Prof. Dr. Sakibe Nalan BÜYÜKKANTARCIOĞLU is a Professor at Çankaya University, Faculty of Arts and Sciences, Department of Translation and Interpreting Studies (English). She previously served as Professor (2007–2019), Associate Professor (2000–2007), and Assistant Professor (1997–2000) at Hacettepe University, Department of English Linguistics. PhD in English Linguistics (Hacettepe University, 1991) MA in English Language Teaching (Middle East Technical University, 1985) BA in English Teaching (Istanbul University, 1978) Her research spans Linguistics, Cognitive Linguistics, Discourse Analysis, Psycholinguistics, Sociolinguistics, Translation Studies , with a focus on figurative language, interjections, political discourse, and endangered languages. She received the European Language Award (2011) and has supervised numerous PhD and MA theses, including work on Turkish idioms, gender representation, and language awareness. A prolific editor and conference participant, she has contributed to interdisciplinary projects and publications on Turkish sociolinguistics.
Mark Steedman is a Professor in the School of Informatics at the University of Edinburgh, where he conducts research in Artificial Intelligence, Computational Cognitive and Social Science, and Natural Language and Speech Processing. He is affiliated with the Institute for Language, Cognition and Computation (ILCC), the Centre for Speech Technology Research (CSTR), and the Human Communications Research Center (HCRC). He also holds an adjunct professorship in Computer and Information Science at the University of Pennsylvania. His research focuses on Combinatory Categorial Grammar (CCG) , computational linguistics , prosody and intonation , temporal semantics , gesture in communication , and computational music analysis . He has authored foundational books including Surface Structure and Interpretation , The Syntactic Process , and Taking Scope . The recent publications reflect a strong trend toward integrating formal grammatical frameworks like CCG with modern neural and distributional models, particularly in semantic parsing, entailment reasoning, and cognitive modeling. His work bridges symbolic and statistical approaches in NLP, often focusing on robust, wide-coverage parsing and semantic interpretation. Best Paper Award at AACL/IJCNLP 2023 for 'Smoothing Entailment Graphs with Language Models' Best Paper Award at ACL 2023 for 'Extrinsic Evaluation of Machine Translation Metrics' Influential Paper Award 2017 from IFAAMAS for 'Animated Conversation' Mark Steedman has supervised numerous PhD students and collaborated widely across institutions. He leads research in formal grammar applications to cognitive modeling, dialogue, and multimodal communication. His lab contributes to CCG software and semantic parsing tools, and he continues to be actively involved in advancing the integration of symbolic and neural AI.
James Glass is a Senior Research Scientist at the Massachusetts Institute of Technology (MIT) and heads the Spoken Language Systems Group within MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). He is also affiliated with the Harvard-MIT Division of Health Sciences and Technology. His research spans automatic speech recognition, multimodal learning, and spoken language understanding, with applications in healthcare and video analysis. Education: SM and PhD in Electrical Engineering and Computer Science from MIT His work focuses on paralinguistic speech analysis, health markers in speech, and the intersection of speech and natural language processing. Recent trends emphasize audio-visual alignment, recursive reasoning, and AI applications in cognitive disorder diagnosis. Scientific awards include IEEE Fellow, ISCA Fellow, and Associate Editor for IEEE Transactions on Pattern Analysis and Machine Intelligence. His group explores unsupervised learning, speaker verification, and social text analysis. James leads the Spoken Language Systems Group at CSAIL, collaborating with institutions like IBM and Harvard-MIT Division of Health Sciences and Technology. His research integrates vision-language models, neural audio codecs, and self-supervised frameworks.
David Yarowsky is a Professor in the Department of Computer Science at Johns Hopkins University. He leads the Low-Resource Languages Lab and is a member of the Center for Language and Speech Processing. Harvard University - Bachelor of Arts in Computer Science (1987) University of Pennsylvania - Master of Science in Engineering (1993) and PhD in Computer and Information Science (1996) Research Interests : Natural Language Processing, particularly focusing on word sense disambiguation, minimally supervised induction algorithms, multilingual NLP, and machine translation for low-resource languages. His work bridges theoretical linguistics with practical applications in information retrieval, spoken language systems, and very large text databases. Article Trends : His publications emphasize cross-lingual transfer learning, universal morphology, and low-resource language technologies. Key themes include morphological analysis, computational etymology, and adversarial speech recognition. Scientific Awards : ACL Fellow (2013-present) Professional Service : Served as Treasurer and Executive Committee Member of the Association for Computational Linguistics, Secretary-Treasurer of SIGDAT, and chair/co-chair of major conferences including EMNLP 2013, IJCNLP 2011, and ACL 2014. Labs & Teams : Director of the Low-Resource Languages Lab at JHU and active member of the Center for Language and Speech Processing.
