Find Academic Supervisors

Marc KeuschniggView profile

Professor

• Cultural Dynamics
• Social Norms
• Urban Inequality
• +5 more

Marc Keuschnigg is a Professor of Sociology at Leipzig University and a Professor at The Institute for Analytical Sociology (IAS) at Linköping University. He holds roles as executive director of Leipzig’s Institute of Sociology and member of the steering group of the Swedish Excellence Center for Computational Social Science. His research focuses on cultural dynamics, social norms, and urban inequality, leveraging computational methods to study public discourse, social influence, and spatial inequality. He leads three research groups at IAS: Text Analysis, Cultural Dynamics, and Spatial Inequality. Recent work includes studies on misinformation propagation, cognitive ability among top earners, and urban scaling laws. Education includes a PhD from LMU Munich (2012) and a habilitation from Linköping University (2018). Awards include the Robert K. Merton Award (2017), Karl Polanyi Prize (2016), and Anatol Rapoport Prize (2014). He has supervised multiple PhD students and secured grants totaling millions in computational social science research. His work is published in journals like Nature Human Behaviour, PNAS, and Science Advances.

Eleonora RosatiView profile

Professor

• Intellectual Property Law
• EU Copyright Law
• Fashion Law
• +4 more

Eleonora Rosati is a Professor of Intellectual Property Law at the Department of Law, Stockholm University. She co-directs the LLM in European Intellectual Property Law and teaches modules including Copyright and Transborder Litigation and Fashion and Intellectual Property Law . Her research focuses on EU copyright harmonization, online intermediary liability, AI-related challenges, and fashion industry IP. Education: PhD in EU Copyright Law (European University Institute, 2012) LLM (University of Cambridge, 2009) Laurea Specialistica in Giurisprudenza (University of Florence, 2008) Laurea in Scienze Giuridiche (University of Florence, 2006) Her recent publications analyze AI training data licensing , DSM Directive implementation , and metaverse IP enforcement . She has contributed to policy debates at the European Parliament , WIPO , and EUIPO . Notable awards include the 2022 Adepi Award and recognition as an Influential Woman in IP (2020, 2025) and one of the 50 Most Influential People in IP (2018). She serves as Of Counsel at Bird & Bird and co-edits the Journal of Intellectual Property Law & Practice . As a permanent member of The IPKat team since 2012 and author of over 1,000 posts, she shapes global IP discourse. Her work spans online intermediary responsibilities , text and data mining , and international jurisdiction in digital copyright cases. She has advised on Belgian additional remuneration rights and Italian Supreme Court decisions regarding store layout design protection.

Jalal MalekiView profile

Lecturer

• Natural Language Processing
• Machine Translation
• Computational Linguistics
• +3 more

Jalal Maleki is a Lecturer and Head of Unit at Linköping University's Department of Computer and Information Science (IDA). He serves as Director of Studies and is affiliated with the Human-Centered Systems (HCS) division, focusing on human-centered computing, cognitive science, and information systems. His research emphasizes natural language processing, machine translation, and computational linguistics. Research interests include Arabic relation extraction, transliteration systems, and finite state morphology applications. His work spans conference papers on topics like cross-language learning and statistical machine translation reordering models. Maleki collaborates with institutions like the Division for Human-Centered Systems, contributing to interdisciplinary projects at one of Northern Europe's leading computer science departments. No specific awards are listed, but his contributions to machine translation evaluation (e.g., ELEXR framework) and transliteration systems demonstrate technical expertise. Advising and grant details are not explicitly mentioned, though his role as Head of Unit suggests administrative leadership in academic programs.

Martin BoldtView profile

Senior Lecturer

• Machine Learning
• Data Science
• Computational Criminology
• +1 more

Martin Boldt is Senior Lecturer in Computer Science at Blekinge Institute of Technology, leading the Artificial Intelligence and Data Analytics lab. His research applies machine learning to industrial fault prediction and computational criminology. Current projects funded by Swedish Research Council and Länsförsäkringars forskningsfond analyze crime-CCTV relationships using data-driven approaches. Earlier work with Ericsson Research developed anomaly detection systems for telecom infrastructure. Supervises PhD students in AI applications for customer lifecycle management and crime pattern analysis General chair for SAIS'23 and IEEE EISIC'18 conferences

Wasif AfzalView profile

Professor

• Software Testing
• Model-Based Testing
• Embedded Systems
• +4 more

Wasif Afzal is a Professor at Mälardalen University, affiliated with the School of Innovation, Design and Engineering and the Division of Networked and Embedded Systems. His primary role includes duties as an orientation representative for the school. His research focuses on software testing methodologies, model-based testing, embedded systems, cybersecurity, and blockchain applications, with significant contributions to industrial collaborations. Key research interests include advancing automated testing frameworks, optimizing test case generation and prioritization, and enhancing cybersecurity through innovative approaches like serious gaming for phishing resilience. He has led projects such as the SmartDelta initiative, which addresses automated quality assurance in industrial software systems, and contributed to EU-funded projects like Adeptness for DevOps integration in cyber-physical systems. His recent work explores gaps in software testing education, leveraging machine learning for test reduction, and applying EARS notation for PLC system requirements validation. Over 50 publications reflect his expertise in testing tools, safety-critical systems, and systematic literature reviews in software engineering. Notable projects include developing the Pylc framework for PLC software validation and exploring blockchain applications for food safety. He actively engages in improving test automation strategies and measurable outcomes in safety-critical domains.

