Find Academic Supervisors

• 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.

Dejan KosticView profile

Professor

• Distributed Systems
• Computer Networks
• Operating Systems
• +6 more

Dejan Kostic is a Chair Professor of Internetworking at KTH Royal Institute of Technology, leading the Networked Systems Laboratory. He is affiliated with RISE Research Institutes of Sweden and the Wallenberg AI, Autonomous Systems and Software Program (WASP). His research focuses on distributed systems, computer networks, operating systems, and machine learning applications in networking. Education: Ph.D. in Computer Science, Duke University M.S. in Computer Science, University of Texas at Dallas B.S. in Computer Engineering, University of Belgrade Research Interests: His work spans distributed systems , high-performance networking , smartNIC offload , AI-driven systems , and network function virtualization (NFV) . Recent projects include energy-efficient AI inferencing (Wallenberg Scholar Project) and scalable federated learning for healthcare applications. Key contributions include the PacketMill framework, FAJITA stateful processing, and the LineFS distributed file system. Publications & Awards: ERC Starting (2010) and Consolidator (2018) Awards Wallenberg Scholar (2024) Best Paper at SOSP 2021 and Community Award at NSDI 2022 Advising & Grants: Supervised over 15 doctoral students, including notable alumni like Hamid Ghasemirahni and Alireza Farshin. Leads projects funded by ERC, WASP, and Swedish Research Council (VR). Active in grant review panels for ERC and Swedish Foundations. Labs & Teams: Heads the Network Systems Laboratory (NSLab) at KTH, collaborating with Ericsson and RISE on industrial projects. Key initiatives include the Networked Systems for Machine Learning course and the Connected Intelligence unit at RISE.

Josephine SullivanView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +4 more

Josephine Sullivan is an Associate Professor at KTH Royal Institute of Technology, affiliated with the Division of Robotics, Perception and Learning. She is a core faculty member of Digital Futures, a cross-disciplinary research center addressing global challenges through digital technologies. Her research focuses on AI-driven environmental monitoring, disaster management, and deep learning applications in computer vision and remote sensing. She has supervised projects such as the fusion of radar and optical remote sensing for wildfire monitoring and contributes to initiatives like EO-AI4GlobalChange. Sullivan teaches advanced courses in deep learning, generative models, and machine learning at KTH, emphasizing hands-on training and research integration. Her work bridges theoretical advancements in neural networks with practical solutions for climate-related disasters, leveraging satellite imagery and time-series data. Recent projects highlight innovations in wildfire prediction datasets (Wildfirespreadts) and robust networks for disaster damage assessment (DisasterAdaptiveNet). She collaborates across disciplines at Digital Futures, contributing to labs and teams focused on AI ethics, sustainable technologies, and global change mitigation. Sullivan’s academic leadership includes roles as course examiner and responsible for multiple graduate programs in computer science and engineering.

• Production Engineering
• Industrial Automation
• Digital Twins
• +3 more

Dr. Anna Syberfeldt is a Professor in Production Engineering at the Department of Engineering Science, University of Skövde. She leads the Virtual Production Development research group and serves as research director at the ASSAR Industrial Innovation Arena. With a background in computer science from De Montfort University and habilitation in automation engineering, her work bridges AI, robotics, and immersive technologies with industrial production systems. Professor in Production Technology Research Director at ASSAR Industrial Innovation Arena University of Skövde Affiliation Her research focuses on developing innovative industrial solutions through: Artificial Intelligence and Machine Learning Collaborative Robotics and Human-Robot Interaction Digital Twins and Simulation-Based Optimization Augmented/Virtual/Mixed Reality Applications Smart Manufacturing and Industry 4.0 Worker Well-Being and Productivity Optimization Recent publications demonstrate her leadership in simulation-based optimization, collaborative robotics, and mixed reality applications in manufacturing. Her work emphasizes creating ultra-flexible production systems through cyber-physical approaches while prioritizing environmental sustainability and human-centric design. Key trends in her research include: Unified frameworks for virtual commissioning Knowledge graph applications in production systems Multi-objective optimization balancing productivity and ergonomics Smart glasses evaluation for industrial operators Evolutionary algorithms for complex manufacturing problems Her projects demonstrate practical implementations of: MAXLabs distributed cyber-physical testbed ERAIVA posture identification software Virtual environments for human-robot collaboration testing Digital support functions for factory layout planning

• Biomedical Informatics
• Artificial Intelligence in Healthcare
• Network Medicine
• +5 more

