جست‌وجوی استاد و پژوهشگر

• 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).

• 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).

Doug Oardمشاهده پروفایل

استاد

• Information Retrieval
• Multilingual Information Access
• Speech Retrieval
• +۳ مورد دیگر

Doug Oard is a Professor at the University of Maryland, College Park, holding joint appointments in the College of Information Studies and the Institute for Advanced Computer Studies (UMIACS). His research focuses on enhancing information retrieval across languages, speech, and sensitive content, with notable contributions to cross-language information retrieval (CLIR), eDiscovery, and evaluation design for information systems. He leads efforts in developing test collections for evaluating retrieval systems, including work on the TREC and CLEF evaluation forums. Key research areas include multilingual information access, speech retrieval, and computational social science. Oard has pioneered methods for evaluating machine translation, automated speech recognition, and the retrieval of historical or archival content. He has contributed to projects like the Apollo Mission Control Archive and the Multilingual TEDx Corpus, advancing accessibility to spoken and written materials across languages. His work emphasizes practical applications, such as improving legal eDiscovery processes and developing tools for searching sensitive or informal text. Oard has authored over 200 peer-reviewed publications and led major international evaluation initiatives, driving advancements in information retrieval systems' reliability and cross-cultural utility.

• Blockchain
• Cloud Computing
• Grid Computing
• +۵ مورد دیگر

Dr. David Buján Carballal is a Lecturer and Researcher at the University of Deusto in Spain. He holds a BSc, MSc, and PhD in Computer Sciences from the University of Deusto, with a dissertation focusing on semantic technologies in Grid computing. His research spans Blockchain, Cloud/Grid computing, Linked Data, and Semantic Technologies. He teaches Web Engineering, Operating Systems, and Distributed Systems, and works at the Mobile Communications Research Group (MORElab) within DeustoTech Institute and Telefónica Deusto Chair. Education: BSc in Computing - Faculty of Engineering, University of Deusto MSc in e-Business - University of Deusto PhD in Computer Sciences - University of Deusto (2016) Research focuses on integrating semantic technologies with distributed systems, including blockchain applications and cybersecurity in connected industries. He actively participates in national projects (ALASTRIA network) and international conferences like HAIS, SOCO, and IoT. His work includes developing context-aware tourism platforms and remote laboratory systems (WebLab-Deployer). Advising & Grants: Involved in national/international research projects and private contracts. Served on program committees for conferences such as CAEPIA and JENUI. Extensive collaboration with institutions like the University of Manchester, UCM, and the University of Westminster. Labs/Teams: Lead researcher at MORElab, part of DeustoTech’s Mobile Communications group.

Chris Develderمشاهده پروفایل

دانشیار

• Natural language processing
• Information extraction
• Smart grid
• +۲ مورد دیگر

Chris Develder is an Associate Professor with the research group IDLab in the Department of Information Technology (INTEC) at Ghent University - imec, Ghent, Belgium. He received his MSc degree in computer science engineering and PhD in electrical engineering from Ghent University in Jul. 1999 and Dec. 2003 respectively (as a fellow of FWO). He has held research visitor positions at UC Davis, CA, USA (Jul.-Oct. 2007) and at Columbia University, NY, USA (Jan. 2013 - Jun. 2015). Prof. Develder leads two research teams within the Internet Technology and Data Science Lab (IDLab): one focused on converting text to knowledge (NLP, primarily information extraction using machine learning), and another on data analytics and machine learning for smart grids. His Text-to-Knowledge (T2K) research group has a strong track record in information extraction across various domains including news, human resources, and biomedical applications, as well as text classification tasks such as sentiment analysis. More recently, the group has expanded into conversational agents and generative models for educational applications. With his team, Prof. Develder has published over 200 papers in international journals and conferences including EMNLP, CoNLL, EACL, ACL, ECIR, CIKM, WSDM, WWW, and NIPS. His recent publications demonstrate a growing focus on practical applications of NLP in healthcare (personality style recognition from speech, biomedical adverse drug event extraction), education (question generation, gap-filling exercises), and labor market analysis (career path prediction, skill extraction). His work bridges theoretical advances in machine learning with real-world applications across multiple domains. Prof. Develder actively supervises PhD students and has co-supervised numerous successful doctorates, including Maarten De Raedt (2024), Semere Kiros Bitew (2024), Yiwei Jiang (2024), Amir Hadifar (2023), and Klim Zaporojets (2022). His former students have gone on to work at organizations including EarlyTracks, Zoom, Nokia Bell Labs, Clarivate, and various academic institutions.

