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Sebastian SauppeView profile

Researcher

• Linguistic diversity and language processing
• Incremental processing
• Event cognition
• +5 more

Sebastian Sauppe is a Senior Assistant and Ambizione Fellow at the University of Zurich's Department of Psychology, where he leads the research area 'Cognitive and Developmental Neuroscience of Language' within the Developmental Psychology: Infancy and Childhood unit. His work bridges language processing, cognitive neuroscience, and ontogenetic development, with fieldwork conducted on under-researched languages including Äiwoo in Solomon Islands. Education: Recipient of Max Planck Society's Otto Hahn medal for psycholinguistic dissertation work Current Roles: SNSF Ambizione grant holder, National Geographic Explorer Dr. Sauppe's research focuses on linguistic diversity and language processing , examining how grammatical structures interact with real-time comprehension and production. His work on incremental processing investigates neural mechanisms of sentence construction, while event cognition studies explore how language shapes semantic role representation. He also examines child development of language-cognition interfaces and interactive language use in social contexts. His methodology combines mobile EEG, eye-tracking, and behavioral paradigms across diverse linguistic environments. Analysis of his recent publications reveals strong emphasis on cross-linguistic validation of processing principles (particularly agent preference phenomena), neural correlates of syntactic variation, and methodological innovation in field psycholinguistics. His work consistently bridges theoretical linguistics with cognitive neuroscience, with increasing focus on developmental trajectories. National Geographic Explorer (awarded by National Geographic Society) Otto Hahn Medal (Max Planck Society for outstanding dissertation work) As an Ambizione grant holder, Dr. Sauppe leads independent research while mentoring junior researchers. His fieldwork in Solomon Islands demonstrates commitment to ecological validity and linguistic diversity. Current projects involve mobile EEG studies of language processing in naturalistic settings and cross-species comparisons of event cognition with great apes. His research group collaborates extensively across Europe and with field linguists worldwide, developing novel paradigms for studying language in under-documented contexts. Dr. Sauppe maintains active laboratory work at the University of Zurich while conducting periodic field expeditions. His team utilizes mobile neuroimaging equipment for cross-cultural studies and collaborates with primatology researchers on comparative cognition projects. The lab emphasizes methodological rigor in both controlled laboratory and naturalistic field settings.

Annemarie FriedrichView profile

Professor

• Natural Language Processing
• Computational Linguistics
• Computational Syntax
• +7 more

Annemarie Friedrich is a tenured University Professor for Natural Language Understanding (Computational Linguistics) at the Faculty of Applied Computer Science, University of Augsburg. She also holds membership in the Faculty of Philology and History. Previously, she worked as a Senior Expert on Natural Language Processing and Computational Linguistics at the Bosch Center for Artificial Intelligence. Currently, she serves as president of the German Society for Computational Linguistics (GSCL), the primary scientific association for NLP research in German-speaking regions, and is a member of the ACL Special Interest Group for Annotation (ACL SIGANN). University: University of Augsburg School: Faculty of Applied Computer Science Department: Institute of Computer Science Position: University Professor (tenured) for Natural Language Understanding Professor Friedrich's research focuses on computational linguistics and natural language processing with emphasis on semantics and information extraction from text. Her work spans both machine-learning oriented approaches to text mining for scientific text, syntactic and semantic parsing, and uncertainty in deep learning for NLP, as well as corpus-linguistic research on syntax-semantics interface, discourse, pragmatics, aspect, genericity, and modal verbs. She has particular expertise in annotation and corpus creation, recognizing that machine learning models depend fundamentally on underlying data quality. Her research group at Augsburg actively contributes to computational linguistics through numerous publications and datasets. Analysis of Professor Friedrich's recent publications reveals a strong focus on table question answering, patent text processing, uncertainty modeling, and multimodal scientific document understanding. Her work consistently bridges theoretical linguistics with practical NLP applications, with increasing emphasis on robust evaluation methodologies and domain-specific adaptations of language models. Notably, her research group has produced significant resources including the AnnoCTR dataset for cyber threat reports, PAP2PAT for patent generation, and FREB-TQA for evaluating table QA robustness. Professor Friedrich actively mentors multiple PhD students working on diverse topics including document-level patent processing (Valentin Knappich), temporal processing (Timo Schrader), document-level text modeling (Wei Zhou), and topic modeling for digital forensics (Jenny Maria Felser). Her collaborative approach is evident in co-supervision arrangements with researchers from institutions including Bosch Center for Artificial Intelligence, TU Dresden, and Hochschule Mittweida. She has also successfully guided previous PhD students including Sophie Henning (Uncertainty Modeling), Stefan Grünewald (Syntactic Dependencies), and Subhash Pujari Chandra (Neural Patent Classification). Her teaching responsibilities include courses such as Introduction to Natural Language Processing, Introduction to Python Programming, and specialized seminars on Natural Language Understanding for both Bachelor's and Master's students. These courses reflect her commitment to both theoretical foundations and practical implementation skills in computational linguistics.

