diff --git a/404.html b/404.html index 61afcb59..0cfe2157 100644 --- a/404.html +++ b/404.html @@ -13,12 +13,12 @@ - - + + -
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\ No newline at end of file diff --git a/about/index.html b/about/index.html index 97db9464..0faf8d5b 100644 --- a/about/index.html +++ b/about/index.html @@ -13,12 +13,12 @@ - - + + -
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About Us

Zeno is designed, built, and maintained by a team of interdisciplinary researchers at Carnegie Mellon University. We span research groups in Machine Learning, Human-Computer Interaction, and Language Technologies. We are passionate about empowering people to understand the complexities of AI systems in order to design the future of responsible AI.

Team

Alex Cabrera

PhD Candidate

Alex Bäuerle

Research Scientist

Erica Fu

Undergraduate Student

Ankitha Vasudev

Masters Student

Kathy Yu

Masters Student

Josh Zhou

Undergraduate Student

Donald Bertucci

Undergraduate Student

Ameet Talwalkar

Associate Professor

Graham Neubig

Associate Professor

Jason Hong

Professor

Adam Perer

Assistant Professor

Past Members

Tianqi Wu

Masters Student

Yi-Cheng Huang

Research Associate

Sponsors & Organizations

We are thankful to our sponsors and affiliated organizations. Zeno is part of the Mozilla Technology Fund and developed partly with the support of multiple NSF grants and fellowships.

Mozilla logoMozilla logoCMU logo

Contact

If you have any questions or issues with Zeno please send an email to hello@zenoml.com, ask in our Discord, or open an issue on GitHub

Cite

Please reference our CHI 2023 paper if you would like to cite Zeno:


@inproceedings{cabrera23zeno,
  author = {Cabrera, Ángel Alexander and Fu, Erica and Bertucci, Donald and Holstein, Kenneth and Talwalkar, Ameet and Hong, Jason I. and Perer, Adam},
  title = {Zeno: An Interactive Framework for Behavioral Evaluation of Machine Learning},
  year = {2023},
  isbn = {978-1-4503-9421-5/23/04},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3544548.3581268},
  doi = {10.1145/3544548.3581268},
  booktitle = {CHI Conference on Human Factors in Computing Systems},
  location = {Hamburg, Germany},
  series = {CHI '23}
}
+
Skip to main content

About Us

Zeno is designed, built, and maintained by a team of interdisciplinary researchers at Carnegie Mellon University. We span research groups in Machine Learning, Human-Computer Interaction, and Language Technologies. We are passionate about empowering people to understand the complexities of AI systems in order to design the future of responsible AI.

Team

Alex Cabrera

PhD Candidate

Alex Bäuerle

Research Scientist

Erica Fu

Undergraduate Student

Ankitha Vasudev

Masters Student

Kathy Yu

Masters Student

Josh Zhou

Undergraduate Student

Donald Bertucci

Undergraduate Student

Ameet Talwalkar

Associate Professor

Graham Neubig

Associate Professor

Jason Hong

Professor

Adam Perer

Assistant Professor

Past Members

Tianqi Wu

Masters Student

Yi-Cheng Huang

Research Associate

Sponsors & Organizations

We are thankful to our sponsors and affiliated organizations. Zeno is part of the Mozilla Technology Fund and developed partly with the support of multiple NSF grants and fellowships.

Mozilla logoMozilla logoCMU logo

Contact

If you have any questions or issues with Zeno please send an email to hello@zenoml.com, ask in our Discord, or open an issue on GitHub

Cite

Please reference our CHI 2023 paper if you would like to cite Zeno:


@inproceedings{cabrera23zeno,
  author = {Cabrera, Ángel Alexander and Fu, Erica and Bertucci, Donald and Holstein, Kenneth and Talwalkar, Ameet and Hong, Jason I. and Perer, Adam},
  title = {Zeno: An Interactive Framework for Behavioral Evaluation of Machine Learning},
  year = {2023},
  isbn = {978-1-4503-9421-5/23/04},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3544548.3581268},
  doi = {10.1145/3544548.3581268},
  booktitle = {CHI Conference on Human Factors in Computing Systems},
  location = {Hamburg, Germany},
  series = {CHI '23}
}
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