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Eva PetterssonView profile

Researcher

• Computational Linguistics
• Digital Humanities
• Historical Linguistics
• +5 more

Eva Pettersson is a Researcher in computational linguistics at Uppsala University's Department of Linguistics and Philology. She is affiliated with the Swedish National Language Bank and collaborates with researchers across multiple institutions, including the University of Gothenburg where she works with Lars Borin on corpus linguistics projects. Her academic work bridges computational methods with historical language analysis. Dr. Pettersson's research focuses on the intersection of digital humanities and computational linguistics, specializing in the processing and analysis of historical texts. Her work spans multiple domains including natural language processing for historical documents, historical cryptology, corpus development, and linguistic analysis of diachronic language change. She has made significant contributions to Swedish historical linguistics through her development of specialized resources and tools. Her publication record demonstrates a consistent trajectory of innovation in historical text processing, with recent work focusing on medieval scribal habits, named entity recognition in 19th century Swedish, rhetorical structure analysis of historical petitions, and historical cryptanalysis. These publications reveal a progression from foundational work on spelling normalization to increasingly sophisticated applications of NLP techniques to historical documents across multiple centuries. Dr. Pettersson has developed significant linguistic resources including the Swedish Diachronic Corpus and the HistCorp collection of historical corpora and tools. These resources have become essential for researchers working on historical Swedish language and provide standardized datasets for computational analysis of language change over time. Her collaborative work extends to international projects like the DECRYPT initiative for historical manuscript decryption and the development of specialized databases for historical ciphers. Through these projects, she has established herself as a key figure in the application of computational methods to historical linguistic materials and cryptological challenges.

Amanda WasielewskiView profile

Associate Professor

• Digital Humanities
• Art History
• Artificial Intelligence
• +4 more

Amanda Wasielewski serves as Associate Senior Lecturer of Digital Humanities and Associate Professor (Docent) of Art History at Uppsala University's Department of ALM (Art History, Musicology and Theatre Studies). Her academic work bridges traditional art historical scholarship with cutting-edge digital methodologies, focusing on how computational technologies reshape artistic practice, cultural heritage institutions, and art historical research itself. Dr. Wasielewski's research interests center on the critical examination of digital technologies in art contexts, with particular emphasis on artificial intelligence applications in art history, digital humanities methodologies, virtual reality experiences in cultural institutions, and the transformation of visual culture through digital mediation. Her work consistently interrogates the theoretical implications of computational approaches to art historical study while developing practical digital research methods. Analysis of her recent publications (2021-2025) reveals a pronounced shift toward critical engagement with generative AI systems like Midjourney and DALL-E, examining their impact on artistic creation, photographic authenticity, and art historical representation. Her scholarship demonstrates how AI image generators reanimate historical art canons while exposing fundamental limitations in computational understanding of visual concepts. This work positions her at the forefront of critical digital art history as a distinct scholarly field. Dr. Wasielewski has established herself as a leading voice in critical digital art history through significant publications including the 2023 MIT Press monograph Computational Formalism: Art History and Machine Learning and the forthcoming 2024 edited volume Critical Digital Art History (Intellect Press). Her editorial work includes the 2020 special issue of Digital Culture & Society titled "The Politics of Metadata." Her methodological contributions include developing techniques for bulk processing humanities texts and analyzing art historical datasets, demonstrating both theoretical sophistication and practical research skills. Dr. Wasielewski's work consistently bridges computational methods with deep art historical knowledge, creating a distinctive scholarly position at the intersection of traditional humanities and digital innovation.

Fredrik WahlbergView profile

Assistant Professor

• Computational Linguistics
• Historical Document Analysis
• Handwriting Recognition
• +3 more

Fredrik Wahlberg serves as Assistant Professor in Computational Linguistics at Uppsala University's Department of Linguistics and Philology, where he develops computational methods for historical manuscript analysis. His work bridges computer science and humanities through machine learning applications in paleography and digital archives. His research encompasses: Computational Linguistics for historical texts Handwriting recognition and word spotting in medieval manuscripts Deep learning for scribal attribution and dating Neural networks for archival image restoration AI-driven metadata enrichment in cultural heritage Analysis of his 2011-2022 publications reveals consistent innovation in combining image processing and language modeling for large-scale manuscript analysis. Key trends include advancing convolutional neural networks for dating accuracy, developing quill-curvature features for writer identification, and extending into critical AI applications for gender-inclusive historical collections. His methodologies significantly enhance accessibility of pre-modern documents through computational approaches.

