Science Stories

Bedartha Goswami’s goal is to build a bridge between machine learning and climate science. It’s not easy: when new methods in machine learning are developed, the ways that they can be applied in the climate sector is often not considered. Goswami is a team player, so his solution has been to put together a group with the interdisciplinary expertise needed for real breakthroughs in climate science.

Since the Cyber Valley initiative was established, there has been a controversial debate in Tübingen about research into artificial intelligence (AI) and machine learning. Of all the places in Europe, this picturesque town on the Neckar is supposedly one of the most important centres of AI research? For many, it is hard to imagine.

With a PhD in machine learning, the world seems to lie at your feet. But what to do afterwards: academia, the IT-sector, or something else entirely? For Poornima Ramesh, the answer is clear: she wants to use machine learning to improve people’s lives in places where the problems are most urgent. To further this goal, she joined a global advisory, data analytics, and research organization.

A meeting in Vienna, a lecture in Boston, a conference in London – academic events such as these are a part of researchers’ everyday working lives. They are where researchers meet their scientific communities to discuss their own research, exchange ideas with others and develop new ideas for collaboration. But how do they get to London, Boston, or Vienna?

We can’t change how much the sun shines or how hard the wind will blow. But if we want to utilise renewable energy better, we need to take into account how weather and climate will change over time. Nicole Ludwig, an expert in machine learning and renewable energy systems, develops models that do just that.

Different perspectives advance research. Yet Africa is considered all too rarely in this context. A fellowship program for young researchers aims to change that. It brings five talents from African countries to Tübingen to spend half a year working on research projects in machine learning.

Skepticism about the use of AI systems is widespread. Many say the systems are too opaque. Professor for Explainable Machine Learning Zeynep Akata wants to change that - and has made the user’s perspective the focal point of her research.

Algorithms are becoming better and better at analyzing medical images and recognizing diseases. Researchers Christian Baumgartner and Sergios Gatidis – one an expert on artificial intelligence (AI), the other a radiologist – expect that algorithms will fundamentally change doctors’ work.

Computer Science Professor Ulrike von Luxburg speaks in an interview about the opportunities and challenges of trimming machine learning systems to fairness. Prof. von Luxburg also explains why she is convinced that people, rather than machines should resolve certain questions.