جستجوی استاد راهنمای دانشگاهی

Lisa Beinbornمشاهده پروفایل

استاد

• Natural Language Processing (NLP)
• Multilingual Language Models
• Cognitive Plausibility in NLP
• +۵ مورد دیگر

Lisa Beinborn is a Professor for Human-Centered Data Science at the University of Göttingen, leading the Human-Centered Data Science group. Her research bridges natural language processing with cognitive science, focusing on multilingual models and interpretability. PhD in Computer Science (2016), Technische Universität Darmstadt MSc in Computational Linguistics (2010), Saarland University & Bolzano, Italy BSc in Computational Linguistics (2008), Saarland University & Barcelona, Spain Her research explores cognitive plausibility in NLP, analyzing how language models process language differently from humans. Key areas include multilingual model interpretability, semantic drift, eye-tracking, and readability prediction. Recent work examines input representation stability in neural models, cross-lingual transfer of complexity, and aligning language models with human cognitive patterns. Her team has presented findings at EMNLP, CoNLL, ACL, and CoLING. VENI Grant for "Interpretability of Transfer in Multilingual Models" Early Career Partnership by Royal Dutch Academy of Science "Most Interesting Paper" Award at BabyLM Challenge "Best Project Award" by Network Institute She has taught courses like Language as Data and Advanced NLP at University of Göttingen, VU Amsterdam, and TU Darmstadt. Her group collaborates with institutions like Gemeente Amsterdam and NT2 on multilingual text simplification and learner correction.

Michael Krauthammerمشاهده پروفایل

استاد

• Clinical Data Science
• Translational Bioinformatics
• Machine Learning in Healthcare
• +۳ مورد دیگر

Michael Krauthammer is a Professor of Medical Informatics and Chair of the Department of Quantitative Biomedicine at the University of Zurich, affiliated with the University Hospital of Zurich. His lab focuses on Clinical Data Science and Translational Bioinformatics, leveraging AI and machine learning to address healthcare challenges. Key areas include cancer genomics, federated learning, and automated medical imaging analysis. Education and affiliations: Krauthammer leads an interdisciplinary team supported by major funding agencies. His research spans bioinformatics, clinical decision support systems, and multimodal data integration. Notable projects include AI-assisted diagnosis in rheumatology and prime editing efficiency prediction. Recent work emphasizes longitudinal cfDNA analysis, drug interaction modeling, and personalized oncology. The lab collaborates across disciplines, with projects funded by Swiss and international grants. Students and postdocs work on topics like machine learning for radiology reports, longitudinal disease trajectories, and protein design. Key projects include the NTCIR-18 RadNLP challenge, prime editing prediction models (Nature Biotechnology 2024), and vision transformers for capillaroscopy analysis. The lab advocates for reproducible data science and ethical AI in healthcare.

Daniel Hershcovichمشاهده پروفایل

استادیار

• Natural Language Processing
• Cross-Cultural NLP
• Computational Linguistics
• +۷ مورد دیگر

Daniel Hershcovich is a Tenure-Track Assistant Professor at the Natural Language Processing section of the Department of Computer Science, University of Copenhagen. His research focuses on cross-cultural adaptation of language models, integrating human values into AI systems, and evaluating AI's real-world impact in domains like law, literature, and food culture. Research Themes Cultural value alignment in LLMs Multimodal models for accessibility Cross-cultural recipe and food knowledge Historical Scandinavian text analysis Ethical AI and bias mitigation Scientific Recognition SAC Highlight Award (ACL 2025) Outstanding Paper Award (ACL 2017) Advising & Grants : Mentions collaborations on multiple EMNLP/ACL/CoNLL papers. Leads Independent Research Fund Denmark project ALIKE (2025-2027) and contributes to Innovation Fund Denmark's XHAILe (2025-2028). Co-organized SemEval 2019 and CoNLL 2019/2020 shared tasks. Labs & Teams : Leads the CoAStaL research group. Collaborates with teams at IBM Research Haifa, University of Manchester, and Wuhan University of Science and Technology.

