ETH Zurich promotes data science research
Intelligent data science approaches are changing science, the economy and society. In a new interdisciplinary initiative, ETH researchers from the fields of mathematics, computer science and information technology are therefore increasingly dedicating themselves to the foundations of data science.
Anyone who takes a photo with their smartphone nowadays automatically gets complete photo albums including titles, dates, maps and location names that are digitally compiled. That all happens without the users having to do anything themselves. The results are astoundingly good, or at least so good that it isn’t easy to decide who put the album together: a human being or a machine?
This is just one example of how computers can solve certain everyday tasks that used to seem reserved for humans, because they require intelligence and the ability to learn. Catchy names like “artificial intelligence” or “machine learning” have become established for the technologies and methods that make this possible.
They typically refer to calculation methods that have the capacity to learn – so-called intelligent algorithms – that can be used to automate intelligent problem solving. In machine learning, a computer learns from example data how to independently recognise patterns and regularities in data sets.
How data become knowledge
Efficient, intelligent algorithms that can themselves learn how to find the data they need not only have an effect on private users and industrial processes, but also change the way that researchers and computers share their work. Especially for very large, complex and heterogeneous data sets, these algorithms can enable valuable insights that would otherwise remain hidden.
Peter Bühlmann is both an observer and a designer of this rapid development in data-driven methods. The ETH professor’s background is as a statistician. He has headed the new ETH initiative for the “ETH Foundations of Data Science” since the start of the year. Finding ways to obtain information and knowledge from data has always been the focus of statistics.
The data-driven methods are different from the classic methods, however, explains Peter Bühlmann, with his characteristic wit: “The classical statistical approach would be for a researcher to start with a scientific hypothesis and then to carefully consider which data he would collect with which method in order to derive the most informative conclusions. You could say that because nowadays the data are automatically dropping right out of the sky, that’s often no longer the case.”
A new dimension
The new approaches that use intelligent algorithms can automatically extract interesting information from existing data sets without relying on planned out data collection. The subject of data science has arisen in recent years from these new possibilities. Today it’s an interdisciplinary research and development field that intersects statistics, computer science, information technology and mathematics.
“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,” says Bühlmann. ETH Zurich is strengthening the exploration of the fundamentals of data science with the new initiative by bundling existing expertise. Eleven professors from three ETH departments are involved, representing Statistics, Machine Learning and Information Technology and Electrical Engineering.
It focuses on the foundational issues of mathematical theories and algorithmic methods. There are two programmes, one for postdoctoral students and one for visiting researchers. The initiative complements activities in education (Master in Data Science, DAS in Data Science) and in the transfer of knowledge and technology between the disciplines and industry (external pageSwiss Data Science Center). It started on 1 January 2019. It receives 2.7 million Swiss francs in funding from “ETH+”, the ETH-wide initiative for supporting interdisciplinary projects.
Responsibility and fair algorithms
Because data science developments affect many users throughout scientific research, the economy and society, research on the fundamentals of data science carries a special responsibility, says Bühlmann. He sees one challenge in developing algorithms that can deliver causally correct, stable, reliable and easily interpretable results even in the case of complex data sets. For his research on stability and causality (cause and effect), Bühlmann was awarded the prestigious “Guy Medal in Silver” by the Royal Statistical Society and an “ERC Advanced Grant” in 2018.
Ultimately, the automation doesn’t always work as elegantly as it does in the photo app. The application of intelligent algorithms can sometimes become problematic. If, for example, computers can use characteristic data (age, sex, nationality, health, etc.) to determine who is creditworthy, or if they can provide judges with results about the probability that a defendant could be guilty, then it shouldn’t result in any disadvantages.
So “interpretable machine learning” and “fair algorithms” are two major research issues that Bühlmann takes a great personal interest in. “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,” says Bühlmann. “That’s my position.”
Enabling innovation: ETH+ Grants
The ETH+ initiative supports interdisciplinary projects by students, researchers and other ETH members that contribute to better harness the potential for innovation at the borders between disciplines and departments.
The new ETH+ Grants focus on researchers and promoting interdisciplinary research. They support projects in which a minimum of three researchers from different ETH departments or groups participate.
An ETH+ Grant can comprise up to 3 million Swiss Francs for three to four years and cover three quarters of the project costs. It can be used by postdocs and/or more experienced researchers. ETH researchers can apply twice a year.
Next submission deadlines: 1 March and 1 September 2019.