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James HendersonView profile

Researcher

• Natural Language Processing
• Machine Learning
• Deep Learning
• +3 more

James Henderson is a Senior Researcher at Idiap Research Institute where he heads the Natural Language Understanding group. He currently serves as Action Editor for Transactions of the Association for Computational Linguistics (TACL) and was recently awarded an ERC Advanced Grant for his project 'Interpretable Beliefs and Programmable Knowledge with Bayesian Attention in Large Language Models' (BALM). Previously, Henderson held positions as Chargé de Cours at University of Geneva's Department of Computer Science and Principal Scientist at Xerox Research Centre Europe (now Naver Labs Europe). Henderson's research focuses on machine learning methods for natural language processing, with pioneering work on recurrent neural networks for syntactic and semantic parsing. His current investigations include representation learning for language semantics, graph-to-graph deep learning models, entity induction, and variational-Bayesian attention-based representation learning. His research bridges Bayesian inference, transformer architectures, and structured prediction for NLP tasks. His publication portfolio demonstrates consistent contributions to core NLP methodologies, with recent emphasis on transformer optimization, Bayesian neural methods, efficient model architectures, and graph-based language representations. Research frequently appears in top venues including ACL, EMNLP, ICLR, and NeurIPS. Honors: ERC Advanced Grant (2023) Henderson leads the Natural Language Understanding group at Idiap, currently recruiting PhD students and postdoctoral researchers for his ERC project. He obtained his PhD and MSc from University of Pennsylvania and BSc from Massachusetts Institute of Technology, all in computer science.

• Natural Language Processing
• Computational Linguistics
• Dependency Syntax
• +5 more

Prof. Zdeněk Žabokrtský is a Professor at the Institute of Formal and Applied Linguistics (ÚFAL) within the Faculty of Mathematics and Physics at Charles University in Prague. He serves as the head of the PhD study program in Computational Linguistics and teaches several courses including Language Data Resources, Variability of Languages in Time and Space, Natural Language Processing, and Introduction to Language Technologies. His office is located in room S 409 on the 4th floor in the Lesser Town area of Prague. Prof. Žabokrtský's research spans multiple areas of Natural Language Processing and Computational Linguistics. His primary interests include building multilingual morphological resources, developing NLP applications that utilize parallel corpora, studying dependency syntax and valency frameworks, coreference resolution, theoretical studies on formal representations of natural languages, and applying Machine Learning techniques to linguistic problems. His work bridges theoretical linguistics with practical applications in language technology. Analysis of Prof. Žabokrtský's recent publications reveals a strong focus on morphological analysis, coreference resolution across multiple languages, and cross-lingual NLP. His research spans diverse languages including Czech, Turkish, Russian, and various Indic languages. He has made significant contributions to morphological resources, word-formation networks, and multilingual coreference systems, often creating and utilizing linguistic resources while developing novel computational approaches to linguistic phenomena. As an academic leader, Prof. Žabokrtský has supervised numerous PhD students and contributed to major research projects at the Institute of Formal and Applied Linguistics. His work has been supported by various grants that have enabled the development of important linguistic resources and NLP tools. He has been instrumental in establishing the PhD study program in Computational Linguistics at Charles University. Prof. Žabokrtský is a key member of the Institute of Formal and Applied Linguistics research teams, contributing to projects focused on developing language technologies, creating linguistic resources, and advancing theoretical understanding of natural language processing. His work is closely integrated with the Prague Dependency Treebank project and other major linguistic resources developed at Charles University.

• interpretation of deep neural networks
• machine learning
• dependency syntax
• +3 more

David Mareček is a researcher at the Institute of Formal and Applied Linguistics (ÚFAL) within the Faculty of Mathematics and Physics at Charles University. His work focuses on neural network interpretation, machine learning, dependency syntax analysis, and machine translation, with a particular emphasis on unsupervised and semi-supervised parsing techniques. Current projects: GenderBias (2023–present), EduPo (2024–present) Long-term contributions in dependency treebanks (HamleDT, 2012–2016) and neural machine translation frameworks (Treex, TectoMT) His research explores linguistic structure in neural architectures, including BERT and Transformer models, through methods like structural probing and attention analysis. Recent publications examine gender bias mitigation, automated poetry analysis, and theatre script generation. Key collaborations include Rudolf Rosa, Tomáš Musil, and Tomasz Limisiewicz. He supervises students in projects spanning multilingual representations, language model probing, and bias detection in AI systems. Technical leadership in developing tools like THEaiTRobot and DEPFIX demonstrates his commitment to applied computational linguistics and robust NLP systems.

