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Sara Stymne is a Senior Lecturer in Computational Linguistics at the Department of Linguistics and Philology, Uppsala University, where she has been working since 2012. She initially joined as a post-doc (2012-2015), then worked as a researcher (2015-2017), and served as an assistant professor (2017-2023) before her current position as Senior Lecturer. Prior to Uppsala, she was a researcher at Linköping University's Department of Computer and Information Science. Dr. Stymne earned her PhD in Computational Linguistics from Linköping University in 2012 with the thesis 'Text Harmonization Strategies for Phrase-Based Statistical Machine Translation,' following a Licentiate degree in Computational Linguistics (2009) and a Master's degree in Cognitive Science (2006), both also from Linköping University. During her doctoral studies, she spent the autumn of 2010 and spring of 2009 at Xerox Research Centre Europe in Grenoble, France. Her primary research interests focus on cross-lingual natural language processing and digital humanities, with particular emphasis on multilingual dependency parsing. Dr. Stymne is passionate about applying computational linguistics to solve research questions in other fields, including language history, literary analysis, and political science. Her earlier work concentrated on machine translation, with specific interests in discourse-aware translation, compound processing, and error analysis. She has made significant contributions to the development of language technology tools for analyzing dialogue, narrative, and stylistic features in literature. Analysis of Dr. Stymne's recent publications reveals a strong focus on cross-lingual and cross-domain natural language processing. Her work spans multiple subfields including dependency parsing across genres and topics, discourse relation analysis in low-resource languages like Egyptian Arabic, direct speech identification in Swedish literature, and causality detection in governmental documents. A notable trend is her application of NLP techniques to digital humanities problems, particularly in analyzing literary texts and historical language change. Her research often involves creating and utilizing specialized datasets for specific linguistic phenomena across multiple languages. Dr. Stymne actively supervises graduate students, having guided numerous master's and bachelor's theses on topics ranging from speech recognition to multilingual parsing and causality detection. She leads or participates in several research projects including 'Fictional prose and language change' (funded by VR, 2021-2023) and 'Enabling climate-resilient development' (funded by Marianne and Marcus Wallenberg Foundation, 2023-2027), demonstrating her commitment to interdisciplinary research with practical applications. Her work has resulted in several notable software resources including uuPronPred for cross-lingual pronoun prediction, uuparser for dependency parsing, and Docent for document-level machine translation. Within the Computational Linguistics and Language Technology group at Uppsala University, Dr. Stymne contributes to multiple research initiatives focused on developing language technology tools for digital humanities applications. Her team works closely with literary scholars and historians to create computational methods for analyzing large corpora of literary texts, particularly focusing on Swedish literature across different historical periods. Her research bridges the gap between theoretical computational linguistics and practical applications in the humanities, creating new methodologies for quantitative analysis of literary and historical texts.

• Computational Linguistics
• Natural Language Processing
• Cross-lingual NLP
• +7 more

Zenun Kastrati is an Associate Professor at the Department of Informatics, Linnaeus University. His research focuses on Artificial Intelligence, Natural Language Processing, Machine Learning, Semantic Web, Sentiment Analysis, and Learning Technologies. He contributes to the Data-driven Business Innovation (DBI) and Interaction Design Research Groups, leading projects like Forest 4.0, RAPID, and IGNITE. His recent work involves Explainable AI, medical imaging, and multilingual NLP. Ph.D. in Computer Science (NTNU, 2018) Master's in Computer Science (EU TEMPUS Programme) Previous Lecturer/Researcher at University of Prishtina His research spans AI applications in medical diagnostics , NLP , sentiment analysis , and semantic technologies . Key projects include Forest 4.0 (environment monitoring) and RAPID (online education in Pakistan). Publications highlight his expertise in deep learning , transformer models , and context-aware systems . Recent publications demonstrate trends in Explainable AI (XAI) for healthcare, medical imaging techniques, and multilingual NLP frameworks. Other work explores social media analytics , student feedback analysis , and pedagogical document classification . Zenun's teaching includes Fundamentals of Programming , Object-Oriented Programming , Web Applications , Data Analytics , and Adaptive Web courses at BSc and MSc levels.

