جست‌وجوی استاد و پژوهشگر

Ilia Markovمشاهده پروفایل

استادیار

• Computational Linguistics
• Natural Language Processing
• Artificial Intelligence
• +۶ مورد دیگر

Ilia Markov is an Assistant Professor at the Vrije Universiteit Amsterdam, affiliated with both the Faculty of Social Sciences and Humanities and the Network Institute. His research focuses on computational linguistics, natural language processing, and artificial intelligence, with particular emphasis on hate speech detection and counterspeech strategies. Education: PhD in Computer Science (with honors) from the National Polytechnic Institute, Mexico (2018) Dr. Markov's research aims to foster more inclusive and respectful online communication by mitigating online hate speech and developing context-aware, personalized counterspeech strategies tailored to authors' sociodemographic profiles. He also investigates cross-domain scalability and the reasoning capabilities of AI models. His work bridges computational linguistics with social computing, focusing on practical applications for creating safer online environments. His research demonstrates technical sophistication in developing novel computational methods while maintaining awareness of social implications. His publication record shows a clear trajectory from foundational work in authorship analysis and native language identification to current research on hate speech detection and counterspeech generation. Recent publications emphasize multimodal approaches that integrate text and image analysis, cross-lingual transfer learning techniques, and ethical considerations in deploying AI systems for social media moderation. His work increasingly addresses low-resource language scenarios and the challenges of adapting models across different social contexts. Scientific Awards and Recognition: Outstanding Academic Performance Award for PhD students at the National Polytechnic Institute Senior Area Chair for LREC-COLING 2024 Area Chair for LREC 2026 Dr. Markov has co-organized several international competitions on automatic authorship identification and serves in editorial roles for major conferences in computational linguistics. His collaborative work spans multiple institutions across Europe and Latin America, reflecting the international nature of his research community. He has published over 65 peer-reviewed papers in journals and conference proceedings, demonstrating consistent productivity and impact in his field. As part of the Network Institute at VU Amsterdam, Dr. Markov contributes to interdisciplinary research that combines computational methods with social science perspectives to address contemporary challenges in digital communication. His affiliation with this institute provides a rich environment for exploring the societal implications of his technical work while maintaining rigorous methodological standards.

Michael Collinsمشاهده پروفایل

استاد

• Natural Language Processing
• Machine Learning
• Computational Linguistics
• +۳ مورد دیگر

Michael Collins is the Vikram S. Pandit Professor of Computer Science at Columbia University, where he has been since January 2011. He previously held roles at MIT and AT&T Labs-Research. His research focuses on natural language processing (NLP) and machine learning, emphasizing statistical methods and their applications in parsing, question answering, and cross-lingual systems. Collins teaches courses such as COMS W4705 (Natural Language Processing) and has online teaching materials from a Coursera course in 2013. Affiliations include Columbia University and Google NYC. He is affiliated with the Department of Computer Science and leads research in NLP. His work spans theoretical foundations like parsing algorithms and practical applications such as machine translation and semantic grounding. Collaborations with students and postdocs have produced influential models and frameworks (e.g., Qed for explainable QA systems). He has also contributed to benchmark datasets like the Natural Questions corpus. Collins' awards include the NSF Career Award, ACL Fellowship, and multiple best paper awards. His advising includes students like Avner May and Karl Stratos. Research outputs span 20+ years, with recent focus on attribution methods, cross-lingual parsing, and QA systems. His NLP notes and course materials are widely referenced in academic and industrial settings.

