جستجوی استاد راهنمای دانشگاهی

Arnab Nandiمشاهده پروفایل

استاد

• Database Systems
• Human-Computer Interaction
• Human-in-the-Loop Data Analytics
• +۵ مورد دیگر

Arnab Nandi is a Professor of Computer Science and Engineering at The Ohio State University, with a courtesy appointment in Biomedical Informatics. He holds leadership roles including Steering Committee Member for the Human-in-the-Loop Data Analytics (HILDA) Workshop and has served as Workshops co-chair for SIGMOD 2025-26 and Demonstrations co-chair for SIGMOD 2024. His research focuses on bridging human interaction and data infrastructure, spanning database systems, human-in-the-loop data analytics, and next-generation query interfaces. Nandi's work emphasizes interactive data exploration through projects like DICE (Distributed Interactive Cube Exploration), GestureDB (Querying Beyond Keyboards), and Omni (Multimodal Data Exploration). His recent research explores integrating LLMs into database education, augmented reality interfaces for data analytics, and multimodal approaches to video querying. Nandi has received numerous honors including the NSF CAREER Award, Google Faculty Research Award, IEEE TCDE Early Career Award, and the University's Alumni Award for Distinguished Teaching. He was also named to Columbus Business First's '40 under 40' and became an ACM Distinguished Member in 2024. As an educator, he teaches courses including CSE 3241 (Introduction to Database Systems), CSE 5242 (Advanced Database Systems), and CSE 5251 (Introduction to Software Startups). His educational innovations include DBTutor, which integrates LLMs into database systems education. At Ohio State, Nandi co-founded the OHI/O Program, which fosters tech culture through hackathons, and The STEAM Factory, an interdisciplinary research collaboration network. Prior to academia, he was founder and CEO of Mobikit, a connected vehicles data analytics startup acquired by Azuga Inc. (a Bridgestone company). His research has been supported by the NSF and industry partnerships, with applications spanning precision agriculture (CropFusion), clinical data pipelines (ICARUS), and interactive visualization systems (Perceptvis).

Amir Aryaniمشاهده پروفایل

دانشیار

• Data Science
• Social Data Analytics
• Public Health Informatics
• +۵ مورد دیگر

Amir Aryani is an Associate Professor at the School of Business, Law and Entrepreneurship , Swinburne University of Technology . He leads the Social Data Analytics (SoDA) Lab within the Social Innovation Research Institute , focusing on data-driven solutions for health and social challenges. His work involves large-scale cross-institutional projects with international collaborators including the British Library , ORCID , and NIH . Research Focus: Data modeling, real-time analytics, and information retrieval for social-good initiatives Collaborations: CERN, Data Archiving and Networked Services (DANS), and Global Information Systems (GESIS) His research explores data science applications in mental health, community resilience, and sustainable development. Key trends in his publications include: Mapping research to United Nations Sustainable Development Goals Developing hybrid expert-finding models using NLP and graph algorithms Creating interoperable research graphs for cross-platform discovery Assessing social impact of data projects in non-profit sectors Amir is actively involved in PhD supervision and has secured funding from Australian Research Council , National Health and Medical Research Council , and philanthropic foundations . He also contributes to community data projects through the SoDA Lab , which builds tools for social connection analysis and humanitarian response optimization.

Jamie Callanمشاهده پروفایل

استاد

• Information Retrieval
• Neural Search Engine Architectures
• Knowledge Graphs
• +۵ مورد دیگر

Jamie Callan is a Professor at Carnegie Mellon University's Language Technologies Institute (School of Computer Science), where he leads research in Information Retrieval and Neural Search Architectures . He teaches advanced courses on search engine design and mentors students in multiple programs. Research Focus: Federated retrieval, knowledge graph integration in search, ClueWeb dataset development, and neural approaches to document ranking Leadership: Past SIGIR Treasurer/Chair, Co-founding Editor of Foundations and Trends in IR, former TOIS Editor-in-Chief His recent work explores: Neural Retrieval: Latent vocabulary for sparse systems, hypothetical documents for dense vector retrieval Dataset Innovation: Maintenance and distribution of ClueWeb09, ClueWeb12, and ClueWeb22 datasets Search Efficiency: Selective search architectures with 90% reduced computational costs Scientific Recognition: International ACM SIGIR Conference Leadership Co-founding Editor-in-Chief, Foundations and Trends in IR Former Editor-in-Chief of ACM TOIS Dr. Callan's Lemur Project has produced Indri/Galago search engines and supported TREC evaluations through dataset contributions.

