The AI Seminar is a weekly meeting at the University of Alberta where researchers interested in artificial intelligence (AI) can share their research. Presenters include both local speakers from the University of Alberta and visitors from other institutions. Topics can be related in any way to artificial intelligence, from foundational theoretical work to innovative applications of AI techniques to new fields and problems.

Abstract: Identifying chemicals from mass spectra is essential in medicine, food, and environmental science. Traditional methods match spectra to libraries, an approach that is limited by incomplete library coverage. Predicting spectra can improve identification by augmenting real libraries, yet existing models struggle with resolution, scalability, and interpretability. We introduce FraGNNet, a probabilistic approach that efficiently and accurately predicts high-resolution spectra. FraGNNet leverages a structured latent space to reveal underlying processes that define the spectrum, enhancing interpretability and insight into chemical fragmentation.

Presenter Bio: Fei Wang is a PhD student in the Computing Science Department of the University of Alberta, interested in machine learning models for small molecules and mass spectra.