جست‌وجوی استاد و پژوهشگر

Anjalie Fieldمشاهده پروفایل

استادیار

• Natural language processing
• Computational social science
• AI ethics
• +۳ مورد دیگر

Anjalie Field is an Assistant Professor in the Computer Science Department at the Whiting School of Engineering, Johns Hopkins University. She is also affiliated with the Data Science and AI Institute and the Center for Language and Speech Processing (CLSP). Dr. Field completed her PhD at the Language Technologies Institute at Carnegie Mellon University under Yulia Tsvetkov, where she was a member of TsvetShop. Prior to joining Johns Hopkins, she was a postdoctoral researcher in the Stanford NLP Group and at the Stanford Data Science Institute, working with Dan Jurafsky and Jennifer Eberhardt. She also spent time as a visiting student at the University of Washington from 2021 to 2022. Her research focuses on the ethics and social science aspects of natural language processing, developing computational models to address societal issues like discrimination and propaganda while critically assessing and improving privacy, transparency, and fairness in AI pipelines. Her work spans social media analysis, bias detection in multilingual content, police accountability systems, and ethical considerations in large language model applications. Dr. Field's publications demonstrate a consistent focus on identifying and addressing biases in language technologies, with particular attention to racial, gender, and cultural dimensions. Her recent work has expanded into domain-specific applications of NLP in astronomy, healthcare, and social justice contexts, showing the breadth of impact that ethical AI considerations can have across disciplines. Among her notable recognitions are: AI2050 Fellow by Schmidt Sciences 2022 Wikimedia Foundation Research Award of the Year Best Paper nomination at Socinfo (2020) Dr. Field teaches AI Ethics and Social Impact (Fall 2023; Fall 2024) and NLP for Computational Social Science (Spring 2024; Spring 2025). She plans to take PhD students for the 2024-2025 admissions cycle. Her work bridges technical NLP research with important social considerations, making significant contributions to both the technical community and broader societal discourse around AI ethics. She is an active member of the Center for Language and Speech Processing, where she collaborates with researchers working at the intersection of language technologies and real-world applications. Her research focuses on developing methods that not only advance NLP capabilities but also ensure these technologies serve diverse communities equitably.

Véronique Hosteمشاهده پروفایل

استاد

• Computational Linguistics
• Natural Language Processing
• Machine Learning
• +۷ مورد دیگر

Véronique Hoste is Senior Full Professor of Computational Linguistics at Ghent University's Faculty of Arts and Philosophy, where she serves as Department Head of Translation, Interpreting and Communication and Director of the LT3 language technology research team. She also holds the position of Research Director for the Faculty of Arts and Philosophy. Her educational background includes a PhD in Computational Linguistics from the University of Antwerp (2005) focused on optimization in machine learning for coreference resolution. Key research areas encompass machine learning for natural language processing, semantic and discourse modeling, including specialized work in event detection, entity/event coreference resolution, irony detection, and emotion analysis. Hoste's publication trends reveal strong interdisciplinary focus, with recent work bridging NLP with crisis communication, digital humanities, and ethical AI. Her team develops high-quality datasets (e.g., EmoTwiCS for emotion trajectories) and collaborates extensively with commercial partners on projects like SentEMO for aspect-based sentiment analysis. Current research emphasizes multimodal emotion analysis, fuzzy rough set methods for sentiment detection, and cross-document event coreference. Elected member of the Royal Flemish Academy of Belgium for Science and the Arts (KVAB) Francqui Chair appointment by Université Libre de Bruxelles (2023-2024) Co-founded LT3 spin-off AlfaSent (2024) for customer feedback analysis Authored first Dutch-language book on NLP: "Taaltechnologie ontrafeld" She actively supervises multiple PhD students on projects including Common-sense knowledge in irony detection (Common-sense), cross-document event coreference (Encore), and empathy modeling in conversational agents (FlandersAI). Her team secures funding through interdisciplinary collaborations like NewsDNA for news recommendation and METRICS for emotion trajectory analysis. Hoste also engages in public outreach through the "AI at school" initiative and advises on language technology integration in high school curricula. The LT3 laboratory under her leadership maintains strong industry partnerships and develops practical NLP tools including EmotioNL and Automatic Term Extraction systems, while advancing core research through projects like CLARIAH-VL for data curation.