Jakob Beetzمشاهده پروفایل

استاد

• Building Information Modeling (BIM)
• Digital Twins
• Artificial Intelligence in Construction
• +۳ مورد دیگر

Prof. Dr. Jakob Beetz serves as a University Professor at RWTH Aachen University's Faculty of Architecture, leading the Design Computation (DC) research group. His work addresses critical challenges in sustainable built environments through digital innovation, focusing on integrating knowledge, information, and data across disciplines to reduce the sector's energy and material consumption—which accounts for over one-third of global totals—while advancing climate goals under the European Green Deal. His research spans Building Information Modeling (BIM), digital twins, and artificial intelligence, with emphasis on graph-based data federation, semantic web technologies, and large language models in construction. Key interests include evidence-based planning, parametric design optimization, building physics simulation, and networked knowledge modeling. Recent projects explore federated digital twin ecosystems for infrastructure management, intelligent damage assessment systems, and AI-driven solutions for wood structure preservation, directly contributing to sustainable development targets. Analysis of his 2024-2025 publications reveals a cohesive trajectory toward decentralized data environments and AI integration in Architecture, Engineering, and Construction (AEC). His work bridges theoretical foundations in knowledge representation with practical applications in bridge maintenance, road infrastructure, and timber construction, demonstrating consistent innovation in spatial data querying, federated issue management, and ontology-based process modeling. Prof. Beetz actively supervises PhD candidates, as evidenced by DC.Promotions 2024, and drives international collaboration through events like the Forum Construction Informatics 2025 and CIB W78 conferences. His research group engages with industry standards including Industry Foundation Classes (IFC) and Common Data Environments (CDEs), emphasizing open data principles and interoperability to transform construction workflows.

Xuming Heمشاهده پروفایل

دانشیار

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +۹ مورد دیگر

Xuming He is an Associate Professor at the School of Information Science and Technology (SIST), ShanghaiTech University, where he leads the PLUS Lab. His research spans computer vision and machine learning with a focus on developing algorithms that operate effectively under limited supervision and evolving data conditions. His core research interests include weakly-supervised and few-shot learning for scenarios with sparse annotations, continual learning frameworks for knowledge retention during sequential task acquisition, semantic segmentation techniques for scene understanding, and multimodal vision-language representations. He emphasizes interpretable machine learning to build transparent AI systems capable of human-understandable reasoning, addressing critical challenges in model trustworthiness and deployment reliability. Recent publications reveal strong trends toward novel class discovery in long-tailed recognition scenarios, physics-informed generative modeling for scientific applications, and robust segmentation under distribution shifts. His work increasingly integrates large language models for multimodal reasoning while maintaining focus on efficiency in resource-constrained environments like robotic grasping and medical imaging analysis. He actively mentors students, having supervised Qian He to PhD completion and Chuanyang Hu to Master's degree in 2023. He welcomes prospective graduate students through ShanghaiTech's Computer Science & Technology program and offers undergraduate research projects requiring minimum six-month commitments. The PLUS Lab under his direction drives innovation in learning under supervision constraints, with recent work spanning medical tumor analysis, cross-view geolocation, photonic computing, and semiconductor design verification. The lab's research bridges theoretical advances with practical applications across healthcare, robotics, and scientific discovery domains.

