Professor Bernd Möbius is a leading academic in Phonetics and Phonology at the Department of Language Science and Technology, Saarland University. His research bridges phonetic theory with speech technology applications, focusing on text-to-speech systems, prosody modeling, and computational simulations of speech processes. Current research projects: DFG SFB 1102, C1: Information density and phonetic structure predictability DFG SFB 1102, C4: Slavic intercomprehension and surprisal theory (INCOMSLAV) Research Themes: Key areas include text-to-speech synthesis, speech prosody analysis, experimental methods in speech production/perception, information density in phonetics, and cross-linguistic studies of Slavic-Germanic languages. Scientific Contributions: Recent work explores Parkinson-induced dysarthria detection, breath noise acoustics, surprisal-driven speech behaviors, multilingual BERT models for idiomaticity, and perceptual consequences of acoustic adjustments.
Aage Hill-Madsen is an Associate Professor at the Department of Culture and Learning, Aalborg University (Denmark), affiliated with the Communication, Language, and Discourse (CLD) research unit. His work focuses on translation studies, medical communication, and systemic-functional linguistics. He holds a Cand.mag. et ling.merc. and a PhD in English linguistics. Research Interests: Translation theories (especially intralingual translation) Medical terminology simplification and communication Systemic-functional grammar applications Legitimation Code Theory in educational contexts Genre analysis of specialized texts Semiotic approaches to knowledge transmission Recent Publications Trends: His 2024-2025 work emphasizes medical translation between technical and lay audiences, with studies on EPAR summaries, patient information leaflets, and historical East-West medical terminology exchanges. He combines functionalist translation theory with systemic-functional frameworks to analyze textual transformations. Grants & Projects: Participated in the 2024-ongoing project Udvikling af kommunikative kompetencer gennem frivilligt brobygningsarbejde i NGO’en Social Sundhed , exploring communication skills through NGO activities. Academic Engagement: Active peer reviewer for journals like Globe and Across Languages and Cultures , with conference presentations on intralingual translation strategies and medical text accessibility.
Bistra Andreeva is a full Professor of Phonetics and Phonology at Saarland University, Department of Language Science and Technology. She is also a principal investigator of the DFG-funded project “Judeo-Spanish in Bulgaria: a contact language between archaism and innovation” (2022-2025) and leads or co-leads several other externally funded projects on prosody, information structure, and language contact. She teaches and has taught at Saarland University, Sofia University “St. Kliment Ohridski”, and other European universities. Education & Academic Background Habilitation (kumulative Habilitationsschrift): “Contrastive Prosody: Bulgarian vs. German”, Saarland University, 2016. PhD (Dr. phil.): “Zur Phonetik und Phonologie der Intonation der Sofioter-Varietät des Bulgarischen”, Saarland University, 2007. Research Interests Andreeva’s research focuses on the phonetics and phonology of intonation and rhythm, cross-language and individual differences in the production and perception of syllabic prominence, the relation between intonation and information structure, minority languages (especially Judeo-Spanish in Bulgaria), and the interaction between information density and prosodic structure. Her methodological expertise includes experimental phonetics, corpus-based studies, and acoustic analysis. Major Projects SFB 1102 C1: “Information Density and the Predictability of Phonetic Structure” (DFG, 2014-2026) JuSpa: “Judeo-Spanish in Bulgaria: a contact language between archaism and innovation” (DFG, 2022-2025) Prosodic aspects of Bulgarian compared to other languages with lexical stress (Bulgarian National Science Fund, 2019-2024) Prosodic manifestations of focus in French and English from a Bulgarian point of view (DAAD & Sofia University, 2021-2023) Scientific Awards & Recognition While no major named prizes are listed, Andreeva’s work has been continuously supported by competitive grants from the German Research Foundation (DFG), DAAD, and Bulgarian National Science Fund. Her publications appear in top-tier journals and conference proceedings (Journal of the Acoustical Society of America, Interspeech, Speech Prosody, International Congress of Phonetic Sciences). Teaching & Supervision She offers a broad spectrum of courses including Experimental Phonetics, Prosody, Phonetics & Phonology of Slavic Languages, Introduction to Instrumental Phonetics, and seminars on language contact. She has supervised numerous BA, MA, and PhD theses and regularly teaches at the PhD School of Sofia University. Labs & Teams She is affiliated with the Phonetics Laboratory at Saarland University and collaborates closely with the Institute for Bulgarian Language (Bulgarian Academy of Sciences), working in interdisciplinary teams involving phoneticians, computational linguists, and field linguists.