• Natural Language Processing
• Question Answering
• Human-AI Collaboration
• +۷ مورد دیگر

Jordan Boyd-Graber is a Professor in the Department of Computer Science at the University of Maryland's College of Computer, Mathematical, and Natural Sciences. He serves as a leading researcher in Natural Language Processing with significant contributions across multiple NLP subfields. His work bridges theoretical advances with practical applications requiring human-AI collaboration. His research interests span Natural Language Processing , Question Answering systems , Human-AI collaboration , Machine Translation , and Topic Modeling . He focuses on developing systems that work effectively with humans rather than replacing them, emphasizing interpretability and user-centered design. His work often involves creating evaluation frameworks that better capture real-world utility rather than just technical metrics. His publication record shows consistent leadership in the field, with numerous papers at top venues including ACL, EMNLP, and NAACL. Recent work (2023-2024) demonstrates strong engagement with LLMs, human evaluation methodologies, and practical applications in health and translation domains. His research often involves student collaborators, indicating active mentorship. ACL Fellow (2021) Program Chair for ACL 2023 Organizer of prompt hacking competition Leader in human-centered NLP evaluation Boyd-Graber has secured substantial funding for his research, particularly in projects involving human-AI collaboration and question answering systems. His work often involves interdisciplinary teams spanning computer science, linguistics, and domain-specific applications. He has mentored numerous graduate students who have gone on to successful careers in academia and industry. He leads research groups focused on developing interpretable NLP systems that work effectively with humans, particularly in high-stakes domains like healthcare and education. His lab frequently develops novel evaluation methodologies that better capture real-world utility rather than just technical metrics.

• Digital Humanities
• Art History
• Urban History
• +۱۱ مورد دیگر

Isabella Di Lenardo is a Lecturer and Scientist at the Digital Humanities Institute (DHI) at École Polytechnique Fédérale de Lausanne (EPFL), where she also serves as the coordinator of the EPFL Time Machine Unit and the European Local Time Machines. She holds affiliations across multiple departments, including DHI-GE, SAR-ENS, SHS-ENS, and EDDH-ENS, reflecting her interdisciplinary role in teaching and research. Her educational background includes a PhD in Theories and Art History, with postdoctoral and faculty experience at institutions such as INHA (Paris), EPFL, and IUAV (Venice). Her research spans Digital Humanities, Art History, Urban History, and GIS , with a focus on digital urban reconstruction, historical cadastres, and AI applications in cultural heritage. She employs advanced computational methods including machine learning, 4D modeling, and semantic segmentation to analyze historical maps, cadastral records, and art archives. Her work bridges humanities scholarship with computer science, particularly in reconstructing urban evolution and analyzing visual patterns. The recent publications reveal a consistent trend in AI-powered historical data analysis , especially in processing non-standardized historical documents, reconstructing urban spaces, and developing open-source tools for digital heritage. Her work frequently involves large-scale datasets from Venice, Lausanne, Paris, and Jerusalem, demonstrating a transnational and interdisciplinary approach. She has contributed to significant collaborative projects such as the Venice Time Machine , Parcels of Venice , and Time Machine Organization , often acting as a principal investigator or project leader. Her role involves coordinating diverse teams of researchers, engineers, and cultural institutions. Scientific contributions include: Development of the Morphograph tool for visual pattern recognition in art archives Automatic vectorization and analysis of Napoleonic cadastres Creation of 4D models for historical cities AI-driven text and pattern extraction from historical maps Building discovery engines for digital art history She actively teaches ex cathedra courses in Digital Urban History and Art History at EPFL and internationally. Her work in grants and projects emphasizes open data, reproducibility, and interdisciplinary collaboration. She has led research funded by organizations supporting digital heritage innovation. She is a key member of the Digital Humanities Laboratory at EPFL and the Time Machine Organization , where she fosters collaboration between computer scientists, historians, and cultural institutions. Her work in the Replica Project and ARCHiVe center highlights her leadership in digitizing and making accessible large art historical archives.

Elisa Pellegrinoمشاهده پروفایل

پژوهشگر ارشد

• Phonetics
• Computational Linguistics
• Prosody
• +۷ مورد دیگر

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

• German as a Foreign Language
• German as a Second Language
• Plurilingual Literacy
• +۳ مورد دیگر

Triantafyllia Liana Konstantinidou serves as a Professor of German as a Foreign and Second Language at the Institute of Language Competence , Zurich University of Applied Sciences (ZHAW). Her work bridges academic research with practical applications in vocational education contexts. Current Affiliation: Director of European Literacy Network (2023-2028) Advisor to Internationaler Verband für Deutschlehrer:innen (IDV) since 2021 Her research focuses on vocational literacy , plurilingual competence development , and technology-enhanced language learning . Key projects include: Digital Literacy Skills (completed 2024) Literacy for Entrepreneurship (completed 2024) Integrated Reading-Writing Support (completed 2024) Recent publications analyze writing competence profiles in vocational contexts and explore scenario-based literacy education across diverse professional fields. Her work demonstrates strong connections between corpus linguistics , language testing , and educational policy .

