جست‌وجوی استاد و پژوهشگر

• Artificial Intelligence
• Reinforcement Learning
• Generative AI
• +۱۰ مورد دیگر

Álvaro Jesús López López is a researcher at the Instituto de Investigación Tecnológica (IIT) and a professor at the Higher Technical School of Engineering (ICAI), both part of Comillas Pontifical University. He holds the position of Associate Research Fellow and serves as a principal investigator at the Chair of Smart Industry, where he leads applied research on digital technologies such as generative AI, computer vision, and synthetic data generation. He also directs executive programs in generative artificial intelligence for professionals at Comillas Onexed. His research interests include Artificial Intelligence, Reinforcement Learning, Generative AI, Smart Industry, and Railway Systems . His work focuses on applying advanced AI techniques to industrial and transportation challenges, particularly in energy efficiency and automation. He has published extensively in high-impact journals such as Engineering Applications of Artificial Intelligence , Applied Intelligence , and Transportation Research Part C . The recent articles highlight a strong trend toward applied AI in industrial and robotic systems , with a focus on sim-to-real transfer, keyphrase extraction, and reinforcement learning enhanced with semantic knowledge. His work bridges the gap between theoretical AI and real-world industrial applications. Scientific Awards: Honorable Mention from the Industry 4.0 Observatory (2021) Honorable Mention at the 6th Connected Industry Promotion Award Erasmus Teaching/Research Staff Training Grant (2014) He has advised multiple PhD students and leads several industry-funded research projects with organizations such as Ferrovial, Endesa, Metro de Madrid, and the European Commission. He has also organized scientific events and delivered numerous invited talks on AI and digitalization. His research has led to practical applications, including a patent on utilizing regenerative braking energy in railways. He completed a research stay at the Royal Institute of Technology (KTH) in Stockholm and has contributed to books and policy reports on digitalization in industry.

• Artificial Intelligence
• Semantic Web
• Natural Language Processing
• +۷ مورد دیگر

Philipp Cimiano is a Professor at Bielefeld University, Germany , with a prolific research record in Artificial Intelligence, Semantic Web, Natural Language Processing, Knowledge Graphs, Explainable AI, Clinical Decision Support Systems, Ontology Engineering, and Federated Learning . His work bridges theoretical AI concepts with practical applications in healthcare and robotics. Key research themes include dialogue-based XAI for user understanding, federated learning for healthcare data privacy, and LLM-driven robotics for embodied commonsense reasoning. Recent publications analyze dynamic explanatory interactions , perspectivized argumentation frameworks , and benchmarks for robot manipulation using large language models. His collaborations span institutions in Germany and Europe, with frequent co-authorship on topics like counterfactual generation , stakeholder group analysis , and lexicalization in QALD systems .

• Natural Language Processing
• Computational Linguistics
• Text Generation
• +۳ مورد دیگر

Elizabeth Clark is a researcher at the University of Washington, specializing in Natural Language Processing (NLP) and Computational Linguistics. Her work focuses on improving text generation systems through rigorous evaluation frameworks and datasets. Co-developer of the SEAHORSE dataset for multilingual summarization evaluation Principal investigator in context-aware rewriting and dialect-robust metrics Contributor to GEM benchmark standardization for NLG evaluation Her research explores challenges in: Human evaluation reliability for machine-generated text Counterfactual reasoning in narratives Contextual rewriting to enhance stylistic coherence Clark has published extensively in top conferences like ACL, EMNLP, and NAACL, often collaborating with Noah A. Smith and other NLP experts. Her work highlights critical gaps in automated evaluation metrics and proposes innovative solutions like CtxSimFit and NANO for robust assessment.

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.

