My research interests lie in building emergent models of intelligence that are helpful, robust and aligned with human values. To pursue this, my current research direction is in post-training large language models that are capable of complex instruction following and agentic behaviour, and which are human-aligned. My prior research also spanned the intersection of language + X ∈ {learning, representation, bias, reasoning} and technology + Y ∈ {ethics, culture, government, security}.

Currently at Cohere, I am the technical lead for post-training recipes. I lead a research team to determine the best post-training recipes and apply them to develop Cohere's flagship large language models that power our agentic AI platform for enterprises. Our Command {A, R7B, R+, R} and Aya models are available as open-weights for the research community.

Apart from research, I also have experience in products and policy. At Waymo, I developed models to determine the true collision rate of self-driving cars in simulation given real and simulated driving episodes, for the purpose of accurate safety benchmarking. At Facebook, I used transformer-based models for the detection of harmful comments on Instagram. At Singapore's Smart Nation and Digital Government Office, I authored strategy papers and analyses on the societal harms of AI and the responsible deployment of human-centered AI systems.

At Yale, I was advised by Dragomir Radev (LILY Lab), Robert Frank (CLAY Lab) and Elisa Celis (Controlling Bias in AI Group). I also worked with John Lafferty to create a curriculum for, and teach, text data science.