Simon Clematideمشاهده پروفایل

مدرس

• Natural Language Processing
• Computational Linguistics
• Text Mining
• +۷ مورد دیگر

Simon Clematide is an Academic Associate at the Department of Computational Linguistics within the Faculty of Arts and Social Sciences at the University of Zurich, where he has been actively engaged in research and teaching since the early 2000s. His work spans computational linguistics, natural language processing, and text mining with a particular focus on historical document processing, multilingual applications, and practical implementations of machine learning techniques. Dr. Clematide's research interests encompass Natural Language Processing , Computational Linguistics , Text Mining , Machine Learning , Sentiment Analysis , Named Entity Recognition , and Historical Document Processing . His interdisciplinary approach bridges computer science with humanities applications, particularly in analyzing historical newspapers and multilingual corpora. His work demonstrates strong expertise in developing practical NLP systems that address real-world challenges in document analysis and language processing. Over the past five years, his publication record reveals a consistent focus on historical text processing, multilingual NLP applications, and shared task competitions. His research shows particular strength in named entity recognition for historical documents (CLEF HIPE shared tasks), grapheme-to-phoneme conversion (SIGMORPHON shared tasks), and OCR post-processing for historical newspapers. The interdisciplinary nature of his work is evident in collaborations spanning computer science, linguistics, history, and geography. Dr. Clematide has been instrumental in organizing and participating in numerous shared tasks including CLEF-HIPE (2020-2022), SIGMORPHON (2017-2021), and CoNLL-SIGMORPHON (2017-2020), where his team achieved multiple first and second places. His teaching portfolio includes courses on Text Mining, Machine Learning for NLP, Deep Learning in Language Technology, and Sentiment Analysis, demonstrating his commitment to educating the next generation of computational linguists. He has led or participated in diverse research projects including NFP 77, impresso, Citizen Linguistics Projects (tonaccent.ch, dindialaekt.ch), SPARCLING, KTI project with Eurospider, and biomedical text mining initiatives (MANTRA, SASEBio). His interdisciplinary work extends to collaborations with material scientists and social scientists on concept extraction and text zoning applications. Dr. Clematide has contributed to the development of several computational tools and resources, including finite-state morphology systems for Rumansh Grishun, Standard German, and Swiss German, as well as the CLab web-based virtual laboratory for computational linguistics. His work on crowdsourcing OCR ground truth for heritage corpora demonstrates practical solutions to real-world digitization challenges.

Joshua Weidlichمشاهده پروفایل

استادیار

• Educational Technology
• Learning Analytics
• Online Learning
• +۳ مورد دیگر

Prof. Dr. Joshua Weidlich is an Assistant Professor for Digital Higher Education at the University of Zurich (UZH), collaborating with Zurich University of Education as a DIZH Bridge Professor. His academic background includes a PhD in Educational Technology from FernUniversität in Hagen, where he earned the Early Career Award for Outstanding Dissertation. He holds a BA in Educational Science from TU Chemnitz and an MA in E-Learning and Media Education from the University of Education Heidelberg. His research focuses on leveraging digital technologies to enhance higher education, emphasizing personalized feedback, feedback literacy, learning analytics, AI-driven environments, and social presence in online learning. Prior to UZH, he worked at the DIPF | Leibniz Institute for Research and Information in Education, contributing to both research and teaching. Key achievements include pioneering work on social presence in distance learning, validated through frameworks like the Social Presence Measure, and exploring gender dynamics in STEM education. His recent studies address the impact of AI tools like ChatGPT in education and the role of feedback literacy in student success. Prof. Weidlich’s publications span educational technology, gender studies, and learning analytics, reflecting his commitment to advancing digital pedagogy and equitable educational practices. He actively contributes to the Digital Society Initiative (DSI) at UZH, fostering interdisciplinary dialogue on societal impacts of technology.