• Natural Language Processing
• Computational Linguistics
• Large Language Models
• +۷ مورد دیگر

Yulan He is an active researcher in Natural Language Processing and Computational Linguistics with numerous publications in top-tier conferences including ACL, EMNLP, and COLING from 2023-2025. Their work spans both theoretical advancements in Large Language Model architectures and practical applications in healthcare, social media analysis, and information retrieval. Research interests focus on Large Language Model optimization , including improving faithfulness in rationale generation, enhancing reasoning capabilities, personalizing outputs to user preferences, and optimizing computational efficiency. Significant contributions include frameworks for debiasing opinion summarization, improving depression detection in clinical interviews, and developing methods for Theory-of-Mind reasoning in LLMs. Their work addresses critical challenges in LLM reliability, interpretability, and efficiency. Analysis of recent publications reveals consistent focus on bridging the gap between theoretical LLM capabilities and practical applications , with particular attention to healthcare contexts, social media analysis, and complex reasoning tasks. Their research demonstrates how to make LLMs more reliable, efficient, and aligned with human needs across diverse domains. Scientific contributions include: Novel frameworks for LLM faithfulness and reasoning (Drift, EnigmaToM) Efficient inference methods (SCOPE, PECAN) Bias mitigation techniques (LASS, Rehearse With User) Personalization approaches (PROPER) Healthcare applications (Explainable Depression Detection) As evidenced by senior authorship positions across numerous publications, Yulan He leads research projects and likely supervises graduate students in NLP research. Their work demonstrates strong technical expertise combined with practical problem-solving approaches to real-world NLP challenges.

• First Language Acquisition
• Typical and Atypical Language Development
• Genetic Disorders
• +۸ مورد دیگر

Christiana Christodoulou is a Research Assistant Professor of Linguistics in the Department of Modern Languages at the University of Mississippi , with a focus on experimental and theoretical linguistics. She teaches courses such as Language and Mind , Psycholinguistics , and Syntax . B.A. in Linguistics, Aristotle University of Thessaloniki (1999) M.A. in Linguistics, University of Essex (2001) DB.A. in English Language and Literature, Aristotle University of Thessaloniki (2011) Ph.D. in Linguistics, University of British Columbia (2011) Her experimental research explores First Language Acquisition , Typical and Atypical Language Development , Genetic Disorders , and Neurolinguistics , particularly in Down Syndrome populations. The theoretical research focuses on Morphology , Syntax , and Functional Categories (e.g., case, tense, aspect) as well as Subjunctives and Interface Effects across linguistic domains. Christodoulou’s publications span topics from morphosyntactic development in Down Syndrome children to subjunctive clauses, coreference in Greek, and phonetic analysis of Cypriot Greek. Her work frequently intersects with Developmental Psychology , Neurolinguistics , and Formal Grammar . Marie Skłodowska-Curie Global Grant (2017–2021) Marie Curie Career Integration Grant (2012–2015) Canadian Commonwealth Scholarship Program (2005-2010) AHEPA Award for Outstanding Academic Achievement and Community Service (2009) Fieldwork and Research Award (2008) Her interdisciplinary work involves collaborations in Clinical Linguistics , Genetic Disorders , and Language Documentation , particularly in endangered Indigenous languages like Wenatchi.

• Natural Language Processing
• Corpus Linguistics
• Augmentative and Alternative Communication
• +۵ مورد دیگر

Anaïs LEFEUVRE-HALFTERMEYER is a Senior Lecturer at the University of Orleans, where she is affiliated with the LIFO (Laboratoire d'Informatique Fondamentale d'Orléans) research laboratory. She serves as a member of the DIAMS steering committee and is an associate member of both LIFAT and LLL research groups. Her academic profile reflects strong engagement with both teaching responsibilities and research activities in computer science with a human-centered focus. Dr. LEFEUVRE-HALFTERMEYER's research spans multiple interconnected domains within computational linguistics and human-computer interaction. Her primary interests include Natural Language Processing , Corpus Linguistics , and Augmentative and Alternative Communication systems. She has developed significant expertise in Explainable Artificial Intelligence approaches, particularly for image captioning systems, and has conducted substantial work on social media analysis, temporal annotation, and accessibility technologies. Her research consistently demonstrates a commitment to practical applications that address real-world communication challenges, especially in rehabilitation contexts. Analysis of her publication record reveals a clear trajectory from foundational corpus linguistics work toward increasingly applied research with strong human impact. Early publications focused on French spoken language treebanks and syntactic annotation tools, while more recent work addresses accessibility technologies, ethical considerations in AI, and explainability in vision-language models. There's a notable emphasis on bridging theoretical linguistic concepts with practical implementation, particularly for assistive technologies that serve people with communication disabilities. Dr. LEFEUVRE-HALFTERMEYER actively contributes to the academic community through mentorship and organizational roles. She has served as a mentor for student projects, including work on French language model comparison, and previously organized the JOLICO (JOurnées jeunes chercheurs de la LInguistique de COrpus) conference in 2015 as part of the scientific committee. Her institutional involvement includes membership on the DIAMS steering committee, demonstrating leadership within her research community.