Maisha T. Winn serves as the Excellence in Learning Graduate School of Education Professor at Stanford University and is Faculty Director of the Stanford Accelerator for Learning's Equity in Learning Initiative. She leads the Futuring for Equity Lab as Principal Investigator and holds significant leadership positions including President-Elect of the American Educational Research Association and membership in the National Academy of Education. Dr. Winn's research examines how non-dominant youth and communities develop literate trajectories across historical and contemporary settings. As an ethnographer by training, she investigates how communities depicted as under-resourced create their own educational practices, processes, and institutions. Her scholarship bridges historical analysis with contemporary educational practice to build more just, collaborative, and equitable futures in education. Her work spans restorative justice in education, Black literacy studies, and transformative justice approaches, with particular attention to the school-to-prison pipeline, independent Black institutions, and futures-oriented educational frameworks. Dr. Winn analyzes how historical educational models can inform contemporary practices that center community knowledge and cultural identity. Andrew W. Mellon Fellow at CASBS (2022-23) American Educational Research Association Fellow Member of the National Academy of Education Dr. Winn advises doctoral students including Christina Hewko and Misbah Naseer, and leads research initiatives focused on educational equity. Her Futuring for Equity Lab develops frameworks for understanding how marginalized communities have historically created educational spaces that affirm their identities and knowledge systems, with direct implications for contemporary educational practice and policy. Her research team collaborates with community organizations and schools to translate scholarly insights into practical applications that promote educational justice, particularly in areas of school discipline reform and literacy education for marginalized youth.
Karen Livescu is a Professor at the Toyota Technological Institute at Chicago (TTIC), a philanthropically endowed graduate institute for computer science located on the University of Chicago campus. She also serves as a courtesy faculty member in the Department of Computer Science at the University of Chicago and is an Affiliated Scholar at the Data Science Institute there. Her research focuses on advancing speech and language processing through innovative machine learning approaches. Education: PhD in Electrical Engineering and Computer Science from MIT (2005) S.M. from MIT Department of Electrical Engineering and Computer Science (1999) A.B. in Physics from Princeton University (1996) Karen's research spans multiple dimensions of speech and language processing with particular emphasis on speech recognition, spoken language understanding, and multimodal processing. She has made significant contributions to articulatory feature-based speech recognition, self-supervised learning for speech representation, and sign language processing. Her work consistently bridges machine learning techniques with linguistic and speech science knowledge, focusing on creating more robust, interpretable, and inclusive speech processing systems that can handle diverse languages and modalities. Her recent publication trajectory reveals a strong focus on self-supervised learning for speech representation, multilingual speech processing, and sign language understanding. She has been instrumental in developing benchmark frameworks like SUPERB and ML-SUPERB that have become standard evaluation tools in the speech community. Her work increasingly addresses critical challenges in low-resource language scenarios, language disparities in speech technology, and ethical considerations in real-world deployment. Scientific Awards: Best Paper award at EMNLP 2024 for 'Towards robust speech representation learning for thousands of languages' Best Student Paper Award at ASRU 2023 Best Short Paper Award at CRAC 2021 Top system at WMT-SLT 2023 Karen has successfully advised numerous PhD students and postdoctoral researchers who have gone on to faculty positions at institutions like University of Waterloo, University of Edinburgh, and Stellenbosch University, as well as industry roles at major technology companies including Google, Meta, and NVIDIA. Her research group has secured significant funding for projects including the development of the SLUE benchmark for spoken language understanding and the SUPERB framework for evaluating self-supervised speech models. She has been actively involved in organizing workshops and symposia that bring together researchers in speech and language processing. Karen leads the Speech and Language at TTIC (SL@TTIC) research group, which maintains a strong collaborative relationship with researchers at the University of Chicago and other institutions. The group has been particularly active in advancing sign language processing through projects like ChicagoFSWild and OpenASL, while also making significant contributions to spoken language understanding and multilingual speech recognition. Her team regularly participates in community challenges and benchmarks, helping to push the field forward through open science and collaborative evaluation frameworks.