• Applied Linguistics
• Media Linguistics
• Text Production Research
• +۴ مورد دیگر

Daniel Perrin is Professor and Dean at the Zurich University of Applied Sciences (ZHAW) School of Applied Linguistics . His research focuses on text linguistics , methodology of applied linguistics , media communication , and transdisciplinary research . He has contributed extensively to understanding text production in journalistic and professional contexts, emphasizing the interplay between language, media, and sociocultural practices. Network Memberships: AILA (International Association of Applied Linguistics), Swiss Academy of Humanities and Social Sciences (SAHSS), Swiss Association for Applied Linguistics, SACM (Swiss Academy of Communication and Media Research) ORCID ID: 0000-0001-5477-849X Perrin's work spans media linguistics , financial communication , multilingualism , and narrative structures in journalism . His recent articles explore phenomena like serendipity in newswriting and transdisciplinary approaches to text production . He has led projects such as " Modeling Writing Phases " and " Professionalizing the Swissnex Website " under Swiss National Science Foundation grants. Key Publications: Authored/edited books like Medienlinguistik (3rd ed.), Doing Applied Linguistics , and The Routledge Handbook of Language and Media . His methodology often combines progression analysis , ethnographic observation , and multimodal studies to investigate real-world writing and translation processes.

• Human-Machine Interaction
• Usability Testing
• User Experience (UX)
• +۵ مورد دیگر

Jürgen Sauer is a Full Professor at the University of Fribourg , affiliated with the Department of Psychology under the Faculty of Letters and Human Sciences . With over 120 publications, his research focuses on Human-Machine Interaction , Usability Testing , User Experience (UX) , and Automation Design , particularly in high-stakes environments like X-ray baggage screening and spaceflight simulations . Email: juergen.sauer@unifr.ch Phone: +41 26 300 7622 Address: RM 01 bu. C-1.117, Rue PA de Faucigny 2, 1700 Fribourg Orcid: 0000-0003-2105-1694 His research projects, funded by the Swiss National Science Foundation (FNS), include: Improving work design for airport security officers (2019-2024): Developed pictorial scales for measuring psychological constructs in security environments. Social stress and support in hybrid teams (2018-2023): Investigated machine-induced social stressors and mitigation through social support. Automation in visual inspection tasks (2014-2018): Examined adaptable automation for baggage screening and system reliability effects. Usability testing effectiveness (2012-2016): Analyzed cultural background impacts and non-usability product features influencing test outcomes. Key contributions include the Luggage Inspection Simulation (LIS) environment for modeling work environments and the development of pictorial usability scales for multilingual applicability. His work bridges ergonomics , human factors , and applied psychology , with notable collaborations with researchers like Adrian Schwaninger and Andreas Sonderegger . His recent publications (2025-2014) analyze: Human-machine performance under false alarms and miscues Social stressor dynamics in hybrid teams Usability scale animation effects Phubbing behavior in professional contexts Accessible website design for non-disabled users

• Artificial Intelligence
• Natural Language Processing
• AI in Education
• +۳ مورد دیگر

Anna Sotnikova is a Lecturer at the École polytechnique fédérale de Lausanne (EPFL), affiliated with the Natural Language Processing (NLP) Group within the School of Computer and Communication Sciences (IC). She holds a dual role as a Scientific Collaborator (Collaboratrice scientifique) in NLP and a Course Lecturer (Chargée de cours) in the EDIC-ENS unit. Her research focuses on AI ethics, multilingual language models, and the societal implications of NLP systems, particularly in education and bias mitigation. Her work bridges technical advancements in AI with their real-world applications, addressing challenges such as bias propagation in multilingual models and AI's impact on higher education. Recent projects include evaluating ChatGPT's potential to disrupt engineering education and analyzing how LLMs perpetuate human stereotypes across languages. Anna collaborates with institutions like Cornell University and MIT, and her research is supported by funders such as the Swiss National Science Foundation. She teaches advanced courses like Topics in Natural Language Processing and maintains an active presence in EPFL's interdisciplinary research ecosystem.

