Paweł Garbacz is an Assistant Professor at the Department of Computer Science Fundamentals within the Faculty of Philosophy at the Catholic University of Lublin. His work bridges formal logic, ontology, and their applications in computer science and philosophy. He has authored two books: Sentence Logic - One or Many and Logic and Artifacts . Research Focus: Formal logic, computational ontologies, philosophy of technical artifacts, and semantic interoperability. Publications: His recent work explores identity criteria, temporal logic, and the intersection of metaphysics with artificial intelligence. Collaborations: Active in interdisciplinary projects connecting philosophy with computer science, including contributions to the FOIS (Formal Ontology in Information Systems) conference series.
Prof. Dr. Michael Kohlhase is a Professor in the Computer Science department at FAU Erlangen-Nürnberg and an Adjunct Associate Professor at Carnegie Mellon University . His work spans Computer Science and Formal Methods , focusing on Mathematical Knowledge Management (MKM), Knowledge Representation , and Natural Language Processing (NLP) in STEM domains. He leads the KWARC (Knowledge Adaptation and Reasoning for Content) research group, driving projects like OMDoc , sTeX , and MathHub . His research emphasizes modular logical foundations for mathematical knowledge and large-scale document corpora analysis, with applications in computer-supported education and semantic document systems . Collaborations include extended visits to institutions such as SRI International , University of Amsterdam , and University of Edinburgh . Key projects include developing scalable module systems for formal mathematics, theory graph models for knowledge integration, and mathematical search engines like MathWebSearch . His work bridges formal logic with practical software systems , ensuring interoperability across proof assistants , computer algebra systems , and educational tools .
Prof. Allan Hanbury is a full professor at TU Wien's Faculty of Informatics, leading the E-Commerce Research Area. His work focuses on domain-specific systems for information extraction and retrieval, with expertise in machine learning, natural language processing, and medical informatics. Key projects include developing tools like CRUISE-Screening for systematic literature reviews and VoMBaT for high-recall search evaluation. He coordinates initiatives in transparent automated content moderation and image mining for patents. His research bridges theoretical advancements with practical applications in healthcare, legal domains, and digital humanities. Publications span over 240 works, emphasizing reproducibility, explainability in AI, and cross-lingual systems. Active in organizing conferences like PatentSemTech and contributing to CLEF challenges. Advises on thesis topics involving hate speech detection, legal text analysis, and graph-based methods. Collaborates internationally on projects like CSMeD (clinical trial matching) and BRISE (legal corpus development). His work integrates NLP, IR, and data mining to address real-world challenges in information access and AI transparency.
Johannes Lutzeyer is an Assistant Professor in the Data Science and Mining Team at the Computer Science Department of École Polytechnique in France. His academic journey includes a PhD in Mathematics from Imperial College London (supervised by Prof. Andrew Walden) and a postdoctoral fellowship at École Polytechnique with Prof. Michalis Vazirgiannis. Current role: Assistant Professor in Computer Science at École Polytechnique PhD: Mathematics Department, Imperial College London Postdoc: École Polytechnique His research lies at the intersection of Graph Representation Learning , Statistics , and Computer Science , focusing on Graph Neural Networks (GNNs) and spectral properties of graph shift operator matrices. Recent work explores robustness against adversarial attacks, virtual nodes, and theoretical advancements in GNN expressiveness. The 15 most recent publications span Machine Learning , Deep Learning , and Network Science , with subfields including GNN Robustness , Hyper-Heuristics , Bioinformatics Applications , and Spectral Analysis . Collaborations include leading conferences like ICML, ICLR, and AAAI. Scientific Recognition LOG Top Reviewer Awards (2024) ICML Best Reviewer Awards (2024) NeurIPS Top Reviewer Awards (2023) Johannes actively engages in academic knowledge dissemination through keynote talks, including titles like “Understanding Virtual Nodes in Graph Neural Networks” (ICLR 2025) and “Methodological Advances in Graph Neural Networks” (Ericsson Research 2024). He also invites applications for PhD positions, reflecting his mentorship role.