Benoît Sagot is a Senior Researcher in Natural Language Processing and Computational Linguistics at Inria , currently holding the 2023-2024 Informatics and Digital Sciences Annual Chair at Collège de France. He directs the ALMAnaCH research team and contributes to the PRAIRIE Institute for AI research. Research Focus: His work spans neural language models, machine translation, text simplification, multimodal NLP, and lexical resource development for French and low-resource languages. He explores computational morphology, etymology, and historical linguistics, with applications in opinion mining and computational oenology. Recent Articles emphasize language model interpretability, cross-lingual transfer, and multimodal integration (speech, image). Tools & Resources: He has developed morphological lexicons (Le fff, Alexina), corpora (OSCAR, CAMEMBERT), and parsing pipelines (SxPipe). Projects: Involved in initiatives like ANR BASNUM (Furetière's dictionary digitization) and 3IA PRAIRIE (AI research). His career combines foundational work in syntactic analysis with evolving deep learning approaches.
Anna Shusterman is a Professor in the Psychology Department at Wesleyan University, where she has taught and directed research in the Cognitive Development Laboratories since 2007. She co-founded and serves as co-chair of Wesleyan's College of Education Studies, established in 2020. Her educational background includes: Bachelor's degree in Neuroscience from Brown University Graduate studies and post-doctoral fellowship at Harvard University's Laboratory for Developmental Studies under Elizabeth Spelke and Susan Carey Dr. Shusterman's research centers on conceptual development in children, with a focus on the interplay between language and cognition in spatial and numerical domains. She investigates how language shapes children's understanding of numbers and space, and develops methodological approaches for translating developmental science into practical preschool classroom applications. Her work explores how young children perceive and learn about the world through simple, engaging games. Analysis of her recent publications (2019-2025) reveals consistent emphasis on numerical cognition, language acquisition, and spatial reasoning. Key themes include language's role in math development, early numeracy in diverse populations (particularly deaf and hard of hearing children), and implementation of research-based math activities in preschool settings. Her work demonstrates how language exposure in any modality supports numerical concept development and how grammatical structures influence number word acquisition. Dr. Shusterman has secured significant research funding including NSF CAREER grants and collaborative research awards. She mentors undergraduate researchers in her laboratory and actively bridges academic research with practical educational applications through her work with the College of Education Studies. She directs the Blue Lab within Wesleyan's Cognitive Development Laboratories, located in Judd Hall. The NSF-funded lab conducts studies with children under 12 from central Connecticut preschools, schools, and daycare centers, using fun games to investigate how children think about numbers, space, language, and social concepts.
David Palmer is an Affiliate Associate Professor in the Department of Astronomy and Astrophysics. He is affiliated with Los Alamos National Laboratory (LANL). His research focuses on speech recognition, natural language processing, and multilingual systems, with particular emphasis on information extraction from audio and speech data. His work bridges computational linguistics and machine learning, addressing challenges in automated systems for audio comprehension and cross-language processing. Key research interests include robust information extraction from speech transcriptions, error detection in speech recognition, and multilingual processing for operational users. He has contributed to advancements in speaker identification, text preprocessing techniques, and domain adaptation in speech processing systems. His publications span over two decades, reflecting a consistent focus on improving automated systems for handling audio and text data in dynamic environments. While no specific awards or grants are listed, his extensive publication record highlights sustained contributions to the fields of speech technology and computational linguistics. His work at LANL likely involves collaborative research in applied computational sciences, though specific lab affiliations or teams are not explicitly mentioned.
