Title: The Usage and Improvement of Neurosymbolic AI for Biomedical Applications
Speakers: Lauren DeLong
Neurosymbolic AI describes a hybrid field of AI between symbolic methods, which tend to be robust and interpretable, and deep learning methods, which are more scalable and tend to perform more competitively. Since these two areas tend to see tradeoffs between the types of benefits they offer, methods in neurosymbolic AI try to leverage the perks of each while mitigating their respective weaknesses. As neurosymbolic AI has recently increased in popularity, many various approaches have been introduced, and several of these approaches possess unique features that neither symbolic nor deep learning methods had alone. Consequently, neurosymbolic methods have potential to provide novel solutions to longstanding research challenges. The goal of my thesis is to use and improve neurosymbolic AI to demonstrate how these unique characteristics are especially useful for challenges which are particularly prominent in biomedical data science. Specifically, I will discuss challenges like meaningful multimodal data integration for clinical datasets, discovering drug mechanisms of action via long-range dependencies, and few shot learning to predict and understand rare, serious side effects.