Alejandro Rodríguez González is a Full Tenured Professor at the Computer Languages and Systems and Software Engineering Department of Universidad Politécnica de Madrid , where he has held academic positions since 2015. He leads the Medical Data Analytics Laboratory (MEDAL) at the Centro de Tecnología Biomédica . His academic career includes previous roles at Universidad Internacional de La Rioja (2012-2015), Universidad de Oviedo (2015), and Universidad Carlos III de Madrid (2011-2012). His research focuses on Biomedical Informatics , Network Medicine , and Computational Drug Repurposing . Key areas include artificial intelligence applications in healthcare, clinical natural language processing , and health data ecosystem transparency . He has developed ontologies like EBOCA and contributed to platforms including DISNET for rare disease research and DRIVE for disease visualization. Recent publications demonstrate expertise in protein folding prediction analysis , scRNA-seq integration , and graph neural network applications in drug repurposing. His work emphasizes biological pathway analysis , gender perspectives in pharmacology , and AI-driven disease network modeling . He has secured funding from H2020 , EIT Health , and Ministerio de Ciencia e Innovación . Scientific Awards : Best Research Trajectory Award (UPM, 2017) II Premio al espíritu innovador (Treelogic, 2008) Mejor Idea EUITIO (CEEI Asturias, 2008) He contributes to ERC grant proposal writing , serves on editorial boards for journals like International Journal On Advances in Intelligent Systems , and participates in conferences such as IEEE CBMS and Health Informatics Conference . His technical projects involve Semantic Web Services , Heterogeneous Data Integration , and IoT security frameworks .

Beata MegyesiView profile

Professor

• Computational Linguistics
• Historical Cryptology
• Artificial Intelligence in Humanities
• +3 more

Beata Megyesi is a Professor of Computational Linguistics at Stockholm University , leading groundbreaking work in Historical Cryptology and Digital Humanities . Her research bridges Artificial Intelligence with Philology to decode secret historical documents through projects like DESCRYPT (2025-2032) and DECRYPT (2018-2024), funded by Riksbankens Jubileumsfond and Vetenskapsrådet . Current Chair of Swedish Research Council's Linguistics Review Group (2024-2025) Director of the international Master's Program in AI and Language Advisor to PhD candidates Micaella Bruton and Crina Tudor Research Focus includes: Automatic analysis of 17th-19th century ciphers with AI Development of Linked Open Data infrastructure for cryptology Decryption of papal and diplomatic correspondences from 1500-1965 Creation of HistCorp multilingual historical corpus collection Key Collaborations span institutions in Sweden, Norway, Spain, Germany, Hungary, and the USA , with notable projects like DECODE2LOD and Swe-CLARIN . Her lab has pioneered automated key extraction and neural network-based alignment of encrypted manuscripts to plaintext.

Eva BlomqvistView profile

Associate Professor

• Artificial Intelligence
• Knowledge Representation
• Semantic Web Technologies
• +5 more

Eva Blomqvist is an Associate Professor in the Knowledge Modelling and Cognitive Systems (KMACS) group at the Department of Computer and Information Science (IDA), Linköping University. She holds a Ph.D. and Docent in Computer Science, specializing in AI, knowledge representation, and Semantic Web technologies. Her research focuses on ontology engineering, knowledge graphs, and their applications in security, sustainability, and healthcare domains. She leads projects like Onto-DESIDE and Trace4Value, advancing decision support systems through semantic technologies. Blomqvist has extensive editorial contributions, including co-editing proceedings for major Semantic Web conferences (ESWC, ISWC). She supervises students in ontology design, semantic systems, and data visualization. Her work integrates interdisciplinary approaches, collaborating on projects such as VaccEval (e-health) and LOD-Sweden (linked open data). She is a member of the Human-Centered Systems (HCS) division and actively contributes to ontology pattern methodologies like the eXtreme Design approach. Her publications emphasize ontology alignment, knowledge graph applications in circular economies, and semantic technologies for emergency response. She collaborates internationally, publishing in venues like Semantic Web Journal and ACM Computing Surveys . Blomqvist’s research also extends to data-driven approaches in criminal intelligence analysis and vaccine surveillance through projects like SPIRIT.

Rickard ArmientoView profile

Associate Professor

• Materials Science
• Theoretical Physics
• Artificial Intelligence
• +4 more

Rickard Armiento is an Associate Professor and Head of the Materials Design and Informatics unit at Linköping University's Department of Physics, Chemistry and Biology (IFM). His research focuses on computational methods, AI, and materials design, particularly in semiconductor defects, quantum materials, and high-throughput modeling. He leads the OPTIMADE initiative for materials data standardization and develops tools like ADAQ for defect analysis. His work integrates theory and informatics to discover new materials for quantum technologies and energy applications. Recent research includes predicting magnetic properties in alloys, automated symmetry analysis of defects, and AI-driven crystal structure prediction via models like WyckoffDiff and WREN. Armiento co-organized the OMDI 2021 workshop on materials data interoperability and contributes to open science through ontology development (e.g., materials design ontology, units of measure ontology). His group’s tools and APIs enable global collaboration in materials discovery.

