Lisa Anne Hendricks
Also known as Lisa A. Hendricks, Lisa Hendricks, Lisa Anne Marie Hendricks
Lisa Anne Hendricks is listed with Google DeepMind on a 2025 paper, and her AI research connects images and language. She earned a B.S.E.E. from Rice University in 2013 and a Ph.D. from UC Berkeley in 2019, where Trevor Darrell advised her. Her 2015 CVPR paper, Long-Term Recurrent Convolutional Networks for Visual Recognition and Description, and her 2018 ECCV paper, Women also Snowboard, both work on how models describe images. In 2022 she co-authored An empirical analysis of compute-optimal large language model training, which won an Outstanding Paper award at NeurIPS.
Timeline
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2013
A B.S.E.E. from Rice
She receives a B.S.E.E. from Rice University in 2013, summa cum laude.
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2015
Long-term recurrent networks
She co-authors Long-Term Recurrent Convolutional Networks for Visual Recognition and Description, published in the CVPR 2015 proceedings.
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2018
Women also snowboard
She writes Women also Snowboard: Overcoming Bias in Captioning Models as first author, published in the ECCV 2018 proceedings.
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2019
A Ph.D. under Trevor Darrell
She completes a Ph.D. at UC Berkeley in 2019, advised by Trevor Darrell.
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2022
An Outstanding Paper award
She co-authors An empirical analysis of compute-optimal large language model training, which wins an Outstanding Paper award at NeurIPS in 2022.
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By 2025
Working at Google DeepMind
She co-authors CulturalFrames, a study of cultural expectation alignment in text-to-image models, which appears on arXiv in June 2025 with Google DeepMind as her affiliation.
Sources and credits
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- Lisa Anne Hendricks – Rising Stars in EECS 2017
- CVPR 2015 Open Access Repository
- ECCV 2018 Open Access Repository
- Lisa Hendricks - The Mathematics Genealogy Project
- NeurIPS 2022 Awards
- An empirical analysis of compute-optimal large language model training
- [2506.08835] CulturalFrames: Assessing Cultural Expectation Alignment in Text-to-Image Models and Evaluation Metrics
- CulturalFrames: Assessing Cultural Expectation Alignment in Text-to-Image Models and Evaluation Metrics