Artemii Novoselov, Stanford University This seminar introduces PhaseHunter, a deep learning framework initially designed for the precise estimation and uncertainty quantification of seismic phase onset times. Building upon this foundational capability, PhaseHunter has evolved to handle a broader range of seismic applications through a probabilistic deep learning regression approach. This enables the framework to analyze both continuous and binary properties of seismic signals, thereby extending its potential applications to include earthquake location, seismic tomography, source discrimination, and earthquake early warning systems. The seminar will explore the technical aspects and practical applications of PhaseHunter, offering insights into how this tool could serve various facets of seismological research and hazard assessment. As an open-source project, PhaseHunter also encourages community contributions for ongoing improvements and adaptations.