Summary
Sebastian Riedel is a research scientist and professor with 15 years of experience at the intersection of NLP, machine learning, and probabilistic programming, currently working at DeepMind and UCL. He specializes in extracting relational structure from raw text and simplifying system design for that purpose, with deep expertise in inference, relational learning, Markov logic, semantic role labeling, and event extraction. His work blends theoretical rigor from a PhD in Artificial Intelligence with practical software engineering and integer programming techniques to build deployable information-extraction systems. Based in London, he is notable for bridging academic research and industrial-scale ML, translating complex probabilistic models into usable pipelines. An implicit throughline of his career is a focus on making structured prediction and relational inference more accessible and efficient for real-world applications.
15 years of coding experience
MSc., Informatics, MSc., Informatics at The University of Edinburgh
Abitur, Abitur at Albert-Schweitzer-Gymnasium
Dipl. Ing., Computer Science and Engineering, Dipl. Ing., Computer Science and Engineering at Hamburg University of Technology
German, English, Japanese