• Natural Language Processing
• Question Answering
• Human-AI Collaboration
• +۷ مورد دیگر

Jordan Boyd-Graber is a Professor in the Department of Computer Science at the University of Maryland's College of Computer, Mathematical, and Natural Sciences. He serves as a leading researcher in Natural Language Processing with significant contributions across multiple NLP subfields. His work bridges theoretical advances with practical applications requiring human-AI collaboration. His research interests span Natural Language Processing , Question Answering systems , Human-AI collaboration , Machine Translation , and Topic Modeling . He focuses on developing systems that work effectively with humans rather than replacing them, emphasizing interpretability and user-centered design. His work often involves creating evaluation frameworks that better capture real-world utility rather than just technical metrics. His publication record shows consistent leadership in the field, with numerous papers at top venues including ACL, EMNLP, and NAACL. Recent work (2023-2024) demonstrates strong engagement with LLMs, human evaluation methodologies, and practical applications in health and translation domains. His research often involves student collaborators, indicating active mentorship. ACL Fellow (2021) Program Chair for ACL 2023 Organizer of prompt hacking competition Leader in human-centered NLP evaluation Boyd-Graber has secured substantial funding for his research, particularly in projects involving human-AI collaboration and question answering systems. His work often involves interdisciplinary teams spanning computer science, linguistics, and domain-specific applications. He has mentored numerous graduate students who have gone on to successful careers in academia and industry. He leads research groups focused on developing interpretable NLP systems that work effectively with humans, particularly in high-stakes domains like healthcare and education. His lab frequently develops novel evaluation methodologies that better capture real-world utility rather than just technical metrics.

Nianwen Xueمشاهده پروفایل

استاد

• Machine Learning
• Natural Language Processing
• Chinese language processing
• +۴ مورد دیگر

Nianwen Xue is a Professor in the Computer Science Department and Language & Linguistics Program at Brandeis University, leading the Chinese Language Processing Group. He holds affiliations with the Michtom School of Computer Science, Benjamin and Mae Volen National Center for Complex Systems, and serves on editorial boards of Computational Linguistics and Lingua Sinica. His research focuses on computational linguistics, machine learning, and cross-linguistic meaning representations. He previously held roles at the University of Colorado and University of Pennsylvania. Education: Ph.D. in Linguistics from University of Delaware, M.A. and B.A. in Linguistics from Nankai University. Research projects include the Chinese Treebank, OntoNotes, and initiatives funded by NSF, DARPA, and IARPA. Teaching includes courses on Natural Language Systems and Computational Corpus Linguistics. He has organized major NLP conferences like CoNLL Shared Tasks and DMR workshops. Research interests emphasize semantic parsing, discourse analysis, and computational social science, with over 100 publications in top venues like ACL, EMNLP, and Computational Linguistics. His work bridges theoretical linguistics and applied NLP, advancing cross-lingual resources and formal meaning representations.

Marieke Meelenمشاهده پروفایل

دانشیار

• Historical Linguistics
• Comparative Syntax
• Information Structure
• +۵ مورد دیگر

Dr Marieke Meelen is an Associate Professor in Historical Linguistics at the University of Cambridge, affiliated with the Faculty of Modern and Medieval Languages and Linguistics. She serves as a Fellow, Tutor, and Director of Studies at Trinity Hall. Research Interests: Historical Linguistics, Comparative Syntax, Information Structure, Grammaticalisation & Pragmaticalisation, NLP for low-resource languages, Celtic & Tibeto-Burman languages Projects: PI for ELDP-funded endangered language documentation in Nepal; collaborator on ERC-funded 'PaganTibet' and AHRC-funded 'Emergence of Egophoricity' Key Contributions: Development of ASR and HTR models for Tibeto-Burman languages; historical treebank of Welsh; computational approaches to Celtic and Tibeto-Burman syntax Publications & Grants focus on syntactic reconstruction, corpus creation, and NLP for endangered languages. She mentors PhD students working on Celtic or Tibeto-Burman languages and consults for computational linguistics projects.

Anke Hollerمشاهده پروفایل

استاد

• Descriptive Grammar of Contemporary German
• Formal Grammar and Language Theory
• Grammar-Pragmatics Interface
• +۴ مورد دیگر

Prof. Dr. Anke Holler is a Professor of German Linguistics at the University of Göttingen , specializing in formal grammar, discourse analysis, and computational linguistics. Her career spans roles in academic administration, including Vice President for Appointments since 2021, and leadership in DFG and Leibniz Association committees. Education : University of Tübingen, University of Leipzig, and University of Massachusetts, Amherst Current Projects : GRK 2636 'Form-Meaning Mismatches', DFG 'Structuring Literature' (SPP 2207), VW Foundation 'Uncertain Attribution' Research Interests focus on the intersection of grammar theory, experimental linguistics, and computational modeling. She explores discourse structures, narrative perspective, and constraint-based parsing, with applications in digital humanities and text mining. Her recent work involves neural networks for speaker attribution and computational analysis of literary reflexivity. Collaborations include the Carl Friedrich Lehmann-Haupt Doctoral Program and the 'Textstrukturen' center. She serves as editor for the Zeitschrift für Sprachwissenschaft and coordinates interdisciplinary initiatives.