Grzegorz Chrupałaمشاهده پروفایل

دانشیار

• Computational Linguistics
• Artificial Intelligence
• Cognitive Science
• +۳ مورد دیگر

Grzegorz Chrupała is an Associate Professor at the Department of Cognitive Science and Artificial Intelligence , Tilburg University, where he leads research in computational approaches to multimodal communication. Previously, he was a postdoctoral researcher at Saarland University's Spoken Language Systems group and earned his PhD from Dublin City University's School of Computing. His research bridges biological and artificial computation , focusing on enabling machines to learn language from multimodal data (speech, gestures, visual-auditory stimuli) as children do naturally. This involves developing and interpreting deep learning architectures, analyzing emergent representations, and advancing speech technology for under-resourced languages. Key themes include Visually grounded speech modeling Feature attribution and model interpretability Human-inspired learning paradigms BlackboxNLP workshop leadership His recent publications examine speech model reliability , lexical tone encoding , and contextual dependencies in NLP systems. He mentors a team of PhD candidates and alumni working on topics like user-centric interpretability, bioacoustics, and disentangled speech representations. He also serves on the board of the Dutch Open Speech Technology Foundation, chairs Interspeech 2025 tutorials, and contributes as an Action Editor for TACL.

Anna Rogersمشاهده پروفایل

دانشیار

• Natural Language Processing (NLP)
• Large Language Models (LLMs)
• Model Interpretability
• +۴ مورد دیگر

Anna Rogers is a tenured Associate Professor at the IT University of Copenhagen , where she leads research in the Data Science Section . Her work bridges NLP , large language models (LLMs) , and sociotechnical impacts of AI , with a focus on interpretability, robustness, and ethical frameworks. She serves as co-editor-in-chief of ACL Rolling Review and is a Villum Young Investigator and ELLIS fellow . Education: PhD in Computational Linguistics (University of Tokyo), Postdocs in Machine Learning for NLP (University of Massachusetts, Lowell) and Social Data Science (University of Copenhagen) Her research explores how LLMs can be designed for transparency and fairness, with recent grants like the Villum Synergy and Inge Lehmann supporting interdisciplinary collaborations. She organizes workshops (e.g., Dagstuhl seminar 24052) and co-leads projects like CAISA (National Centre for Artificial Intelligence in Society). Key themes include: Interpretability in NLP Robustness against synthetic content Data governance and ethical use Peer review systems and academic publishing Her lab has hosted researchers such as Max Müller-Eberstein (funded by DFF) and is actively recruiting for projects on generalization benchmarks and data attribution. She emphasizes collaboration across academia, industry, and policy, shaping Denmark's data science future through roles like the Danish Data Science Academy committee. Recent articles highlight trends in corpus analysis , temporal annotation , and LLM governance .

Natalia Umanskyمشاهده پروفایل

پژوهشگر ارشد

• Computational Social Science
• Political Communication
• International Relations
• +۴ مورد دیگر

Natalia Umansky serves as a Postdoctoral Research Fellow in Political Science at the University of Zurich's Faculty of Arts and Social Sciences, affiliated with the Institute of Political Science and the Digital Democracy Lab within the Digital Society Initiative (DSI). Her institutional profile highlights interdisciplinary engagement across DSI's Cybersecurity, Democracy, and Digital Humanities communities. Academic Background: PhD in Politics and International Relations (defence pending) from University College Dublin Iseult Honohan Scholar at University College Dublin's Connected_Politics Lab Her research pioneers Computational Social Science methodologies to analyze social media ecosystems, specializing in securitization theory applications to digital political communication. Current projects examine agenda-setting constellations in financial policy, hate speech detection frameworks, and TikTok's impact on youth political engagement. Methodologically, she integrates network analysis, natural language processing, and comparative case studies to investigate how security narratives propagate across digital platforms and influence democratic processes. Publication trends reveal a strategic evolution from foundational work on terrorism-migration linkages (2016) toward cutting-edge examinations of AI moderation systems (2024) and generative AI's role in security discourse (2025). Her scholarship consistently bridges political theory, data science, and platform governance with emphasis on empirical validation of computational models. Scientific Recognition: Iseult Honohan Scholar (University College Dublin) As a core member of Zurich's Digital Democracy Lab, Umansky contributes to high-impact collaborative projects analyzing digital threats to electoral integrity and developing transparency standards for algorithmic governance. Her work positions her at the forefront of computational political science, with emerging leadership in establishing methodological best practices for social media analysis in security contexts.