I am a Senior Principal Researcher at Microsoft Research.  My research interests span causal inference, machine learning, and AI’s implications for people and society.

I am working to broaden the use of causal methods for decision-making across many application domains; and, in the broad area of AI’s implications for society, my projects include work at the intersection of security and machine learning.  I have a strong interest in computational social science questions and social media analyses, especially that require causal understanding of phenomenon in health, mental health; issues of data bias; and understanding how new technologies affect our awareness of the world and enable new kinds of information discovery and retrieval.

Recent highlights

New working paper: Causal Reasoning and Large Language Models: Opening a New Frontier for Causality, details how LLMs can open new frontiers for advancing the research, practice, and adoption of causality by capturing common sense and domain knowledge about causal mechanisms and supporting translation between natural language and formal methods.

The DoWhy library for causal inference and the PyWhy organization for causal libraries and tools is growing! Learn more here: http://pywhy.org/

Our ICLR 2023 paper, “Modeling the Data-Generating Process is Necessary for Out-of-Distribution Generalization” was accepted with Notable-Top 25% distinction.

I recently had a great time talking with Jon Krohn on the SuperDataScience Podcast (Episode 613).