# About Me

I am a 3rd-year PhD student at the Centre for Artificial Intelligence, University College London, working on Causal Inference and Causal Machine Learning, fortunately supervised by Matt Kusner and Ricardo Silva. So far in my PhD, I have been lucky enough to work extensively with Arthur Gretton on the intersection of Kernel Methods and Causal Inference. From summer 2022 to spring 2023, I was hosted by Dominik Janzing at Amazon Research Tuebingen, and Cheng Zhang at Microsoft Research Cambridge, where I worked on causal abstraction and inference with many measurements, respectively.

I am broadly interested in abstracting causal models, and causal inference under weak observability conditions, specifically methods applicable to social sciences. Right now, I am especially interested in the role of causality in understanding the behaviour of modern deep learning architectures.

Prior to starting my PhD, I earned a master’s degree in Machine Learning from University College London and my undergraduate degree in Mathematics from the University of Cambridge.

## News

**01.2024** **New work**: Meaningful Causal Aggregation and Paradoxical Confounding has been accepted to **CLeaR 2024**.

**04.2023** I gave an invited talk on Causal Inference Under Treatment Measurement Error: A Nonparametric Instrumental Variable Approach at Causality Seminar (China). Talk slides are here.

**04.2023** I gave an invited talk on confounded causal inference with proxies or measurement error at Causal Inference Meets Statistics Quarterly Meeting. Talk slides are here and the poster is here.

**01.2023** I am thrilled to join Microsoft Research Cambridge to work with Cheng Zhang on causal inference with many measurements from 01.2023 to 03.2023.

**06.2022** I am thrilled to join Amazon Science Tuebingen to work with Dominik Janzing on abstracting causal models from 06.2022 to 12.2022.

**05.2022** **New work**: Causal Inference Under Treatment Measurement Error: A Nonparametric Instrumental Variable Approach has been invited for an **oral presentation at UAI 2022**.

**05.2022** I am presenting my work on causal effect estimation with latent variables(slides) at the Statistics for Data-Centric Engineering Seminar Series, Alan Turing Institute and on structured treatment effect estimation(slides) at Professor Menggang Yu’s group, University of Wisconsin, Madison.

**10.2021** **New work**: Causal Effect Estimation for Structured Treatments has been accepted to **NeurIPS 2021**.

**05.2021** **New work**: Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restrictions has been accepted to **ICML 2021**.