Simone Paolo Ponzettoمشاهده پروفایل

استادیار

• Knowledge acquisition
• Natural Language Processing
• Semantic Web technologies
• +۵ مورد دیگر

Simone Paolo Ponzetto is an Assistant Professor (Juniorprofessor) at the University of Mannheim since 2013, affiliated with the Research Group Data and Web Science. His research focuses on Semantic Web technologies, Natural Language Processing (NLP), and knowledge acquisition, particularly leveraging collaboratively built resources like Wikipedia. Prior to Mannheim, he held postdoctoral roles at Sapienza University of Rome and research positions at the University of Heidelberg and Stuttgart. His work includes pioneering projects like BabelNet, a multilingual semantic network. Ponzetto earned his PhD in Computational Linguistics from the University of Stuttgart, with interdisciplinary contributions to coreference resolution, semantic relatedness, and ontology learning. Research Interests: Unsupervised/weakly-supervised knowledge extraction Multilingual ontology learning and semantic networks Lexical semantics (word sense disambiguation, semantic similarity) Discourse semantics (coreference resolution, coherence modeling) Professional Contributions: Guest editor for a Artificial Intelligence Journal special issue on AI and Wikipedia Area chair for EMNLP-CoNLL 2012 and EACL 2014 Program committee member for ACL, AAAI, and other top conferences Lab/Team: Active in the Research Group Data and Web Science at Mannheim, advancing AI and NLP applications in collaborative knowledge systems.

• Computational Linguistics
• Natural Language Processing
• Artificial Intelligence
• +۵ مورد دیگر

Massimo Poesio is a Professor of Computational Linguistics at Queen Mary University of London and a full Professor of Natural Language Understanding at Utrecht University. He leads the ARCIDUCA project (EPSRC-funded) on conversational agents and the Dealing with Meaning Variation project (NWO-funded) exploring NLP applications. His research focuses on anaphora resolution, games-with-a-purpose, and applying NLP to combat deception and misinformation. He is a Fellow of the Alan Turing Institute and supervises students in PhD programs like IGGI and Health Data in Practice. Education: Previous affiliations include University of Essex and University of Trento (founded CLIC Lab). Research Projects: DALI (ERC), SENSEI (EU), AnaWiki (EPSRC), and LiveMemories (Provincia di Trento). His work combines formal semantics with empirical methods, including brain data and corpus analysis. He has developed tools like Phrase Detectives and BART for anaphora resolution, and co-founded the Lingotowns platform for language learning.

Julian Houghمشاهده پروفایل

دانشیار

• Natural Language Processing
• Human-Robot Interaction
• Human-Computer Interaction
• +۳ مورد دیگر

Dr. Julian Hough is an Associate Professor of Human-Computer Interaction at Swansea University, affiliated with the School of Mathematics and Computer Science under the Faculty of Science and Engineering. His research focuses on improving human-agent interaction through Natural Language Processing (NLP) and AI, emphasizing ethical and quality outcomes in human-robot collaboration. His work spans Human-Robot Interaction (HRI), dialogue systems, and cognitive applications of speech technology. Notable projects include the FLUIDITY initiative exploring virtual reality platforms for HRI and the ARCIDUCA project annotating dialogue using conversational agents in games. He has secured significant grants, including a £587,000 EPSRC New Investigator Award for FLUIDITY and a £1.09M EPSRC grant for ARCIDUCA. Research interests include multimodal communication, disfluency analysis in dialogue, and applying LLMs to word sense disambiguation. His work often bridges computational linguistics with practical robotics and health technology, such as analyzing wearable sleep-tracker subjectivity and detecting Alzheimer’s through speech patterns. Collaborations span institutions globally, with contributions to workshops and conferences on HRI and dialogue systems. He actively supervises postgraduate research in areas like incremental intention recognition and computational law semantics.

