Research focus
About the research
How can learned graph representations be measured and designed to improve downstream tasks and robustness?
Read the full research overview
The group develops geometry- and topology-based methods and graph-based models for drug discovery and chemical informatics. Researchers analyze social networks combining structure with attributes like sentiment and trust to model team decision dynamics and trust evolution. The group builds methods to integrate multimodal biological and imaging data to find discriminative sub-networks that predict global clinical or brain network states. They design scalable algorithms for querying and modeling dynamic composite networks to detect unusual patterns and enable efficient sampling.
Selected publications
2 listedNormalized Space Alignment: A Versatile Metric for Representation Analysis
Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Global Human-guided Counterfactual Explanations for Molecular Properties via Reinforcement Learning
Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
People & alumni
Team details haven’t been added. See the lab’s website for its current members.
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Sources & listing information
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