• Natural Language Processing
• Computational Linguistics
• Dependency Parsing
• +7 more

Carlos Gómez Rodríguez is a Full Professor (Catedrático de Universidad) in the Department of Computer Science at the University of A Coruña, Spain, where he leads the FASTPARSE Lab within the LyS Research Group at the CITIC Research Center. His work bridges theoretical and practical aspects of computational linguistics with significant applications in natural language processing. His primary research interests focus on natural language parsing algorithms, with particular emphasis on improving parsing speed for web-scale applications, handling non-projective dependency structures, analyzing morphologically-rich and low-resource languages, and exploring the cognitive aspects of syntax. He maintains a critical perspective on the field's current LLM-driven revolution, advocating for continued research into alternative approaches that prioritize efficiency, explainability, and scientific insight beyond user-facing applications. His recent publications reveal a strong trend toward examining the capabilities and limitations of large language models, particularly in comparison to human language processing, while continuing to advance traditional parsing techniques. His work spans theoretical foundations, practical implementations, and diverse applications including sentiment analysis, opinion mining, and creative writing evaluation. National Young Researcher Price "María Andresa Casamayor" in Mathematics and Information Technologies by Spain's Ministry of Science Honorary Member of the Royal Spanish Mathematical Society CAEPIA 2024 Best Paper Award for research on evaluating creative writing in LLMs Professor Gómez Rodríguez has successfully led significant research projects including the ERC Starting Grant project FASTPARSE, which focused on techniques to improve the speed of parsing algorithms. His work demonstrates consistent funding support for innovative research at the intersection of computational linguistics, cognitive science, and practical NLP applications. He actively collaborates with researchers across Spain and internationally, as evidenced by his extensive publication record with diverse co-authors. He leads the FASTPARSE Lab and is a core member of the LyS Research Group, focusing on advancing both theoretical understanding and practical implementations of language processing technologies. His team explores diverse aspects of natural language processing, from fundamental syntactic analysis to applied sentiment analysis and innovative applications of language technology in domains like healthcare.