Orphée De Clercqمشاهده پروفایل

استادیار

• Natural Language Processing
• Machine Learning
• Readability Prediction
• +۷ مورد دیگر

Orphée De Clercq is an Assistant Professor at Ghent University, specializing in language technology for educational applications. Her research focuses on leveraging Natural Language Processing (NLP) and Machine Learning (ML) to enhance computer-assisted language learning , readability prediction , and automated writing evaluation . She also explores sentiment analysis , emotion detection , and event coreference resolution in Dutch and multilingual contexts. Education : PhD in 2015 with groundbreaking work in readability prediction and fine-grained sentiment analysis for Dutch. Research Trends in her recent publications emphasize: Readability across domains and languages Emotion Detection using transformers and affect lexica Event Coreference in cross-document news Automated Writing Evaluation through NLP Implicit Sentiment Analysis in user-generated content Cross-Lingual Transfer with multilingual datasets She co-supervises four PhD students and contributes to interdisciplinary projects like Steunpunt Toetsen , SentEMO , and NewsDNA . Her teaching includes courses on digital communication and Computer-Assisted Language Learning .

• Natural Language Processing
• Sociophonetics
• Social Meaning Analysis
• +۵ مورد دیگر

Rob Voigt Assistant Professor of Linguistics and courtesy faculty in Computer Science at Northwestern University, affiliated with the Institute for Policy Research and Cognitive Science Program. Specializes in computational linguistics, focusing on natural language processing (NLP) applied to social science questions including policing, mental health, and immigration. Director of the Linguistic Mechanisms Lab, exploring how language reflects and shapes societal structures. Education PhD in Linguistics (2019), Stanford University Postdoctoral Scholar (2019-2020), Stanford University M.A. in East Asian Studies (2013), Stanford University B.A. in Chinese (2008), Vassar College Research Interests Combines computational methods with sociolinguistics to study: Police-community interaction through body-worn camera data Linguistic markers of procedural justice and racial disparities Language in marginalized communities and social movements LLM applications to clinical language analysis (autism spectrum disorders) Historical discourse analysis of immigration policies Recent Work Recent projects include analyzing 140 years of political immigration rhetoric (PNAS 2022), evaluating officer communication training via bodycam NLP (PNAS Nexus 2024), and developing stereotype analysis pipelines (EMNLP 2024). Active in policy-relevant research through grants on police training (NIJ) and mental health language analysis (NIH). Awards & Recognition Holds Cozzarelli Prize (2017) for groundbreaking policing research and Cialdini Prize for field methods innovation. Recipient of interdisciplinary fellowships from Stanford and NIH grants totaling $8M+. Teaching Current courses include Text Processing for Linguists . Known for ungrading pedagogy emphasizing intrinsic motivation and collaborative learning. Develops open-access computational linguistics curricula using Unix/Python. Labs & Collaborations Linguistic Mechanisms Lab focuses on socially responsible NLP. Collaborates with criminologists, cognitive scientists, and sociologists on projects like the Media Accountability Project (racial disparities in gun violence reporting).

Daniel Hardtمشاهده پروفایل

دانشیار

• Computational Linguistics
• Natural Language Processing
• Artificial Intelligence
• +۴ مورد دیگر

Daniel Hardt serves as Associate Professor in the Department of Management, Society and Communication at Copenhagen Business School. His interdisciplinary research bridges computational linguistics, artificial intelligence, and social analysis, with particular focus on natural language processing applications and theoretical linguistic phenomena. His primary research domains include Computational Linguistics (specializing in ellipsis resolution and sluicing phenomena), Natural Language Processing (developing methods for psychographic classification and sentiment analysis), and Artificial Intelligence (examining large language model capabilities and limitations). Recent work analyzes travel behavior during crises, gender effects in evaluations, and GDPR policy comprehension through NLP techniques. His publications span top venues including Linguistic Inquiry , Tourism Management , and ACL proceedings. Hardt actively engages with practical business applications through 27 media contributions discussing AI implementation, ChatGPT transparency, and data-driven leadership strategies. His academic service includes organizing events like the 2019 "Fake News" conference at CBS and presenting at international venues including JSAI 2024. With 28 supervised academic works documented, he maintains substantial mentoring activity while contributing to public discourse on digital transformation challenges.

