I’m a last year PhD student in Statistics at ETH Zürich, Seminar for Statistics. Previously I was a PhD student at Copenhagen Causality Lab, University of Copenhagen. I’m co-supervised by Jonas Peters and Niklas Pfister. During my PhD I was fortunated to visit Pradeep Ravikumar at Carnegie Mellon University’s Machine Learning Department and Susan Murphy at the Statistical Reinforcement Learning lab, Harvard University.

I completed my master’s degree in Machine Learning at University College London in 2019, during which I worked with Ricardo Silva. Prior to the master’s study, I worked as a data scientist at Agoda for four years. I received my bachelor’s degree in Statistics from Chulalongkorn University in 2014.

My research interest lies in the intersection between Causality, Invariance and Robustness in Machine Learning.

News

• (Feb 2024) Our paper “Identifying Representations for Intervention Extrapolation” has been accepted to ICLR!
• (Dec 2023) Our paper “Effect-Invariant Mechanisms for Policy Generalization” has been accepted to JMLR!
• (April 2023) I’ve now moved to ETH Zürich, Seminar for Statistics for my last year of PhD.
• (Jan 2023) Excited to start my research visit with Pradeep Ravikumar at Carnegie Mellon University’s Machine Learning Department.
• (Dec 2022) Our paper “Invariant Policy Learning: A Causal Perspective” has been accepted to TPAMI!
• (Nov 2022) Our paper “Statistical Testing under Distributional Shifts” has been accepted to JRSS-B!
• (June 2022) Our paper “Exploiting Independent Instruments: Identification and Distribution Generalization” has been accepted to ICML 2022!
• (Dec 2021) I’m thrilled to start my research visit at the Statistical Reinforcement Learning lab, Harvard University, where I’m advised by Susan Murphy.
• (Jun 2021) Our paper entitled “Counterfactual Mean Embeddings” has been accepted to JMLR.
• (Jun 2021) Two new preprints “Invariant Policy Learning: A Causal Perspective”, “Statistical Testing under Distributional Shifts” are out.