Michael Strubeمشاهده پروفایل

استاد

• Natural Language Processing
• Computational Linguistics
• Semantics and Discourse Pragmatics
• +۷ مورد دیگر

Michael Strube is an Honorary Professor at the Department of Computational Linguistics at Heidelberg University and leads the Natural Language Processing (NLP) Group at HITS (Heidelberg Institute for Theoretical Studies) in Germany. He has been with HITS (previously EML Research and European Media Laboratory) since 2003 and became an Honorary Professor at Heidelberg University in 2010. He is also a Fellow of the Association for Computational Linguistics (2019). Dr. Strube received his PhD from the Computational Linguistics Department at the University of Freiburg in December 1996 under the supervision of Udo Hahn. Between 1997 and 1999, he was a postdoctoral fellow at the Institute for Research in Cognitive Science at the University of Pennsylvania, Philadelphia. Michael Strube's research focuses on semantics and discourse pragmatics, graph-based methods for text representation and analysis, extraction of world knowledge from Wikipedia for computational linguistics, and development of methods to synchronize multilingual content. His work spans coreference resolution, discourse processing, text summarization, entity linking, and natural language generation. He has made significant contributions to coherence modeling, anaphora resolution, and the application of geometric deep learning in NLP. His recent publications demonstrate strong trends in discourse processing, coreference resolution, and the application of geometric approaches to NLP problems. Strube has pioneered work in hyperbolic space for entity typing and graph embeddings, while maintaining his foundational work in discourse and coherence. His research bridges theoretical linguistics with practical NLP applications across multiple languages. Dr. Strube has received several prestigious awards, including: Fellow of the Association for Computational Linguistics (2019) Best Paper Award for "Fine-grained entity typing in hyperbolic space" (2019) Honorable Mention for the IJCAI-JAIR best paper prize 2010 for "Knowledge Derived from Wikipedia for Computing Semantic Relatedness" Professor Strube has advised numerous PhD students who have gone on to successful careers in academia and industry. His current PhD students include Yi Fan, Wei Liu, Haixia Chai, Mehwish Fatima, and Sungho Jeon, working on topics such as discourse structure, discourse relations, coreference resolution, and cross-lingual summarization. His former students include Federico Lopez, Benjamin Heinzerling, Mohsen Mesgar, and Nafise Moosavi, who now hold positions at institutions like Argo AI, RIKEN, Bosch Center for AI, and the University of Sheffield. As group leader of the NLP Group at HITS, Strube oversees a team focused on advancing natural language processing through research in discourse analysis, coreference resolution, text generation, and knowledge extraction. The group has been involved in numerous collaborative projects and has made significant contributions to the field through publications, shared tasks, and community building via workshops and conferences.

David A. Robbمشاهده پروفایل

پژوهشگر ارشد

• Human-Computer Interaction
• Human-Robot Interaction
• Image-based emotion feedback
• +۵ مورد دیگر

