جست‌وجوی استاد و پژوهشگر

• Natural Language Generation
• Computational Linguistics
• Referring Expression Generation
• +۴ مورد دیگر

Kees van Deemter is a Professor in the Department of Computing Science at the University of Aberdeen's School of Natural and Computing Sciences. With a research career spanning over three decades, he has established himself as a leading figure in Natural Language Generation and Computational Linguistics. His work bridges theoretical linguistics with practical NLP applications, with particular focus on referring expression generation, multilingual processing, and evaluation methodologies. Van Deemter's research interests center on Natural Language Generation, with special emphasis on Referring Expression Generation (REG), computational modeling of linguistic phenomena across languages, and the development of robust evaluation frameworks for NLP systems. His work spans theoretical foundations to practical applications, including logic-to-text generation, cross-linguistic studies comparing East Asian and European languages, and addressing challenges in hallucination and reproducibility in NLP. His recent publications demonstrate a consistent focus on improving evaluation methodologies in NLP, with particular attention to task-based assessments rather than purely ratings-based approaches. He has made significant contributions to understanding how linguistic features influence reference production across languages, with extensive work on Chinese and Mandarin processing. His research group has developed several important corpora including QTUNA for quantifier studies and Mtuna for Mandarin referring expressions. Co-authored the influential survey 'Computational Generation of Referring Expressions: A Survey' (2012) Developed the Mtuna corpus for Mandarin referring expressions Contributed foundational work on the incremental algorithm for referring expression generation Pioneered research on vague language generation from a game-theoretic perspective Investigated cross-linguistic differences in reference production between English and East Asian languages Van Deemter has supervised numerous PhD students who have become active researchers in NLP, including Guanyi Chen, Fahime Same, and Albert Gatt. His collaborative work extends across multiple institutions and has significantly shaped current approaches to referring expression generation and evaluation in NLP. His recent work critically examines the limitations of current evaluation practices in NLP and proposes more scientifically grounded assessment frameworks.

• Natural Language Processing
• Computational Linguistics
• Logic-to-Text Generation
• +۲ مورد دیگر

Eduardo Calò is a PhD Candidate in Natural Language Processing (NLP) at the Department of Information and Computing Sciences , Utrecht University, under Prof. Kees van Deemter. He works on the Interactive Natural Language Technology for Explainable Artificial Intelligence (NL4XAI) project, funded by EU Horizon 2020 under a Marie Sklodowska-Curie grant. Research Focus: Logic-to-text generation, formula simplification, and explainable AI Projects: LoLa system development, GECko+ error correction tool Expertise: Computational linguistics, hybrid symbolic-neural approaches, multilingual NLP His work spans four key areas: (1) translating logical formulae into natural language with hybrid methods, (2) evaluating text quality through faithfulness and fluency metrics, (3) simplifying first-order logic expressions, and (4) developing writing assistance tools. He seeks to bridge symbolic logic with neural language models while addressing cross-linguistic challenges. Recent publications demonstrate technical depth in formula minimization using QBF solvers, UX optimization for NLP interfaces, and discourse-level error correction systems. Collaborations include Albert Gatt, Jordi Levy, and Kees van Deemter across multiple EU-funded initiatives.

Mihai Dascăluمشاهده پروفایل

استاد

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

Mihai Dascălu is a faculty member at the Faculty of Automatic Control and Computers , University Politehnica of Bucharest , with additional affiliation at the Academy of Romanian Scientists . His work spans Natural Language Processing , Intelligent Tutoring Systems , and Learning Analytics , focusing on automated comprehension modeling, text summarization, and psychomotor training systems. Research Interests : Reading comprehension, cohesion network analysis, large language models (ChatGPT), automated assessment, dialogism in education, and psychomotor skill development. Key Contributions : Development of the ReaderBench framework for collaborative learning analysis, validation studies integrating AI with educational psychology, and datasets for large-N educational research. Publications : 62 items (2008–2024), including 15 recent works on multitask scoring, domain adaptation, and AI-driven educational tools. Collaborations : Frequent co-authorship with Dragos Corlatescu, Laurentiu Neagu, Stefan Ruseti, and Robert Botarleanu across institutions like Arizona State University, Mines ParisTech, and Romanian Academy.