Professor Nigel Collier is a leading academic in Natural Language Processing at the University of Cambridge, holding positions as Professor of Natural Language Processing, Fellow of the Alan Turing Institute, Co-Director of the Language Technology Lab, and Professorial Fellow of Murray Edwards College. He serves within the Faculty of Modern and Medieval Languages and Linguistics, Department of Theoretical and Applied Linguistics. His educational background includes a BSc in Computer Science from the University of Leeds (1992), MSc in Machine Translation (1994), and PhD in Computational Linguistics (1996) from the University of Manchester. His doctoral research focused on English-Japanese Lexical Transfer using Hopfield Neural Networks. Professor Collier's research spans core machine learning for NLP with particular expertise in Information Extraction, Text Mining, Social Media Analysis, Textual Inference, and Generation. His work integrates text with knowledge graphs, addresses fact verification challenges, and explores applications in biomedicine, epidemiology, and public health. Recent research focuses on LLM evaluations including adversarial attacks, policy violations, uncertainty modeling, and synthetic personalities. His publication record demonstrates consistent contributions to top-tier venues including ACL, EMNLP, and CoNLL, with research themes evolving from early biomedical text mining systems like BioCaster to contemporary large language model research. His work shows strong interdisciplinary connections between linguistics, computer science, and healthcare applications. Fellowship, Alan Turing Institute for data science and artificial intelligence (2017) EPSRC Experienced Research Fellow (2014) Marie Curie International Research Fellowship (2012) Japan Science and Technology Agency Research Fellowship (2008) Japan Society for the Promotion of Science Visiting Fellowship (2002) Toshiba Corporation Research Fellowship (1996) Professor Collier actively supervises PhD students and has mentored numerous researchers who now hold prominent positions at institutions including Google DeepMind, Cohere, Amazon Alexa, and academic posts worldwide. His research has been funded by major agencies including EPSRC, ESRC, MRC, EU FP7, and JST. He co-founded Trismik, a spinout company launched in May 2025, serving as Chief Scientist. The Language Technology Lab, which he co-directs, serves as the primary research hub for his team's work in computational linguistics and NLP. The lab maintains strong connections with the Alan Turing Institute and focuses on both theoretical advances and real-world applications of language technology.
Frederic Bechet is a researcher affiliated with Aix-Marseille Université, CNRS, and the LIF UMR 7279 laboratory. His work spans Natural Language Processing, Computational Linguistics, and Machine Learning, focusing on task structure analysis, factual knowledge robustness, and semantic-driven evaluation methodologies. Research Interests: Semantic Parsing, Question Answering, Text Summarization, and Multitask Learning. Recent Trends: Empirical studies on task inclusion via statistical deficiency, POS-driven specialization in Mixture-of-Experts models, distractor-based factual evaluation, and temporal knowledge decay in LLMs. His work on WikiFactDiff introduces a realistic framework for atomic fact updates in causal language models. Collaborations: Co-authored with experts in semantic annotation (Géraldine Damnati), QA systems (Alexis Nasr), and adversarial learning (Gabriel Marzinotto). Labs/Teams: Involved in the DECODA corpus for call-center analysis and the MEDIA corpus for dialogue understanding.
Wilker Ferreira Aziz is an Assistant Professor at the Institute for Logic, Language and Computation (ILLC) within the Faculty of Science at the University of Amsterdam, where he leads the Probabilistic Language Learning group. His primary affiliation is with the Natural Language Processing & Digital Humanities research unit. His research focuses on the intersection of machine learning, natural language processing, and probabilistic modeling. Key areas of interest include language modeling, machine translation, syntactic parsing, text classification, and question answering. He develops techniques for probabilistic inference, gradient estimation, and uncertainty quantification in neural language models. Dr. Aziz's recent publications demonstrate a strong focus on uncertainty in natural language generation, with multiple papers at top-tier conferences like EACL, EMNLP, and ICLR. His work examines how language models represent uncertainty compared to humans, calibration issues when humans disagree on labels, and methods for more robust decision-making in text generation. Best Paper Award at Coling 2020 He actively supervises both PhD and MSc students, with several ongoing PhD projects focusing on uncertainty in language models and neural text generation. Dr. Aziz serves on program committees for major ML and NLP conferences including ACL, EMNLP, NeurIPS, and ICLR, and has acted as area chair for several of these venues. His research has been supported through positions at the Mercury Machine Learning Lab, a collaboration between Booking.com, TU Delft, and the University of Amsterdam.
