Daniel Hershcovich is a Tenure Track Assistant Professor at the Department of Computer Science (Faculty of Science, University of Copenhagen) specializing in Natural Language Processing and Machine Learning . His research focuses on cross-cultural adaptation of language models, integrating human values into AI, and analyzing food-related cultural narratives for sustainable diets. Education: Ph.D. in Computational Neuroscience from Hebrew University of Jerusalem B.Sc. in Mathematics and Computer Science from Open University of Israel Recent publications highlight his work on multimodal models (haptic captioning, visual assistants for the blind), historical text analysis (Danish/Norwegian literature, euphemism detection), and cross-cultural NLP (recipe adaptation, cultural value alignment, climate awareness). His projects frequently combine AI ethics with domain-specific applications like food studies, historical linguistics, and accessibility research. Key collaborative networks include institutions in Denmark, Israel, and international partnerships through conferences like ACL, EMNLP, and workshops on cross-cultural NLP. The NLP section at DIKU serves as his primary affiliation for these efforts.
Véronique Hoste is Senior Full Professor of Computational Linguistics at Ghent University's Faculty of Arts and Philosophy, where she serves as Department Head of Translation, Interpreting and Communication and Director of the LT3 language technology research team. She also holds the position of Research Director for the Faculty of Arts and Philosophy. Her educational background includes a PhD in Computational Linguistics from the University of Antwerp (2005) focused on optimization in machine learning for coreference resolution. Key research areas encompass machine learning for natural language processing, semantic and discourse modeling, including specialized work in event detection, entity/event coreference resolution, irony detection, and emotion analysis. Hoste's publication trends reveal strong interdisciplinary focus, with recent work bridging NLP with crisis communication, digital humanities, and ethical AI. Her team develops high-quality datasets (e.g., EmoTwiCS for emotion trajectories) and collaborates extensively with commercial partners on projects like SentEMO for aspect-based sentiment analysis. Current research emphasizes multimodal emotion analysis, fuzzy rough set methods for sentiment detection, and cross-document event coreference. Elected member of the Royal Flemish Academy of Belgium for Science and the Arts (KVAB) Francqui Chair appointment by Université Libre de Bruxelles (2023-2024) Co-founded LT3 spin-off AlfaSent (2024) for customer feedback analysis Authored first Dutch-language book on NLP: "Taaltechnologie ontrafeld" She actively supervises multiple PhD students on projects including Common-sense knowledge in irony detection (Common-sense), cross-document event coreference (Encore), and empathy modeling in conversational agents (FlandersAI). Her team secures funding through interdisciplinary collaborations like NewsDNA for news recommendation and METRICS for emotion trajectory analysis. Hoste also engages in public outreach through the "AI at school" initiative and advises on language technology integration in high school curricula. The LT3 laboratory under her leadership maintains strong industry partnerships and develops practical NLP tools including EmotioNL and Automatic Term Extraction systems, while advancing core research through projects like CLARIAH-VL for data curation.
Patrick Wu is a Professor in the Department of Computer Science at American University, with additional affiliations as Faculty Fellow at the Center for Data Science and Faculty Affiliate at the Center for Security, Innovation, and New Technology. He holds a PhD in Political Science and Scientific Computing from the University of Michigan, an MA in Statistics from Michigan, and a BA in Political Science and Statistics from the University of Chicago. His research develops AI/ML and natural language processing approaches for computational social science, focusing on: Political elite and non-elite ideology measurement Affective polarization on social media platforms Detection of hateful/abusive speech and memes Application of large language models to political science research Recent work explores innovative methods for political attitude measurement using LLMs, in-context learning techniques for social media analysis, and frameworks for multimodal representation learning. His publications demonstrate consistent innovation in applying NLP and machine learning to political discourse analysis, with emerging focus on generative AI's impact on political science education and methodology. Wu teaches courses including Object-Oriented Programming and topics in Natural Language Processing/Text as Data.