David A. Robb is a Research Fellow in the School of Mathematical and Computer Sciences at Heriot-Watt University, where he has been actively contributing to academic research since completing his PhD in 2015. His career progression shows a clear trajectory from PhD student (2011-2015) to Research Associate (2015-2020) to his current Research Fellow position. Robb is currently leading research efforts across three major EPSRC/UKRI projects: DeMILO (studying expert laser aligners' thinking and strategies), @tas_trust (investigating trust in autonomous systems), and HUME (focusing on human-machine teaming for AUVs). Robb's research interests span multiple interconnected domains within human-centered computing. His primary focus areas include Human-Computer Interaction (HCI) and Human-Robot Interaction (HRI), where he investigates how humans interact with and trust autonomous systems. Additional interests include image-based emotion feedback mechanisms, image summarization techniques, cognitive aspects of human-computer interaction, computer-supported cooperative work (CSCW), and visualization of complex data. His work often bridges theoretical understanding with practical applications in industrial and service environments. Analysis of Robb's publication record reveals a strong trajectory of scholarly contribution with 54 publications to date. His recent work demonstrates a clear focus on practical applications of human-robot interaction in real-world settings, particularly in industrial automation (laser alignment systems) and service robotics (robo-barista studies). A notable trend is his increasing focus on understanding human factors in automation adoption, trust dynamics in human-machine teams, and the practical implementation challenges of deploying autonomous systems in complex environments. His publications span top-tier venues in HCI, HRI, and robotics, demonstrating both breadth and depth in his scholarly contributions. ACM ICMI2021 Best Reviewer Awards (Top 5%) ACM ICMI 2023 Outstanding Reviewer Award ACM ICMI 2024 Outstanding Reviewer Award Honourable Mention ACM CHI 2017 Research Papers and Notes Honourable Mention ACM DIS 2017 Research Papers and Notes Robb has been instrumental in securing and executing multiple significant research grants, particularly through EPSRC/UKRI funding mechanisms. His current role as Experimental Lead for the HRI theme of the ORCA Hub project (a major £5.7 million EPSRC-funded initiative) demonstrates his capability in managing substantial research programs. While specific details about student supervision aren't prominently featured in the provided materials, his active research program and teaching responsibilities (Experimental Design and Web Design and Databases) suggest engagement with graduate students. His work extends beyond pure research to practical implementation, as evidenced by projects like the MIRIAM multimodal interface for autonomous systems and the robo-barista field studies. Robb is affiliated with the Strategic Futures Lab at Heriot-Watt University and has been involved with the ORCA Hub (Offshore Robotics for Certification of Assets), a major UK Robotics and Artificial Intelligence Hub. His collaborative network is extensive, with co-authors from multiple departments at Heriot-Watt as well as international collaborators. Recent projects like the DeMILO study of laser alignment expertise and the @tas_trust project on human trust in autonomous systems indicate his work is increasingly focused on translating fundamental HRI research into practical industrial applications.

Mirella Lapataمشاهده پروفایل

استاد

• Natural Language Processing
• Machine Learning
• Computational Linguistics
• +۲ مورد دیگر

Mirella Lapata is a Professor at the School of Informatics, University of Edinburgh, with visiting roles at the University of Washington and MIT. Her research focuses on natural language processing, machine learning, computational linguistics, and natural language generation. She has held academic positions since 2002, progressing from Lecturer at the University of Sheffield and University of Edinburgh to Reader (2007) and eventually Professor (2012). Key awards include Fellowships from the Royal Society of Edinburgh (2019) and ACL (2019), as well as the Royal Society Wolfson Merit Award (2018). She leads major grants such as the £6.8M UKRI Centre for Doctoral Training in Natural Language Processing (2019) and the €1.9M ERC Consolidator Grant (2016). Her work spans semantic parsing, neural networks, text generation, and machine learning applications. She has delivered keynote speeches at major conferences like ACL, EMNLP, and ICLR. Professional service roles include associate editorships for Royal Society Open Science and JAIR, and conference chairs for EACL and EMNLP.

Sonia Mandinمشاهده پروفایل

پژوهشگر

• Educational Technologies
• Learning Sciences
• Computer-Assisted Learning
• +۵ مورد دیگر