LEE Wee Sun is a Professor in the Department of Computer Science at the National University of Singapore (NUS), where he previously served as Head of Department, Vice Dean of Undergraduate Studies, and Vice Dean of Research. His academic journey began with a B.Eng. in Computer Systems Engineering from the University of Queensland (1992) and a Ph.D. from the Australian National University (1996), followed by research roles at the Australian Defence Force Academy and MIT. Education: Ph.D., Australian National University, Canberra, Australia (1996) B.Eng. in Computer Systems Engineering, University of Queensland, Brisbane, Australia (1992) Research Focus: Professor Lee pioneers work in Machine Learning , Planning Under Uncertainty , and Approximate Inference , with emphasis on integrating AI subfields for holistic reasoning. His current projects include "Learning to Decompose for Reasoning and Planning" (enhancing LLMs via self-supervised problem decomposition) and "Learning to Reason with Visual-Linguistic Inputs" (unifying vision, language, and reasoning in single architectures). Publication Trends: Recent work (2023-2025) centers on bridging LLMs with classical AI techniques, featuring breakthroughs in uncertainty quantification, multi-task optimization, and graph-based reasoning. Key themes include sparsity-aware vehicle routing, epistemic uncertainty for reliable LLMs, and differentiable neural solvers for combinatorial problems. Awards: IJCAI-JAIR Best Paper Prize (2022) RSS Test of Time Award (2021) RoboCup Best Paper Award (IROS 2015) HRATC 1st Place (2015) IPPC POMDP Track 1st Place (2011, 2014) UAI Google Best Student Paper (2014) Semeval-1 1st/2nd Place (2007) J.G. Crawford Prize (ANU 1996) Leadership & Service: As steering committee chair for ACML and area chair for NeurIPS/ICML/AAAI/IJCAI, Professor Lee shapes global AI discourse. His administrative roles at NUS and collaborations with MIT/Singapore-MIT Alliance demonstrate commitment to advancing AI education and research infrastructure. While student advisees aren't listed, his leadership positions imply extensive mentoring. Research Ecosystem: His work drives NUS's AI initiatives including Knowledge@Computing projects on reasoning frontiers. Current efforts focus on making AI systems robust through uncertainty-aware planning and multi-modal integration, with applications in robotics, verification systems, and combinatorial optimization.
Stefania Degaetano-Ortlieb is an Associate Professor of English Linguistics and Corpus Linguistics at Saarland University's Department of Language Science and Technology. She serves as Principal Investigator for the Collaborative Research Center (SFB 1102) 'Information Density and Linguistic Encoding,' leading Project B1 on diachronic information density in English scientific writing (17th century-present). Her interdisciplinary work bridges computational methods with sociolinguistics, focusing on register variation, language change, and digital humanities. Research interests center on text mining, data analytics, and probabilistic modeling of language variation. Key areas include: Diachronic evolution of scientific registers and linguistic densification Information-theoretic approaches to language efficiency Computational sociolinguistics and register diversification AI applications in humanities education (e.g., ChatGPT integration) Her publications show a strong trend toward quantitative diachronic analysis, with recent work emphasizing: interpretable AI models for linguistic change detection; propagandistic narrative analysis in conflict zones; and multi-word expression dynamics in scientific discourse. Cross-disciplinary collaborations frequently intersect with history, psychology, and media studies. Awards include the Fellowship Excellence Program for Young Female Scientists (2015-2018). Current grants: EU Horizon MSCA Doctoral Network 'CASCADE' (€521K to UdS, 2024-2027) Data-Pin Project for AI in education (€50K, 2023-2024) SFB 1102 Project B1 (€595K, 2022-2026) Advises PhD candidates in the EU CASCADE project on computational semantic change. Leads a research team exploring Russian media narratives, personality modeling in LLMs, and multi-word expressions. Directs teaching modules integrating AI tools for humanities students.