• Natural Language Processing
• Explainable Artificial Intelligence
• Mechanistic Interpretability
• +۳ مورد دیگر

Simon Ostermann serves as a Senior Lecturer at Saarland University and Senior Researcher & Deputy Director at the Multilinguality and Language Technology (MLT) lab of the German Research Center for Artificial Intelligence (DFKI). He leads the Efficient and Explainable NLP (E&E) research group and contributes to major projects including lorAI (Low Resource AI), TRAILS (Trustworthy Machines), PERKS (Procedural Knowledge), DAM-S (Semantic Search), and DisAI (Disinformation Combat). His research centers on democratizing language technology through transparent, robust models—specializing in mechanistic interpretability to reverse-engineer LLM internals and enhance efficiency for low-resource languages. Key focus areas include reducing model size for constrained environments, improving cross-lingual transfer via adapters, and developing structured input techniques. His work bridges theoretical interpretability with practical applications in resource-limited settings. 2025 publications reveal concentrated efforts in low-resource adaptation (language adapters, graph-enhanced embeddings), explainable AI (counterfactual generation, conversational XAI datasets), and multilingual fact-checking systems. Notable trends include systematic neuron manipulation frameworks, rigorous evaluation of synthetic data strategies, and cross-lingual claim verification benchmarks. Ostermann advises six PhD candidates (Anikina, Oguz, Bäumel, al Ghussin, Gurgurov, Vykopal) and multiple MSc students on topics spanning RAG hallucinations, multilabel classification, and adapter interpretability. His research receives funding through DFKI-led consortia with European and international partners focusing on trustworthy, efficient AI deployment. The E&E group under his leadership drives innovation in efficient NLP through biweekly seminars, collaborative coding sessions, and partnerships with institutions like KInIT. Current initiatives prioritize green computing for language models and real-world deployment in industrial procedural knowledge systems.

Mahsa Yarmohammadiمشاهده پروفایل

پژوهشگر

• Natural Language Processing
• Deep Learning
• Automatic Speech Recognition
• +۴ مورد دیگر

Mahsa Yarmohammadi is an Assistant Research Scientist at the Center for Language and Speech Processing (CLSP), where she leads research in cross-lingual language and speech applications. Her work focuses on deep learning techniques for multilingual data representation, automatic speech recognition (ASR), and speech translation. She develops novel algorithms for ASR decoders and explores interfaces between ASR and machine translation systems. Education: PhD in Computer Science and Engineering, Oregon Health & Science University (2016) Master’s in Computer (Software) Engineering, Shahid Beheshti University, Iran (2007) Undergraduate degree in Computer (Software) Engineering, Amirkabir University of Technology (Tehran Polytechnic) (2004) Research Interests: Her expertise spans Natural Language Processing (syntactic/semantic parsing, information extraction), multilingual systems, deep learning, and ASR. She specializes in resource transfer across languages, neural lattice embeddings, and end-to-end system integration for speech and language technologies. Publication Trends: Recent articles (2019–2025) emphasize multilingual challenges: cross-lingual retrieval, contextual ASR, coreference resolution in dialogue, and zero-shot information extraction. Her work frequently addresses low-resource settings, neural algorithm design, and real-world dataset creation (e.g., earnings call analysis, COVID-19 QA pairs). Grants & Teams: As a prime researcher in multi-site programs, she collaborates on large-scale multilingual projects. She was mentored by Benjamin Van Durme (Johns Hopkins CS) and Sanjeev Khudanpur (Johns Hopkins ECE) during her CLSP post-doctoral fellowship (2021).

Stav Atirمشاهده پروفایل

استادیار

• Decision-making
• Judgment
• Diversity, Equity, and Inclusion
• +۵ مورد دیگر

Stav Atir serves as an Assistant Professor in the Management and Human Resources Department at the Wisconsin School of Business, University of Wisconsin-Madison, where she joined in June 2021. She maintains dual affiliations with the Psychology department and the Institute for Diversity Science. Her academic credentials include: B.Sc. in Psychology from Yale University Ph.D. in Social Psychology from Cornell University Professor Atir's research examines pervasive misperceptions in expertise evaluation and learning processes, investigating why individuals overclaim knowledge and select ineffective learning strategies. Concurrently, she explores gender dynamics in professional communication, analyzing how linguistic patterns (e.g., surname usage for male professionals) perpetuate status disparities and bias in workplace contexts. Her publication portfolio reveals strong thematic consistency across cognitive and social psychology domains. Key trends include experimental investigations of overclaiming phenomena, gendered language effects on professional perception, and the counterintuitive benefits of stranger interactions for knowledge acquisition. These works frequently bridge theoretical psychology with organizational behavior applications. Professor Atir actively mentors doctoral students and secured competitive funding through the Institute for Diversity Science's 2024 Seed Grant Program for research at the business-diversity science intersection. She directs the Knowledge Lab, which employs rigorous experimental methodologies to advance understanding of expertise calibration, learning optimization, and communication dynamics in professional environments.