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

Chris HaffendenView profile

Researcher

• Cultural History
• Memory Studies
• Digital Humanities
• +3 more

Chris Haffenden is a researcher at Uppsala University's Department of History of Ideas and part-time research coordinator at KBLab (Royal Library, Stockholm). He holds a PhD from Uppsala University (2018), an MPhil from the University of Cambridge, and a BA from the University of Sussex. His interdisciplinary research focuses on 19th-century cultural memory, digital heritage, and AI applications in humanities. Research interests span memory studies, canonization processes, celebrity history, and the intersection of cultural heritage with digital infrastructure. His work examines how societies construct value and legacy through material practices and digital transformation. Publications demonstrate strong focus on: Digital preservation of cultural heritage (HTR, AI models) Memory/erasure dynamics in 19th-century Britain Library science innovation through AI Romantic-era cultural practices At KBLab, he develops infrastructure for large-scale analysis of Sweden's cultural collections and leads workshops on AI methods in humanities. He previously taught IB History/English and supervises student work on canonicity and classical texts.

• Cybersecurity
• Cyber-Physical Systems
• Vulnerability Analysis
• +4 more

Yuning Jiang holds a doctoral degree from the University of Skövde, with research focused on cybersecurity for cyber-physical systems (CPS) and critical infrastructure protection. Affiliated with the School of Informatics, Jiang's work bridges information technology (IT) and operational technology (OT) security domains through advanced modeling and machine learning techniques. Education: PhD in Information Security (2022), University of Skövde Research interests include: Cybersecurity analysis for cyber-physical production systems (CPPS) Enterprise architecture modeling for vulnerability assessment Machine learning applications in security data inconsistency resolution Digital twin technology for attack scenario simulation Article trends show consistent focus on: CPS vulnerability quantification across 8 publications 11 papers addressing critical infrastructure protection 6 studies on dependency modeling in hybrid systems Awarded grants from: EU Internal Security Fund (ISF) Knowledge Foundation (KKS), Sweden SYMBIO-TIC project ELVIRA project Contributions include: Developing semantic frameworks for human-in-the-loop security Designing cross-linked vulnerability databases Advancing ensemble machine learning models for threat analysis Creating taxonomy for cyber-physical functional dependencies

• Ontology Engineering
• Semantic Web
• Data Stream Processing
• +3 more

Robin Keskisärkkä is a Researcher at the Department of Computer and Information Science at Linköping University. His work focuses on ontology engineering, semantic web technologies, data stream processing, and their applications in areas like circular economy, healthcare informatics, and sustainability. He has contributed to advancing methods for handling uncertainty in real-time data streams and developing domain-specific ontologies for diverse fields such as ice hockey and criminal investigations. Key research interests include RDF stream processing, semantic complex event processing (CEP), and the integration of large language models for ontology generation. His recent work explores digital product passports for circular economy frameworks and longitudinal health outcomes analysis in oncology. He has also conducted studies on text simplification techniques for Swedish language. His technical contributions span query languages for streaming data (e.g., RSP-QL*), semantic event boundary detection, and cross-domain ontology networks. His research bridges theoretical computer science with practical applications in environmental sustainability, public health, and criminal justice systems.

