PhD
Chair of Statistics and Data Science in Social Sciences and the Humanities at LMU
Institut für Statistik
Ludwig-Maximilians-Universität München
Ludwigstr. 33
80539 München
Biosketch
Unai is a PhD candidate at LMU’s Chair of Statistics and Data Science in Social Sciences and the Humanities at the Institute of Statistics. His research focuses on the reliability of machine learning in high-stakes decision settings, particularly in the public sector. Currently, he’s focused on increasing model robustness under distribution shifts and leveraging causal machine learning techniques.
Unai holds a Bachelor’s and Master’s in Physics from the University of Heidelberg, where he specialised in reconstructing dynamical systems, cooperative networks, and generative recurrent neural networks. He also worked as an associate researcher at the Hertie School in Berlin, exploring the intersection of machine learning and government.
relAI Research
Machine Learning for Reliable Decision-Making
My research explores how machine learning can guide resource allocation problems, recognizing that better predictions do not necessarily translate into better outcomes. By studying the design and deployment of predictive algorithms, I aim to highlight tradeoffs between accuracy, administrative capacity, and fairness in real-world resource-constrained decision environments. Methodologically, my PhD research bridges applied and theoretical work in algorithmic fairness, causal machine learning, and distribution shifts. For details, see Fischer-Abaigar et al. (2024): https://doi.org/10.1016/j.giq.2024.101976