Rate My Professor Ben Adams

Ben Adams is an Associate Professor and Associate Head of Department in the Department of Computer Science and Software Engineering at the University of Canterbury, within the Faculty of Engineering. He earned his PhD from the University of California, Santa Barbara. Prior to his current role, Adams served as a Research Associate at the University of Auckland's Centre for eResearch from 2013 to 2017. His academic career emphasizes bridging computational systems with human discovery, particularly in aligning information-rich technologies with learning and communication processes.

Adams' research specializations include spatial data science, semantic web technologies, big data, text mining, ontologies, natural language processing, digital humanities, information extraction, and information retrieval. He investigates advanced computing applications, such as AI and machine learning, to tackle environmental and social challenges, alongside dynamic scientific workflows, natural language data representation, and decentralization for privacy-focused tools. Key focus areas encompass geographic information retrieval, environmental narratives, eScience, geoinformatics, exploratory search interfaces, spatial humanities, spatial cognition, and location-based services. He contributes to groups like UCVision and the New Zealand Institute of Language, Brain & Behaviour. Adams supervises Master's and Doctoral students and teaches courses including SENG303 on Android app development. He has received an Erskine Grant. His scholarly impact is evident in over 1,600 citations across 100+ publications. Prominent works include the book Unlocking Environmental Narratives: Towards Understanding Human Environment Interactions through Computational Text Analysis (2022); "Five stars of Linked Data vocabulary use" (2014, Semantic Web, 186 citations); "A data-synthesis-driven method for detecting and extracting vague cognitive regions" (2017, International Journal of Geographical Information Science, 168 citations); "Inferring thematic places from spatially referenced natural language descriptions" (2012); "A weighted multi-attribute method for matching user-generated Points of Interest" (2014, Cartography and Geographic Information Science, 103 citations); and recent papers like "Spatially explicit models for exploring COVID-19 lockdown strategies" (2020, Transactions in GIS) and "KEA Explain: A Neurosymbolic Framework for Explaining LLM Hallucinations" (2026).