Susan Leavyمشاهده پروفایل

استادیار

• Artificial Intelligence
• Text Mining
• Digital Humanities
• +۶ مورد دیگر

Susan Leavy is an Assistant Professor at the School of Information and Communication, University College Dublin, affiliated with the Insight Centre for Data Analytics. Her research bridges artificial intelligence, text mining, and digital humanities, focusing on bias mitigation in machine learning algorithms and cultural analytics. PhD in Artificial Intelligence & Bias Detection, Trinity College Dublin MPhil in Gender and Women’s Studies MSc in Artificial Intelligence BA in Philosophy and English Literature Her work spans gender bias in AI , ethical data curation , multimodal bias analysis , and algorithmic transparency . Recent publications highlight interdisciplinary approaches to auditing generative models, mitigating bias through inclusive language, and socio-technical frameworks for AI safety. Key trends in her research include: cross-disciplinary methods for bias detection, ethical AI design informed by feminist theory, and security applications of machine learning for content moderation and network protection. She has developed tools like CURATR for historical text analysis and ARTAI for societal risk assessment in recommender systems, while advocating for feminist epistemology in data-centric AI.

• Natural Language Processing
• Artificial Intelligence
• Data Science
• +۳ مورد دیگر

Jorge Turmo Borras es profesor del Departament de Ciències de la Computació en la Universitat Politècnica de Catalunya. Forma part del Grup de Processament del Llenguatge Natural (GPLN) i del Centre de Tecnologies i Aplicacions del Llenguatge i la Parla (TALP). Su investigación se centra en procesamiento del lenguaje natural, inteligencia artificial y minería de datos clínicos. Investigación: NLP, Deep Learning, Salud Digital Colaboraciones: IDEAI-UPC, XarTEC Salut Sus intereses principales incluyen extracción de información de documentos médicos, resolución de anáforas mediante hipergrafos, y normalización léxica de microtextos. Ha publicado sobre modelado de temas para enlazado de entidades y detección de negaciones en textos clínicos. En los últimos 5 años ha trabajado en sistemas de asistencia para diagnóstico , procesamiento de tweets médicos , y redes neuronales para historiales de salud . Sus artículos destacan aplicaciones de Transformers, CRF y modelos híbridos. Patrocinadores: HORIZON 2020, Plan Estatal de Investigación 2021-2023, RIS3CAT. Colabora con investigadores como Alicia Ageno, Lluís Padró y Horacio Rodríguez-Hontoria.

• Natural Language Processing
• Computational Linguistics
• Language Model Interpretability
• +۷ مورد دیگر

Anna Rogers is a leading researcher in Natural Language Processing (NLP), known for her foundational and critical work on language model interpretability, dataset quality, and the ethics of AI systems. She has published extensively at top-tier venues including ACL, EMNLP, and NAACL, often collaborating with prominent researchers like Anna Rumshisky and Isabelle Augenstein. Her research interests center on data curation , model generalization , responsible data use , and transparency in large language models . She has conducted influential surveys on community perspectives regarding intelligence in AI and has contributed to improving peer review practices in NLP conferences. Her work on BERTology, including papers like A Primer in BERTology and Revealing the Dark Secrets of BERT , has become key reading in the field. Recent publications highlight her focus on data transparency (e.g., the ROOTS Search Tool), synthetic content detection , and temporal annotation frameworks like NarrativeTime. She also investigates the robustness of models, probing techniques, and the limitations of current benchmarks. Her work consistently emphasizes methodological rigor and ethical considerations in NLP research. She has advocated for better data practices and more thoughtful evaluation paradigms across the community. Natural Language Processing Computational Linguistics Language Model Interpretability Dataset Curation and Quality Ethics in NLP Peer Review in Academic Conferences Anna Rogers has played a key role in shaping meta-discussions in the NLP field, including peer review reform and the societal impact of language technologies. She has co-organized workshops on negative results and contributed to tutorials on reviewing and data use. Her work bridges technical depth with critical reflection on the direction of the field. She has advised or collaborated with numerous researchers, though specific students are not listed in the available data. She has not received any explicitly mentioned scientific awards in the provided text. Anna Rogers leads or contributes to several open-source initiatives and shared resources, including the DECAF framework, the ROOTS Search Tool, and various datasets such as TimeBankNT and RuSentiment. Her work often includes released code and tools to promote reproducibility and further research.

