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.
In my research, I’m driven to understand out-of-distribution generalization: how can we train and adapt machine learning models such that they generalize beyond their training distributions? In particular, my interests center on:
- Robustness. Models can fail unexpectedly and inequitably when distributions shift within a task, e.g., over time or data sources. How do we ensure that ML systems perform reliably in the real world?
- Transfer across tasks. Models that generalize beyond a pretraining distribution to new task distributions are especially useful. How do we design pretraining datasets that enable broad transfer?
I’m especially excited about the intersection of these topics with interactive learning.
Outside of research, I love Art History! Here’s a random sample of a favorite work.
Selected Publications
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Out-of-distribution Robustness via Targeted AugmentationsIn NeurIPS Workshop on Distribution Shifts 2022.
* denotes equal contribution.