Kihong Heoمشاهده پروفایل

دانشیار

• Program Analysis
• Programming Language
• Machine Learning
• +۳ مورد دیگر

Kihong Heo is an Associate Professor in the School of Computing and Graduate School of Information Security at KAIST (Korea Advanced Institute of Science and Technology) in South Korea. His academic career includes serving as an Assistant Professor at KAIST from 2017-2019 before being promoted to Associate Professor in 2020, following his postdoctoral research at the University of Pennsylvania. He earned both his Ph.D. and B.S. in Computer Science & Engineering from Seoul National University. Dr. Heo's research focuses on developing program reasoning systems for safe and reliable software, with specific interests in AI-based program analysis systems for detecting deep semantic software bugs, general-purpose program simplification systems for secure and efficient software, and scalable program synthesis systems for automatic software generation and repair. His work bridges the gap between programming languages, program analysis, and machine learning techniques to create next-generation programming systems. Analysis of his recent publications reveals a strong trend toward integrating machine learning techniques with traditional program analysis methods, with significant contributions in compiler validation, software security, fault localization, and program debloating. His research has practical impact, with some of his work incorporated into Facebook's Infer static analyzer. ACM SIGSOFT Distinguished Paper Award, FSE 2025 Amazon Research Award, 2024 The Soo-Young Lee Teaching Innovation Award, KAIST, 2024 Prize for Excellence in Teaching, KAIST, 2024 Best Artifact Award, ICSE 2022 ACM SIGPLAN Distinguished Paper Award, PLDI 2019 ACM SIGSOFT Distinguished Paper Award, ICSE 2019 Dr. Heo actively mentors graduate students, currently advising several Ph.D. candidates including Yeonhee Ryou, Taeeun Kim, and Sujin Jang, as well as master's students. He has served on program committees for major software engineering and programming language conferences including PLDI, ICSE, POPL, and SPLASH, demonstrating his active role in the academic community. His laboratory, the Programming Systems Laboratory at KAIST, focuses on creating innovative programming systems that leverage both semantic-based program analysis and AI techniques.

• deep learning
• convolutional neural network
• deep features
• +۵ مورد دیگر

Fabrizio Falchi is a researcher at the Artificial Intelligence for Media and Humanities (AIMH) Lab of the Institute of Information Science and Technologies (ISTI) within Italy's National Research Council (CNR). He also maintains an associate position at the Biorobotics Institute of Scuola Superiore Sant'Anna. His work focuses on developing advanced multimedia retrieval systems, with the VISIONE platform being his most notable contribution, which has won international competitions including the Video Browser Showdown in 2024 and placed second in 2023. Falchi's educational background includes: Ph.D. in Information Engineering from University of Pisa (Italy) Ph.D. in Informatics from Faculty of Informatics of Masaryk University of Brno (Czech Republic) M.B.A. from Scuola Superiore Sant'Anna in Pisa His research spans deep learning, convolutional neural networks, deep features extraction, similarity search algorithms, distributed indexing systems, multimedia information retrieval, computer vision applications, and peer-to-peer systems. Falchi has made significant contributions to fine-grained visual understanding, cross-modal retrieval (particularly image-text matching), and robustness of deep learning systems against adversarial attacks. His work demonstrates a strong focus on practical applications of these technologies, particularly in video retrieval systems and safety monitoring solutions. Analysis of Falchi's recent publications reveals a strong focus on video and image retrieval systems, with the VISIONE platform being central to his work. His research shows increasing emphasis on fine-grained understanding in computer vision, cross-modal retrieval, and addressing practical challenges like cross-resolution face recognition. Recent work demonstrates innovation in making these systems more efficient through techniques like knowledge distillation (ALADIN) and leveraging virtual worlds for training data. His publications consistently bridge theoretical advances with practical applications in surveillance, safety monitoring, and multimedia search. Falchi's work has received significant recognition: Best paper award at CBMI 2024 for 'Is ClLIP the main roadblock for fine-grained open-world perception?' VISIONE 2024 won the Video Browser Showdown competition in Amsterdam VISIONE obtained second place at Video Browser Showdown 2023 in Bergen Best Paper Award for 'Learning Safety Equipment Detection using Virtual Worlds' at CBMI 2019 Falchi collaborates extensively with researchers at ISTI-CNR, particularly within the AIMH Lab. His work on VISIONE involves collaboration with Giuseppe Amato, Paolo Bolettieri, Fabio Carrara, Claudio Gennaro, Nicola Messina, Lucia Vadicamo, and Claudio Vairo. As co-chair of Ital-IA 2023, the 3rd National Conference on Artificial Intelligence, he plays an active role in the academic community. He is a member of ACM (since 2012), the Computer Vision Foundation, the Italian Association for Computer Vision Pattern Recognition and Machine Learning (CVPL), and the CINI Lab on Artificial Intelligence and Intelligent Systems. Falchi is a key member of the Artificial Intelligence for Media and Humanities (AIMH) Lab at ISTI-CNR, where he leads research on video retrieval systems. The lab has developed the award-winning VISIONE platform, which combines multiple scientific results in content-based video retrieval. His team focuses on developing systems that enable users to search for target videos using textual prompts, drawing objects and colors, or images as query examples. The lab's work demonstrates strong interdisciplinary collaboration, bridging computer science with practical applications in media, safety monitoring, and urban environments.