Maria BrandénView profile

Professor

• Segregation Dynamics
• Social Inequality
• Machine Learning
• +4 more

Maria Brandén is a Professor and Head of Division at Linköping University, affiliated with the Department of Management and Engineering (IEI) and the Institute for Analytical Sociology (IAS). She has been part of IAS since its founding in 2014 and became its director in January 2021. Her primary role involves leading research and fostering interdisciplinary collaborations, particularly in computational social science. She is based at Campus Norrköping and can be reached at maria.branden@liu.se. Education: Maria Brandén holds a PhD in Sociological Demography from Stockholm University (2014), which focused on couple decisions related to residential mobility. She has since expanded her research into broader societal issues. Research Interests: Her work examines how segregation (school, housing, workplace) impacts individuals’ life chances, leveraging Sweden’s extensive register data. Key areas include neighborhood effects, socio-economic inequalities in health outcomes (e.g., during the pandemic), and computational methods such as machine learning to analyze complex social dynamics. She emphasizes understanding heterogeneous effects and long-term societal impacts of segregation. Grants & Projects: As Principal Investigator (PI), she secured SEK 13 million from the Swedish Research Council (2020–2023) to combine machine learning with register data for studying social inequality. Additional projects funded by Forte and the Swedish Research Council address school and housing segregation. She also leads the Institute’s strategic growth and international collaborations. Labs/Teams: Director of IAS, which focuses on agent-based simulations, social network analysis, and text mining. She co-founded the SweCSS (Swedish Centre for Computational Social Science) and oversees its Visiting Fellows Program to bridge social and computational sciences.

Mohammad BawanehView profile

Assistant Professor

• Time Series Data Mining
• Smart Cities
• Machine Learning
• +5 more

Mohammad Bawaneh is an Assistant Professor at the Department of Networked Systems and Services (BME-HIT) at Budapest University of Technology and Economics, Hungary. He holds a PhD in Computer Engineering from the same university (2023), with prior degrees from Yarmouk University, Jordan (BSc 2014, MSc 2017). He is affiliated with the Multimedia Networks and Services Laboratory (MEDIANETS). Education: BSc and MSc in Computer Engineering, Yarmouk University (2014, 2017) PhD in Computer Engineering, Budapest University of Technology and Economics (2023) His research focuses on: - Time series data mining for smart city services - Machine learning in transportation management systems - Greening big data storage through dimensionality reduction - AI-driven solutions for traffic prediction and congestion detection No awards or publications are explicitly listed in the provided text. He teaches courses such as C++ Programming, Communication Networks, and Multimedia Systems. He supervises BSc/MSc projects in data science topics.

• Artificial Intelligence
• Machine Learning
• Virtual Sensors
• +3 more

Stefan Byttner is a Professor at the School of Information Technology, Halmstad University. His research focuses on artificial intelligence, machine learning, virtual sensors, safe AI (XAI), and monitoring of heterogeneous mechatronic systems. He supervises doctoral students in XAI and leads major initiatives like the "MAISTR" program and "Innovation platform AI.m". Master's Program Manager (2015-2018) Director of Doctoral Program (2012-2018) Head of Department ISDD (2018-) Acting Dean, School of Information Technology (2022-2023) Collaboration Leader at ITE (2017-2018) His research integrates AI with mechatronics and industrial IoT , emphasizing explainability and safety in deployed systems. Recent work explores graph neural networks for clinical and spatiotemporal forecasting applications, as well as diffusion models for time series imputation. Key publication trends include neuroimaging AI , time series modeling , and industrial anomaly detection . Applications span district heating systems , vehicle diagnostics , and healthcare analytics with PET scans. Teaching areas include artificial intelligence, data mining, thesis supervision at bachelor's and master's levels, and technology-democracy intersections. No awards or student names are explicitly mentioned in available texts.

• Machine Learning
• Deep Learning
• Health Data Analysis
• +3 more

Mattias Ohlsson is a Visiting Professor at the School of Information Technology , Halmstad University . His research focuses on machine learning and deep learning for analyzing diverse health data, particularly patient trajectory modeling with multimodal approaches. Primary affiliation: IT - Computer Department Collaboration areas: Healthcare sector and industry His work emphasizes explainable AI in clinical contexts, including survival analysis, cardiac event prediction, and cross-domain applications in satellite poverty mapping. Recent projects explore temporal healthcare data analysis using transformer architectures and self-supervised learning . Key article trends include: 2025: AI integration with medical expertise for emergency diagnostics 2024: Survival model evaluation, temporal data challenges, and graft failure prediction 2023: Multi-robot routing optimization and fatty liver disease etiology modeling 2022: Heart failure mortality algorithms and anomaly detection systems Current affiliations include the CAISR Health research group, with technical expertise spanning CNNs, transformers, and robust imputation methods.