• Artificial Intelligence
• Natural Language Processing
• Machine Learning
• +4 more

Isak Samsten is a Senior Lecturer at Stockholm University's Department of Computer and Systems Sciences (DSV), specializing in data science and machine learning. He leads research in temporal machine learning, counterfactual explanations, and interdisciplinary applications in healthcare and environmental science. His work includes developing the wildboar Python module for time series analysis. Current research projects focus on AI for insurance fraud detection and environmental remediation. Samsten is affiliated with the Data Science Research Group, which bridges algorithmic innovation with practical decision-making. He holds an ORCID identifier (0000-0002-3056-6801) and is active in publishing influential papers on topics like time series classification, ESG performance prediction, and clinical decision support systems. Education: Unspecified in text (assumed doctoral degree given academic rank) Affiliations: DSV, Stockholm University; Data Science Research Group Research Interests: Time series analysis, interpretable machine learning, healthcare informatics, environmental sustainability metrics, and AI ethics. Key contributions include shapelet-based classification methods (e.g., Castor algorithm) and counterfactual explanation frameworks (e.g., Glacier system). Grants & Awards: None explicitly listed in provided text. Labs/Teams: Leads the Data Science Research Group, collaborating on projects like AI to detect unclear insurance claims and Toxicity guided inverse design of materials .

• Data Science
• Machine Learning
• Time Series Analysis
• +3 more

Ehsan Doostmohammadi is a Researcher at the Artificial Intelligence and Integrated Computing Systems (AIICS) department of Linköping University . His work focuses on Natural Language Processing (NLP) and Language Model Optimization , with particular interest in Retrieval-Augmented Models , Multimodal Learning , and Low-Resource Language Processing for Persian and Swedish. Research Interests : Retrieval-Augmented Language Models Swedish Language Processing Persian Language Technology Medical Text Analysis Cross-lingual Transfer Learning Publications : 15 recent articles spanning 2018–2025 Key themes: AI , NLP , Language Model Efficiency

• Artificial Intelligence
• Natural Language Processing (NLP)
• Language Models
• +5 more

Nina Tahmasebi is a Professor at the University of Gothenburg, affiliated with the Department of Linguistics and Theory of Science. Her research focuses on computational methods for studying lexical semantic change, particularly through projects like the 6-year RJ-funded 'Change is Key!' initiative. She leads interdisciplinary teams collaborating with institutions such as the University of Lund and Queen Mary University of London. Her work bridges computational linguistics and the humanities, emphasizing data science applications in historical text analysis, sentiment lexicon creation, and argument mining at Språkbanken . Her research interests include developing robust evaluation frameworks for semantic change detection, leveraging AI and NLP for humanities research, and improving accessibility of historical parliamentary data. She teaches statistics for humanities researchers and leads study circles on data science applications in the field. Key contributions include the DWUGs dataset for multilingual semantic change analysis, the DURel annotation tool, and frameworks like TRoTR for evaluating text reuse. Her publications span semantic shift detection, word sense induction, and benchmarking Swedish language models (e.g., Superlim). Advising and grants involve leading teams of researchers and engineers on EU and national grants. She collaborates with the Center for Digital Humanities (CDH) and contributes to Swedish NLP infrastructure through initiatives like SentSALDO and parliamentary data enhancement projects.

