Puja Trivedi

I am a CSE PhD candidate in the Graph Exploration and Mining at Scale (GEMS) Lab at the University of Michigan, where I am fortunate to be advised by Prof. Danai Koutra. I also often collaborate with Dr. Jay Thiagarajan at Lawrence Livermore National Laboratory.

I am broadly interested in understanding how self-supervised learning can be performed effectively and reliably for non-euclidean and graph data by incorporating domain invariances and designing grounded algorithms. My recent work has focused on understanding the role of data augmentations in graph contrastive learning.

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We introduce G-ΔUQ, an accurate and scalable strategy for obtaining reliable uncertainty estimates for node classification and graph classification tasks.

We study how adaptation protocols can induce safe and effective generalization on downstream tasks through the lens of feature distortion and simplicity bias.

We rigorously study the effectiveness of popular scoring functions under distribution shifts and corruptions

We provide a novel generalization analysis for graph contrastive learning with popularly used, generic graph augmentations. Our analysis identifies several limitations in current self-supervised graph learning practices.

We contextualize the performance of several unsupervised graph representation learning methods with respect to inductive bias of GNNs and show significant improvements by using structured augmentations defined by task-relevance.