Jamie Macbeth’s research is focused on building and studying intelligent computing systems that demonstrate a human-like capability for in-depth understanding and production of natural language, and thus can achieve richer interactions with human users. He is especially keen on building systems that decompose the meaning of language into complex conceptual structures that reflect humans’ embodied cognition, memory, imagery and knowledge about social situations.

Macbeth uses crowdsourcing and machine learning techniques to build these systems to scale, but he also often crafts systems by hand because it informs his research as to how to use crowds and machine learning methods better. Because these systems can comprehend narratives and participate in discourse, he studies how they may support safe and positive experiences for users of social media platforms by preventing the spread of harmful behaviors on them.

Macbeth also performs system evaluations and empirical studies with human participants using qualitative and quantitative methods. He teaches courses in artificial intelligence, intelligent user interfaces, algorithms and introductory computer science.

Selected Publications

“Automated Narrative Understanding Technologies for Intervention Against Cyberbullying,” in Narratives in Research and Interventions on Cyberbullying among Young People. Edited by Heidi Vandebosch and Lelia Green. Springer, Berlin/Heidelberg, forthcoming.

“Monitoring Scene Understanders with Conceptual Primitive Decomposition and Commonsense Knowledge,” with Leilani H. Gilpin and Evelyn Florentine, Advances in Cognitive Systems 6, (2018): 45–63.