Burcu Can BuglalilarView profile

Lecturer

• Natural Language Processing
• Unsupervised Learning
• Statistical Methods
• +6 more

Dr. Burcu Can Buglalilar is a Lecturer in Computing Science at the Department of Computing Science and Mathematics, University of Stirling, where she is a member of the Data Science and Intelligent Systems Research Group. She previously held academic positions at Hacettepe University (2015-2020) and University of Wolverhampton (2020-2022). Her research focuses on Natural Language Processing with particular emphasis on unsupervised learning techniques for morphology, syntax, and semantics in agglutinative languages like Turkish. She applies both statistical methods (including nonparametric Bayesian learning) and deep learning approaches to language representation problems. Dr. Can Buglalilar's recent work shows a clear trajectory toward Large Language Models and their applications, with upcoming lectures scheduled for 2025 on both Large and Small Language Models. Her publications consistently address fundamental challenges in representing and processing morphologically rich languages. Scientific Recognition: Best Paper Award at RepL4NLP, ACL 2018 TUBITAK Project Performance Award (2022) As an active member of the NLP community, Dr. Can Buglalilar serves as Senior Associate Editor for ACM TALLIP and Associate Editor for Journal of Natural Language Engineering. She has organized workshops including the 8th Representation Learning for NLP (Repl4NLP) at ACL 2023. She is currently developing the Turkish Neural NLP Toolkit and welcomes MSc and PhD students interested in natural language processing, computational linguistics, and machine learning for language.

Sharon GoldwaterView profile

Professor

• Computational Linguistics
• Natural Language Processing
• Machine Learning
• +9 more

Sharon Goldwater is a Professor in the Institute for Language, Cognition and Computation at the University of Edinburgh's School of Informatics. Her research focuses on computational models of language acquisition, particularly in unsupervised learning of linguistic structure, Bayesian and neural network models, and cross-linguistic studies. She is affiliated with the Edinburgh NLP group and the Centre for Doctoral Training in Responsible NLP (CDT). Education & Background: PhD from Brown University (2007), postdoctoral research at Stanford University, and extensive academic roles including program chair for EACL 2014 and governance roles in the field. Research Interests: Speech perception, phonetics, morphology, and the integration of computational models with cognitive science. She explores how humans and machines learn language structures from limited data, with applications to zero-resource languages and sociolinguistic analysis. Key Contributions: Pioneered Bayesian models for word segmentation, developed self-supervised speech representations, and contributed to understanding phonetic learning dynamics. Recognized with awards like the 2016 Roger Needham Award and multiple paper prizes. Grants & Awards: Includes the 2024 Outstanding Paper Award, 2021 Best Research Paper award, and leadership in academic service. Her work bridges theoretical linguistics, machine learning, and cognitive science. Labs & Teams: Active in the Edinburgh NLP group and CDT, fostering collaborative research on NLP ethics and computational linguistics. Her lab's work spans from foundational algorithms to applied sociolinguistic studies using computational methods.

• Computational Linguistics
• Low-resource NLP
• Unsupervised Learning
• +3 more

Dr. C.M. Downey is an Assistant Professor in Linguistics and Data Science at the University of Rochester, specializing in natural language processing for low-resource languages. His research develops methods for unsupervised learning, multilingual modeling, and transfer learning to address data scarcity in computational linguistics. He received his PhD in Computational Linguistics from the University of Washington (2024), with dissertation work on segmental language models and cross-lingual transfer. Professional experience includes positions at Microsoft Research (cryptography and privacy) and Apple (AI/ML). Research addresses fundamental challenges in language technology for under-resourced languages through novel neural architectures, including masked segmental models, hierarchical adaptation techniques, and communication-based translation frameworks. His contributions enable NLP applications for languages lacking large text corpora. Analysis of publications shows consistent focus on multilingual embedding spaces (3 papers), segmentation/transduction models (2 papers), and low-resource adaptation techniques (2 papers), with recent emphasis on communication-inspired learning paradigms. Current projects investigate dynamic vocabulary adaptation and emergent communication protocols for translation systems. Teaching includes graduate courses in computational linguistics and deep learning for NLP.

Tiina Lindh-KnuutilaView profile

Researcher

• Neuroscience
• Magnetoencephalography
• Semantic Processing
• +3 more

Tiina Lindh-Knuutila is a Researcher in the Department of Neuroscience and Biomedical Engineering at Aalto University, holding a Doctoral degree in Engineering and Technology (2014) and a Master's degree from Helsinki University of Technology (2005). Her work bridges neuroscience and computational modeling to investigate brain dynamics during language processing. Education: Doctoral degree in Engineering and Technology, Aalto University (2014) Master's degree in Engineering and Technology, Helsinki University of Technology (2005) Research Interests: Her expertise centers on Magnetoencephalography (MEG) and semantic processing , with deep specialization in computational modeling of neural representations . She pioneers methods to decode brain responses to written language using vector space models and unsupervised learning, advancing understanding of how semantic features activate cortical networks during reading and cognitive tasks. Recent Research Trends: Her 2023-2025 publications reveal a cohesive focus on decoding semantic representations from MEG data, particularly examining morphologically complex words, misspelled-word effects, and cross-linguistic semantic norms. This work consistently applies machine learning to neural time-series data, highlighting evidence accumulation dynamics in semantic processing and establishing robust brain-decoding pipelines for linguistic stimuli. Grants and Projects: She contributes to two Academy of Finland-funded initiatives: the active consortium project Salmelin_konsortio: Aivojen, mielen ja taudin kulun toiminnallinen yksilöllisyys (2023-2027) investigating individual brain variability, and the completed Dyslexia: genes, brain functions, interventions project (2015-2019) that integrated MEG with dyslexia research. Collaborative Network: As a core member of Riitta Salmelin's research group, she engages in interdisciplinary collaborations spanning neuroscience, engineering, and computer science, with documented international exchanges including visiting researcher positions in 2008-2009.