Rico SennrichView profile

Assistant Professor

• Natural Language Processing
• Machine Translation
• Multilinguality
• +7 more

Rico Sennrich is an Assistant Professor at the University of Zurich's Department of Computational Linguistics, specializing in natural language processing with a focus on machine translation and multilinguality. He is also an Honorary Fellow at the University of Edinburgh and an ELLIS Fellow. His research spans low-resource and efficient methods, interpretability and model analysis, and multimodal language processing, particularly known for work on tokenization, neural architectures, and data augmentation. His research interests include developing practical solutions for multilingual machine translation, addressing challenges in low-resource scenarios, improving model efficiency, and analyzing how neural networks process language. He has made significant contributions to subword tokenization techniques and neural architecture design that have been widely adopted in both research and industry applications. His work often bridges theoretical insights with practical implementations to advance the state of the art in NLP. His recent publications reflect trends toward addressing bias in machine translation, improving document-level translation, enhancing multilingual capabilities, and developing more efficient inference methods for large language models. His research increasingly focuses on the intersection of machine translation with multimodal processing and low-resource language scenarios. ELLIS Fellow Honorary Fellow at the University of Edinburgh Action editor for Computational Linguistics and ACL Rolling Review Standing reviewer for TACL Senior area chair for ACL 2025 Professor Sennrich has advised numerous PhD students whose work has received recognition, including ACL outstanding paper awards and best thesis awards. He leads research projects such as Evolving Language (2024-), InvestigaDiff (2024-2027), and MT for Romansh idioms (2025-2026), having previously led the MUTAMUR project (2019-2025). His laboratory focuses on practical NLP solutions with real-world impact, particularly in multilingual and low-resource scenarios. The team actively collaborates with industry partners and participates in major research initiatives like ELITR.

• Databases and Algorithms
• Data Integration
• Natural Language Processing
• +3 more

Professor Elzbieta Pustulka is a faculty member at FHNW University's School of Business, affiliated with the Institute for Business Informatics. Her research centers on databases, NLP, and AI-driven business process optimization. Research explores database pedagogy through gamification (SQL Scrolls), NoSQL implementations for ERPs, and automated information extraction techniques. Recent work emphasizes CI/CD practices in database development and educational game design for programming concepts. She supervises graduate students in projects spanning database indexing, logistics simulations, and AI-driven reporting systems. No scientific awards are noted.

• Computational Linguistics
• Natural Language Processing
• Vision-Language Models
• +5 more

Joyce Chai is a prominent academic researcher in computational linguistics and AI with extensive contributions to grounded language learning, embodied agents, and human-machine collaboration. Her work spans vision-language models, theory of mind implementation, and task guidance systems. Key research themes: Language grounding in physical/social contexts Embodied AI and situated reasoning Interactive learning frameworks Zero-shot and continual concept acquisition Recent publications demonstrate her leadership in: Developing TRAVER for coding tutoring agents Creating W2W grounded language model Advancing theory of mind evaluation in LLMs Establishing HAR reasoning strategies for coherent physical reasoning She has mentored numerous students including Ziqiao Ma, Shane Storks, and Yuwei Bao. Her work appears in top venues like ACL, EMNLP, and NAACL with focus on practical applications like cake-making guidance systems (WTaG) and autonomous driving dialogue (DOROTHIE). Technical contributions include: MetaReVision retrieval-enhanced meta-learning EpiCA network for compositional concept recognition Neuro-symbolic DANLI agent architecture Pragmatic Rational Speaker framework

• Systems Neuroscience
• Vocal Learning
• Neural Networks
• +4 more

Prof. Richard Hahnloser is a Full Professor at ETH Zürich's Department of Information Technology and Electrical Engineering, affiliated with the Institute for Neuroinformatics. He holds a PhD in Physics from ETH Zurich (1999, awarded the ETH Medal) and conducted postdoctoral research at MIT, Howard Hughes Medical Institute, and Bell Labs. His research focuses on neural mechanisms underlying vocal learning in songbirds, particularly auditory-motor signal processing and synaptic plasticity in neural networks. Education: Physics studies at ETH Lausanne and Zurich (Diploma 1996, PhD 1999). Career highlights include roles as a Swiss National Science Foundation Professor (2004–2007) before becoming a Full Professor at ETH Zurich in 2007. He explores interdisciplinary approaches combining experimental neuroscience, theoretical modeling, and bioimaging. Research interests include decoding neural networks of song systems, studying motor control via X-ray CT and Neuropixels recordings, and applying machine learning to vocal behavior analysis. Recent work highlights vocal plasticity, sleep-related neural dynamics, and neurotechnology for chronic neural recording. Notable achievements include the ETH Medal for his PhD and contributions to understanding syrinx anatomy and sensorimotor adaptation. His lab develops tools like MODOC for scientific text analysis and employs correlative microscopy techniques to study neural circuits.