David H Laidlaw is a Professor of Computer Science at Brown University, specializing in virtual reality, scientific visualization, and medical imaging. His work spans interdisciplinary applications in neuroscience, biomedical research, and educational tools. Brown University Affiliation Department of Computer Science His research focuses on: Immersive visualization for complex data analysis Diffusion MRI and neuroimaging techniques Human-computer interaction in virtual environments 3D interaction methods for scientific exploration Collaborative visualization tools for multidisciplinary teams Recent trends in his publications highlight advancements in: Graph neural networks for biomedical data Memory-efficient segmentation algorithms Perceptual studies in VR environments Annotation and analysis of placental vasculature Technological innovations in foot dynamics research He teaches courses in virtual reality design and scientific visualization, including: CSCI 1370 - Virtual Reality Design for Science CSCI 1951S - Virtual Reality Software Review CSCI 1951T - Surveying VR Data Visualization Software CSCI 2370 - Interdisciplinary Scientific Visualization
Prof. Jacco van Ossenbruggen is a Full Professor in Intelligent Information Systems at Vrije Universiteit Amsterdam (VU), affiliated with the Network Institute. He serves on the Management Board of ODISSEI, a national research infrastructure for social sciences and economics. His academic background includes a PhD in Computer Science (2001) from VU’s Faculty of Science, focusing on hypermedia processing. Research Interests: His work centers on cultural AI, FAIR data principles, ontology engineering, and semantic web technologies. Key areas include inclusive cultural heritage metadata, bias mitigation in AI systems, and knowledge discovery via linked data. Recent projects involve leveraging large language models (LLMs) for metadata enrichment and ontology construction. Key Contributions: He leads initiatives like the Cultural AI Lab, exploring AI applications for cultural heritage. His research bridges technical innovations (e.g., semantic integration of restricted-access data) with societal impacts (e.g., ethical AI frameworks for public-sector applications). Developed frameworks for evaluating entity alignment in knowledge graphs Pioneered FAIR-aligned data management plans for scientific communities Designed tools like Alter Heritage for collaborative metadata curation Grants & Projects: Principal Investigator of the ODISSEI Portal project (2020–2024), advancing open data infrastructures. Active in funding initiatives promoting reproducible research and ethical data practices. Labs/Teams: Cultural AI Lab at VU, focusing on AI-driven solutions for cultural heritage preservation and accessibility.
Ziming Zhang is an Assistant Professor in the Department of Electrical and Computer Engineering at Worcester Polytechnic Institute (WPI) , with additional affiliations in Data Science and Robotics Engineering. He previously held research roles at Mitsubishi Electric Research Laboratories (MERL) and Boston University. PhD in Computing (2013) from Oxford Brookes University , UK MS in Computing Science (2010) from Simon Fraser University , CA BS in Computer Science and Technology (2005) from Northeastern University , China Research interests span computer vision , machine learning , and their applications in point cloud processing , medical imaging , autonomous driving , and IoT . He leads the Vision, Intelligence, and System Laboratory (VISLab) at WPI. Recent publications focus on 3D reconstruction , hyperbolic learning , and robust classifiers . Awards include the R&D100 Award 2018 and NSF funding for data-efficient deep learning. PhD Students: Yecheng Lyu (co-supervised), Guojun Wu (co-supervised), Hangrui Zhang, Xuechu Yu Master's Students: Yun Yue, Yuping Shao Visiting Scholars: Fangzhou Lin His lab partners with industry and academic institutions, focusing on autonomous systems , robotics , and scientific imaging projects.
Professor Leif Isaksen is a leading scholar in Digital Humanities and Ancient History at the University of Exeter, holding a chair in the Department of Classics, Ancient History, Religion and Theology. He also serves as Theme Lead for Humanities, Heritage and the Creative Industries at the Institute for Data Science and Artificial Intelligence (IDSAI). With a background in archaeology and a focus on Spatial-temporal representation in humanities Intelligent systems for historical data Digital skills development in arts and humanities , his work bridges classical scholarship and computational innovation. His research includes Pelagios Network projects like Recogito and Peripleo , which revolutionized semantic annotation and geospatial analysis of ancient texts. He directed the Cluny Hill Dig and co-founded initiatives like Hot Source! and DISKAH to democratize digital humanities skills. Key professional roles include: Executive Board Chair of ADHO (2019-2021) Co-Director of Web Science CDT at Southampton Active leadership in CAA, EADH, and ISHMap Scientific honors include fellowships at the Society of Antiquaries of Scotland and former affiliations with the Alan Turing Institute and Society of Antiquaries of London . His work aligns with UN Sustainable Development Goals 9: Industry, Innovation and Infrastructure and 4: Quality Education .
