Biosketch
Daniela obtained her Bachelor’s and Master’s degree in Mathematics at the Technical University of Munich. During her studies she spend one semester at the University of Pisa and did an internship in Process Mining. Since December she works as a PhD candidate at the Mathematical Statistics chair of TUM and is a relAI member. Her research focuses on graphical models and causal discovery.
relAI Research
Causal Discovery with Linear Models
I specialize in causal discovery, which aids AI reliability by distinguishing genuine causal relationships from spurious correlations that can lead to biased or unreliable predictions. Moreover, by integrating causal reasoning, AI systems can generalize better to new situations and become more interpretable. Beyond this, my work includes adversarial attacks in CNNs, as well as root cause analysis.