Postdoctoral Fellow
Research group | Statistics and Data Science
Main supervisor | Johan Pesar
Co-supervisor | -
Affiliation | Department of Mathematics, UiO
Contact | camiling@math.uio.no
Short bio
I received my PhD in Biostatistics from the University of Cambridge in 2023. My thesis, titled “Joint Network Modeling of Omics Data for Understanding Complex Diseases”, was focused on the development of novel Bayesian methodology for integrative modelling of conditional dependencies in extremely high-dimensional data, with applications in autoimmune disease and cancer omics.
I am currently a Postdoctoral Fellow at The Norwegian Centre for Knowledge-driven Machine Learning Integreat, at the Department of Mathematics. My project is aimed at advancing the field of machine learning by developing new supervised learning methods that, by integrating causal inference principles, exhibit enhanced robustness, particularly in their ability to generalise adequately to new domains.
Research interests and hobbies
I am interested in the development of statistical and machine learning methods with increased accuracy, reliability, and applicability. Some key topics of interest include causal learning, graphical modelling, Bayesian statistics, statistical omics, statistical machine learning, and high-dimensional statistics.
Outside of work, I enjoy spending time with friends and family, travelling, cooking, running, lifting weights, and listening to Taylor Swift.
DSTrain project
Towards Robust Machine Learning through Causal Multi-Task Learning
The primary goal of this project is to advance the field of machine learning by developing new supervised learning methods that exhibit enhanced robustness, particularly in their ability to generalise adequately to new domains. This objective is motivated by the need for reliable machine-learning algorithms that perform consistently in unpredictable real-world conditions. Robust algorithms are essential for mitigating errors in critical applications, such as healthcare and environmental monitoring, and ensuring Artificial Intelligence (AI)'s equitable performance across diverse populations. This enhances the fairness and reliability of machine learning in everyday applications.
This project seeks to integrate causal inference principles with supervised machine learning, addressing the limitation that models trained to minimise standard empirical risk often face - degradation in performance when exposed to domain shifts. Unlike association-based relationships, causal relationships are stable across domains, making them invaluable for predictions in varying contexts and scenarios. This work aspires to increase machine learning's robustness, applicability, versability, computational efficiency, and fairness by being inspired by causality and multi-task learning.