Dr. Varsha Suresh serves as a Postdoctoral Researcher at Saarland University's Computer Science and Computational Linguistics department, affiliated with Prof. Vera Demberg's research group since May 1, 2024. She also contributes to the DFG-funded SFB-1102 project "Information Density and Linguistic Encoding" as part of project B2 focused on "Cognitive modelling of information density for discourse relations". Her research expertise spans multiple dimensions of language technology: Development of knowledge-augmented language models to enhance language understanding capabilities Creation of multimodal language models incorporating non-verbal communication cues Cognitive modeling approaches to information density in discourse relations Integration of gestures, body language, and speech features with linguistic processing Dr. Suresh's work represents an interdisciplinary fusion of computational linguistics, cognitive science, and artificial intelligence, aiming to create language models that more accurately reflect human communication patterns through multimodal data integration. Her research has significant implications for advancing human-computer interaction systems and natural language understanding technologies.
Dr. Tyll Robin Lemke is a researcher at Saarland University in the Department of Modern German Linguistics (Neuere deutsche Sprachwissenschaft). He serves as a scientific staff member in Project B3 of the Collaborative Research Center SFB 1102. His academic focus includes experimental linguistics, syntax, and psycholinguistics, with specialized expertise in ellipsis phenomena and fragment analysis. Lemke's research explores the cognitive underpinnings of language production and comprehension, particularly investigating how predictability, context, and information theory shape linguistic structures. His work employs diverse methodologies including corpus analysis, psycholinguistic experiments, computational modeling, and gamified experimental paradigms to examine ellipsis, fragments, and syntactic phenomena in German. Analysis of Lemke's recent publications reveals consistent themes: the role of predictability in language production, constraints on ellipsis resolution, information-theoretic approaches to language efficiency, and experimental validation of syntactic theories. His research bridges theoretical linguistics with cognitive science through innovative experimental designs. In the upcoming 2025/26 winter semester, Lemke is teaching courses on Experimental Linguistics and Ellipsis in Theory and Experiment. He maintains an active research program within the SFB 1102 collaborative framework and regularly presents at international linguistics conferences.
Richard Futrell is an Associate Professor at the University of California, Irvine (UCI), affiliated with the Department of Language Science. He leads the Language Processing Group, focusing on computational models of human and machine language processing. His work bridges information theory, Bayesian cognitive modeling, and natural language processing (NLP) interpretability. University of California, Irvine Department of Language Science Language Processing Group leader His research examines how linguistic structures emerge from cognitive and communicative pressures. Key areas include dependency locality, surprisal theory in sentence processing, and efficiency-driven language evolution. He investigates how memory constraints, predictability, and information density shape syntactic and morphological patterns across languages. Recent publications analyze code-switching efficiency, syntactic priming, ERP component modeling, and agent-based language contact simulations. His work frequently employs Bayesian modeling, neural network analysis, and cross-linguistic corpora to uncover universal principles in language processing. ACL Best Paper Award (2024) Best Paper Award for Computational Modeling of Language (2023) Marr Prize for Best Student Paper (2017) He has developed datasets like SPACER for error repair analysis and contributed to phonotactic learning frameworks. His collaborations span cognitive scientists, computational linguists, and neuroscientists, advancing understanding of language production, comprehension, and structural optimization.
