Unit Vienna

We fervently believe that scientific excellence emerges not from diligent forward planning, but from giving first-class researchers the freedom and resources to focus on curiosity-driven research, while exposing them to a constant influx of new impulses and ideas. Our guiding principles are foundational thinking, emphasizing principled solutions and answers, and interdisciplinarity, reflecting our conviction that machine learning and related fields flourish when they are in a rich interaction with other scientific disciplines, including computer science, engineering, mathematics, as well as the natural sciences.

• Trustworthy machine learning
• Theory of deep learning
• Discrete and continuous optimization
• Statistical and causal models for the natural sciences and engineering