• Information Retrieval
• Natural Language Processing
• Dialogue Systems
• +۱۰ مورد دیگر

Prof. Dr. Udo Kruschwitz is a Full Professor of Information Science at the University of Regensburg since July 2019. Previously, he held a professorship at the School of Computer Science and Electronic Engineering, University of Essex. His research bridges Information Retrieval (IR) and Natural Language Processing (NLP), focusing on adaptive search systems, dialogue modeling, gamification for annotation, and ethical issues in search technology. Key projects: COURAGE (Volkswagen Stiftung-funded virtual companion for social media safety), SENSEI (Horizon 2020 conversational analytics), and collaborative works with industry (Signal Media, Minority Rights Group). Research themes include: IR/NLP integration Conversational systems Gamified annotation platforms (Phrase Detectives) Arabic language resources Privacy-preserving search strategies His publications span query suggestion algorithms, crowdsourcing validation techniques, and hate speech detection. He has co-organized major events like Search Solutions and GamifIR workshops, and founded the Data Science @ Regensburg meetup. Scientific awards include the 2015 InnovateUK Knowledge Transfer Partnership Project of the Year. Current teaching includes Advanced Topics in IR and Natural Language Engineering modules. Serves as Senior Academic Advisor for Signal AI and mentor for numerous doctoral candidates.

Vinicius Woloszynمشاهده پروفایل

پژوهشگر

• Natural Language Processing
• Text Summarization
• Question Answering
• +۹ مورد دیگر

Vinicius Woloszyn is currently a Project Leader and Post-Doc Researcher at the Quality and Usability Lab within Deutsche Telekom Laboratories at Technical University of Berlin. His work focuses on natural language processing applications for social good, particularly in combating disinformation and addressing climate change through technological solutions. Dr. Woloszyn received his Ph.D. from the Federal University of Rio Grande do Sul (Brazil) with research on Unsupervised Machine Learning Methods for Natural Language Processing. His academic journey includes: Visiting Scholar at Grenoble Informatics Laboratory (France) in 2014 Research at Austrian Research Institute for Artificial Intelligence (Austria) in 2016 Visiting Scholar at Universitat Pompeu Fabra (Spain) in 2017 Researcher at Leibniz Universität Hannover (Germany) in 2018 Dr. Woloszyn's research spans multiple interdisciplinary areas at the intersection of artificial intelligence and societal challenges. His primary interests include Natural Language Processing with specific focus on Text Summarization, Question Answering, and Named Entity Recognition. He has made significant contributions to Open Science initiatives, particularly regarding Open Data and the Usability of Research Data. A substantial portion of his recent work addresses Disinformation through Fake News Detection and Knowledge Base Creation. More recently, he has expanded his research to apply AI techniques to Climate Change issues and Learning Analytics. His publication record demonstrates a clear trajectory toward developing AI solutions for societal challenges. The most recent works focus on regulatory implications of AI content moderation, automatic detection of green claims to combat greenwashing, and systems for improving fact-checking processes. These publications reveal a researcher deeply engaged with real-world applications of NLP technology to address misinformation and environmental sustainability. Dr. Woloszyn actively contributes to the academic community through service on scientific committees including the Annual Meeting of the Association for Computational Linguistics (ACL 2020), International Conference on Language Resources and Evaluation (LREC 2020), and The SIGNLL Conference on Computational Natural Language Learning (CoNLL 2019). He also serves as a reviewer for various conferences and journals. As Project Leader, he oversees several significant initiatives including the Berlin Open Science Platform, Untrue.News (a search engine for fake stories), and CLIFA (a collaborative platform for climate change facts). His current working projects span from language model evaluation to climate change applications, Python interfaces for research data, learning analytics platforms, and multilingual fact-checking systems.

