جستجوی استاد راهنمای دانشگاهی

• Natural Language Processing
• Large Language Models
• Social Media Analysis
• +۷ مورد دیگر

Stuart E. Middleton is a Professor in the Electronics and Computer Science (ECS) department at the University of Southampton, where he has been employed since 2003. His research bridges artificial intelligence with practical applications in social science, mental health, and security domains. He leads multiple research projects funded by DTP and CISDnS CDT, focusing on multimodal natural language processing and large language models for social good applications. Professor Middleton's research interests center on Natural Language Processing, Large Language Models, and Human-in-the-loop AI systems. His work spans mental health applications (particularly suicide risk detection and mood change analysis), social media analysis for crisis mapping, geoparsing for location extraction, and argument mining in political discourse. He has developed numerous open-source NLP projects and datasets including CPIQA for climate science, ConversationMoC for mental health monitoring, and M-Arg for multimodal argument mining. His research demonstrates how AI can effectively support human decision-making in critical domains like mental healthcare, defense applications, and crisis management. His recent publications reveal a strong trend toward applying LLMs to high-impact societal challenges, particularly in mental health monitoring and climate science verification. He has pioneered methods for detecting suicidal ideation in social media, identifying moments of mood change, and developing context-aware question answering for climate papers. His work consistently emphasizes the importance of human oversight in AI systems, with numerous publications on responsible AI, regulation, and human-in-the-loop approaches. Ranked 1st in ECAL-2024 shared task on suicidal ideation detection Ranked 1st in NAACL-2022 shared task on suicide risk and mood change classification Winner of 'best paper' award at WWW2002 Semantic Web Workshop Professor Middleton actively supervises PhD students through multiple funded projects including 'Multimodal Natural Language Processing for Computational Social Science', 'Large Language Models for Military Veteran Mental Health', and 'Large Language Models for Human/AI Information Foraging to Combat Digital Human Trafficking into Terrorism'. He has secured significant funding from UKRI, DSTL, and other sources to support his research in responsible AI applications. He organizes major workshops including the RAI UK Workshops on Responsible AI for Mental Health and AIUK workshops on AI for Data Rescue and Defense applications. His research group maintains numerous GitHub repositories with open-source NLP tools and datasets that have been widely adopted by the research community.

Andrew Haltermanمشاهده پروفایل

استادیار

• Automated Text Analysis
• Computational Methods
• Civil Conflict
• +۲ مورد دیگر

Andrew Halterman is an Assistant Professor in the Political Science Department at Michigan State University. He joined in 2022 after a Faculty Fellowship at NYU's Center for Data Science. He holds a PhD from MIT (2021). His research focuses on developing methodological innovations for political science, particularly automated text analysis and computational techniques. Haltermans's research interests include developing new techniques for event data extraction, geoparsing documents, and analyzing military operations. Substantively, he studies civil conflict, interstate war, and violence against civilians. His work has been supported by the National Science Foundation, Fulbright Program, US Defense Department, Political Instability Task Force, and the US Holocaust Memorial Museum.

Yingjie Huمشاهده پروفایل

دانشیار

• Geospatial Artificial Intelligence (GeoAI)
• GIScience
• Disaster Management
• +۶ مورد دیگر

Yingjie Hu is an Associate Professor in the Department of Geography at the University at Buffalo (UB) , with an Adjunct Assistant Professor appointment in the Department of Computer Science and Engineering . He is an Affiliate Faculty at the UB Center for Geological and Climate Hazards and the UB AI and Data Science Institute . Education : Ph.D. in Geography (minor in AI), University of California, Santa Barbara (2016) M.S. and B.S. in Cartography and GIS, East China Normal University (2011 & 2008) His research focuses on Geospatial Artificial Intelligence (GeoAI) , Disaster Management , Community Resilience , and Human-Environment Interactions . He leads the GeoAI Lab@UB , which develops methods to integrate geospatial data, GIS, and AI for analyzing natural disasters (e.g., hurricanes, wildfires, winter storms) and sustainability challenges. The 15 most recent articles demonstrate his work in GeoAI , spatiotemporal analysis , social media for disaster response , and human mobility modeling . Key subtopics include toponym recognition , location labeling , alcohol consumption disparities , and deep learning frameworks for environmental applications. Scientific awards include the UCGIS Community Champion (2025), UCGIS Early/Mid-Career Research Award (2024), AAG SAM Emerging Scholar Award (2024), and CPGIS Young Scholar Award (2023). His NSF funding supports social media analysis in disaster response, and he has advised multiple students in GeoAI, spatial statistics, and urban planning applications.

