Hi! I am a researcher at Google DeepMind. I also founded and have been co-organizing the Algorithmic Fairness through the lens... workshop series at NeurIPS (afciworkshop.org).
The overarching goal of my work is to build trustworhthy machine learning systems that can be deployed safely. I am particularly interested in using causal approaches to better understand and improve the fairness and safety of such systems.
Please see my google scholar for an updated list of publications.
[Preprint] Check out our new paper on Globalizing algorithmic fairness with a case study on Health in Africa. Accepted at Deep Learning Indaba and the PML4DC and Machine Learning & Global Health workshops at ICLR23
[Service] Excited to organize a 4th edition of our workshop on Algorithmic Fairness at NeurIPS 2023 (details at Algorithmic Fairness through the lens of Time)!
The proceedings at PMLR for the Algorithmic Fairness through the lens of Causality and Privacy workshop is out!
[Service] Excited to organize a 3rd edition of our workshop on Algorithmic Fairness at NeurIPS 2022 (details at Algorithmic Fairness through the lens of Causality and Privacy)!
[Service] I am Program Chair for the Montreal AI Symposium 2022. Looking forward to helping organize this great conference!
[Research activity] Honoured to be invited to participate in the Interpretable Machine Learning program at the Simons Institute for the Theory of Computing, UC Berkeley this summer.
The proceedings at PMLR for the Algorithmic Fairness through the lens of Causality and Robustness workshop is out!
[Preprint] New review paper on transportability of causal effects using RCT and observational data!
[Research activity] Joined the SAMSI (Statistical and Applied Mathematical Sciences Institute) working group on Causal Inference and Missing Data!!