Frank Kellerمشاهده پروفایل

استاد

• Natural Language Processing
• Cognitive Science
• Computational Narrative
• +۲ مورد دیگر

Frank Keller is a Professor in the School of Informatics at the University of Edinburgh, affiliated with EdinburghNLP, the Natural Language Processing Group. His research spans natural language processing, cognitive science, and computational narrative, with dual foci on language-vision integration and narrative modeling. Professor Keller's primary research encompasses natural language processing and cognitive science, specifically investigating language and vision tasks such as image description, visual grounding, video summarization, and visual story telling. His secondary focus involves computational narrative modeling, where he develops frameworks for analyzing characters, plot turning points, and suspense in long-form texts including movie scripts and books—addressing challenges in LLM comprehension of extended narratives. Recent publications (2023-2025) reveal strong trends in multimodal narrative generation, particularly visual story creation with grounded characters and movie script summarization. His work bridges cognitive modeling with practical NLP applications, exploring human reading mechanisms through neural attention models and advancing procedural video understanding through implicit argument prediction. Scientific Awards: EMNLP Best Paper Award (2002) Nominated for ACL Best Short Paper Award (2019) Professor Keller actively supervises eight current PhD students including Anil Batra and Gautier Dagan, and has mentored 26 alumni now at institutions like MIT, Google DeepMind, and Copenhagen University. His research receives substantial funding through the UKRI Centre for Doctoral Training in Designing Responsible NLP, supporting next-generation NLP researchers. As a core member of EdinburghNLP, he leads interdisciplinary teams collaborating with the Pioneer Center for AI and Copenhagen University. Current projects integrate cognitive science with multimodal learning, focusing on narrative structure analysis, visual grounding, and human-AI interaction systems for complex tasks like trailer creation and scientific poster summarization.

Milan Strakaمشاهده پروفایل

استاد

• Machine Learning
• Artificial Neural Networks
• Deep Learning
• +۱۲ مورد دیگر

Milan Straka is a professor at the Institute of Formal and Applied Linguistics (ÚFAL) within the Faculty of Mathematics and Physics at Charles University, Prague. His research focuses on machine learning, artificial neural networks, deep learning, and structured prediction with applications to natural language processing (NLP) tasks including POS tagging, dependency parsing, named entity recognition, and optical music recognition. University: Charles University School: Faculty of Mathematics and Physics Department: Institute of Formal and Applied Linguistics Straka has developed several NLP tools like UDPipe and NameTag, contributing to multilingual coreference resolution, Czech grammar error correction, and historical language processing pipelines. His recent work involves contextualized embeddings, semantic parsing, and large-scale dataset creation for Czech NLP tasks. He collaborates on European Language Grid initiatives and has participated in multiple shared tasks (CRAC, MRP, W-NUT). His publications span topics from algorithm design (functional data structures) to modern transformer-based language models (RoBERTa) with emphasis on Czech language resources.

• Natural Language Processing
• Machine Learning
• Dialogue Systems
• +۳ مورد دیگر

