Louk Smalbil

PhD Candidate in Machine Learning, Quantitative Data Analytics Group, Department of Computer Science
Research focus
My research focuses on developing algorithms that model interventions and learn cause-effect patterns from data. Inspired by applications of machine learning to concrete domain problems from fields such as epidemiology, I am particularly interested in extending existing machine learning frameworks and developing new models by integrating insights from supervised learning, causal inference and reinforcement learning.


Keywords: Deep learning, causal learning, reinforcement learning, epidemiology, causal inference, supervised learning