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

Mikko Aulamo is a Doctoral Researcher at the University of Helsinki, Faculty of Arts, Department of Digital Humanities, specializing in Language Technology. His work focuses on natural language processing, machine translation, and computational linguistics, with particular emphasis on multilingual datasets, low-resource language processing, and open-source translation tools. His research interests span across several key areas in computational linguistics. Aulamo has made significant contributions to the development of high-performance language technologies, particularly through the HPLT (High Performance Language Technologies) project and the OPUS ecosystem. His work addresses critical challenges in multilingual machine translation, parallel corpus processing, and resource development for underrepresented languages. He has published extensively on topics including neural machine translation, corpus filtering techniques, rule-based translation systems, and the creation of multilingual resources that support language technology applications across diverse linguistic contexts. Aulamo's recent publications demonstrate a clear trajectory toward creating more accessible, high-quality language technologies. His work on the HPLT project has produced substantial multilingual datasets and models, while his contributions to OPUS-MT have democratized neural machine translation. His research particularly emphasizes solutions for low-resource languages, including indigenous languages of the Americas and Scandinavian minority languages like Northern Sámi. Aulamo has been actively involved in several major research projects: High Performance Language Technologies (2022-2025) - EU Innovation actions OPUS-MT: Open Translation Models, Tools and Services (2020-2021) FoTran: Found in Translation - Natural Language Understanding with Cross-Lingual Grounding (2018-2024) - ERC Consolidator Grant fiskmö: Creation of a parallel corpus for Finnish and Swedish translation (2018-2021) He is a key contributor to the Language Technology research group at the University of Helsinki, working closely with Professor Jörg Tiedemann's team on advancing the state of the art in multilingual language processing and translation technologies.

Akhilesh Kakolu Ramaraoمشاهده پروفایل

پژوهشگر

• Computational Morphology
• Natural Language Processing
• Linguistics
• +۳ مورد دیگر

Akhilesh Kakolu Ramarao is a Natural Language Researcher and Ph.D. Student at the Department of English and American Studies, English Language and Linguistics, Heinrich Heine University Düsseldorf. His research focuses on Computational Morphology, supervised by Prof. Dr. Kevin Tang and mentored by Dr. Dinah Baer-Henney. He contributes to the HHU SLAM Lab and engages in interdisciplinary projects bridging linguistics and technology. Education : Currently pursuing a Ph.D. in English Language and Linguistics at Heinrich Heine University Düsseldorf. Research Interests : Explores computational methods for analyzing morphological patterns, transliteration systems, speech technology, and language accessibility. His work combines theoretical linguistics with practical applications in NLP, such as improving machine learning models for irregular morphological structures and developing datasets for second-language speech analysis. Publications : Recent work spans transliteration systems for Persian, modeling Spanish morphology with Transformers, and creating speech datasets for L2 English education. His contributions highlight advancements in both foundational linguistic theory and applied technology. Awards/Grants : No explicit awards listed, but his involvement in shared tasks (e.g., SIGMORPHON 2022) and collaborative projects indicates active participation in competitive academic initiatives. Labs/Teams : Part of the HHU SLAM Lab, focusing on research in language and technology. Engaged with interdisciplinary teams like KlarTextCoders for accessible language systems.

• Inclusive Communication Technologies
• Translation Technology
• Machine Translation
• +۶ مورد دیگر

Dr. Martin Kappus serves as Head of the Professorship for Accessibility (ad interim) at the ZHAW School of Applied Linguistics in Zurich. His research focuses on inclusive communication technologies, translation ergonomics, and the integration of language technology in academic contexts. He leads projects like 'Inclusive Information and Communication Technologies' and explores machine translation applications for academic texts. Key research interests include optimizing translation tool interfaces, accessibility standards for multilingual communication, and the human factors in translation workflows. His work bridges applied linguistics with technology development, emphasizing user-centered approaches in language industry practices. Recent projects include investigating automatic text simplification evaluation frameworks and comparing subtitling guidelines across Swiss regions. He has presented at international conferences such as EST Congress and HumEval, contributing to both theoretical and practical advancements in accessible communication. Publications span peer-reviewed journals like Revista Tradumàtica and Handbook of the Language Industry , reflecting his contributions to translation studies, technology, and accessibility. His edited volumes address barriers in communication design for educational and digital content.