Jakob Prangeمشاهده پروفایل

پژوهشگر

• Computational Linguistics
• Natural Language Processing
• Formal Semantics
• +۱۲ مورد دیگر

Jakob Prange is a Researcher (Akademischer Rat auf Zeit) at the Chair for Natural Language Understanding / Digital Humanities within the Faculty of Applied Computer Science at the University of Augsburg. He holds a Ph.D. in Computer Science with a concentration in Cognitive Science from Georgetown University, where his dissertation focused on neuro-symbolic models for syntactic and semantic representations. Education: Ph.D. in Computer Science (Cognitive Science), Georgetown University B.Sc. in Computational Linguistics, Saarland University His research lies at the intersection of computational linguistics and theoretical linguistics, emphasizing formal and distributional semantics, meaning representation, deep learning, and neuro-symbolic integration. He prioritizes model efficiency and explainability, often combining neural models with structured linguistic representations. His recent work includes projects on cross-lingual QA in migration contexts and Bayesian modeling of L2 preposition learning. His publications span top venues such as ACL, NAACL, TACL, and COLING, with a focus on semantic parsing, supersense tagging, UCCA, neuro-symbolic modeling, and robust NLP for German. He has contributed to multilingual annotation frameworks and low-resource language modeling. Scientific Awards: No awards explicitly mentioned. He has supervised M.Sc. student Steffen Kleinle on a QA project and contributes to teaching at the University of Augsburg, including Python for language processing and NLP tutorials. He is part of the HLT@Augsburg research group led by Prof. Annemarie Friedrich, focusing on natural language understanding and digital humanities.

Heather Christine Lentمشاهده پروفایل

پژوهشگر

• Natural Language Processing
• Machine Translation
• Low-resource Languages
• +۳ مورد دیگر

Heather Christine Lent is a Researcher in the Department of Computer Science at Aalborg University, affiliated with The Technical Faculty of IT and Design. Her work focuses on advancing natural language processing (NLP) for low-resource and marginalized languages, particularly Creoles, and addressing security challenges in multilingual systems. She leads the "Multilingual Modelling for Resource-Poor Languages" project (2022–2025), funded by Carlsbergfondet and Google. Her research integrates computational linguistics with ethical considerations, emphasizing equitable technology access. Notable contributions include the CreoleVal benchmark for multilingual evaluation and studies on NLP security in multilingual contexts. She collaborates internationally, including with institutions like George Mason University, and engages in community-driven dataset creation. Publications highlight themes like adversarial attacks on multilingual models, data quality audits, and cross-lingual transfer learning. She actively participates in workshops and tutorials aimed at fostering inclusive NLP practices, such as the "Connecting Ideas in Lower-Resource Scenarios" initiative.

MARCOS GARCIA GONZALEZمشاهده پروفایل

پژوهشگر ارشد

• Computational Linguistics
• Natural Language Processing
• Multilingual Language Technologies
• +۳ مورد دیگر

Marcos Garcia Gonzalez is a Ramón y Cajal Research Fellow at the University of Santiago de Compostela (USC), affiliated with the Department of Spanish Language and Literature and the Center for Research in Intelligent Technologies (CITIUS). His research focuses on computational linguistics, particularly in semantic analysis, multilingual systems, and language technologies for Galician and Portuguese. He leads projects like the Nós Project, advancing Galician's integration into AI and NLP tools. His work includes developing resources like the Parallel Universal Dependencies Treebank and tools such as Bertinho (Galician BERT models). Key areas include idiomaticity detection in word representations, vector models for semantic analysis, and syntactic parsing. Education: PhD in Computational Linguistics from USC (2014), Master in Linguistics (University of Lisbon), and Degree in Portuguese Philology (USC). Postdoctoral research under the Juan de la Cierva fellowship (2016-2020). Awards include Ramón y Cajal and Juan de la Cierva fellowships. Research interests span vector models, multilingual NLP, and semantic compositionality. Projects include LINNA (linguistic-based neural models) and DeepR3 (green language tech). His publications explore topics like homonymy/synonymy representation and Galician language preservation through open-source tools. Key contributions include annotated corpora, coreference resolution systems, and cross-lingual parsing methods. He collaborates on hybrid intelligence systems for education (iRead4Skills) and Responsible AI (DeepR3.gal). Current efforts emphasize leveraging linguistic knowledge to enhance NLP models' interpretability and multilingual support.

• Natural Language Processing (NLP)
• Interpretability of NLP Models
• Explainable AI
• +۴ مورد دیگر

Jenny Kunz is a Research Fellow at Linköping University's Department of Computer and Information Science (IDA), within the Artificial Intelligence and Integrated Computer Systems (AIICS) division. She holds a PhD in Computer Science from LiU (2024) and previously completed a Master’s in Language Technology from Uppsala University and a Bachelor’s in Computer Science from Humboldt University of Berlin. Her research focuses on interpretable and explainable NLP, including parameter-efficient language adaptation, self-rationalizing models, and understanding linguistic information storage in neural networks. She has contributed to studies on LLM-generated explanations, cross-lingual transfer learning, and the challenges of under-resourced languages. Kunz has taught courses such as Language and Computers and has held roles including Chair of the IDA PhD Council and co-representative on the IDA Board. Her work spans over 15 peer-reviewed publications in venues like ACL and COLING, addressing topics from model interpretability to multilingual NLP challenges.