Kavita Balaمشاهده پروفایل

استاد

• Computer Vision
• Artificial Intelligence
• Computer Graphics
• +۶ مورد دیگر

Kavita Bala is the 17th Provost of Cornell University and a Professor of Computer Science. She previously served as the inaugural Dean of the Cornell Ann S. Bowers College of Computing and Information Science, leading its transition to a degree-granting college by 2025, and as Chair of Cornell’s Department of Computer Science. Her academic leadership includes expanding faculty, establishing research programs like the Bowers CIS Undergraduate Research Experience (BURE), and securing a new research facility for computing and information science. Education: B.Tech (IIT Bombay), M.S. and Ph.D. (MIT, Computer Science) Bala’s research focuses on computer vision, artificial intelligence, and computer graphics , with groundbreaking work in material and style recognition using deep learning. Her innovations in crowdsourced training data and differentiable rendering have advanced visual search technologies and translucent material modeling, powering her startup GrokStyle. She pioneered AI techniques applied to environmental monitoring through projects like MONITRS and AllClear , addressing Earth observation challenges. Her scientific awards include: American Academy of Arts and Sciences (2025) SIGGRAPH Computer Graphics Achievement Award (2020) IIT Bombay Distinguished Alumnus Award (2021) ACM Fellow (2019) SIGGRAPH Academy Fellow (2020) As Provost, Bala drives strategic initiatives like the Cornell AI Initiative , creating interdisciplinary minors in AI and AI in Society, and establishing the Schmidt AI in Science postdoctoral program. She co-chaired a task force for generative AI guidelines in education.

Craig Knoblockمشاهده پروفایل

استاد پژوهش

• Knowledge Graphs
• Data Semantics
• Schema Alignment
• +۵ مورد دیگر

Craig Knoblock serves as Keston Executive Director of the Information Sciences Institute (ISI) at the University of Southern California (USC), Vice Dean of the USC Viterbi School of Engineering, and Research Professor of Computer Science and Spatial Sciences. He also directs the Data Science Program and the Center on Knowledge Graphs at USC. His educational background includes a Ph.D. and M.S. in Computer Science from Carnegie Mellon University (1991, 1988) and a B.S. with honors in Computer Science from Syracuse University (1984). Knoblock's research focuses on data semantics , specializing in source modeling, schema and ontology alignment, entity and record linkage, data cleaning, Web data extraction, and knowledge graph construction. His work bridges computer science, geospatial analysis, and artificial intelligence to solve complex data integration challenges. Recent projects emphasize historical map digitization, geospatial knowledge graphs, and smart city applications. His 300+ publications demonstrate consistent contributions to knowledge graphs and geospatial data integration, with a growing emphasis on historical map analysis and urban applications. The research trajectory shows increasing interdisciplinary collaboration across computer vision, geoinformatics, and domain-specific applications. IEEE Fellow (2020) ACM Fellow (2017) AAAI Fellow (2004) Robert S. Engelmore Memorial Lecture Award (2014) Donald E. Walker Distinguished Service Award (IJCAI, 2018) Use-Inspired Research Award (USC Viterbi, 2018) As Executive Director of ISI, Knoblock oversees one of USC's premier research centers with significant federal funding. His leadership extends to directing the Center on Knowledge Graphs and the Data Science Program. While specific grant details aren't provided, his extensive publication record and leadership roles indicate substantial research funding across data integration, knowledge representation, and geospatial applications. His work bridges theoretical computer science with practical applications in historical preservation, urban planning, and resource management through collaborative projects with government agencies and industry partners. Knoblock leads the Center on Knowledge Graphs at USC, focusing on developing techniques for building and utilizing knowledge graphs across diverse domains. His team combines expertise in artificial intelligence, geospatial analysis, and data integration to tackle challenges in historical map digitization, urban applications, and resource discovery. The research group maintains strong connections with both academic and government partners through the Information Sciences Institute's extensive network.