Professor Marie Roch is a distinguished faculty member in the Department of Computer Science at San Diego State University within the College of Sciences . Her groundbreaking research bridges Bioacoustics and Machine Learning , focusing on advanced algorithms for automated detection, classification, and analysis of marine mammal vocalizations using passive acoustic monitoring. Core research in marine bioacoustic signal processing and deep learning applications for echolocation click detection Published extensively in Journal of the Acoustical Society of America , Biological Reviews , and IEEE Transactions Developed deep learning frameworks for whale whistle extraction without human annotation Created open-source tools like Silbido Profundo for automated marine mammal call analysis Marie's work has been supported by over $3 million in grants from the DOD Office of Naval Research , Bureau of Ocean Energy Management , and Human Frontier Science Program . She actively mentors graduate students and serves on numerous thesis committees, with recent advisees working on deep learning for baleen whale calls and terrestrial animal recognition . Her Marine Acoustic Research Lab (MAR Lab) leads in developing the Tethys metadata workbench for ocean acoustic data management.
Alexander Hollberg is an Associate Professor in the Division of Building Technology at Chalmers University of Technology, within the School of Architecture and Civil Engineering. His academic role focuses on Computational Sustainable Design, emphasizing the development of digital tools for sustainable building and urban design. He holds a PhD in Parametric Life Cycle Assessment (2016) from Bauhaus University Weimar, an MSc in Architectural Engineering (2011), and a BSc in Civil Engineering (2008) from Technical University of Munich (TUM). His research interests include Sustainable Design Optimization, Stakeholder Interaction, Artificial Intelligence, and Life Cycle Assessment (LCA). He co-founded CAALA, a software and consulting startup in Munich, Germany, advancing tools for real-time environmental performance evaluation in early design stages. Recent work includes studies on digital twins for urban planning, robust renovation strategies, and AI-driven facade optimization. Hollberg was promoted to Docent (Associate Professor) in Computational Sustainable Design in 2022, focusing on bridging computational methods with sustainable environmental transitions. His collaborative projects address tool development for stakeholder engagement, BIM integration, and circular economy frameworks in construction. Key Projects: Development of Bombyx and Twinable tools for real-time LCA and urban simulation Leading the Nordic Build-LCA PhD forum and BIM-based LCSA applications Contributions to IEA EBC Annex 72 guidelines on life cycle environmental impacts Education Background: PhD in Parametric Life Cycle Assessment, Bauhaus University Weimar, 2016 MSc in Architectural Engineering, Bauhaus University Weimar, 2011 BSc in Civil Engineering, Technical University of Munich, 2008 His research outputs prioritize early design-stage decision support through parametric modeling and AI, with a focus on carbon neutrality and material circularity in construction.
Dr. Silvana Deilen is a Researcher at the Institute for Translation Studies & Technical Communication within the Faculty of Language and Information Sciences at the University of Hildesheim. She joined the university in 2023 after working as a Research Associate at Johannes Gutenberg University Mainz from 2018-2024. Her primary research focus centers on accessible communication, particularly in the areas of Easy Language and Plain Language translation, with special emphasis on cognitive aspects of translation processes and AI-assisted translation technologies. Dr. Deilen earned her B.A. in Multilingual Communication from Cologne University of Applied Sciences (2012-2015), followed by an M.A. in Specialized Translation from the same institution (2015-2018). She completed her doctoral studies (Dr. phil.) in Translation Studies at Johannes Gutenberg University Mainz (2018-2021) with summa cum laude distinction, supervised by Prof. Dr. Silvia Hansen-Schirra and Prof. Dr. Arne Nagels. Her research interests span multiple interconnected domains within translation and communication accessibility. A significant portion of her work examines the cognitive processing of compound words in Easy Language, utilizing eye-tracking methodologies to investigate how visual segmentation affects reading behavior and cognitive load. She has pioneered research on AI-assisted translation for health communication, particularly focusing on how large language models can support the creation of accessible health information. Her work bridges theoretical translation studies with practical applications in healthcare, government communication, and digital accessibility. Dr. Deilen's publication record reveals a clear trajectory toward increasingly sophisticated integration of technology and accessibility. Her recent work shows strong emphasis on evaluating AI systems like ChatGPT for translation tasks, developing editorial workflows for AI-assisted translation of health information, and investigating cognitive aspects of compound translation. The interdisciplinary nature of her research connects linguistics, cognitive science, health communication, and artificial intelligence, demonstrating how translation studies can address real-world accessibility challenges. 