaging with data to do impactful science. Recent examples include:

The driving forces behind the Data Science Centre

Given these compelling examples of data-driven research already taking place at the university, we asked ourselves how can we further support and expand the use of such techniques in all research domains across the UvA? In our discussions with researchers and faculties, one of the clear needs was for skilled data scientists and engineers to be part of research teams. We see this from Chetty’s experience, where the availability of data scientists and engineers as part of the team was central to being able to answer the research question. They were able to develop the required data pipelines, integrate heterogeneous datasets and anonymize data correctly. Personally, it’s clear to me that this team science oriented approach is critical for data-driven research.  The second area where we saw opportunities for acceleration was interdisciplinary cooperation oriented around data. We know that from large scale studies that communities arise around datasets and that these communities often draw from different disciplines and methodological backgrounds. For example, in the AI4Science lab, researchers who study methods of causal discovery are working with those who work on gene regulatory networks to further research in both areas. In the case of studying urban environments mentioned above, urban geographers and political scientists came together around street view datasets.  To address these needs, the UvA will launch a Data Science Centre (DSC) in 2021. The centre was inspired by similar initiatives at NYU, Berkley and more locally by the Netherlands e-Science Centre and the University of Maastricht. However, we aim to have our own unique flavour that will leverage the UvA’s strengths.

Data Science Centre activities

First, the DSC will embed data scientists and engineers throughout every faculty in the university. We will create a community where knowledge sharing is key. Data scientists and engineers will share knowledge and experience through weekly sessions at the Library. The Library has always served as an intellectual crossroads within universities where scientists acquire knowledge and skills making it a natural home for the DSC. We aim to have 35 dedicated staff by 2025 and will offer training opportunities for faculty and staff members more broadly. This will ensure that faculty members will be able to act on new skills gained and the ideas and knowledge sharing happening in the DSC. Second, following the success of collaborative interdisciplinary PhDs and researcher assistants (as seen in the UvA’s research priority areas, AI4 Science and ICAI Amsterdam labs), the DSC will adopt a similar program to innovate data science methods through engagement with hard domain specific research questions.

The Data Science Ecosystem

Just as the UvA helped spur the Amsterdam Data Science (ADS) ecosystem over five years ago, it is now harnessing the ecosystem to improve its broader research. The DSC will work together with ADS to facilitate data science related collaboration and networking in and outside the university. Indeed, this last point shouldn’t be underestimated: data driven research often requires collaboration with parties outside the research domain. Again, reflecting on the COVID-19 economy-tracking example, the data used is supplied by multiple commercial parties. Thus, connecting to the wider data science community through ADS is critical for enabling world-class research. Using the strengths of the UvA and ADS ecosystem, I’m excited to see the new discoveries that the UvA Data Science Centre will make possible. If you are interested in collaborating with the centre, please contact DSC@uva.nl.

AI Research with China: to Collaborate or not to Collaborate – is that the Question?

The opinions in this blog item are the author’s own and do not necessarily reflect those of the organisations she represents. Amsterdam has a historically strong connection with Chinese culture, housing one of the oldest Chinatowns in the Netherlands. While our perception of Chinese culture is perhaps based predominantly on its cuisine, we have to reassess any biases of the past and understand the dynamic, creative and innovative world of AI in China today.

Shifting our perspective of China

Our image of China in Europe comes predominantly through the eyes of Western media [1]. This mixes images of peasant farmers working with technologies of the past, vast modern cities with millions of citizens, and, more recently, the threat of 5G technology being used to infiltrate our state security systems. This results in a biased perception when we are confronted with issues in our own field. China takes a long-term view and this can be seen in its investments in AI research innovation, and particularly its tech talent. Huge efforts have been made to attract successful AI researchers back to their home country to carry out internationally competitive research and to educate new generations of talent. Furthermore, China’s presence in the international AI research community is growing. This can be seen by the increasing percentage of papers in the top international AI conferences that are co-authored by Chinese colleagues, working from China or from abroad [2].

Cultural differences in AI applications

While better, and more, researchers across the globe is generally good news for academic research, in AI we need to remain cautious. China’s enormous investments in AI have led to domination in a narrow set of sub-fields around machine learning, with an emphasis on computer vision and language recognition. This domination could be perceived as cause for concern from an international standpoint. For example, computer vision techniques can be developed for facial recognition to track the movements of citizens, different cultures perceive the benefits and dangers differently. Using these same techniques for other applications, such as recognising the differences between cancerous and benign cells is, however, universally perceived as “good”.

To collaborate or not to collaborate?

This brings us to the difficult political and scientific choices that need to be made as to when and how to collaborate with China, and when to politely decline. Do we need to completely halt all collaboration with Chinese academics and companies? In doing so, we would isolate our colleagues in China. Furthermore, cessation of collaboration would be counter to the established international research culture of openness and dialogue. It is common for European researchers to collaborate with large corporations based in the US. They fund research collaborations and attract high-profile staff to work with them. At the same time, they have created the data economy that led to the passing of EU law to give European citizens at least some control of the data that they (often unknowingly) give to these corporations. There is little discussion in academia, at least to my knowledge, as to whether we should think carefully about collaborations with these US-based companies. While it would be nice to have concrete national guidelines, for example those developed by Frank Bekkers and colleagues [3], or have every AI academic take a course in ethics before signing a contract with a large corporation, this is unrealistic. That said, when working with any large corporation, be they US or China-based, it is essential to retain academic freedom to choose with whom we work and on what research topics.

We cannot not collaborate with the Chinese

So what are my recommendations in this complex and sometimes contradictory collaboration puzzle? China is a world-leader in AI research, technology and innovation. As investment into this field continues to grow this will only become more pertinent. We therefore cannot ignore the relevance of China in our own research and development but we can be considered in our approach to collaborations and make informed decisions on a case-by-case basis. Within the Amsterdam Data Science academic network we have a number of connections with Chinese universities and research institutions, such as the Chinese Academy of Sciences Institute of Automation, Tsinghua University and the Wuhan University of Science & Technology. Alongside my role as director of Amsterdam Data Science, I am the European Director of LIAMA, an organisation for stimulating research collaboration in maths and computer science between CWI, Inria and the Chinese Academy of Sciences. Our goal – just as any international research collaboration – is to stimulate creative and innovative research through the mix of local research cultures. If you would like to collaborate with a Chinese research lab or company then reach out.  Make friends with a Chinese colleague and learn about their culture. Watch some of the Ruben Terlou documentaries. Read the “AI Superpowers” book by Kai Fu Lee, which gives insights into taking the Silicon Valley start-up culture and transferring it to China, while at the same time metamorphosing it to the rules of a new “Wild East”. Learn Chinese and (when we can all travel again) visit your colleagues in China. In the 17th century, Amsterdam was one of the few harbours of religious freedom in the world. Let us continue this tradition by welcoming researchers from other cultures and, through collaboration, understand more about the cultures they come from.   References
  1. A refreshing change, for those who understand Dutch, are the VPRO series about China by Ruben Terlou https://www.rubenterlou.com
  2. Elsevier, 2018. ‘ArtificiaI Intelligence: How knowledge is created, transferred, and used. Trends in China, Europe, and the United States‘ 
  3. Frank Bekkers, Willem Oosterveld, Paul Verhagen “Checklist for Collaboration with Chinese Universities and Other Research Institutions“, The Hague Centre for Strategic Studies, January & September 2019
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