Timotheus KampikView profile

Assistant Professor

• Intelligent Systems
• Automated Reasoning
• Formal Argumentation
• +5 more

Timotheus Kampik is a Tenure-Track Assistant Professor at the Department of Computing Science , Umeå University , Sweden. He combines his academic work with an industry role as Principal Scientist at SAP , focusing on strategic research engagement related to intelligent processes, decisions, and transformation. Kampik's research centers on principle-based automated reasoning and the engineering of intelligent systems with applications in multi-agent systems, formal argumentation, and business process management. Kampik is a WASP Fellow supported by the Wallenberg AI, Autonomous Systems and Software Program. His work often bridges theoretical foundations with practical implementations, reflected in both his academic publications and industry contributions. He has served as co-chair for the International Workshop on Argumentation for eXplainable AI (ArgXAI) and organized sessions at major conferences like AAMAS and ECAI. Research Interests: Abstract and Quantitative Bipolar Argumentation Multi-Agent Systems Engineering Explainable AI and Human-AI Interaction Business Process Intelligence Formal Reasoning and Decision Theory Key Publications: Disagree and Commit (2025) - Argumentation-based agreements Kettle Logic in Abstract Argumentation (2024) Change in Quantitative Bipolar Argumentation (2024) Business Process Improvement with AB Testing (2024) Scientific Recognition: Best Paper Award, ICPM 2024 Best Program Committee Member Award, AAMAS 2022 Best Post-Graduate Student Paper Award, WESAAC 2020 WASP Fellowship recipient Teaching & Supervision: Lecturer for courses including Software Engineering and Artificial Intelligence at Umeå University Supervised multiple Master's and Bachelor's theses on topics like machine learning integration, human-aware planning, and deception detection Co-developed open-source agent programming libraries like JS-son

• Automation Engineering
• Functional Programming
• Optimization
• +7 more

Richard Senington is a Senior Lecturer in Automation Engineering at the School of Engineering Science, University of Skövde. He holds a PhD from the University of Leeds (UK) obtained in 2013 and has been working at the University of Skövde since 2016 after several years as a software consultant. His primary research and teaching focus on automation technologies within industrial production systems. Senington completed his doctoral studies at the University of Leeds in 2013 before working as a software consultant for several years. He returned to academia in 2016 when he joined the University of Skövde, where he has since established himself as a researcher in automation engineering and related fields. His research interests span Functional Programming, Optimization techniques, Industrial Internet of Things, and Human-Robot Collaboration. Senington's work particularly focuses on applying advanced computational methods to industrial production problems. His recent research has explored Knowledge Graphs for manufacturing decision support, Monte Carlo Tree Search applications in production systems, and containerization technologies for factory software management. He actively investigates how these technologies can improve production efficiency, decision-making processes, and system resilience in manufacturing environments. His publication record shows consistent output in industrial simulation and production engineering venues, with a focus on practical applications of computational methods in manufacturing. His most recent work (2024-2025) centers on Knowledge Graph applications for production systems, while his earlier work (2018-2021) focused on containerization technologies and Monte Carlo Tree Search applications. Senington is currently involved in two major research projects: 'Future-Proof Factories: The SMART Way to Resilient Production' (December 2025 - November 2029), which explores how factories can maintain operations during disruptions like rising energy prices or machine failures, and 'Virtual Factories of the Future' (October 2018 - September 2026), an eight-year research profile focused on knowledge-driven optimization in manufacturing. He previously contributed to the 'SUDO' project (October 2020 - September 2024), which addressed lock-in effects in IT system development for complex application domains. Within the EU-funded SYMBIO-TIC project, Senington collaborates with Magnus Holm on Human Robot Collaboration research. His teaching responsibilities include multiple courses at both bachelor's and master's levels in engineering disciplines, with a focus on advanced production technologies and automation systems.

• Cloud Computing
• Big Data Pipelines
• Machine Learning
• +4 more

Mihhail Matskin is a Professor at the Royal Institute of Technology (KTH) within the Department of Software Logic & Computer Systems. He specializes in cloud computing, big data pipelines, machine learning, and distributed systems. His work emphasizes optimizing cloud resource allocation, developing graph-based cost models, and advancing AI-driven systems like SQL recommendation engines and relation extraction frameworks. He actively contributes to KTH's educational mission through roles as examiner and course manager in advanced computer science and engineering courses, including Distributed AI, Cloud Storage Optimization, and Big Data Workflow Design. His research spans over two decades, focusing on scalable data management solutions, edge computing applications in healthcare, and semantic analysis of social media. Notable projects include the DataCloud initiative for big data pipeline orchestration and the DEF-PIPE DSL visualization framework. Matskin’s work bridges theoretical advancements with practical implementations, addressing challenges in reproducibility of LLM-based systems, containerized edge computing, and constraint programming for robotics. His academic contributions include pioneering studies on cloud cost modeling, rule-based storage tiering, and contrastive learning for NER tasks. He has advised numerous advanced-level degree projects across computer science, embedded systems, and communication technologies. Matskin’s lab focuses on innovative solutions at the intersection of distributed systems, AI, and data engineering.

