Title: Multimodal learning with graphs for biomedical applications
Speaker: Yasha Ektefaie
Abstract:
Artificial intelligence for graphs has achieved remarkable success in modelling complex systems, ranging from dynamic networks in biology to interacting particle systems in physics. However, the increasingly heterogeneous graph datasets call for multimodal methods that can combine different inductive biases - assumptions that algorithms use to make predictions for inputs they have not encountered during training. Learning on multimodal datasets is challenging because the inductive biases can vary by data modality and graphs might not be explicitly given in the input. To address these challenges, graph artificial intelligence methods combine different modalities while leveraging cross-modal dependencies through geometric relationships. Diverse datasets are combined using graphs and fed into sophisticated multimodal architectures, specified as image-intensive, knowledge-grounded and language-intensive models. Using this categorization, we introduce a blueprint for multimodal graph learning, use it to study existing methods and provide guidelines to design new models. This talk will focus on biomedical applications of multimodal graph learning, emphasizing how this blueprint can enable future innovation in this space.
Speaker Bio:
Yasha is a PhD candidate in the Bioinformatics and Integrative Genomics program at Harvard Medical School co-advised by Marinka Zitnik and Maha Farhat. With publications in Nature Machine Intelligence, Nature Breast Cancer, and Lancet Microbe, he is interested in designing the next generation of machine learning methods to predict phenotypes from genotypes. Specifically, he is interested in understanding and improving the ability of these models to generalize to new and unseen genotypes. Before Harvard, he received a B.S. in electrical engineering and computer science (EECS) and bioengineering at UC Berkeley where he did research on designing computational methods to understand bacterial communities.
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