Patrick T. Brandt is a Professor of Political Science, Public Policy, and Political Economy at the University of Texas at Dallas , affiliated with the School of Economic, Political and Policy Sciences . His work integrates advanced statistical methods with political science, focusing on time series analysis, machine learning, and Bayesian modeling to study political dynamics. His research spans international relations , political economy , terrorist targeting , and conflict forecasting . He specializes in developing novel models for event count time series, including the Bayesian Poisson Vector Autoregression and MS-BVAR packages for R. His NSF-funded projects focus on event data generation and real-time conflict forecasting. Recent publications emphasize domain-specific language models (ConfliBERT variants), graph neural networks for conflict prediction, and machine translation challenges in political text analysis. He maintains the OpenEvent Data Repository and develops software like MSBVAR and PESTS for academic use. Scientific Awards : Robert H. Durr Award for Best Methodology Paper, Midwest Political Science Association (2006)
Swiss Federal Institute of Technology in LausanneSwitzerland
Martin Rajman is a Senior Scientist at École Polytechnique Fédérale de Lausanne (EPFL) with multiple affiliations across the institution. He holds positions in the School of Computer and Communication Sciences (SIN - Teaching, SCI IC MR Group, SSC - Teaching) as well as in the Vice Presidency for Strategic Development (VPS Artificial Intelligence) and the Vice Presidency for Academic Affairs (SNAI Administration). He serves as the Executive Director of Nano-tera.ch, a large Swiss Research Program funding collaborative multi-disciplinary projects in Health and the Environment. Rajman's research spans the intersection of artificial intelligence, natural language processing, and information retrieval. His work demonstrates a consistent focus on developing practical applications of computational linguistics and machine learning techniques. Early in his career, he contributed significantly to syntactic parsing, stochastic language models, and vector space representations for text. More recently, his research has expanded into deep learning applications for 3D reconstruction, empathetic conversational agents, and distributed analytics systems. His publications reveal a trajectory from foundational NLP research toward increasingly applied and interdisciplinary work connecting AI with healthcare, environmental monitoring, and human-computer interaction. Analysis of his recent publications (2015-2024) shows a clear evolution toward more applied AI research with strong interdisciplinary connections. While maintaining his core expertise in natural language processing and information retrieval, his work has expanded into computer vision, healthcare applications, and sustainable computing. The publications demonstrate increasing collaboration across disciplines, with applications in medical imaging, mental health support systems, environmental monitoring, and human-centered AI. His leadership role in the Nano-tera.ch program reflects this interdisciplinary approach, connecting computing research with real-world challenges in health and environmental contexts. Rajman has mentored several PhD students including Ailomaa Marita, Eckard Emmanuel, Melichar Miroslav, and Veselý Martin. His research has been supported through the Nano-tera.ch program, which has funded more than 100 research projects with over 95 million CHF in public funding. He has also managed more than 20 European projects during his tenure as Director of the EPFL Global Computing Center. As Executive Director of Nano-tera.ch, Rajman leads a significant research initiative connecting EPFL with national and international partners. His work bridges academic research with industry applications, notably through collaborations with eBay on product ranking technology and with Elsevier on article recommendation systems. His leadership extends to managing large-scale research programs while maintaining an active research agenda and mentoring the next generation of computer scientists.
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.
Timothy Baldwin is a Professor at the University of Melbourne, School of Computing and Information Systems, with additional affiliation at Mohamed bin Zayed University of Artificial Intelligence in UAE. His research spans natural language processing, large language models, and multilingual AI systems. His research interests focus on the safety, reliability, and ethical aspects of large language models. He investigates bias evaluation and debiasing techniques, uncertainty quantification methods, fact-checking systems, and multilingual model safety. His work addresses critical challenges in making AI systems more transparent, reliable, and culturally aware, with particular attention to low-resource languages and cross-cultural differences. Baldwin's recent publications demonstrate a strong focus on evaluating and improving the safety of language models across diverse linguistic contexts, developing tools for fact verification, and understanding the internal mechanisms of large language models. His research shows increasing emphasis on practical applications with real-world impact, particularly in multilingual settings and safety-critical domains. His scientific contributions include foundational work on multilingual NLP, bias mitigation techniques, and frameworks for evaluating LLM safety across different cultural contexts. His research has been published in top-tier venues including ACL, NAACL, EMNLP, and ICLR. Baldwin actively mentors students and junior researchers, with frequent collaborations with Haonan Li, Xudong Han, and Fajri Koto, among others. His research group appears to focus on practical applications of NLP with strong ethical considerations, particularly regarding model safety and cultural sensitivity.