Gudrun Reijnierseمشاهده پروفایل

استادیار

• Science Communication
• Metaphor Studies
• Linguistics
• +۶ مورد دیگر

Gudrun Reijnierse is an Assistant Professor of Language & Communication at the Department of Language, Literature and Communication, Vrije Universiteit Amsterdam. She holds affiliate roles in the Network Institute. Her primary research focuses on science communication, metaphor analysis, and linguistic methods. Current affiliations include teaching in BA/MA Communication programs and supervising three PhD projects on climate tech metaphors, primary education metaphor competencies, and quantum science communication. Research Themes: Language of science communication across journalism, education, and healthcare Metaphor use/effects in expert-public discourse Development of computational tools for metaphor analysis (e.g., MSDIP method) Key Projects: NWO-funded metaphor in science reporting project Network Institute research on climate tech communication EU Horizon 2020 postdoc collaboration with Nynke Bos Teaching includes courses on communication & cognition, genre design, and document design. She has developed influential methods like MSDIP for metaphor coding and contributed to Dutch fact-checking corpora. Lab/Team Affiliations: Active in interdisciplinary collaborations through the Network Institute and EU/NWO projects. Leads metaphor analysis initiatives with global academic partners.

Tal Augustمشاهده پروفایل

استادیار

• Human-Computer Interaction (HCI)
• Natural Language Processing (NLP)
• Science Communication
• +۵ مورد دیگر

Tal August is an Assistant Professor at the University of Illinois at Urbana-Champaign's Siebel School of Computing and Data Science. His research focuses on adapting language to improve communication between humans and technology, emphasizing personalized language systems for science, health, and legal domains. He holds a PhD from the University of Washington (Allen School of Computer Science and Engineering), advised by Katharina Reinecke and Noah Smith. Research Interests: Human-Computer Interaction (HCI) : Designing tools that bridge communication gaps through language adaptation. Natural Language Processing (NLP) : Developing AI systems for automatic language personalization. Science Communication : Studying how language affects knowledge sharing in technical domains. Interdisciplinary Collaboration : Breaking down barriers through tailored communication strategies. Awards : Honored on the List of Teachers Ranked as Excellent by Their Students (2025). Recognized for contributions to personalized language tools via grants from the Allen Institute for AI. Advising & Grants : Recruiting 1-2 PhD students for Fall 2025. Recent grant supported work on personalizing research literature searches for scholars. Courses Taught : CS 598 TAL - Language, Interfaces, and Communication (focuses on HCI/NLP intersections). Key Projects : Innovations include TermSight (simplifying legal contracts), Qlarify (expandable scientific summaries), and Semantic Reader (AI-augmented reading interfaces).

Sergio Rodriguez Mendezمشاهده پروفایل

پژوهشگر ارشد

• Knowledge Graphs
• Ontology Engineering
• Linked Data & Semantic Web
• +۷ مورد دیگر

Sergio Rodriguez Mendez is a Research Fellow in Knowledge Graph Engineering at the School of Computing, Australian National University. His work focuses on advancing ontology engineering, linked data, semantic web technologies, and their applications in domains like astronomy, brain-computer interfaces, and IoT. He is a member of the Australian Government Linked Data Working Group (AGLDWG) and the W3C Knowledge Graph Construction Community Group, and holds a senior role at the Software Innovation Institute (SII). His research interests include Knowledge Graphs, Ontology Engineering, Linked Data, Data Science, Machine Learning, Natural Language Processing, Brain-Computer Interfaces, IoT, Cloud/Fog/Edge Computing, and Software Engineering. He has contributed to frameworks like pathfinder for astronomical literature review and Doc-KG for document-to-KG conversion. Recent work emphasizes integrating large language models (LLMs) with knowledge graphs, such as AstroLLaVA for astronomical data unification and hybrid frameworks for entity linking. His publications span conferences like WWW, JCDL, and the ACM/IEEE Joint Conferences. He actively supervises research students and is involved in initiatives like the ASKG project for enriching scholarly knowledge graphs. His work addresses challenges in semantic data representation, automated query processing, and syntactic complexity reduction, with a focus on domain-specific applications in science and cultural heritage.

Demetris P. Gerogiannisمشاهده پروفایل

پژوهشگر

• Computer Vision
• Image Processing
• Image Segmentation and Registration
• +۷ مورد دیگر

