ETH promotes data science research

Zurich – The Swiss Federal Institute of Technology Zurich (ETH) has launched an initiative in which researchers active in various disciplines can dedicate themselves to the field of data science. Here, the focus will be on mathematical theories and algorithmic methods.

A total of eleven professors from the Departments of Mathematics, Computer Science and Information Technology at ETH Zurich are increasingly dedicating their work to the foundations of data science as part of a new initiative. The focus is on fundamental questions of mathematical theories and algorithmic methods. Two programs have been established: one for postdoctoral students and one for visiting researchers, as ETH Zurich explained in a press release

The new initiative was launched in January and, according to reports, has received support from ETH+ in the form of 2.7 million Swiss francs. The ETH+ initiative is the university’s vehicle for supporting interdisciplinary projects.

Peter Bühlmann, Professor of Mathematics, comments: “Data science is something new. It’s not just statistics, not simply computer science and not merely information technology, but instead a combined effort of all three fields”. ETH Zurich is strengthening the exploration of the fundamentals of data science with the new initiative by bundling existing expertise.

According to Bühlmann, a particular challenge will be developing algorithms “that can deliver causally correct, stable, reliable and easily interpretable results even in the case of complex data sets.” This is particularly relevant in the application of intelligent algorithms in sensitive areas – for example in court proceedings: if computers can provide judges with clues about the probability that a defendant could be guilty, then it shouldn’t result in any disadvantages. Discussing his area of research, Bühlmann explains: “As a researcher of the fundamentals of the subject, I want to produce something useful for society. I want to know when an application delivers reliable results, and when they are less reliable”. 

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