I am an assistant professor in the Amsterdam Machine Learning Lab at the University Amsterdam and a Research Scientist at MIT-IBM Watson AI lab. My research focuses on three directions, causal representation learning, causality-inspired machine learning and how can causality ideas help RL adapt to new domains and nonstationarity faster. The goal is to leverage ideas from causality to make ML methods robust to distribution shift and generalizable across domains and tasks. I also continue working on my previous research on causal discovery, i.e. learning causal relations from data.

Previously I was a postdoctoral researcher at IBM Research NY, working on methods to design experiments that would allow one to learn causal relations in a sample-efficient and intervention-efficient way. I received a PhD at the VU Amsterdam on learning causal relations jointly from different experimental settings, even with latent confounders and small samples. During Spring 2022, I was visiting the Simons Institute in Berkeley for a semester on Causality.