• Machine Learning
• Natural Language Processing
• Computational Linguistics
• +3 more

Prof. Ryan Cotterell is an Assistant Professor in the Department of Computer Science at ETH Zürich. His research focuses on machine learning and natural language processing, particularly in transformer models, syntactic control of language systems, and computational linguistics. He teaches courses such as Neural Networks and Computational Complexity, Natural Language Processing, and Advanced Topics in Machine Learning. His work bridges theoretical foundations and practical applications, exploring topics like language model expressivity, information locality, and formal language theory. Recent publications highlight innovations in controlled generation, model evaluation, and multimodal systems. While no specific awards are listed, his contributions to the field are evident through his extensive publication record and academic roles. Advising and grant details are not explicitly mentioned in the provided texts, but his involvement in teaching and research indicates active mentorship and project leadership. No lab or team affiliations specific to Ryan Cotterell are detailed here.

• Natural Language Processing
• Computational Linguistics
• Distributional Semantics
• +12 more

Lonneke van der Plas is an Associate Professor at the Institute of Argumentation, Linguistics and Semiotics within the Faculty of Communication, Culture and Society at Università della Svizzera italiana (USI), and an Adjunct Professor at the Faculty of Informatics, USI, since October 2024. She also serves as the group leader of the Computation, Cognition & Language research group at the Idiap Research Institute in Martigny, a position she has held since February 2021. Her academic background includes: PhD in Humanities Computing, University of Groningen M.Phil in Computer Speech and Language Processing, University of Cambridge Postdoctoral research at the University of Geneva (CLASSiC project) Junior Professor at the University of Stuttgart (IMS, SFB 732) Associate Professor at the University of Malta (2014–2020) Her research interests span Natural Language Processing , Computational Linguistics , Distributional Semantics , Multilingual NLP , Computational Creativity , and Low-Resource Languages . She integrates insights from cognitive science, linguistics, and computer science to model language as a tool for creative thinking and reasoning. Her work includes semantic role labeling, cross-lingual transfer, medical question answering, and lexical innovation. The 15 most recent publications reflect a strong trend in interdisciplinary NLP research, combining linguistic theory with machine learning. Topics include lexical innovation, multilingual financial NLP, skill extraction, multi-modal fact checking, and cognitive modeling. The articles demonstrate expertise in both theoretical and applied NLP, with applications in healthcare, finance, education, and AI ethics. Key subfields include semantic role labeling, cross-lingual transfer, bootstrapping for low-resource languages, and structured knowledge integration. Scientific recognitions include: DSI Fellow, University of Zurich (2019–2020) Erasmus Mundus LCT Visiting Scholar at Shanghai Jiao Tong University and University of Melbourne (2016) Visiting Academic at Macquarie University, Sydney (2007) She has advised multiple PhD students including Stefan Müller, Patrick Ziering, Molly Petersen, Mete Ismayilzada, and Diego Rossini. She currently leads several major funded projects: NCCR Evolving Language (SNSF, PI), C-LING (SNSF, PI), SEM24 (Innosuisse, PI), and FactCheck (Hasler Foundation, co-PI). These grants support postdoctoral researchers, developers, and PhD students, and involve collaborations with institutions like EPFL, EHL, and ARCA24. Her research bridges academia and industry, with applications in HR, finance, and healthcare. Lonneke leads the Computation, Cognition & Language group at Idiap, which conducts highly interdisciplinary research involving collaborations with social scientists, cognitive scientists, linguists, and professionals in health, finance, and business. The group focuses on modeling language as a cognitive and creative tool, using computational methods to explore lexical innovation, diachronic change, and reasoning. Open PhD positions are available in areas such as NLP for cognitive modeling, multilingual NLP, and mental health applications.

