Yuhao Zhang

Hey there, welcome!

I am currently a scientist in the founding team of Samaya AI. We are on a journey to improve knowledge discovery by harnessing the power of large language models.

Before Samaya, I was a scientist at Amazon AWS AI where I worked on core AWS services relevant to enterprise search. I obtained my PhD degree from Stanford University, where I was jointly advised by Prof. Chris Manning in the Stanford NLP Group and Prof. Curtis Langlotz in the Stanford AIMI Center. My PhD work has focused on natural language processing and its applications in medicine.

Before that, I obtained a M.S. degree in the Computer Science Department at Stanford University, and a bachelor’s degree from the Department of Electronic Engineering at Tsinghua University, China.

research interest

I care about NLP systems and their impact in real-world applications. My work has covered the following areas:

retrieval and retrieval-augmented generation;
information extraction;
summarization;
multimodal learning;
syntactic analysis and open-source NLP toolkit (I am a co-author of the widely used Stanza NLP library).

contact

You can reach me now at {first-name} ~at~ cs.stanford.edu. You can also find my various social accounts at the bottom of this page.

selected publications

For a complete list, see the publications page, or my google scholar page.

(*=equal contribution)

ACL Findings

RobustQA: Benchmarking the Robustness of Domain Adaptation for Open-domain Question Answering

Rujun Han , Peng Qi , Yuhao Zhang, and 6 more authors

In Findings of the Annual Meeting of the Association for Computational Linguistics (ACL) , 2023

Bib HTML

@inproceedings{chen2023improving,
  title = {RobustQA: Benchmarking the Robustness of Domain Adaptation for Open-domain Question Answering},
  author = {Han, Rujun and Qi, Peng and Zhang, Yuhao and Liu, Lan and Burger, Juliette and Wang, William and Huang, Zhiheng and Xiang, Bing and Roth, Dan},
  booktitle = {Findings of the Annual Meeting of the Association for Computational Linguistics (ACL)},
  year = {2023},
}

ACL Findings

Improving Cross-task Generalization of Unified Table-to-text Models with Compositional Task Configurations

Jifan Chen , Yuhao Zhang, Lan Liu , and 5 more authors

In Findings of the Annual Meeting of the Association for Computational Linguistics (ACL) , 2023

Bib HTML

@inproceedings{chen2023improvinh,
  title = {Improving Cross-task Generalization of Unified Table-to-text Models with Compositional Task Configurations},
  author = {Chen, Jifan and Zhang, Yuhao and Liu, Lan and Dong, Rui and Chen, Xinchi and Ng, Patrick and Wang, William Yang and Huang, Zhiheng},
  booktitle = {Findings of the Annual Meeting of the Association for Computational Linguistics (ACL)},
  year = {2023},
}

EMNLP Findings

Tokenization Consistency Matters for Generative Models on Extractive NLP Tasks

Kaiser Sun , Peng Qi , Yuhao Zhang, and 3 more authors

In Findings of EMNLP , 2023

Bib HTML

@inproceedings{sun2023tokenization,
  title = {Tokenization Consistency Matters for Generative Models on Extractive NLP Tasks},
  author = {Sun, Kaiser and Qi, Peng and Zhang, Yuhao and Liu, Lan and Wang, William Yang and Huang, Zhiheng},
  booktitle = {Findings of EMNLP},
  year = {2023},
}

MLHC

Contrastive Learning of Medical Visual Representations from Paired Images and Text

Yuhao Zhang, Hang Jiang , Yasuhide Miura , and 2 more authors

In Proceedings of the 7th Machine Learning for Healthcare Conference , 2022

Bib HTML

@inproceedings{zhang2022contrastive,
  title = {Contrastive Learning of Medical Visual Representations from Paired Images and Text},
  author = {Zhang, Yuhao and Jiang, Hang and Miura, Yasuhide and Manning, Christopher D and Langlotz, Curtis P},
  booktitle = {Proceedings of the 7th Machine Learning for Healthcare Conference},
  pages = {1--24},
  volume = {182},
  year = {2022},
  series = {Proceedings of Machine Learning Research},
  publisher = {PMLR},
  dataset = {https://github.com/yuhaozhang/convirt},
}

Thesis

Deep Understanding and Generation of Medical Text and Beyond

Yuhao Zhang

Stanford University PhD Thesis, 2021

Bib HTML

@article{zhang2021deep,
  title = {Deep Understanding and Generation of Medical Text and Beyond},
  author = {Zhang, Yuhao},
  year = {2021},
  journal = {Stanford University PhD Thesis},
  school = {Stanford University},
}

JAMIA

Biomedical and Clinical English Model Packages for the Stanza Python NLP Library

Yuhao Zhang, Yuhui Zhang , Peng Qi , and 2 more authors

Journal of the American Medical Informatics Association, 2021

Bib HTML

@article{zhang2021biomedical,
  title = {Biomedical and Clinical English Model Packages for the Stanza Python NLP Library},
  author = {Zhang, Yuhao and Zhang, Yuhui and Qi, Peng and Manning, Christopher D and Langlotz, Curtis P.},
  journal = {Journal of the American Medical Informatics Association},
  volume = {28},
  number = {9},
  pages = {1892--1899},
  year = {2021},
  publisher = {Oxford University Press},
}

ACL

Stanza: A Python Natural Language Processing Toolkit for Many Human Languages

Peng Qi* , Yuhao Zhang*, Yuhui Zhang , and 2 more authors

In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL): System Demonstrations , 2020

Bib HTML

@inproceedings{qi2020stanza,
  title = {Stanza: A Python Natural Language Processing Toolkit for Many Human Languages},
  author = {Qi*, Peng and Zhang*, Yuhao and Zhang, Yuhui and Bolton, Jason and Manning, Christopher D},
  booktitle = {Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL): System Demonstrations},
  year = {2020},
}

EMNLP-CoNLL

Universal Dependency Parsing from Scratch

Peng Qi* , Timothy Dozat* , Yuhao Zhang*, and 1 more author

In Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies , 2018

Bib HTML

@inproceedings{qi2018universal,
  title = {Universal Dependency Parsing from Scratch},
  author = {Qi*, Peng and Dozat*, Timothy and Zhang*, Yuhao and Manning, Christopher D},
  booktitle = {Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies},
  year = {2018},
}