I am a PhD student at the Department of Mathematics, investigating the causal interlinkages amongst the United Nations' Sustainable Development Goals (SDGs). I view the 17 SDGs with their 169 targets as a network of 17 or 169 nodes, respectively, and try to find directed causal edges between them. Data for the indicators of the SDGs is publicly available here and I would like to see many more data scientists working with it.
My research interests lie in kernel methods, causal discovery and network theory, and I find it interesting to use them to learn more about the connections between macro-economics, societies, and natural environments — which eventually underpin the urge to govern sustainably.
I obtained a MSc in Engineering from the Technical University of Denmark (DTU) and Aarhus University with a focus on (Bayesian) machine learning.
Laumann F, von Kügelgen J, Barahona M, 2020, Non-linear interlinkages and key objectives amongst the Paris Agreement and the Sustainable Development Goals. ICLR workshop on Tackling Climate Change with Machine Learning. arXiv preprint arXiv:2004.09318
Shridhar K, Laumann F, Liwicki M, 2019, Uncertainty Estimations by Softplus normalization in Bayesian Convolutional Neural Networks with Variational Inference. arXiv preprint arXiv:1806.05978
Laumann F, Tambo T, 2018, Enterprise Architecture for a Facilitated Transformation from a Linear to a Circular Economy. Sustainability, 10(11), p.3882