Dr. Andreas van Cranenburgh is an Assistant Professor of Digital Humanities and Information Sciences at the University of Groningen, Faculty of Arts. His work focuses on computational linguistics, statistical parsing, and computational literary studies, with expertise in information science, language & linguistics, and artificial intelligence. He leads projects on historical text normalization, authorship attribution, and narrative analysis frameworks like the GOLEM Triple Store. His research integrates NLP techniques with literary analysis, addressing topics such as gender bias in literary prizes, coreference resolution in Dutch literature, and psycholinguistic applications in speech disorder detection. He collaborates on corpora like OpenBoek and Dutch Novels 1800-2000, advancing digital humanities infrastructure. Notable contributions include developing Dutchcoref systems for literary text processing, exploring machine learning approaches to literary quality, and advancing graph-based narrative representations. His work bridges computational methods with humanistic inquiry, impacting both academic research and cultural heritage preservation.
Michael Strube is an Honorary Professor at the Department of Computational Linguistics at Heidelberg University and leads the Natural Language Processing (NLP) Group at HITS (Heidelberg Institute for Theoretical Studies) in Germany. He has been with HITS (previously EML Research and European Media Laboratory) since 2003 and became an Honorary Professor at Heidelberg University in 2010. He is also a Fellow of the Association for Computational Linguistics (2019). Dr. Strube received his PhD from the Computational Linguistics Department at the University of Freiburg in December 1996 under the supervision of Udo Hahn. Between 1997 and 1999, he was a postdoctoral fellow at the Institute for Research in Cognitive Science at the University of Pennsylvania, Philadelphia. Michael Strube's research focuses on semantics and discourse pragmatics, graph-based methods for text representation and analysis, extraction of world knowledge from Wikipedia for computational linguistics, and development of methods to synchronize multilingual content. His work spans coreference resolution, discourse processing, text summarization, entity linking, and natural language generation. He has made significant contributions to coherence modeling, anaphora resolution, and the application of geometric deep learning in NLP. His recent publications demonstrate strong trends in discourse processing, coreference resolution, and the application of geometric approaches to NLP problems. Strube has pioneered work in hyperbolic space for entity typing and graph embeddings, while maintaining his foundational work in discourse and coherence. His research bridges theoretical linguistics with practical NLP applications across multiple languages. Dr. Strube has received several prestigious awards, including: Fellow of the Association for Computational Linguistics (2019) Best Paper Award for "Fine-grained entity typing in hyperbolic space" (2019) Honorable Mention for the IJCAI-JAIR best paper prize 2010 for "Knowledge Derived from Wikipedia for Computing Semantic Relatedness" Professor Strube has advised numerous PhD students who have gone on to successful careers in academia and industry. His current PhD students include Yi Fan, Wei Liu, Haixia Chai, Mehwish Fatima, and Sungho Jeon, working on topics such as discourse structure, discourse relations, coreference resolution, and cross-lingual summarization. His former students include Federico Lopez, Benjamin Heinzerling, Mohsen Mesgar, and Nafise Moosavi, who now hold positions at institutions like Argo AI, RIKEN, Bosch Center for AI, and the University of Sheffield. As group leader of the NLP Group at HITS, Strube oversees a team focused on advancing natural language processing through research in discourse analysis, coreference resolution, text generation, and knowledge extraction. The group has been involved in numerous collaborative projects and has made significant contributions to the field through publications, shared tasks, and community building via workshops and conferences.
Yiwei Wang is an Assistant Professor at the Department of Computer Science, University of California, Merced, leading the UC Merced NLP Lab . He holds a Ph.D. from National University of Singapore (2023), M.Phil from Hong Kong University of Science and Technology (2019), and B.S. from Southeast University (2017). His research focuses on natural language processing , large language models , and graph machine learning , with emphasis on trustworthy AI systems.