2014 & 2016: PROMOS Scholarships 2018-2021: Doctoral Scholarship from Gutenberg Young Researchers College 2020: Best Student Paper Award at Swiss Conference on Barrier-free Communication 2023: Award for Outstanding Dissertation from Johannes Gutenberg University Mainz 2023: Multiple research grants from University of Hildesheim, Wort & Bild Verlag, and Niedersachsen Zukunftsdiskurse 2025: DAAD Postdoctoral Research Grant Dr. Deilen actively collaborates on significant research projects including the KI-GesKom project (AI-Supported Health Communication in Plain Language), which receives funding from the state of Niedersachsen. She works closely with Prof. Dr. Ekaterina Lapshinova-Koltunski, Prof. Dr. Christiane Maaß, and Sergio Hernández Garrido as part of the Research Center for Easy Language. Her work with the Apotheken Umschau demonstrates practical application of research, translating health information into accessible formats for people with communication limitations. Dr. Deilen also contributes to the academic community as a program chair and scientific committee member for international conferences including UCCTS 2025 and Translation in Transition 2024.
Serge Belongie is a Professor at the Department of Computer Science (DIKU) at the University of Copenhagen, where he holds dual affiliations with the Pioneer AI research section and the Image Analysis, Computational Modelling, and Geometry section. His academic position places him at the forefront of interdisciplinary research connecting computer vision with language models, geospatial analysis, and cultural understanding. Professor Belongie's research program encompasses several critical domains in modern artificial intelligence: Advanced computer vision and image analysis techniques Vision-language model integration and multimodal systems 3D point cloud processing and semantic segmentation Geospatial representation learning for environmental applications Fine-grained object recognition and detection Cultural context understanding in AI systems His recent publication record reveals a sophisticated trajectory toward developing precise control mechanisms for vision-language models, with applications spanning forensic analysis, cultural heritage preservation, and social media understanding. The research demonstrates increasing sophistication in handling cultural context and enabling fine-grained manipulation of visual content through natural language interfaces. Professor Belongie maintains an active research group producing significant scholarly output, with over 280 research publications documented in his academic profile. His work is supported by research funding that enables cutting-edge exploration in multimodal AI systems with practical societal impact. He plays a key role in the Pioneer AI center at the University of Copenhagen, which focuses on advancing artificial intelligence through interdisciplinary collaboration and innovative research approaches that bridge theoretical computer science with real-world applications.
Professor Tony McEnery is a Distinguished Professor of English Language and Linguistics at Lancaster University, where he has held various senior roles, including Dean of the Faculty of Arts and Social Sciences (2008–2014) and Head of the Department of Linguistics and English Language (2000–2005). His research focuses on corpus linguistics, applied linguistics, and sociolinguistics, with notable contributions to corpus-based analyses of historical medical discourse, media representation of Islam, and swearing in English. He has led major research projects, including the ESRC Centre for Corpus Approaches to Social Science (CASS) and the Trinity Lancaster Corpus. McEnery has secured over £10 million in research funding and holds visiting professorships at institutions worldwide, including Zhejiang University of Media and Communications and Shanghai International Studies University. He is a Fellow of the Academy of Social Sciences, Trinity College London, and the Royal Society for the Arts. Education: No explicit details provided, but his career trajectory implies advanced degrees in linguistics. Research Interests: Corpus linguistics applications in theoretical and applied linguistics, historical language analysis, and language as a socially situated phenomenon. McEnery’s work extends to interdisciplinary collaborations, including projects with the Environment Agency and the Home Office. His teaching includes summer schools in corpus linguistics and a Futurelearn MOOC, reaching tens of thousands globally. He advises on research funding for international bodies like the Irish Research Council and the Qatar National Research Foundation. Grants and Awards: Queen’s Anniversary Prize (2015), Changjiang Chair (2021), and multiple fellowships. Current grants include the Lancaster-Northern Arizona corpus and the Trinity Lancaster Corpus projects. Advising and Labs: Supervises postgraduate students in linguistics and collaborates with teams like the ESRC CASS Centre and UCREL (University Centre for Computer Corpus Research on Language).