Teri Schamp-BjeredeView profile

Researcher

• Language Didactics
• Computer-Assisted Language Learning
• EFL Writing
• +3 more

Teri Schamp-Bjerede is an active doctoral candidate and researcher at Lund University's Centre for Languages and Literature within the Faculty of Humanities and Theology. She specializes in language didactics with a focus on English as a Foreign Language (EFL) writing and computer-generated feedback systems. Her research examines digital writing processes, pedagogical affordances of technology, and L2 acquisition strategies. Education: • Bachelor's in English (Blekinge Institute of Technology, 2004) • MSc in IT (University of Liverpool, 2009) Research Focus: Her work intersects digital pedagogy, linguistic analysis, and educational technology. Primary interests include: computer-assisted writing feedback, affective aspects of digital learning, sociolinguistics in online environments, and interdisciplinary methodologies merging computational linguistics with language education. Publication Trends: Schamp-Bjerede's recent articles demonstrate a strong focus on computational linguistics and digital pedagogy, particularly analyzing social media discourse, sentiment detection, and educational technology efficacy. Her work frequently employs interdisciplinary approaches combining linguistic theory with data visualization and machine learning techniques. Projects: Co-leads the interdisciplinary project Representation of war as autobiographical media: Walter MacKay Draycot, P.P.C.L.I. exploring digital remediation of historical texts. Previously developed the Walter Draycot Web Project, integrating academic standards with digital accessibility.

• Computational Linguistics
• Digital Humanities
• Historical Text Analysis
• +4 more

Vera Danilova is a Postdoctoral Research Fellow at Uppsala University's Department of History of Science and Ideas, specializing in computational approaches to historical text analysis. Her work bridges digital humanities and natural language processing, with a focus on extracting meaningful patterns from historical periodicals and archives. Her primary research interests include: Genre classification in historical magazines (1875-1990) Post-OCR correction using large language models Cross-genre transfer learning in dependency parsing Topic modeling for medical history periodicals Development of multilingual NLP resources Recent publications reveal a strong trajectory in applying machine learning to historical document analysis, particularly in genre identification and text restoration for German and Russian periodicals. Her work frequently addresses challenges in historical OCR output and develops specialized tools like UD-MULTIGENRE for linguistic annotation. No scientific awards were documented in the provided sources. Information regarding academic advising, grant funding, or laboratory affiliations was not present in the available materials.

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

Tomohiko SakaoView profile

Professor

• Ecodesign
• Product/Service Systems (PSS)
• Circular Economy
• +6 more

Tomohiko Sakao is a Professor at Linköping University's Department of Management and Engineering (IEI) within the Environmental Technology and Management (MILJÖ) division. His research focuses on ecodesign, product/service systems (PSS), and circular economy strategies, emphasizing transdisciplinary approaches to address environmental challenges while enhancing industrial competitiveness. He leads initiatives such as the Catena-D project, integrating AI and digital threads for circular systems, and contributes to ISO standardization for sustainability. Education: Ph.D. in Precision Machinery Engineering, The University of Tokyo (1995–1998) Master of Engineering, The University of Tokyo (1993–1995) Bachelor of Engineering, The University of Tokyo (1989–1993) Guest Researcher, Darmstadt University of Technology (2005–2007) Research Interests: Ecodesign methodologies and cognitive aspects of sustainable design PSS design frameworks for resource efficiency and circular business models AI-driven lifecycle engineering and digital threads Remanufacturing scalability and policy impacts on circular transitions Articles Overview: Recent work explores challenges in PaaS design, cognitive design processes, and AI applications for resource efficiency Focus on bridging theory and practice through industry collaborations and policy analysis Awards: IVA Research2Business 100-list (2020, 2023, 2024) Young Scientists’ Prize (2009) Alexander von Humboldt Fellowship (2005–2007) Grants & Teaching: Funded by EU, VINNOVA, Mistra, and industry partners Responsible for courses like 'Trans-disciplinary Approach to Circular Economy Research' and 'Resource Efficient Products' Labs/Teams: Unit for Product Service Innovation (PSI) Catena-D project collaboration with Volvo and Bosch