Hayder Murad is a researcher with expertise in machine learning, sentiment analysis, and hybrid filtering algorithms, particularly applied to intelligent tutoring systems and medical diagnostics. His work bridges computational methods with practical applications in education and healthcare. He earned his PhD from the University of Portsmouth in 2019 with a thesis titled An integrated approach to recommending online video materials through sentiment analysis and hybrid filtering algorithms . His recent publications focus on knowledge graph construction, large language model applications, and open science initiatives. Notable contributions include enhancing systematic literature reviews, improving dataset interoperability, and advancing FAIR data principles across disciplines. Research Interests Machine learning for healthcare diagnostics Hybrid recommendation systems Knowledge graph development Open science infrastructure Intelligent tutoring systems LLM-based research synthesis
Nitin Gupta is a researcher with affiliations spanning institutions like Google, Cornell University, and Carnegie Mellon University. His work bridges theoretical and applied computer science, focusing on database systems, data-driven applications, and knowledge graphs. Research interests include: Structured data on the web and semantic publishing Scalability in virtual environments and declarative programming Integration of large language models (LLMs) into scholarly workflows FAIR data principles and research data management Recent publications explore hybrid question answering datasets, LLM applications for literature reviews, and knowledge graph construction for software engineering. His contributions emphasize machine-actionable metadata, open science, and enhancing reproducibility through standardized research artifacts. Collaborations span institutions and disciplines, with coauthorships on topics like: Game development and data coordination Non-invasive brain stimulation analysis Neuro-symbolic approaches for digital libraries Ontology matching and FAIR digital objects
Philipp Cimiano is a Professor at the Faculty of Engineering, Bielefeld University, and leads the Semantic Databases Group. He holds additional roles as Coordinator of the Cognitive Interaction Technology Center (CITEC) and Director of the Joint Artificial Intelligence Institute (JAII). His research focuses on the intersection of language, semantics, and knowledge representation, with applications in Explainable AI , Knowledge Graphs , and AI in Medicine . Education: University of Stuttgart, University of South Australia, Karlsruhe Institute of Technology (KIT) Key Research Areas: Knowledge Representation, Ontologies, Explainable AI, Clinical Decision Support His recent work emphasizes dialogue-based XAI , federated learning , and semantic data integration . He has secured funding from the German Research Foundation (DFG) and the European Union for projects like TRR 318 "Constructing Explainability" and Pret-a-LLOD. Scientific Awards Carl Adam Petrie Prize, KIT Faculty of Business and Economics Editorial Roles Co-editor, Journal of Applied Ontology Area Editor, Semantic Web Journal Editorial Board, Journal of Web Semantics Notable Projects TRR 318 (Subprojects B01, C05, INF) 3B: Bots Building Bridges for online deliberation LLM4KMU: Open Source LLMs for SMEs
Nadeen Fathallah is a researcher at the University of Stuttgart, affiliated with the Analytic Computing group at KI. Her work spans AI applications for accessibility, computer vision, and knowledge engineering. Research Focus: Web accessibility, ontology learning, and LLM-based solutions for Deaf/Hard of Hearing communities Projects: Key contributor to the IKILeUS project (Integrated AI in Teaching) at the University of Stuttgart Teaching: Has served as teaching assistant and assistant lecturer at German International University, German University in Cairo, and The Knowledge Hub Her research explores: Automated detection/correction of web accessibility violations (e.g., AccessGuru platform) Improving video captions using large language models Accessibility tools for tabular data (EchoTables) Ontology learning pipelines (NeOn-GPT, LLMs4Life) Recent work shows a focus on combining LLMs with domain-specific challenges across multiple fields, particularly emphasizing inclusive design principles. Contact details: Office at Universitätsstraße 32, Stuttgart, Germany (Room: 2.312b). Available via +49 711 685 88130.
Hoifung Poon is General Manager at Microsoft Health Futures and affiliated faculty at University of Washington Medical School. He leads Real-World Evidence (RWE) research focusing on AI applications for precision health. Poon earned a B.S. with Distinction in Computer Science from Sun Yat-Sen University and a Ph.D. in Computer Science and Engineering from University of Washington. Specializes in biomedical AI research Focuses on structuring unstructured medical data Co-PI for DARPA Big Mechanisms projects Research strength lies in biomedical multimodal learning (text, radiology, pathology, genomics) and causal learning for real-world evidence generation. His team develops methods for LLM self-verification , multi-modal fusion , and biases correction in observational data. Publications show expertise in Nature , Nature Methods , and NEJM AI , covering topics from digital pathology to clinical text analysis. Scientific recognition includes: Best Paper Awards at NAACL, EMNLP, and UAI Winner of ACM Health Best Paper Award Named Technology Champion 2022 by Puget Sound Business Journal