• Computational Lexical Semantic Change Detection
• Data Science for the Humanities
• Natural Language Processing (NLP)
• +3 more

Thomas Vakili is a doctoral researcher at the Department of Computer and Systems Science , Stockholm University , affiliated with the Natural Language Processing Research Group . His work intersects language technology, artificial intelligence, and privacy protection , focusing on mitigating data leakage risks in large language models while maintaining utility. Education: MSc in Computer Science and Engineering (KTH Royal Institute of Technology) Research explores privacy-preserving NLP through techniques like pseudonymization and de-identification, with applications in clinical data and Swedish electronic health records . Recent projects include End-to-End Pseudonymization of BERT models and analysis of eponyms in clinical text . Publications in BMC Medical Informatics , LREC , and NoDaLiDa conferences address privacy risks , model utility , and cross-lingual NLP . He supervises bachelor's and master's theses and teaches courses in NLP , Internet Search Techniques , and Digital Business Strategies .

• Natural Language Processing
• Artificial Intelligence
• Data Privacy
• +4 more

Meriem Beloucif serves as an Assistant Professor in Computational Linguistics at Uppsala University's Department of Linguistics and Philology, focusing on neural machine translation and lexical semantics for low-resource languages. Her work bridges theoretical linguistics and practical NLP applications, particularly for underrepresented language communities. Education: PhD in Machine Translation from HKUST (Hong Kong) Prior research appointments at Hamburg University (Comparative QA) and Copenhagen University (Semantic Parsing) Research Interests: Core expertise in low-resource language processing for African and Indigenous languages Specialization in sentiment analysis benchmarks (AfriSenti) and semantic role labeling Investigation of pre-trained language model capabilities through probing techniques Development of preprocessing strategies for challenging languages like Inuktitut Her publication trajectory (2021-2023) reveals concentrated efforts in creating evaluation frameworks for African languages, advancing cross-lingual transfer methods, and analyzing semantic compositionality in transformers. Key contributions include standardized datasets for semantic relatedness and novel approaches to zero-shot sentiment classification in Nigerian Pidgin. Collaborative work with researchers like Shamsuddeen Muhammad and Micaella Bruton demonstrates active mentorship in computational linguistics projects. While specific grant details aren't documented, her leadership in SemEval shared tasks indicates significant community engagement in NLP evaluation standards.

• Natural Language Processing
• Machine Translation
• Lexical Semantics
• +3 more

Jenny Kunz is a Research Fellow at Linköping University's Department of Computer and Information Science (IDA), within the Artificial Intelligence and Integrated Computer Systems (AIICS) division. She holds a PhD in Computer Science from LiU (2024) and previously completed a Master’s in Language Technology from Uppsala University and a Bachelor’s in Computer Science from Humboldt University of Berlin. Her research focuses on interpretable and explainable NLP, including parameter-efficient language adaptation, self-rationalizing models, and understanding linguistic information storage in neural networks. She has contributed to studies on LLM-generated explanations, cross-lingual transfer learning, and the challenges of under-resourced languages. Kunz has taught courses such as Language and Computers and has held roles including Chair of the IDA PhD Council and co-representative on the IDA Board. Her work spans over 15 peer-reviewed publications in venues like ACL and COLING, addressing topics from model interpretability to multilingual NLP challenges.

• Natural Language Processing (NLP)
• Interpretability of NLP Models
• Explainable AI
• +4 more

Marcel Bollmann is an Associate Professor at the Department of Computer and Information Science (IDA) at Linköping University, Sweden, where he leads research in the Artificial Intelligence and Integrated Computer Systems (AIICS) division. His work focuses on natural language processing (NLP), machine learning, and computational linguistics, particularly in under-resourced languages, multilinguality, and historical document analysis. Education: He holds a PhD in Computational Linguistics from Ruhr-Universität Bochum (2018), funded by projects on historical German language corpora. Previously, he was a postdoc at the University of Copenhagen (2018–2021), supported by an EU Marie Skłodowska-Curie Fellowship, and an assistant professor at Jönköping University (2021–2023), where he developed AI Engineering courses. Research: Bollmann’s interests include linguistically-informed NLP approaches, historical text normalization, and improving NLP accessibility across language genres. His work has addressed challenges like morphology-aware models and low-resource language support. Awards: His 2021 paper on morphology’s role in error analysis won the EACL Best Long Paper Award. He also contributed to the ACL Anthology as Site Development Lead, advancing NLP infrastructure. Advising & Grants: He has supervised projects on historical language corpora and received funding from EU initiatives. His lab collaborations focus on advancing AI through interdisciplinary methods. Labs/Teams: Active in the AIICS division at IDA, contributing to WASP (Wallenberg Autonomous Systems Program) and other interdisciplinary AI initiatives.