Sonia Mandin is a Postdoctoral researcher in educational sciences specializing in educational technologies at the French Institute of Education (EducTice) at École Normale Supérieure de Lyon. She has been working as a Post-Doc researcher with the Eductice team since October 2015. Her professional background includes positions as a teaching assistant at DILIPEM, Grenoble-3 since 2014, and previous postdoctoral research with the LIRIS - CNRS SILEX team from 2013-2015. Her educational background is impressive and interdisciplinary. She earned her doctorate in educational sciences from LSE, Grenoble 2 in 2009 with 'Very honorable mention with congratulations from the jury.' Prior to this, she completed a DEA in educational sciences (2003), a Master's in Educational Sciences (2002), and a degree in educational sciences (2001), all from SEAD, Lille 3 with honors. Her technical foundation was established with a DUT in computer science engineering from IUT A, Lyon 1 in 1998. Dr. Mandin's research focuses on the complex process of learning from an interdisciplinary perspective, integrating insights from psychology, didactics, and computer science. Her work centers on assisting learners through appropriate digital activities by developing computational models that can be implemented in educational technology tools. A significant portion of her research has focused on summarization skills, exploring how to help students better understand texts through effective summarization techniques. She has developed expertise in Latent Semantic Analysis (LSA) and its application to educational contexts, particularly in the development of the Résum'Web system for improving students' summarization abilities. Analysis of Dr. Mandin's recent publications reveals a strong focus on adaptive learning systems, competency-based education, and the application of natural language processing to educational contexts. Her work demonstrates a consistent trajectory from foundational research on text summarization to broader applications in personalized learning environments. Key themes include learner modeling, knowledge representation through ontologies, and the development of feedback mechanisms that support student learning. Her research bridges cognitive science with practical educational technology applications, particularly in mathematics education and writing instruction. Dr. Mandin has been actively involved in several significant research projects including the e-education project 'OCINAEE' (2015-2016), the 'Cartographie des savoirs' project (2013-2015), the European 'Language Technologies for Lifelong Learning' project (2008-2011), and the ACI Ecole et Sciences Cognitives project (2003-2005). She has also served as a speaker for the Canopé Network (formerly CNDP), the National Agency for the Use of ICT in education. Her work spans multiple research teams and institutions, including collaborations with the Knowledge Media Institute at the Open University of Milton Keynes, and research positions at LIRIS - CNRS and LSE, Grenoble. This diverse institutional engagement has allowed her to develop a rich perspective on educational technology applications across different educational contexts.

Serena Yeungمشاهده پروفایل

استادیار

• Computer Vision
• Machine Learning
• Deep Learning
• +۴ مورد دیگر

Serena Yeung is an Assistant Professor of Biomedical Data Science at Stanford University School of Medicine, with courtesy appointments in Computer Science and Electrical Engineering. She leads the Medical AI and Computer Vision Lab (MARVL) at Stanford and serves as Associate Director of Data Science for the Stanford Center for Artificial Intelligence in Medicine & Imaging (AIMI). She is also affiliated with the Stanford Clinical Excellence Research Center (CERC). Dr. Yeung's research focuses on computer vision, machine learning, and deep learning with applications to healthcare. Her work spans surgical video analysis, medical imaging AI, human motion analysis, and vision-language models for clinical applications. She has pioneered approaches for analyzing patient activities in ICU settings, surgical skill assessment, and hand hygiene monitoring in hospitals using computer vision systems. Her recent publications reveal a strong trend toward multimodal AI systems applied to medical imaging, with increasing focus on foundation models, surgical AI, medical vision-language systems, and 3D human motion analysis for clinical applications. Her work bridges computer vision fundamentals with practical healthcare implementations. Best Paper Award at NIPS 2017 Machine Learning for Health Workshop Dr. Yeung leads significant research initiatives including the Medical AI and Computer Vision Lab (MARVL) and serves as Associate Director of Data Science for the Stanford Center for Artificial Intelligence in Medicine & Imaging (AIMI). Her research has been supported by collaborations with major healthcare institutions and technology companies, focusing on translating computer vision research into clinical practice. Dr. Yeung directs the Medical AI and Computer Vision Lab (MARVL), which focuses on developing computer vision systems for healthcare applications. The lab's work spans surgical video analysis, ICU patient monitoring, medical imaging, and clinical decision support systems using state-of-the-art computer vision and deep learning techniques.

