Hi! I’m Irena, a senior at Stanford University. [B.S. ‘22 in Computer Science & M.S. ‘23 in Statistics]

My research interests lie in studying Machine Learning Under Distribution Shifts with the aim of improving model robustness. I’m excited about ideas like domain generalization, adaptation, and lifelong learning. I’m grateful to be a part of Percy Liang’s group.

Outside of research, you can also find me in dance.

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 NeurIPS Workshop on Distribution Shifts 2021
  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
  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.