Tanel Alumäe is an Associate Professor of Speech Processing at Tallinn University of Technology's School of Information Technologies, Department of Software Science. With over 15 years of academic experience, he has held various research and teaching positions at the university since 2006, progressing from Research Fellow to Tenured Associate Professor. His work focuses on speech and language technologies with a particular emphasis on Estonian language applications. PhD in Information and Communication Technology (2006), Tallinn University of Technology Research Master's Degree in Informatics (2002), Tallinn Technical University MSc studies at Tallinn Technical University (1999-2002) and Universität Erlangen-Nürnberg, Germany (1999-2000) Diploma in Computer and Systems Engineering (1994-1999), Tallinn Technical University Alumäe's research spans automatic speech recognition, speaker recognition, natural language processing, and computational linguistics with a focus on Estonian language technology. His work addresses challenges in multilingual speech processing, deep learning applications for speech technologies, and developing practical systems for real-world applications including broadcast media processing and accessibility solutions. He has made significant contributions to low-resource language processing and specialized applications for children's speech and emotion recognition. His recent publications demonstrate a strong focus on cutting-edge speech processing techniques including deepfake detection, multi-speaker systems, speech-to-speech translation, and applying large language models to speech applications. The research shows a consistent pattern of addressing both theoretical challenges in speech processing and practical implementations for Estonian language technology. Award 'Keeletegu 2019' from the Ministry of Education and Research Award 'Keeletegu 2011' from Estonian Ministry of Education and Research 3rd award at the Tallinn University of Technology contest for applied scientific projects (2011) Boris Tamm stipend (2007) First prize at the national contest of students' scientific works (2007) Ustus Agur stipend of Estonian Information Technology and Telecommunications Association (2005) Alumäe has supervised postdoctoral researchers including Rena Nemoto (2012-2015) on pronunciation modeling for speech recognition. He serves in editorial and review capacities for major journals including Nature, Computer Speech & Language, and IEEE Transactions. His administrative roles include Secretary of the Northern European Association for Language Technology Board and membership on the Department of Software Science Council at TalTech. His research group at Tallinn University of Technology actively participates in international challenges (IWSLT, Interspeech, Odyssey) and collaborates with institutions worldwide. The team has developed open-source platforms for Estonian speech transcription and created systems for automatic closed captioning of Estonian broadcasts, demonstrating strong practical applications of their research.
Dr. Jacek Kudera is a post-doctoral researcher in the Department of Phonetics at the University of Trier, coordinator of the LODinG project at the Trier Center for Digital Humanities, and adjunct faculty at WSB Merito University in Wrocław. His work bridges phonetics, Slavic linguistics, digital humanities, and forensic speech science. Education 2022 – PhD, Department of Language Science and Technology, Saarland University, Germany 2019 – MA (Linguistics), Department of Linguistics, Cognitive Science and Semiotics, Aarhus University, Denmark 2015 – Magister (Slavic Philology), Institute of Slavic Studies, University of Wrocław, Poland Research Interests His research focuses on phonetic and prosodic aspects of Slavic languages , cross-linguistic speech perception , forensic automatic speaker recognition , and human-robot interaction . He employs experimental methods such as eye-tracking, articulatory measurements (EMA), and large-scale digital corpora to investigate how speakers of closely related languages understand one another and how machines can replicate or support this process. Publication Trends Across more than 25 peer-reviewed articles (2014-2025), Kudera has consistently explored Slavic intercomprehension , speech technology evaluation , and digital humanities infrastructure . Recent work (2024-2025) targets voice cloning security , linked open data for linguistics , and mismatch conditions in forensic speaker recognition . Scientific Awards & Fellowships Visegrad Fellowship, University of Presov (2025) Erasmus+ Fellowships (Ostrava 2025, Zagreb 2014, Rijeka 2012-2013) NAWA Fellowship, Polish Academy of Sciences (2022) Nordlys Fellowship, University of Eastern Finland (2018-2019) CEEPUS & additional Central-European mobility grants (2014-2018) Projects & Funding Coordinator : “Mismatch conditions in machine speaker identification” (University of Trier Research Fund, 2024-2025) Coordinator : LODinG – Linked Open Data in the Humanities (Trier Center for Digital Humanities, ongoing) Coordinator : “Patterns: Linguistic Creativity and Variation” (Trier Center for Language and Communication, 2022-2024) Member : SFB 1102 “Information Density and Linguistic Encoding” (Saarland University, DFG, 2019-2022) Member : Digital Atlas of Dialects of Bosnia and Herzegovina (2017-2018) Member : CLARIN-PL & European Roadmap for Research Infrastructures (2014-2017) Labs & Teams He conducts research within the Phonetics Team at the University of Trier , collaborates closely with the Trier Center for Digital Humanities , and maintains affiliations with the Phonetics Group at Saarland University and the WSB Merito University in Wrocław.