• Systems Neuroscience
• Vocal Learning
• Neural Networks
• +۴ مورد دیگر

Prof. Richard Hahnloser is a Full Professor at ETH Zürich's Department of Information Technology and Electrical Engineering, affiliated with the Institute for Neuroinformatics. He holds a PhD in Physics from ETH Zurich (1999, awarded the ETH Medal) and conducted postdoctoral research at MIT, Howard Hughes Medical Institute, and Bell Labs. His research focuses on neural mechanisms underlying vocal learning in songbirds, particularly auditory-motor signal processing and synaptic plasticity in neural networks. Education: Physics studies at ETH Lausanne and Zurich (Diploma 1996, PhD 1999). Career highlights include roles as a Swiss National Science Foundation Professor (2004–2007) before becoming a Full Professor at ETH Zurich in 2007. He explores interdisciplinary approaches combining experimental neuroscience, theoretical modeling, and bioimaging. Research interests include decoding neural networks of song systems, studying motor control via X-ray CT and Neuropixels recordings, and applying machine learning to vocal behavior analysis. Recent work highlights vocal plasticity, sleep-related neural dynamics, and neurotechnology for chronic neural recording. Notable achievements include the ETH Medal for his PhD and contributions to understanding syrinx anatomy and sensorimotor adaptation. His lab develops tools like MODOC for scientific text analysis and employs correlative microscopy techniques to study neural circuits.

Rodolfo DELMONTEمشاهده پروفایل

استاد

• Computational Linguistics
• Natural Language Processing
• Text Mining
• +۳ مورد دیگر

Rodolfo Delmonte is a retired Professor at Ca' Foscari University of Venice, affiliated with the Department of Linguistics and Comparative Cultural Studies. His research focuses on computational linguistics, natural language processing, and poetry analysis. He has contributed to systems like GETARUNS and SPARSAR, which address text understanding, sentiment analysis, and language modeling. His work spans topics such as machine translation, anaphora resolution, and the application of linguistic tools to literary texts like Shakespeare's sonnets. Delmonte has also explored the intersection of speech synthesis and language learning, developing tools for automated tutoring. His publications reflect a commitment to bridging theoretical linguistics with practical computational methods, including studies on discourse relations, pragmatic processing, and semantic evaluation. Despite no listed advisees, his extensive contributions to academic conferences and workshops highlight his role in advancing computational linguistics research in Italy and globally.

Benjamin Van Durmeمشاهده پروفایل

دانشیار

• Natural Language Processing
• Computational Semantics
• Streaming Algorithms
• +۱۰ مورد دیگر

Benjamin Van Durme is an Associate Professor in the Department of Computer Science at Johns Hopkins University's Whiting School of Engineering, with secondary appointments as Senior Research Scientist at the Human Language Technology Center of Excellence (HLTCOE) and affiliation with the Center for Language and Speech Processing (CLSP). He leads Natural Language Understanding research at HLTCOE and serves as research lead at Microsoft Semantic Machines. His educational background includes B.S. and B.A. (2001), M.S. in Language Technologies (2004), M.S. in Computer Science (2006), and Ph.D. (2009), all from the University of Rochester. Van Durme's research spans multiple facets of artificial intelligence with primary focus on natural language processing, computational semantics, and information seeking systems. His work addresses fundamental challenges in machine learning, reasoning agents, multimodal understanding, factuality, and legal reasoning. Current projects include Decomp.io for decompositional semantics, IterX for structured information extraction, Nellie and Treewise for neuro-symbolic reasoning, and MultiVENT for event detection in videos. Analysis of his recent publications reveals a strong emphasis on improving large language models through context compression, safety alignment, and multilingual capabilities. His work bridges theoretical advances in NLP with practical applications in legal reasoning, scientific communication, and information retrieval systems. Van Durme actively collaborates across multiple institutions and leads research efforts that address critical challenges in AI safety, factuality verification, and efficient reasoning systems. His lab produces work that spans from foundational ML methods to cognitive science applications, with particular attention to creating models that can extract structured information, make logical inferences, and handle uncertainty in multilingual contexts.