Demetris P. Gerogiannis is a researcher in the Department of Computer Science & Engineering at the University of Ioannina, Greece. His academic work focuses on Computer Vision and Image Processing, with specific expertise in image registration, pointset registration, and feature extraction. He has maintained a consistent publication record from 2007 through 2020, primarily collaborating with Christophoros Nikou and A. Likas. Gerogiannis' research interests span multiple areas within Computer Vision including Image Segmentation and Registration, Pointset Registration, Feature Extraction, Object Detection and Recognition, Pattern Recognition, Automatic Image Annotation, and Machine Learning applications. His work demonstrates a consistent focus on developing robust algorithms for geometric problems in vision, with particular attention to handling noise and outliers in visual data. He has made significant contributions to point set registration, shape representation, and vanishing point detection. His publication history reveals a strong trend toward developing mathematically rigorous approaches to Computer Vision problems, particularly utilizing statistical models like Student's t-distributions for robust registration. His work bridges theoretical computer vision with practical applications, including consumer-facing technologies like QR code systems. The 15 most recent publications show progression from foundational work in image registration to more applied research in texture analysis, word spotting, and biomedical applications. Gerogiannis is also an active entrepreneur, currently coordinating QReca!, a startup focused on consumer engagement through QR codes and NFC technology. He has previously worked on a Video On Demand startup and is developing a spin-off to commercialize his Computer Vision research through an API framework. He maintains a commitment to open academic knowledge by providing Matlab code for non-commercial use related to his publications. He has volunteered for the Athens 2004 Olympic Games and maintains a passion for Mathematics, particularly Number Theory and Euclidean Geometry. His academic profile suggests a researcher who bridges theoretical computer vision with practical applications and entrepreneurial ventures.

Hao Tangمشاهده پروفایل

مدرس

• Speech and Language Processing
• Speech Representations
• Discrete Representations
• +۸ مورد دیگر

Hao Tang is a Lecturer in Speech Technology at the School of Informatics, University of Edinburgh, affiliated with the Institute for Language, Cognition and Computation (ILCC) and the Centre for Speech Technology Research (CSTR). He contributes to cutting-edge research in speech and language processing, with a focus on speech representations and self-supervised learning. PhD, Toyota Technological Institute at Chicago (2017–2020), advised by Karen Livescu Postdoctoral Associate, MIT Spoken Language Systems Group (2020–) Master’s, National Taiwan University, advised by Lin-Shan Lee Hao Tang’s research centers on speech representations, particularly discrete and geometric properties in self-supervised models. He investigates how speech systems encode speaker and phonetic information, and how these representations can be improved for tasks like phone segmentation, acoustic word embedding, and text-to-speech. He also works on text summarization, especially opinion and attributable summarization. His work bridges machine learning, cognitive modeling, and practical speech applications. His recent publications span top venues including Interspeech, ICASSP, ACL, NeurIPS, and IEEE/ACM Transactions. Key themes include self-supervised learning, disentangled representations, predictive coding, and efficient speech modeling. He has co-authored papers on discrete speech units, orthogonality in representations, and context modeling in neural speech systems. Best Student Paper Award, Interspeech 2020 Computational Modeling Prize for Perception & Action, CogSci 2024 Speech and Language Processing Student Paper Award, ICASSP 2016 Best Student Paper of Speech and Language Processing, ICASSP 2016 Best Paper Nominee, ASRU 2015 Hao Tang actively supervises PhD and Master’s students, many of whom have published at leading conferences and gone on to positions at Cohere, Apple, Sesame, and top PhD programs. He serves as Associate Member of IEEE SLTC, Meta Reviewer for ICASSP, Area Chair for ACL ARR, Action Editor for TACL, and Area Chair for ICLR and NeurIPS. He teaches core courses such as Machine Learning (INFR10086) and Automatic Speech Recognition (INFR11033) . Hao Tang leads a research group within ILCC and CSTR, collaborating with researchers like Sharon Goldwater, Jim Glass, and Karen Livescu. His team focuses on developing interpretable, efficient, and scalable models for speech and language understanding.