Sándor DarányiView profile

Professor

• Digital Libraries
• Information Retrieval
• Digital Preservation
• +5 more

Sándor Darányi is a Professor at the Swedish School of Library and Information Science, University of Borås (since 2011), with a career spanning over four decades in information science, digital libraries, and cultural heritage preservation. He also holds an Honorary Professorship at Szeged University, Hungary. His research focuses on advanced access to digital libraries, automatic indexing, information visualization, digital preservation, narrative genomics, and quantum interaction models for semantics. Education: Candidate of Science (CSc) in Ethnography, Hungarian Academy of Sciences (1994) PhD in Information Science, Eötvös Loránd University (1989) MA in Library and Information Science, Eötvös Loránd University (1985) MSc in Agricultural Sciences, University of Agriculture (1975) Research Interests: Darányi’s work bridges computational methods with cultural studies, including: - Formalization of folk narratives for big data analysis - Quantum-inspired models of semantic change - Haptic interfaces for accessibility in museums - Evolving semantics in digital ecosystems - Cultural heritage digitization projects Professional Activities: Editorial Board Member of Journal of Information Science Education Organized major conferences such as SEMANTiCS, IC-ININFO, and AMICUS workshops Supervised doctoral students in computer science and information studies Co-developed the PERICLES project on digital preservation Labs/Teams: His research groups have pioneered projects like the MuseIT inclusive museum initiative and the SHAMAN digital preservation framework, leveraging high-performance computing and cloud technologies.

Henrik BjörklundView profile

Associate Professor

• Natural Language Processing
• Formal Languages
• Machine Learning
• +3 more

Henrik Björklund is an Associate Professor at the Department of Computer Science, Umeå University. His research focuses on natural language processing , formal languages , and mitigating social bias in large language models . Primary affiliation: Umeå University Department: Computing Science Role: Faculty member Research interests include: Detecting and mitigating gender bias in NLP systems like ChatGPT Foundational studies in formal languages and automata theory Developing language technology for Swedish, particularly nonbinary pronouns like 'hen' Publication trends show: Recent work (2023-2024) on intersectional bias analysis in LLMs 2017-2023 studies on hyperedge replacement grammars and DAG language transduction 2013-2017 contributions to XPath evaluation and tree pattern queries Contact : henrik.bjorklund@umu.se

Lili JiangView profile

Associate Professor

• Artificial Intelligence
• Privacy Protection
• Fairness in AI
• +4 more

Lili Jiang is an Associate Professor at the Department of Computing Science, Umeå University, where she serves as Director of Studies and Programme Director for the MSc in Computing Science. She holds a PhD in Computer Science with expertise in AI trustworthiness, data mining, and natural language processing. Her research bridges computer science and artificial intelligence, focusing on privacy, fairness, and federated learning systems. Her research interests center on: Developing trustworthy AI systems with emphasis on privacy-preserving techniques Ensuring algorithmic fairness in socio-technical decision-making Advancing few-shot learning methods for NLP tasks Creating robust data federation frameworks for cross-database analysis Recent publications demonstrate strong focus on few-shot learning (particularly in NLP), privacy-preserving AI , and applied data mining for industrial/environmental systems. Her work consistently addresses real-world challenges through multimodal learning and explainable AI techniques. She leads the Deep Data Mining research group and collaborates with the Interpresence institute. Key projects include: Explainable, Safe, Contact-Aware Planning for Heavy Machinery (XSCAVE, 2024-2028) Assessment and Engineering of Equitable AI Systems (2022-2024) Privacy-aware Federated Database Infrastructure for Micro-Data Analysis (2018-present)

Petter EricsonView profile

Researcher

• Responsible AI
• Graph Theory
• Formal Grammars
• +4 more

Petter Ericson is a Researcher at the Department of Computer Science , Umeå University , specializing in Responsible AI . His work bridges graph theory , formal grammars , and ethics , with interdisciplinary interests in music and sociotechnical systems . Affiliated with the Responsible AI Research Group , he explores the alignment of AI with human values. Ericson’s research spans formal computational models , including hyperedge replacement grammars and order-preserving DAGs , alongside AI ethics and fairness frameworks . His publications address sociotechnical limits in AI safety, artistic shifts through AI, and graph language learnability. He has contributed to interdisciplinary projects like Concordia , a musical XR instrument for planetary data sonification, and Musereduce , a hierarchical music analysis framework. Ericson applies computational methods to Bayesian music theory and jazz harmony datasets.