Dr. Frances Yung is a Postdoctoral Researcher at Saarland University's Department of Language Science and Technology within the Department of Computer Science. She has been affiliated with Prof. Vera Demberg's research group since April 2017 and is currently working on the DFG-funded SFB-1102 project "Information Density and Linguistic Encoding," specifically on project B2 "Cognitive modelling of information density for discourse relations." She is pursuing her habilitation, indicating career progression toward a higher academic position in the German university system. Dr. Yung's research focuses on discourse relations at the intersection of NLP, corpus linguistics, and experimental psycholinguistics. Her work explores how information density affects discourse relation marking through cognitive modeling approaches. She has developed expertise in discourse parsing, resource construction, annotation aggregation, and experimental pragmatics, with particular attention to multilingual aspects of discourse phenomena. Her research combines computational modeling with experimental methods to understand how speakers produce and comprehend discourse relations. Analysis of Dr. Yung's recent publications reveals a strong focus on discourse relation resources, particularly multilingual corpora like DiscoGeM 2.0 covering English, German, French, and Czech. Her work increasingly incorporates crowdsourcing methodologies and examines how large language models can be leveraged for discourse annotation tasks. She has made significant contributions to understanding the challenges of implicit discourse relation annotation and the biases introduced by different task designs in crowdsourcing environments. Active reviewer for major computational linguistics conferences (ACL, EMNLP, NAACL, EACL, COLING, IJCNLP) and workshops since 2016 Served as area chair for Sigdial 2024 Regular service on program committees for discourse-related workshops Dr. Yung has supervised multiple Master's theses on topics related to discourse relations, implicit relation identification, and domain adaptation. Her teaching portfolio includes courses on crowdsourcing linguistic annotations, discourse relations from cognitive and NLP perspectives, and recent advances in discourse processing. She has also served as a teaching assistant for data science and AI courses, demonstrating her commitment to interdisciplinary education at the intersection of computer science and linguistics.
Tuukka Ruotsalo serves as Associate Professor in the Machine Learning Section at the Department of Computer Science, University of Copenhagen. His research bridges human cognition with computational systems through brain-computer interfaces and physiological computing. As Academy Research Fellow at University of Helsinki (2019-2024), he maintained dual institutional affiliations while leading cutting-edge work in neuro-linguistic modeling and affective relevance. His research focuses on brain-computer interfaces for information retrieval , where he pioneers methods to decode cognitive states from neural signals to improve search systems. Key areas include affective relevance modeling that integrates emotional states into search algorithms, and neuro-linguistic reconstruction that translates brain activity into language. His work on fairness-relevance tradeoffs in recommender systems established Pareto frontier evaluation frameworks now widely adopted in ethical AI research. Recent publications demonstrate how physiological signals like EEG and galvanic skin response can create more adaptive human-information interaction systems. Ruotsalo's scientific recognition includes the prestigious Academy Research Fellow position. His publications in IEEE Transactions on Human-Machine Systems , Journal of the Association for Information Science and Technology , and Communications Biology reveal growing interdisciplinary impact. His advising spans cognitive neuroscience and machine learning students, with notable collaborations across the SCIENCE AI Centre. Current projects include the TreeSense initiative for remote sensing of global tree resources and development of quantum-inspired neural architectures. His lab leverages the department's powerful compute cluster for large-scale physiological data analysis.
Marten van Schijndel is an Assistant Professor of Computational Linguistics at Cornell University, affiliated with the Cognitive Science Program and the Department of Linguistics within the College of Arts and Sciences. His research focuses on incremental language processing, comparing human linguistic behavior with computational models like neural networks. He organizes the Computational Psycholinguistic Discussions (C.Psyd) and collaborates with the Cornell Computational Linguistics Lab (CLab) and Cornell NLP Group. Van Schijndel’s work bridges computational modeling and psycholinguistics, probing how humans and models process language incrementally, particularly through methodologies like fMRI and behavioral experiments. His research interests include computational modeling of language processing, neural network representations, and the interplay between syntax, semantics, and discourse. Notably, he investigates ungrounded learning in neural models, exploring what linguistic aspects emerge from statistical patterns alone. Recent contributions include studies on semantic change quantification, phonotactic effects in context, and discourse predictability in Hindi word order. Van Schijndel teaches courses such as Computational Linguistics II and leads seminars on natural language processing. He actively advises students like John R. Starr, Ashlyn Winship, and Zander Lynch, whose work spans experimental paradigms for semantic spaces, implicit arguments in sentence processing, and semantic role localization. His recent activities include co-organizing workshops on event representation (PEER 2025) and presenting at the University of Rochester on event construction from language. Media highlights include research on tracking anti-immigrant hate speech and semantic evolution in French texts.