Ana Valeria Gonzalez is a researcher in the Department of Computer Science at the University of Copenhagen's Faculty of Science, specializing in Machine Learning with a focus on human-centered natural language processing. Her work bridges computational linguistics and cognitive science within the department's Machine Learning research section. Her research investigates bias mitigation in multilingual systems, interpretable AI evaluation frameworks, and affect-aware dialogue generation. Key contributions include developing testbeds for gender bias analysis in coreference resolution, novel methodologies for evaluating model interpretability through reverse Turing tests, and retrieval-based approaches for goal-oriented conversational agents. She employs techniques spanning attention mechanisms, BiLSTMs, and domain adaptation to address challenges in low-resource NLP settings. Analysis of her 2019-2021 publications reveals a cohesive research trajectory toward building transparent, equitable NLP systems that account for human cognitive factors. Her work consistently addresses real-world deployment challenges, particularly in dialogue systems requiring emotional intelligence and cross-lingual fairness. Collaborations with Anders Søgaard, Isabelle Augenstein, and other Copenhagen researchers demonstrate strong integration within the university's AI ecosystem. Gonzalez completed her Ph.D. in 2021 with the dissertation "Towards Human-Centered Natural Language Processing". She maintains active research in the Machine Learning group, though specific advising roles and grant details aren't documented in the available materials. Her publication record shows significant impact with multiple Scopus-cited works and substantial reader engagement across platforms.

• Computational Linguistics
• Formal Grammar
• Type-Logical Grammar
• +۴ مورد دیگر

Jose Oriol Valentin Fernandez Gallart is a researcher in the Department of Computer Science at the Faculty of Mathematics and Statistics (FME), Polytechnic University of Catalonia (UPC). With a substantial publication record spanning from 2004 to 2017, his work primarily focuses on computational linguistics and formal grammar systems. Dr. Fernandez Gallart's research centers on Type-Logical Grammar (TLG) , Displacement Calculus , and Lambek Calculus , with significant contributions to understanding grammatical formalisms for natural language. His work addresses complex linguistic phenomena including gapping constructions, anaphora resolution, and syntactic discontinuities. He has developed formal systems that bridge theoretical linguistics with computational applications, particularly in parsing algorithms and semantic interpretation. Analysis of his recent publications reveals consistent advancement in computational models of natural language. His research demonstrates how formal mathematical approaches can effectively model complex linguistic structures while maintaining computational tractability. Key contributions include extensions to displacement calculus for handling linguistic discontinuities and comprehensive analyses of Type-Logical Grammar's computational coverage. Dr. Fernandez Gallart has participated in significant research projects including "Aprendizaje Computacional y Comunicación" and "Gestió i Anàlisi de Dades Complexes," demonstrating broader engagement with computational approaches to language and data analysis. His extensive collaboration with Professor Glyn Morrill Verden (21 joint publications) indicates a strong research partnership in formal linguistics.

Andy Kehlerمشاهده پروفایل

استاد

• Pragmatics
• Discourse Interpretation
• Computational Linguistics
• +۵ مورد دیگر

Andy Kehler is a Professor in the Department of Linguistics at the University of California, San Diego (UCSD), where he directs one of the Computational Linguistics Labs. His research focuses on pragmatics, discourse interpretation, and the computational modeling of linguistic phenomena. He has held academic roles including Chair of the Linguistics Department (2009–2014) and Director of Graduate Studies (2016–2018). He teaches courses such as 'Making and Breaking Codes' and 'The Language Code,' and advises graduate students in computational and theoretical linguistics. His lab explores interdisciplinary topics like discourse coherence, coreference resolution, and ellipsis, blending theoretical, computational, and psycholinguistic approaches. Editorial Roles: Academic Editor at PLOS ONE, Review Editor at Frontiers in Language Sciences, and Associate Editor at Journal of Logic and Computation. Teaching: Extensive record since 2000, including courses on pragmatics, computational linguistics, and semantics. Research Interests: Explores how discourse coherence drives linguistic interpretation, with emphasis on anaphora, ellipsis, and Bayesian models of pronoun resolution. His work bridges formal semantics, computational methods, and experimental psycholinguistics. Recent Articles: Focus on ellipsis theory, LLMs' pragmatic competence, and Bayesian models of German pronouns. These studies advance understanding of discourse structure and computational linguistics applications. Awards & Grants: Not explicitly listed, but his editorial roles and extensive publications reflect scholarly recognition. Active in securing grants for lab research and student support. Labs/Teams: Leads UCSD Computational Linguistics Lab, collaborating on projects like LLM analysis and discourse modeling. Engages with international academic networks through invited talks and visiting fellowships.