About

Hi! I’m Irena, a student at Stanford University studying Computer Science (B.S. with honors) and Statistics (M.S.), graduating in June 2023.

My research interests revolve around model robustness: how can we train, adapt, and evaluate machine learning systems such that they perform reliably on unseen data in the real world? I’m grateful to have worked with professors Pang Wei Koh, Percy Liang, and Tatsu Hashimoto.

Outside of research, I also love Art History. Here’s a random sample of a favorite work.

Current Questions

  • How should we design effective data augmentations for domain generalization?
  • Can we capture challenging visual distribution shifts using language?

Selected Publications

  1. Extending the WILDS benchmark for unsupervised adaptation
    Shiori Sagawa*, Pang Wei Koh*, Tony Lee*, Irena Gao*, Sang Michael Xie, Kendrick Shen, Ananya Kumar, Weihua Hu, Michihiro Yasunaga, Henrik Marklund, Sara Beery, Etienne David, Ian Stavness, Wei Guo, Jure Leskovec, Kate Saenko, Tatsunori Hashimoto, Sergey Levine, Chelsea Finn, and Percy Liang
    In International Conference on Learning Representations 2022 (Oral Presentation)
  2. WILDS: A benchmark of in-the-wild distribution shifts
    Pang Wei Koh*, Shiori Sagawa*, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, Tony Lee, and others
    In International Conference on Machine Learning 2021 (Oral Presentation)
  3. Effect of confidence indicators on trust in AI-generated profiles
    Tommy Bruzzese*, Irena Gao*, Christina Ding*, Alyssa Romanos*, and Griffin Dietz
    In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems 2020

* denotes equal contribution.