I am an assistant professor in the Amsterdam Machine Learning Lab at the University Amsterdam. The goal of my research is to find how can causality improve current machine learning (ML) algorithms, especially in terms of robustness, generalization across domains/tasks, and safety. My research focuses on three directions: causal representation learning (i.e. learning causal factors from high-dimensional data, e.g. sequences of images), causal discovery (i.e. learning causal relations from data), and causality-inspired ML, e.g. how can ideas from causality help ML/RL adapt to new domains, nonstationarity and varying number of objects with different latent parameters.

Roles in Hybrid Intelligence: Participant

Challenge: Assistants & Trust 

Hybrid Intelligence Project(s): 

• TBD.

Research interests: Causality, Causal Discovery, Causal Representation Learning, Causal ML.

Affiliations:

• AMLab, University of Amsterdam

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