After graduating and receiving her PhD in Poland, she worked as a postdoc in the United States. "My work shifted from academic to more applied. As a PhD student, I developed methods for translating numerical data into natural language. In the United States, we gathered a variety of sensor data from senior citizens and translated these into natural language for nurses who cared for the elderly."

Scientists usually find it easy to work with numbers, but the majority of the general population does not. "Most people understand information presented in plain language better than in numerical form. Our car's speedometer is simple to understand, but when the dashboard displays a variety of numerical data, it will become increasingly difficult to comprehend. Given that, for instance, the amount of data produced by a single hospital doubles every two years, it is clear that summarising the data in an understandable format is critical."

Patient-oriented research frequently requires researchers from different hospitals to share medical data. Because of privacy laws, this is becoming an increasingly different task. Federated learning comes to our rescue. "Each participating hospital trains the model locally with its own data. The researchers then collect the results, or partial models, and combine them to form a complete model. This ensures that no patient data leaves the hospital. Currently, federated learning is primarily used in medical research, but it can also be found on smartphones. When you type, you will see suggestions for words you could use. This task is accomplished using federated learning, in which your data never leaves your smartphone.”