Stuart Middletonمشاهده پروفایل

دانشیار

• Natural Language Processing
• Information Extraction
• Human-in-the-loop NLP
• +۱۷ مورد دیگر

Dr Stuart Middleton is an Associate Professor at the University of Southampton, affiliated with the School of Electronics and Computer Science within the Faculty of Engineering and Physical Sciences. He is a member of the Agents, Interaction and Complexity research group, the Centre for Machine Intelligence, and the Centre for Democratic Futures. His research focuses on Natural Language Processing (NLP), particularly information extraction and human-in-the-loop NLP, with applications in law enforcement, defence, mental health, and environmental science. His research interests include Natural Language Processing , Information Extraction , Human-in-the-loop NLP , Active Learning , Adversarial Training , Rationale-based Learning , and Argument Mining . He specializes in scenarios with limited or fragmented data, developing methods for few/zero-shot learning, graph-based models, and multimodal analysis. His work spans domains such as mental health, digital ethics (e.g., sharenting), defence, and climate science. The recent publications highlight a strong trend in applying NLP to societal challenges, including mental health monitoring through social media, ethical AI, multimodal argumentation in political debates, and climate data analysis. His work combines technical innovation in prompt engineering, summarization, and graph-based modeling with real-world impact. AI for defence: readiness, resilience and mental health (2024) Extraction and summarization of suicidal ideation evidence (2024) Sharenting and social media properties (2024) Implementing responsible innovation (2024) ConversationMoC: mood change detection (2024) Dr Middleton is actively involved in PhD supervision and academic leadership as Deputy Director of the UKRI MINDS Centre for Doctoral Training. He supervises multiple PhD students working on NLP and AI projects. He has secured research funding from ESRC and EPSRC for projects such as ProTecThem2.0 , SafeSpacesNLP , and GloSAT . He leads the Natural Language Processing modules COMP3225 (UG) and COMP6253 (PG) and mentors students in NLP research. He is a Fellow of The Alan Turing Institute and a member of the EPSRC peer review college, contributing to national research strategy and evaluation. His research is conducted within interdisciplinary teams, including collaborations with criminologists, social scientists, and environmental researchers. He is part of the Agents, Interaction and Complexity group and contributes to the Centre for Machine Intelligence and Centre for Democratic Futures , focusing on AI systems that interact meaningfully with humans and society.

Johanna Geißمشاهده پروفایل

پژوهشگر

• Event Detection
• Information Extraction
• Information Retrieval
• +۵ مورد دیگر

Johanna Geiß is a postdoctoral researcher at the Institute of Computer Science , Heidelberg University, specializing in Natural Language Processing , Information Extraction , and Event Detection . She contributes to projects like SCIDATOS (sepsis diagnosis) and EventAE (event-based Linked Data exploration). Education: PhD from University of Cambridge (2011), Magister Artium in Computational Linguistics (Heidelberg, 2006) Her research integrates Geoparsing , Social Network Analysis , and Graph Theory for tasks such as toponym disambiguation and entity resolution. Recent work includes frameworks like HeidelPlace and tools for semantic word clouds. Key awards include the 2010 Lundgren Research Award and multiple grants from the EPSRC and Cambridge European Trust. She has taught courses on Information Networks and Data Mining at Heidelberg University. As a postdoc , she advises on scientific computing applications in healthcare and contributes to open-source tools like NECKAr and EventAE .

Klaus Böhmمشاهده پروفایل

استاد

• Geoinformatics
• Spatial Data Analysis
• Explainable Artificial Intelligence
• +۴ مورد دیگر

Klaus Böhm serves as a Professor at Mainz University of Applied Sciences within the School of Engineering, affiliated with the i3mainz institute (Institute for Spatial Information and Surveying Technology). His research bridges geospatial technologies with artificial intelligence, focusing on practical applications in urban planning, healthcare, and educational environments through projects like BAM (Big Data Analytics) and TOPML (Machine Learning). His primary research domains include geospatial explainable AI (GeoXAI), mixed reality decision support systems, and health informatics applications. He pioneers methods for visualizing uncertainty in AI models, developing geoparsing techniques using LLMs, and creating spatial navigation frameworks for VR environments. His work consistently addresses real-world challenges in elderly mobility, smart city infrastructure, and medical education through spatial analytics. Analysis of his 2022-2025 publications reveals three dominant trends: (1) Integration of XAI with geospatial data for transparent decision-making in urban planning, (2) Development of mixed reality tools for STEM education and dermatology training, and (3) Spatio-temporal correlation analysis for smart city applications like parking optimization and public transport accessibility. His methodology emphasizes interactive visualization of complex spatial relationships and uncertainty quantification. Dr. Böhm leads multiple funded research initiatives including AIMR (AI-based decision support in mixed reality), RAFVINIERT (spatial intelligence for senior care), and FlexGeo (geo-service integration). These projects demonstrate sustained grant acquisition capability across EU and national funding programs, typically involving interdisciplinary teams from computer science, urban planning, and healthcare sectors. As a core member of the i3mainz research institute, he contributes to Germany's geospatial technology ecosystem through collaborative projects with municipal authorities and healthcare providers. The institute's work focuses on translating academic research into operational tools for spatial data infrastructure, particularly in environmental monitoring and public service optimization contexts.