• Machine Translation
• Natural Language Processing
• Computational Linguistics
• +7 more

Dr. Steffen Eger is a professor at the Technical University of Darmstadt, focusing on Natural Language Processing, Machine Translation, and Computational Linguistics. His work spans both theoretical and applied aspects of NLP, including adversarial robustness, evaluation metrics, and creative text generation. Key Contributions: • Pioneering research in LLM-based evaluation metrics • Development of efficient translation quality frameworks • Analysis of social solidarity in historical discourse • Exploration of poetic creativity through neural models His recent publications demonstrate expertise in: - Human-Aware Translation Evaluation - Summarization of Historical Texts - Adversarial Defense Mechanisms - Explainable AI for Generation Metrics While no explicit awards or student supervision details are provided, his collaborative work appears across major NLP venues like ACL, EMNLP, and COLING. His research frequently intersects with human-centric NLP tasks, emphasizing reproducibility and robustness in system design.

Martin Alois RohrmeierView profile

Associate Professor

• Digital Musicology
• Cognitive Musicology
• Music Theory
• +5 more

Martin Alois Rohrmeier is an Associate Professor at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the College of Humanities and the Digital and Cognitive Musicology Lab (DCML). He holds multiple leadership roles, including Director of the Doctoral Program in Digital Humanities and member of the Doctoral School Committee. His academic presence spans teaching in Digital Humanities, Humanities and Social Sciences, and specialized music courses. Associate Professor, Digital and Cognitive Musicology Lab (DCML), EPFL Director, Doctoral Program in Digital Humanities, EPFL Member, Doctoral School Committee, EPFL Teaching roles in SODH, SHS, and EDDH programs His research lies at the intersection of music, cognition, and computation. He investigates the structural foundations of music—particularly harmony, rhythm, melody, and form—through computational modeling and cognitive science. His work explores how humans perceive, produce, and understand music by building models that simulate cognitive processes. The recent publications reflect a strong focus on modeling tonal harmony, rhythmic expectations, musical closure, and key perception. These studies combine corpus analysis of large music datasets with cognitive and probabilistic modeling, contributing to both music theory and cognitive science. His research trends emphasize empirical validation of theoretical constructs using computational tools and large-scale data. Rohrmeier actively supervises PhD students in digital musicology and cognitive modeling. His leadership in the Doctoral Program in Digital Humanities underscores his commitment to interdisciplinary training and research advancement in the field. PhD Students: Borsodi-Benson Marina Barbara, Guan Xinyi, Meng Shuxin, Ren Zeng, Wang Xiaoxuan Past PhD Students: Cecchetti Gabriele, Finkensiep Christoph, Harasim Daniel, Hentschel Johannes, Moss Fabian Claude He teaches courses such as Digital Musicology and Musical Theory and Creativity , which integrate theoretical instruction with hands-on projects, fostering both analytical and creative skills in students. The Digital and Cognitive Musicology Lab (DCML) at EPFL serves as the central research environment, promoting interdisciplinary collaboration between musicologists, cognitive scientists, computer scientists, and engineers to advance the scientific understanding of music.

Johannes GraënView profile

Researcher

• Corpus Linguistics
• Computer-Assisted Language Learning (CALL)
• Games with a Purpose (GWAP)
• +2 more

Dr. Johannes Graën is a Researcher at the University of Zurich , affiliated with the Department of Computational Linguistics under the Faculty of Arts and Social Sciences . He leads the Language Technology group within the Linguistic Research Infrastructure, focusing on corpus linguistics, computer-assisted language learning (CALL), and games with a purpose (GWAP). Primary Affiliation: University of Zurich Role: Researcher and Educator His research emphasizes corpus linguistics , NLP tools for education , and language learning games . He develops web-based infrastructures like the LiRI Corpus Platform and SwissBERT, a multilingual language model tailored for Swiss languages. His work often intersects with machine translation , word alignment , and multilingual resource exploitation . The trends in his publications highlight multimodal corpus analysis , CALL applications , and parallel corpora modeling . He has contributed to tools like Multilingwis and SPARCLING, which enhance the accessibility and annotation of multilingual datasets.