• Translation Studies
• Corpus Linguistics
• Machine Translation
• +۵ مورد دیگر

Professor Ekaterina Lapshinova-Koltunski serves as Full Professor (W3) in Multilingual Technical Specialized Communication at the University of Hildesheim since October 2022. She is affiliated with the Institute for Translation Studies & Specialized Communication within Department 3: Linguistics and Information Sciences. Previously, she held positions as Akademische Oberrätin (equivalent to Associate Professor) at Saarland University (2017-2022) and served as Interim Professor at the University of Hildesheim (2020-2021). Her educational background includes: Habilitation in Linguistics, Translation Studies and Corpus Linguistics (2016) from Saarland University PhD in Computational Linguistics (2011) from University of Stuttgart Postgraduate studies in Language Data Processing (2003-2006) from Georg August University of Göttingen Diploma in Translation, Interpreting and Intercultural Communication (1997-2002) from Volgograd State University Professor Lapshinova-Koltunski's research focuses on the intersection of translation studies, corpus linguistics, and artificial intelligence, with particular emphasis on machine translation, plain language communication, and multilingual technical communication. Her work bridges theoretical linguistics with practical applications in healthcare communication, accessibility, and translation technology. She has made significant contributions to understanding cohesion and coherence in translation, cross-linguistic variation, and the cognitive aspects of translation processes. Her recent publications (2024-2025) demonstrate a strong shift toward AI applications in translation, particularly in health communication and plain language translation. These works examine how large language models can support the creation of accessible medical information, analyze gender representation in machine translation, and develop practical frameworks for integrating AI tools into editorial workflows. Her research shows increasing focus on practical applications of translation technology to address real-world communication challenges, especially in healthcare contexts. She actively serves as a reviewer for major funding organizations including DFG and Research Foundation – Flanders, and for numerous prestigious journals and conferences in computational linguistics and translation studies. Her current grant portfolio includes: Project B7 'Translation as Rational Communication' in SFB1102 (2022-2026) 'AI-supported health communication in Plain language' funded by the Ministry of Science and Culture of Lower Saxony (May 2024-August 2025) 'Use of AI tools in intralingual translation of health communication' with Wort & Bild Verlag (October 2023-April 2024) 'Data triangulation in translation process research' startup grant from University of Hildesheim (August 2023-January 2024) Professor Lapshinova-Koltunski holds multiple administrative roles including membership in various examination committees for master's programs in International Communication and Translation, Barrier-free Communication, and Technical Communication, as well as serving on the Faculty Council and Research Committee for Department 3.

Ned Nedialkovمشاهده پروفایل

استاد

• Applied Computing
• Digital & Smart Systems
• Numerical Analysis
• +۳ مورد دیگر

Ned Nedialkov is a Professor in the Department of Computing and Software at McMaster University, within the Faculty of Engineering. He serves as an Undergraduate Advisor for Computer Science and teaches courses such as CAS 708: Scientific Computation (Theory). His research focuses on numerical methods for differential-algebraic equations (DAEs), Taylor series algorithms, multibody dynamics, and automatic differentiation. His work emphasizes computational techniques for solving complex engineering systems, including structural analysis of DAEs, high-index DAE solving, and energy-based modeling of electrical circuits. He has contributed to software tools like DAESA and DAETS for DAE analysis and simulation. Research interests span mechanical systems simulation, computational mechanics, and algorithm optimization for stiff systems and rigorous defect control. He has published extensively on topics such as natural coordinates in multibody dynamics, port-Hamiltonian systems, and symbolic-numeric methods. His interdisciplinary approach bridges numerical analysis, computational physics, and engineering applications.

• Speech Processing
• Machine Learning
• Computational Linguistics
• +۴ مورد دیگر

Okko Räsänen is a Professor at the Department of Computing Sciences, Faculty of Information Technology and Communication Sciences, Tampere University. His research focuses on speech processing, machine learning, and computational models of child language development. He leads projects involving wearable sensor technology for tracking infant motor development and analyzing audiovisual data in naturalistic environments. Key research areas include automatic transcription of child-centered recordings, affective speech analysis, and zero-resource speech processing. He has contributed to tools like ALICE for automated language environment measurements and Meta-analysis frameworks for computational models. His work bridges signal processing, artificial intelligence, and developmental science, with applications in clinical speech assessment and educational technology. Recent studies explore the reliability of acoustic metrics in longitudinal recordings, the impact of audio quality in child-directed speech research, and the integration of multimodal data for speech emotion recognition. His interdisciplinary approach addresses challenges in natural language acquisition, wearable health monitoring, and cross-lingual generalization of speech models.

• Natural Language Processing
• Biomedical Informatics
• Health Communication
• +۳ مورد دیگر

Yue Guo is an Assistant Professor in the Department of Information Sciences at the University of Illinois. Her research focuses on natural language processing (NLP), biomedical informatics, and healthcare communication. She explores how large language models (LLMs) can improve lay language summarization of complex scientific content, particularly in healthcare contexts. Her work emphasizes personalized jargon detection, factuality evaluation of summaries, and accessibility of health information for diverse audiences. Guo has also conducted extensive studies on radiation oncology outcomes, including xerostomia (dry mouth) recovery in head and neck cancer patients and the impact of radiation dose patterns on patient-reported symptoms. Her interdisciplinary research bridges computational methods with clinical needs, aiming to enhance patient understanding and care through advanced NLP techniques. Her recent articles (2021–2025) highlight trends in LLM-driven biomedical communication, evaluation of health summaries, and data-driven approaches to cancer treatment optimization. Notable projects include APPLS (a summarization metric framework) and personalized jargon identification systems for interdisciplinary collaboration. Guo teaches IS 504: Sociotechnical Information Systems , integrating technical and human factors in information systems design.