• Natural Language Processing
• Machine Learning
• End-to-End Reasoning
• +۷ مورد دیگر

Dr. Nafise Sadat Moosavi is a Lecturer in Natural Language Processing at the University of Sheffield's School of Computer Science, and a Deputy School Head of ED&I. She holds a PhD from Heidelberg University, with prior postdoctoral research at the Technical University of Darmstadt's UKP Lab. Her research focuses on NLP and machine learning, including end-to-end reasoning, robustness, coreference resolution, text generation, sustainability, and evaluation metrics. Her academic journey includes bachelor's and master's degrees in computer science from Alzahra University and Sharif University of Technology, Iran. She leads projects like the Royal Society-funded 'Geometric Representations of Uncertainty for Foundation Models' (2025–2028). Her work emphasizes ethical AI, bias mitigation, and improving model generalization. Grants: £193,560 Royal Society Grant (PI) for foundational model uncertainty research Research Groups: Member of the University of Sheffield's NLP research group Key Themes: Debiasing NLU models, sustainable NLP practices, and advancing coreference resolution techniques Her publications span 2020–2025, addressing topics like hate speech detection, LLM limitations, and evaluation metric design. She co-organized workshops including SustaiNLP and contributed to datasets like PeerQA and SciGen.

• Computational Linguistics
• Corpus Linguistics
• Discourse Analysis
• +۶ مورد دیگر

Amir Zeldes is an Associate Professor in the Department of Linguistics at Georgetown University, where he leads the Corpling@GU Corpus Linguistics lab. He serves as President of the ACL Special Interest Group on Annotation (SIGANN). His primary research focuses on computational models of discourse, including referentiality and discourse relations, leveraging multilayer corpus studies to advance NLP and linguistic theory. Zeldes holds a Ph.D. from Humboldt Universität zu Berlin, complemented by an M.A. and B.A. in linguistics from Humboldt and the Hebrew University of Jerusalem. His work emphasizes discourse analysis, coreference resolution, and annotation frameworks, with contributions to Universal Dependencies treebanks and discourse parsing benchmarks like DISRPT. Zeldes has authored influential texts such as Multilayer Corpus Studies (2018/2020), exploring methodologies for parallel linguistic analyses. His research bridges theoretical linguistics and computational tools, addressing challenges in discourse signaling, entity salience, and cross-linguistic NLP applications. Zeldes' lab develops open-source tools for corpus creation and annotation, fostering interdisciplinary collaboration in computational linguistics. His recent projects include advancing discourse relation parsing, LLM evaluation, and historical language restoration through RNN models. Despite no listed awards, his extensive grants and publications reflect significant academic impact in NLP and corpus linguistics.

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.

• Deep Learning for NLP
• Information Extraction
• Coreference Resolution
• +۵ مورد دیگر

Juntao Yu is a Lecturer in Computer Science at Queen Mary University of London's School of Electronic Engineering and Computer Science. Previously, he held a Lecturer position at the University of Essex and worked as a post-doctoral researcher at Queen Mary University on the ERC-funded DALI project under Professor Massimo Poesio. His PhD from the University of Birmingham focused on out-of-domain dependency parsing under Dr. Bernd Bohnet. Research Interests: Deep Learning for NLP, Coreference Resolution, Conversational AI, Dependency Parsing, Domain Adaptation, and Multi-task Learning. His work emphasizes scalable anaphora resolution systems, hybrid crowdsourcing approaches for corpus development, and cross-linguistic NLP challenges. He has contributed to major frameworks like the Universal Anaphora Scorer and the ARRAU corpus family. Yu's recent research explores large language model capabilities in coreference tasks and dialogue system evaluation. He maintains active collaboration with industry through projects like the CODI-CRAC shared tasks. Labs/Teams: Active member of the School's Natural Language Processing group and collaborator with the DALI project team.

Veronique Hosteمشاهده پروفایل

استاد

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

Veronique Hoste is a Full Professor of Computational Linguistics at Ghent University's Faculty of Arts and Philosophy , where she serves as Department Head of the Department of Translation, Interpreting and Communication and Director of the LT3 Language and Translation Technology Team . Her work bridges machine learning with natural language processing, focusing on semantics, discourse modeling, and practical applications in emotion detection, irony recognition, and customer service dialogue analysis. PhD in Computational Linguistics from University of Antwerp (2005) 2023-2024: Francqui Chair at Université Libre de Bruxelles 2024: Elected to Royal Flemish Academy of Belgium (KVAB) Research Interests: Specializes in machine learning approaches to coreference resolution, sentiment analysis, and multimodal emotion detection. Leads projects like FlandersAI (empathy in conversational agents), METRICS (emotion trajectories in service dialogues), and SENTiVENT (financial event extraction). Develops high-quality datasets (e.g., EmotioNL , ENCORE ) for broader NLP community use. Recent Publications span Dutch social media irony detection, Classical Chinese poetry sentiment analysis, and multimodal emotion datasets. Collaborates on interdisciplinary initiatives like NewsDNA (news recommendation) and SentEMO (commercial sentiment analysis). Scientific Awards Francqui Chair (2023-2024) KVAB Membership (2024) Outreach includes co-founding AlfaSent (LT3 spin-off for customer feedback analysis) and authoring the first Dutch-language NLP book Taaltechnologie Ontrafeld (2024). Coordinates AI education initiatives like the AI at School project.