Julie Tibshirani

Julie with California poppies

Hello, world! I’m a software engineer focused on search, database systems, and statistical computing. I work at Elastic on the Elasticsearch search engine, and serve as an Apache Lucene committer and PMC member. I’m also the author of the generalized random forests (grf) package.

I love building software that’s empowering and enjoyable to use. I’m an open source enthusiast, and shared my experience joining the open source search community in Finding a home (and career) in the open source community.

Before Elastic I worked at Palantir Technologies, where I led development for the federated search framework.

Selected work

There’s been a surge of interest in vector search, thanks to a new generation of machine learning models that powerfully represent text and other content as vectors. I helped introduce k-nearest neighbor search in Lucene and Elasticsearch, opening up new possibilites for ranking and recommendations.

Machine learning for causal inference

Causal inference allows for determining the effect of an action on a larger system. The generalized random forests (grf) method combines insights from statistics and machine learning to enable causal analysis. The associated software package is becoming a popular choice for social scientists investigating causal effects.

Information extraction

During my Master’s degree I researched relation extraction and knowledge base population as part of the Stanford Natural Language Processing group. Our work focused on non-traditional supervision techniques, including multi-label learning, partial supervision, as well as methods to address labelling errors in training data.

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