• Natural Language Processing
• Machine Learning
• Under-Resourced Languages
• +3 more

Johan Boye is an Associate Professor at the School of Electrical Engineering and Computer Science at KTH Royal Institute of Technology, part of the Speech, Music, and Hearing department. He leads the SWE-QUEST project, developing automated question generation for Swedish language education, funded by Vinnova and Digital Futures. His research focuses on dialogue systems, natural language processing, and spatial language understanding, with applications in education, robotics, and human-computer interaction. Education background includes a docent title (Swedish associate professor) and extensive academic experience. He has supervised over 20 master’s and PhD theses in NLP and AI, with recent topics including large language models, coreference resolution, and multimodal retrieval systems. Teaching responsibilities include courses on language technology, search engines, and speech processing. His work spans cross-disciplinary collaborations with Stockholm University and RISE Research Institutes in the Digital Futures initiative, addressing societal challenges through digital innovation. Key projects include the SpaceBook EU project (2010–2014) exploring pedestrian navigation via dialogue systems and the NICE game (2005) for child-computer interaction. Research outputs include over 50 peer-reviewed publications in journals like the Journal of Natural Language Engineering and conferences such as ACL and SIGDIAL. He maintains active roles in academic service, including editorial work and program committees for NLP conferences. His lab develops tools like Textinator for NLP evaluation and UDon2 for dependency tree manipulation, emphasizing open-source contributions to the research community.

• Natural Language Processing
• Spoken Dialogue Systems
• Multimodal Dialogue Systems
• +6 more

Tingting Zhang is a Professor at Mid Sweden University's Department of Computer and Electrical Engineering (DET), affiliated with the STC Research Centre. Her work focuses on wireless sensor networks, industrial IoT, and privacy-preserving protocols in vehicular and IoT networks. Research spans VANETs, machine learning for anomaly detection, light field imaging, and secure communication systems. Current projects include PLENOPTIMA, COINS, DAWN, and Smart Industry Sweden. Publications emphasize network reliability, security frameworks for IoT/VANETs, and machine learning applications in sensor networks. She has contributed to patents in wireless communication and video noise reduction. Recent work explores cross-layer optimization, privacy-preserving protocols, and GPU-accelerated light field rendering. Collaborations include institutions in Sweden, Finland, and China.

• Wireless Sensor Networks
• Industrial IoT
• Vehicular Ad-hoc Networks (VANETs)
• +3 more

Jody Foo is a Lecturer at the Department of Computer and Information Science, Linköping University. They are affiliated with the Natural Language Processing Lab (NLPLAB) and the Human-Centered Systems (HCS) research group. Their work bridges computational methods with terminology management. Education: Licenciate degree in Computational Linguistics; Magistersexamen (~MSc) in Cognitive Science. Research Focus Jody’s research centers on computational terminology management , involving: Monolingual and bilingual automatic term extraction Term clustering and semantic mirroring Terminology harmonization for standardization Integration of machine learning and language models Applications in patent analysis and SMT (Statistical Machine Translation) Publication Trends Their publications (2003–2014) span computational linguistics, NLP, and terminology engineering. Notable areas include SVM-based term detection, semantic mirroring for patent terminology, and corpus augmentation for machine translation. Collaborative works with Merkel, Andersson, and others highlight interdisciplinary approaches. Contact Email: jody.foo@liu.se | Office: Room E 3F:482, Linköping