Hua Xuمشاهده پروفایل

استاد

• Clinical Natural Language Processing
• Biomedical Informatics
• Large Language Models for Biomedicine
• +۳ مورد دیگر

Dr. Hua Xu is the Robert T. McCluskey Professor of Biomedical Informatics and Data Science at Yale School of Medicine. He serves as Vice Chair for Research and Development in the Department of Biomedical Informatics and Data Science and as Assistant Dean for Biomedical Informatics at Yale School of Medicine. Dr. Xu leads the Clinical NLP Lab and is Chair of the NLP working group at the Observational Health Data Sciences and Informatics (OHDSI) program. Dr. Xu received his PhD in Biomedical Informatics from Columbia University, an MS in Computer Science from New Jersey Institute of Technology, and a BS in Biochemistry from Nanjing University. Dr. Xu is a renowned researcher in clinical natural language processing (NLP), having developed novel algorithms for important clinical NLP tasks such as entity recognition and relation extraction. His work has been top-ranked in over a dozen international biomedical NLP challenges. He has developed CLAMP, a comprehensive clinical NLP toolkit that has been successfully commercialized and adopted by hundreds of healthcare organizations worldwide. His research focuses on applying NLP technologies to diverse clinical and translational studies to accelerate clinical evidence generation using electronic health records data. Recently, he has been utilizing NLP to harmonize metadata of biomedical digital objects to promote FAIR principles in biomedicine, and his lab is actively working on developing large language models (LLMs) for diverse biomedical applications. Dr. Xu's recent publications demonstrate a clear trend toward leveraging large language models for biomedical applications. His work spans from benchmarking LLMs for clinical NLP tasks to developing specialized architectures like BiomedRAG (retrieval augmented LLMs for biomedicine). His research addresses critical healthcare challenges including adverse event extraction, oncology clinical trial analysis, and EHR-based association studies, showing how NLP can bridge the gap between unstructured clinical text and actionable medical insights. Dr. Xu's lab has achieved top rankings in numerous NLP challenges, including multiple #1 positions in i2b2 Temporal information extraction, SemEval Disease-modifier extraction, BioCREATIVE Chemical-induced disease extraction, and other prestigious competitions. His contributions to clinical NLP have significantly advanced the field's ability to extract meaningful information from complex medical texts. As the leader of the Clinical NLP Lab at Yale, Dr. Xu oversees research that forms a complete ecosystem: developing novel NLP methods, building robust software tools, and applying these technologies to clinical and translational research. His lab's work closes the loop between methodological innovation and practical healthcare applications, ensuring that advances in NLP directly benefit patient care and medical research.

Kai R. Larsenمشاهده پروفایل

استاد

• Information Systems
• Semantic Theory of Survey Response
• Automated Machine Learning
• +۳ مورد دیگر

Kai R. Larsen is a Professor at the Organizational Leadership and Information Analytics division within the Leeds School of Business , University of Colorado Boulder . He holds courtesy faculty appointments in the Department of Information Science at the College of Media, Communication and Information , serves as a Research Advisor to Gallup , and is a Fellow of the Institute of Behavioral Science . Education: Ph.D., Information Science, University at Albany, SUNY Candidatus Magisterii (Software Engineering), The National College for Teachers of Commerce, Norway Adjunkt (Education), The National College for Teachers of Commerce, Norway Diplomkandidat, NHI College of Computer Science, Norway His research focuses on Information Systems , particularly addressing the Jingle Fallacy and developing the Semantic Theory of Survey Response . He leads the Federally Supported Human Behavior Project , creating transdisciplinary frameworks using Large Language Models and Natural Language Processing to predict human behaviors across technology utilization, investor decisions, voter behaviors, and cancer prevention. His scientific awards include INFORMS ISS Design Science Award (2019) Best Prototype Award at WITS (2019) Herbert A. Simon Award (2020) David B. Balkin Innovative Teaching Award (2023) Kolb Teaching Award (2022) National Institutes of Health $355,000 Grant (2022-2023) Larsen's grant funding and mentorship achievements include NIH grant for stress ontology research Outstanding Faculty Mentor Award (2021-2022) Technology Challenge Award (2016) He also maintains the Theories Used in IS Wiki and developed the TheoryOn award-winning ontology-based search engine used by thousands.