Dr. Elisa Pellegrino is a senior Post-Doc in Phonetics at the Department of Computational Linguistics , affiliated with Zeppelin University and the Digital Society Initiative of the University of Zurich . Her research focuses on voice individualization, vocal accommodation, and the role of speaker-specific information in speech temporal variability. Role: Senior Post-Doc in Phonetics University: Zeppelin University School: Faculty of Arts and Social Sciences Department: Department of Computational Linguistics Email: elisa.pellegrino@uzh.ch Research Interests: Prosody and speech rhythm in native and second language acquisition Vocal accommodation in cross-dialectal interactions Speaker individuality and forensic phonetics Age-related speech temporal variability Speech disorders and Parkinson’s disease Applications of speech technology in linguistics and education
Tanel Alumäe is a Tenured Associate Professor of Speech Processing and Head of the Laboratory of Language Technology at Tallinn University of Technology (TalTech). He holds a PhD in Information and Communication Technology from TalTech (2006) and has conducted research at institutions like LIMSI/CNRS, Aalto University, and Raytheon BBN Technologies. His research focuses on speech processing, speaker and language recognition, and low-resource language technologies. Affiliations: Department of Software Science, School of Information Technologies, TalTech. Education: PhD in ICT (2006), MSc in Informatics (2002), Diploma in Computer & Systems Engineering (1999). Research interests include speech recognition, speaker diarization, spoken language translation, and combating DeepFake voices. He leads teams achieving top results in competitions like IARPA BABEL, NIST LRE, and Interspeech challenges. His work emphasizes open-source tools and equitable AI solutions. Key Awards: Best Student Paper at Odyssey 2024 and TSD 2018. Keeletegu Awards (2019, 2011) for contributions to Estonian language technology. Grants & Leadership: Managed the National Programme for Estonian Language Technology (2011–2017). Serves as Secretary of the Northern European Association for Language Technology (NEALT) and Area Chair for ICME, EACL, and Interspeech conferences. Labs & Teams: Directs the Laboratory of Language Technology, focusing on practical applications of speech and language technologies.
Megumi Kameyama was a Senior Research Scientist at the Artificial Intelligence Center (AIC) of SRI International. Her research focused on discourse semantics, computational models for information extraction, and mismatch resolution in machine translation. She contributed to projects like FASTUS, MIMI, and GEMINI, addressing challenges in spoken dialogue summarization and cross-lingual translation. Key projects included the NSF-funded Mismatch Resolution in Machine Translation (1996-1999), where she co-led efforts to develop logical and statistical approaches for resolving linguistic discrepancies between languages. Her work emphasized real-time systems and robust context modeling, particularly for Japanese-English translation and dialogue analysis. Dr. Kameyama maintained active involvement in software development, including MIMI for spoken dialogue extraction and FASTUS for text analysis. Her publications explored dialogue structure, context dependency, and system robustness in natural language processing. She passed away on January 23, 1999.
Afra Alishahi is a Full Professor at Tilburg University's Department of Cognitive Science and Artificial Intelligence within the Tilburg School of Humanities and Digital Sciences. Her research focuses on computational models of human language acquisition and grounded language learning, leveraging neural models to explore how language processing and acquisition occur. She has held roles including Assistant Professor at Tilburg University (since 2011) and Postdoctoral Fellow at Saarland University (2008-2011). Her work bridges computational linguistics, cognitive science, and artificial intelligence, with contributions to understanding language learning mechanisms through models that integrate visual, auditory, and linguistic data. Education: PhD (university unspecified), with prior academic roles in Iran and Germany. Awards: CoNLL 2017 Best Paper Award, 2023 Outstanding Paper Award, NWO Aspasia Grant (2015), and NWO Natural Artificial Intelligence Grant (2015). Her research has been supported by grants such as the Dutch National Research Agenda-funded project on interpreting deep learning models for text and sound. Research Interests: Grounded language learning, interaction effects in language acquisition, and neural model interpretability. Key areas include multi-modal learning (e.g., linking speech to visual scenes), computational modeling of child language learning, and probing neural networks for linguistic knowledge. She co-organized workshops like BlackboxNLP (2018-2020) and has authored over 60 publications, including influential works on phonology encoding in neural models and gender disambiguation in machine translation. Teaching: Courses include Cognitive Models of Language Learning , Computational Linguistics , and Language, Cognition & Computation . She advises master's theses and leads projects in data science and AI. Lab/Team: Leads research on computational modeling, collaboration with interdisciplinary teams (e.g., with Grzegorz Chrupała, Afsaneh Fazly), and involvement in initiatives like the Interpreting Deep Learning Models for Text and Sound project.
