Jihyeon Lee

jihyeon (at) cs.stanford.edu

I am a Senior Research Engineer at Google Research, building AMIE: a multimodal, conversational, and empathetic medical intelligence. My mission is to make healthcare more accessible and more robust for patients who fall in the long tail diagnostically and culturally. Previously at Google, I built SKAI, an AI system that detects damage after natural disasters for the United Nations World Food Programme and aided relief efforts for Hurricane Helene & Milton.

I received my B.S. with Honors & Master’s in Computer Science from Stanford University, specializing in Artificial Intelligence and Human-Computer Interaction. I had the pleasure of being advised by Stefano Ermon and Maneesh Agrawala. My research focused on computer vision to identify pollution sources from satellite imagery and connect them to health outcomes & policy improvements in the Global South, which was recognized with a Firestone medal, granted to the top 10% of theses across all disciplines, and the Siebel Scholarship.

Research Interests: Developing multimodal, robust AI for high-risk decision-making in healthcare & sustainability. As a child of immigrants, I’m passionate about using technology to help marginalized communities. Outside of research, I love making music, dancing, and hiking!

selected publications

PNAS

Scalable deep learning to identify brick kilns and aid regulatory capacity

Jihyeon Lee, Nina R. Brooks, Fahim Tajwar, and 5 more authors

Proceedings of the National Academy of Sciences, 2021

DOI Bib HTML

@article{lee2021scalable,
  author = {Lee, Jihyeon and Brooks, Nina R. and Tajwar, Fahim and Burke, Marshall and Ermon, Stefano and Lobell, David B. and Biswas, Debashish and Luby, Stephen P.},
  title = {Scalable deep learning to identify brick kilns and aid regulatory capacity},
  journal = {Proceedings of the National Academy of Sciences},
  volume = {118},
  number = {17},
  pages = {e2018863118},
  year = {2021},
  doi = {10.1073/pnas.2018863118},
  url = {https://www.pnas.org/doi/10.1073/pnas.2018863118},
}

UIST

Automatically Visualizing Audio Travel Podcasts

Jihyeon Janel Lee, Mitchell Gordon, and Maneesh Agrawala

In Adjunct Publication of the 30th Annual ACM Symposium on User Interface Software and Technology, Québec City, QC, Canada, 2017

DOI Bib HTML

@inproceedings{10.1145/3131785.3131818,
  author = {Lee, Jihyeon Janel and Gordon, Mitchell and Agrawala, Maneesh},
  title = {Automatically Visualizing Audio Travel Podcasts},
  year = {2017},
  isbn = {9781450354196},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3131785.3131818},
  doi = {10.1145/3131785.3131818},
  booktitle = {Adjunct Publication of the 30th Annual ACM Symposium on User Interface Software and Technology},
  pages = {165–167},
  numpages = {3},
  location = {Québec City, QC, Canada},
  series = {UIST '17},
}