Artificial Intelligence for Medicine and Science @ Harvard

Prioritizing network communities

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Defending graph neural networks against adversarial attacks (NeurIPS 2020)

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GraphXAI: Resource to support the development and evaluation of GNN explainers

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Disease pathways in the human interactome

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Self-supervised contrastive learning for time series via time-frequency consistency

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Fast graph-regularized matrix factorization

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Domain Adaptation for Time Series Under Feature and Label Shifts

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TxGNN: Zero-shot prediction of therapeutic use with geometric deep learning and clinician centered design

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SHEPHERD: Few shot learning for phenotype-driven diagnosis of patients with rare genetic diseases

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General Strategy for Unlearning in Graph Neural Networks

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