Mark Steedmanمشاهده پروفایل

استاد

• Computational musical analysis
• Computational linguistics
• Combinatory logic
• +۶ مورد دیگر

Mark Steedman is a Professor of Cognitive Science at the School of Informatics , University of Edinburgh, and an Adjunct Professor in the Department of Computer and Information Science at the University of Pennsylvania. His research bridges Artificial Intelligence , Cognitive Science , and Computational Linguistics , with a focus on Combinatory Categorial Grammar (CCG) , Prosody and Intonation , and Temporal Semantics . He has led the Institute for Language, Cognition, and Computation and contributed to interdisciplinary research at the Human Communications Research Center and Centre for Speech Technology Research . Research Interests : Steedman's work explores the intersection of formal grammar, computational models, and cognitive processes. He investigates how CCG parsing can enhance semantic inference, how prosodic features improve speech processing, and the role of temporal semantics in language understanding. His projects often integrate language models with entailment graphs for question answering and dialogue systems. Scientific Awards : Fellow of the American Association of Artificial Intelligence (1993) Fellow of the Royal Society of Edinburgh (2002) Fellow of the British Academy (2002) Member of Academia Europaea (2006) Best Paper Awards at ACL 2023 and AACL/IJCNLP 2023 Influential Paper Award (IFAAMAS 2017) Recent Trends in Publications : His recent work emphasizes language models for semantic inference , entailment graphs in multilingual settings, and incremental parsing for brain-language interfaces. Papers address challenges in hallucination , cross-lingual transfer , and prosody-text alignment .

• Reinforcement Learning
• Artificial Intelligence
• Natural Language Processing
• +۴ مورد دیگر

Sylvain Lamprier is an Adjunct Professor affiliated with the Institute for Intelligent Systems and Robotics (ISIR) at Sorbonne University, working closely with the Machine Learning and Artificial Intelligence (MLIA) team. His research spans reinforcement learning, adversarial robustness, fairness in AI, and natural language processing. Specializes in robustness of deep RL agents against adversarial attacks Investigates fairness mechanisms without sensitive attributes Develops cooperative learning frameworks for language models Contributes to symbolic regression and query decomposition techniques Recent work includes ensemble methods for adversarial robustness (CAp 2025), distributionally robust auto-encoding (2025), and grounding LLMs in interactive environments (ICML 2023). Publications in top venues like NeurIPS, IJCAI, and ACL reflect his interdisciplinary focus. No scientific awards or part-time status are explicitly mentioned.

Zhicheng Liuمشاهده پروفایل

استادیار

• Human Computer Interaction
• Machine Learning and Data Science
• Data Visualization
• +۳ مورد دیگر

Zhicheng Liu is an Assistant Professor in the Department of Computer Science at the University of Maryland. His research focuses on human-computer interaction, machine learning, and data visualization, emphasizing democratizing visualization design through computational tools and human-centered systems. He received the NSF CAREER Award (2023) for his work on manipulable semantic components in data visualization. Education details are not explicitly stated in the provided texts, but his professional trajectory indicates advanced academic training in computer science. Research interests include interactive visualization authoring, collaborative systems, AI-enhanced design tools, and semantic analysis of visual content. His work bridges computer science with cognitive science, psychology, and graphic design to create impactful interdisciplinary solutions. Liu's recent publications (2023–2025) explore topics like semantic chart decomposition, AI-assisted documentation, and cross-linguistic collaboration tools. His NSF-funded project aims to develop modular visualization components for easier creation of interactive charts. Awards include the NSF CAREER Award, recognizing his potential to advance visualization research. He advises four PhD candidates focusing on visualization and HCI topics. His grants include a $600K NSF award supporting his CAREER project. Collaborative tools developed (e.g., DocDancer, VisAnatomy) demonstrate his focus on practical, user-centric systems. Liu’s lab focuses on responsive documents, AI-driven tutorial systems, and visualization corpus development. He emphasizes real-world impact through tools that simplify data analysis and communication for non-experts.