Semantic, symbolic and interpretable machine learning

This program aims to advance semantic, symbolic, and interpretable machine learning methods that operate at the level of human-understandable abstractions. Its research pillars include multi-relational learning, temporal and structured knowledge graphs, extraction of statistical and logical regularities from data, embedding and graph-based representations, neuro-symbolic and inductive logic programming approaches, and cross-fertilization with related areas such as natural language processing, vision, and geometric deep learning.