Shuhao Fuمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Cognitive Science
• Relational Reasoning
• +۳ مورد دیگر

Shuhao Fu is a Program Postdoctoral Fellow at the Santa Fe Institute (SFI) researching the intersection of machine learning and cognitive science. He completed his Ph.D. in Psychology at UCLA under advisors Hongjing Lu and Ying Nian Wu, following a B.S. in Computer Science and Mathematics from Hong Kong University of Science and Technology. His research examines human-like relational reasoning in AI systems through cognitive modeling and computational approaches. Research focuses on: Bridging human-machine reasoning gaps via analogical mapping Developing explicit relational representations in vision models Structural cognitive modeling for compositional understanding Multimodal reasoning and scene interpretation Relational knowledge representation in biological and artificial systems Publication trends show concentrated work in computational cognitive science (2021-2025), with evolving focus from visual analogy fundamentals to applications in 3D recognition, social interaction modeling, and mental health diagnostics. Recent work demonstrates increased emphasis on transformer architectures, multimodal integration, and human-AI comparative studies. Professional experience includes research internships at Google X and Mineral.ai, with prior affiliation at Johns Hopkins University's CCVL lab under Alan Yuille. Currently serves as reviewer for ICML, ICCV, and Cognitive Science Society conferences.

Lisa Nathanمشاهده پروفایل

دانشیار

• Climate Justice Informatics
• Indigenous Information Studies
• Information Ethics & Policy
• +۲ مورد دیگر

Lisa Nathan is an Associate Professor and current PhD Program Chair at the University of British Columbia's School of Information, situated on unceded Musqueam territory. Her academic home resides within the Department of Library, Archival and Information Studies under the Faculty of Arts, where she directs doctoral studies and teaches specialized courses in information ethics and climate justice. Her research examines the critical intersection of information policy, sustainability, and Indigenous knowledge systems, exploring how information ecosystems shape societal values through frameworks like climate justice and multi-lifespan design. Nathan's work consistently centers on disrupting colonial information practices while developing ethical alternatives through community-engaged scholarship, particularly evident in her collaborations with Indigenous communities on language preservation and cultural protocols. Analysis of her recent publications reveals a strong trajectory toward decolonial computing and environmental justice, with increasing emphasis on Indigenous-led information initiatives and the inclusion of 'other-than-human' participants in design processes. Her scholarly output spans high-impact journals including Journal of Documentation and First Monday, alongside influential books like Digital Technology and Sustainability: Engaging the Paradox. Outstanding Information Science Teacher Award (2017) Honorable Mention Paper Award at ACM CSCW (2016) Leadership in ACM SIGCHI Sustainability initiatives British Columbia Library Association Climate Action Committee Nathan actively supervises doctoral research through UBC's Indigenous Information Studies pathway, currently mentoring Rodrigo dos Santos while having guided recent graduates including Shaffer, Shankar, and Kaczmarek to completion. Her service includes chairing the First Nations Curriculum Concentration (2010-2018) and developing innovative courses like LIBR 564: Information Practice and Protocol in Support of Indigenous Initiatives. As Director of the Centre for Climate Justice research cluster, she fosters interdisciplinary collaborations addressing information policy's role in environmental crises.

Sean Andersonمشاهده پروفایل

مدرس ارشد

• Bioinspired Robotics
• Adaptive and Optimal Control
• System Identification
• +۳ مورد دیگر

Dr Sean Anderson is a Senior Lecturer at the Department of Automatic Control and Systems Engineering , University of Sheffield , with over 15 years of experience in interdisciplinary research spanning robotics, control systems, and computational biology. He earned his MEng and PhD from the University of Sheffield, focusing on control systems and chemical engineering. Education: MEng in Control Systems Engineering, University of Sheffield (2001) PhD in Chemical and Process Engineering, University of Sheffield (2005) Research Interests include: Bioinspired robotics Adaptive and optimal control in biological systems Nonlinear system identification Computational neuroscience Acoustic and visual sensor fusion for localization His recent publications highlight innovations in robotic localization in hazardous environments, interpretable deep learning for control systems, acoustic sensing technologies, and data-driven modeling of complex systems. Key projects involve autonomous navigation in pipe networks, turbulence modeling, and biomedical signal processing. Grants and Funding: He has secured major grants from EU H2020 (£4M), EU FP7 (£2.9M), and EPSRC (£5.7M), focusing on bioinspired control algorithms, robotic safety, and infrastructure assessment. Teaching: He leads the ACS61011 Deep Learning module, emphasizing practical applications in robotics and signal processing.

• 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.