Machine learning is about creating computer programs that can learn to solve complex problems from data. The technology has become part of our daily lives through services like ChatGPT, but it can also be used within various scientific fields.
In my research, I develop machine learning models that can extract information from complex data and make statistical predictions. One example is weather forecasting: a model is trained on historical weather data and can then generate hypothetical future weather developments. The forecasts can not only be made accurate, but the model can also indicate how confident it is, which enables, for example, rational action plans when there's a risk of extreme weather. Another research direction is materials science, where machine learning can be trained on large databases of known materials and then generate new, hypothetical combinations of atoms. In this way, we can more quickly find materials with unique properties, which ultimately can contribute to everything from energy storage to new methods for water purification.
For more information about my background and my research, please visit my external page.