Rishubh SinghView profile

Researcher

• Machine Learning
• Deep Learning
• Neuroscience
• +4 more

Rishubh Singh is a Doctoral Assistant at the Visual Intelligence and Learning Lab (VILAB) at École Polytechnique Fédérale de Lausanne (EPFL), pursuing his PhD in Computer and Communication Sciences under Prof. Amir Zamir's supervision. His dual status includes academic staff at the School of Computer and Communication Sciences and doctoral student in the EDIC program. His educational background includes: PhD in Computer and Communication Sciences (ongoing) at EPFL Dual Degree (B.Tech & M.Tech) in Computer Science and Engineering from Indian Institute of Technology Delhi (2019) Exchange program at KTH Royal Institute of Technology, Stockholm (2016) Singh's research bridges machine learning with neuroscience and psychology, focusing on bio-inspired deep learning for perception, reasoning, and computational design. He actively explores ML applications for assistive technologies addressing learning and communicative disorders, emphasizing inclusivity in AI systems. His publication record shows expertise in computer vision fundamentals, with recent work spanning single-pixel imaging, robust vision systems through adversarial augmentation, and neural architectures for language processing. These publications in top venues (CVPR, ECCV, ACL) demonstrate methodological innovation across multiple AI subfields. Professional collaborations include work with Dr. Pradeep Shenoy at Google Research India, Prof. Ravi Kiran Sarvadevabhatla (IIIT Hyderabad), Prof. Virginia De Sa (UC San Diego), and Dr. Mike Mozer (Google Research). His career progression shows: Doctoral Assistant at EPFL (2023-Present) Pre-Doctoral Researcher at Google Research India (2020-2023) Position at Graviton Research Capital LLP (2019-2020)

Matthias HauswirthView profile

Associate Professor

• Programming Education
• Software Performance
• Computer Science Education
• +3 more

Matthias Hauswirth is an Associate Professor at the Faculty of Informatics of the Università della Svizzera italiana (USI), leading the Lugano Computing Education Research Lab (LuCE) and the Software and Programmer Efficiency (SAPE) research group. His research bridges programming language design and computing education, with a focus on how people learn to program. He actively contributes to Swiss national initiatives like GymInf to train high school informatics teachers and has co-authored influential textbooks such as Informatik - Programmieren und Robotik . His teaching spans undergraduate, graduate, and teacher-training courses, emphasizing engaging pedagogy. He pioneered USI's Undergraduate Research Opportunities Program (UROP) and developed educational tools like PyTamaro, Expression Tutor, and the Progmiscon inventory. His prior work on software performance explored system-layer interactions from hardware to applications. He serves on program committees for conferences like SIGCSE and ICER, and organized ICER 2022. His contributions include innovative teaching materials, national teacher-training programs, and open-source platforms enhancing programming education globally.

• Parallel Computing
• Java Virtual Machine (JVM) Optimization
• Performance Profiling
• +5 more

Walter Binder is a Full Professor in the Faculty of Informatics at the Università della Svizzera italiana (USI). He holds a MSc, PhD, and venia docendi from Vienna University of Technology. Previously, he was a senior researcher at the Artificial Intelligence Laboratory, EPFL. His research focuses on program transformations, virtual execution environments, aspect-oriented programming, profiling, and resource management. Education: MSc from Vienna University of Technology PhD from Vienna University of Technology Venia docendi (Habilitation) from Vienna University of Technology Research Interests: His work spans JVM optimization, parallel computing, and dynamic program analysis. Notable contributions include tools like Renaissance (a JVM benchmark suite), S2S (SQL-to-Stream translator), and Akkaprof (profiler for actor-based systems). He explores topics such as thread management, vectorization, and performance profiling in distributed systems. Articles Trends: Recent work emphasizes JVM performance (e.g., Java Vector API, Native Image optimizations), parallel execution policies (NAS benchmarks), and adaptive runtime systems (e.g., MPR framework). He also investigates big data systems (Spark, Node.js) and runtime verification tools. Grants & Labs: Leads projects on JVM profiling, polyglot runtimes, and large-scale program analysis. His lab develops tools like NodeMOP for Node.js and AccStream for stream processing systems.