Olga Vitek is a Professor at Northeastern University's Khoury College of Computer Sciences, with affiliated faculty status in the Department of Chemistry and Chemical Biology. Her research bridges statistical science and machine learning with mass spectrometry-based proteomics and systems biology, focusing on developing open-source software tools like MSstats and Cardinal for quantitative proteomic analyses and imaging. Education: PhD in Statistics (Purdue University), Postdoc at the Ruedi Aebersold Lab (Institute for Systems Biology) Leadership: Director of the Barnett Institute for Chemical and Biological Analysis Her work emphasizes: Statistical experimental design Signal detection in complex mass spectrometry data Causal inference in biomolecular networks Reproducible computational infrastructure Recent publications highlight advancements in quantitative proteomics , mass spectrometry imaging , and causal modeling , with applications spanning cancer research, immunology, and clinical diagnostics. Notable trends include deep learning integration for image analysis and open-source tool development for scalable, transparent workflows. Scientific accolades: Elected Fellow of the American Statistical Association 2021 Gilbert S. Omenn Computational Proteomics Award NSF CAREER award Chan-Zuckerberg Essential Open-source Software award Senior Member, International Society for Computational Biology
Ana Filipa Sequeira is a researcher affiliated with INESC TEC, Porto, Portugal, and the University of Porto. Her work spans biometrics, fairness in AI, and explainable artificial intelligence. She has contributed to advancements in face recognition, synthetic data applications, and bias mitigation through techniques like knowledge distillation and model compression. Institution: INESC TEC (Porto, Portugal) Research Themes: Face recognition, fairness, explainability, synthetic data, biometric security Her recent publications focus on addressing demographic biases in face recognition systems, developing privacy-preserving explainable methods, and evaluating synthetic data's impact. She collaborates extensively with researchers like Pedro C. Neto, Jaime S. Cardoso, and Naser Damer. Sequeira has participated in organizing and analyzing competitions such as FRCSyn, BIOSIG, and SYN-MAD, emphasizing robust evaluation frameworks. Her work intersects technical innovation with ethical considerations, advocating for responsible AI applications in biometrics.
Christian Wolff is a University Professor and Chair of Media Informatics at the Institute for Information and Media, Language and Culture at the University of Regensburg. Since April 2022, he has served as the founding Dean of the Faculty of Computer Science and Data Science, while maintaining secondary membership in the Faculty of Languages, Literature and Cultural Studies (SLK). His academic career spans over three decades with significant contributions to multiple disciplines at the intersection of computer science and humanities. Wolff's research interests center around multimedia and multimodal information systems, electronic publishing, and text technology, particularly text mining. His work bridges computer science with digital humanities, legal informatics, and social media analysis. Recent publications demonstrate a strong focus on large language models, sentiment analysis applications across various domains, legal technology innovations, and virtual reality research for cognitive studies. His interdisciplinary approach has produced significant contributions in both technical and humanities domains. His recent publication trends reveal a strategic shift toward applied AI research, particularly in legal technology (LegalTech), social media analysis, and sentiment analysis using large language models. The publications show increasing collaboration across disciplines, connecting computer science with law, political science, literature, and psychology. His work on the digital basis document for legal proceedings represents a major practical application of his research in the German justice system. East Bavarian Cultural Prize Doctoral Award of the University of Regensburg Wolff has led numerous interdisciplinary research projects connecting computer science with humanities and legal studies. His leadership extends to institutional roles including Dean of Research, Vice Dean, and Dean of Faculty positions. He has been instrumental in establishing the new Faculty of Computer Science and Data Science at the University of Regensburg, demonstrating significant impact on institutional development and research infrastructure. Wolff directs research initiatives focused on text technology, digital humanities, and legal informatics. His work with the INDIGO - Internet and Digitization Eastern Bavaria initiative and the TRIO project demonstrates commitment to regional technology transfer and innovation. The interdisciplinary nature of his research groups connects computer scientists with legal scholars, linguists, and social scientists to address complex digital transformation challenges.