• Computational Linguistics
• Natural Language Processing
• Terminology Management
• +3 more

Dr. Sriparna Saha is an Associate Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Patna, India. She holds a Ph.D. from the Indian Statistical Institute Kolkata and has held leadership roles including Head of Department (2021-2023) and Associate Dean for Research and Development (2019-2021). Her research focuses on AI, machine learning, natural language processing, bioinformatics, and multiobjective optimization. She has authored over 400 publications with an h-index of 38 and received awards such as the NASI Young Scientist Platinum Jubilee Award and Google India Women in Engineering Award. Her work spans multimodal systems, medical image analysis, and computational social systems. Education: M.Tech (2005) and Ph.D. (2011) in Computer Science from Indian Statistical Institute Kolkata. Research Interests: Multimodal information processing, NLP, machine learning, bioinformatics, and optimization techniques. Awards: Includes Lt. Rashi Roy Memorial Gold Medal, BIRD Award, and multiple fellowships (Humboldt, CNRS, etc.). Administrative Roles: IEEE Student Branch Councilor, Senate Member, and Visvesvaraya Nodal Officer at IIT Patna. Her recent work includes advancements in multimodal recommendation systems, breast cancer prognosis models, and computational social systems for crisis management. She has also contributed to hate speech detection in multilingual contexts and medical imaging diagnostics.

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +5 more

Marco Kuhlmann is a Professor and Deputy Head of Department at the Department of Computer and Information Science (IDA) at Linköping University. He leads the Natural Language Processing Group and is part of the Artificial Intelligence and Integrated Computer Systems (AIICS) division. His research focuses on natural language processing (NLP), combining theoretical computer science and machine learning to develop algorithms for semantic parsing and contextualized language representations. Key projects include collaborations with Chalmers University and Recorded Future to enhance LLM interpretability and the TrustLLM initiative for European languages. He teaches courses such as Language Technology, Natural Language Processing, and Deep Learning for NLP across multiple faculties. His work emphasizes practical applications of NLP in human-computer interaction and text mining. Kuhlmann actively engages in public outreach, explaining AI advancements through events like European Researchers’ Night, where he discusses societal impacts of language models like ChatGPT. His research spans semantic parsing frameworks, neural network architectures, and the integration of contextualized representations. Ongoing efforts address challenges in low-resource languages and scalable learning methods for imbalanced datasets. Kuhlmann’s contributions include benchmark datasets for dependency parsing and community-building initiatives for NLP evaluation standards.

• Natural Language Processing
• Machine Learning
• Artificial Intelligence
• +3 more

Hercules Dalianis is a Professor at the Department of Computer and Systems Sciences, Stockholm University. His research focuses on Natural Language Processing, particularly in clinical text mining for Swedish language data. He leads the Natural Language Processing Research Group and serves as director of the Health Bank - Swedish Health Record Research Bank infrastructure. MSc in Electrical Engineering (1984), KTH PhD in Technology (1996), KTH Professor of Computer and Systems Science (2011), Stockholm University His research addresses privacy-preserving NLP for clinical text analysis, including automated de-identification , domain adaptation of BERT models , and clinical entity recognition . Current projects like DataLEASH and Privacy-Preserving Techniques explore machine learning solutions that balance data utility with patient confidentiality. Key publication trends show emphasis on Swedish clinical text processing , ICD-10 coding automation , and privacy-aware language modeling . Collaborations span Karolinska University Hospital, Nordic healthcare institutions, and international AI research communities. He teaches courses in Internet Search Techniques and Business Intelligence (ISBI) , Natural Language Processing (NLP) , and Principles and Foundations of Artificial Intelligence (PFAI) . His work has produced the open-access textbook Clinical Text Mining: Secondary Use of Electronic Patient Records , establishing foundational frameworks for clinical NLP in low-resource languages.

• Natural Language Processing
• Clinical Text Mining
• Artificial Intelligence
• +7 more