• Machine Learning
• Visual Analytics
• Explainable AI
• +7 more

Juhee Bae is a Senior Lecturer in the Department of Information Technology at the University of Skövde, Sweden. She is affiliated with the AI research group, focusing on machine learning, visual analytics, and explainable AI. Her work bridges research and education, with courses in advanced data science and experience in teaching coordination. Ph.D. in Computer Science, North Carolina State University Merited Teaching Award (2023) Mobility Grant for Belgian University Collaboration (2020) Her research spans machine learning , visual analytics , and explainable AI , with applications in Steel industry process optimization Climate-adaptive water management Migration intention forecasting Smart production logistics Wearable biosensor analysis Recent publications highlight predictive models for weather-driven migration, causal discovery frameworks, and interactive data mining techniques. She contributes to editorial boards and conference organization. Juhee Bae serves as course coordinator for Explainable AI and has been active in international collaborations. Scientific awards include 2023 Merited Teaching Award 2020 Mobility Grant Her projects STRATUS (AI for climate adaptation), INSITE-X (steel industry AI), and Understanding Human Migration demonstrate practical AI applications. She works with interdisciplinary teams and has presented talks on predictive machine learning and explainable AI.

• Scientometrics
• Bibliometrics
• Science and Technology Studies (STS)
• +6 more

Gustaf Nelhans is an Associate Professor at the Department of Library and Information Science, University of Borås. He holds a PhD in Theory of Science and leads the Data as Impact Lab, focusing on machine learning and citation analysis for research impact. Current projects: HUMINFRA (Swedish Research Council 2022–2028), Publishing Platforms (Riksbankens Jubileumsfond 2023–24), Evaluation (Norwegian Research Council) Former projects: Data4Impact (Horizon 2020), INCITE (Västra Götalandsregionen), Chronic Disease Impact Mapping (EU FP7) His research spans scientometrics , open science , digital humanities , and research policy . Recent work includes AI for citation context analysis, policy document citation studies, and societal impact assessment frameworks. Scientific advisory roles: Royal Library's Research Council (Chair 2022–23), SUHF Bibliometrics Working Group, Royal Library Privacy Council Supervision: Main supervisor for Fereshta Westin (2021–), co-supervisor for Camilla Lindelöw (2022–) and Marco Schirone (2018–)

• Machine Learning
• Neural Networks
• Activity Recognition
• +3 more

Kunru Chen is a Research Fellow at Halmstad University 's School of Information Technology , specializing in machine learning and industrial automation. His research focuses on activity recognition for material handling machines, predictive maintenance, and domain adaptation techniques. His work leverages sensor data and recurrent neural networks (e.g., LSTM, GRU) for real-time classification and failure prediction in industrial systems. Recent publications highlight applications in forklift activity recognition and air compressor fault detection. The trends in his publications emphasize time series analysis , industrial IoT , and deep learning optimization for limited-labeled data scenarios. No scientific awards or student advisement details were explicitly mentioned in the provided texts.

• Air Quality Analysis
• Microdata Analysis
• Urban Pollution Modeling
• +6 more

Yves Rybarczyk serves as Professor of Microdata Analysis at Dalarna University's Department of Data and Information Management. His academic profile spans computer science, environmental science, and healthcare technology applications, reflecting a highly interdisciplinary research approach. Currently teaching User-centered Design (GIK348), Data Warehousing (AIK232), and Bachelor's thesis in Microdata Analysis (GMI2C8), he maintains active engagement in both undergraduate education and research supervision. Professor Rybarczyk's research focuses primarily on air quality analysis, urban pollution modeling, and machine learning applications for environmental monitoring. His work demonstrates particular expertise in analyzing PM2.5 effects on urban environments, biosphere-atmosphere interactions, and the application of digital twin technology in home care settings. His research methodology frequently employs causal analysis, convergent cross mapping, and ensemble deep learning approaches to address complex environmental challenges. Analysis of his recent publication record reveals a strong trend toward interdisciplinary environmental data science, with approximately 60% of his work focusing on air quality and pollution modeling, 25% on healthcare technology applications including tele-rehabilitation systems, and 15% on broader data science applications. His publications appear in high-impact journals across multiple disciplines including environmental science, computer science, and public health, demonstrating his ability to bridge academic domains through data-driven approaches. While specific grant information isn't detailed in the available text, Professor Rybarczyk's collaborative publication pattern suggests involvement in international research projects, particularly with colleagues from South American institutions as evidenced by multiple studies focused on Quito, Ecuador. His research appears to receive support for environmental monitoring initiatives and digital health technology development.

• Corporate Sustainability
• Sustainability Reporting
• Accounting Information Systems
• +4 more

Asif M Huq serves as a Senior Lecturer in Information Science at Dalarna University's School of Information and Engineering, where he coordinates the Data Analysis and Visualization course (AMI23A). His academic profile bridges computational methods with sustainability accounting, leveraging institutional partnerships including Trafikverket's sustainable transport initiatives. Dr. Huq's research centers on computational sustainability analysis , specializing in NLP-driven CSR report assessment , materiality measurement frameworks , and governance impacts on corporate sustainability . His methodology integrates text mining and quantitative disclosure analysis to evaluate greenhouse gas reporting quality, with particular focus on Swedish private firms navigating global crises like the pandemic and Ukraine war. Analysis of his 2021-2025 publications reveals three convergent research streams: (1) technical innovations in measuring accountable information through NLP techniques; (2) empirical investigations of how sustainability performance interacts with financial resilience during geopolitical disruptions; and (3) practical applications in sustainable logistics via LivingLab frameworks. His work consistently utilizes Swedish firm datasets to address real-world sustainability reporting challenges. Through the FEDeRATED project, Dr. Huq contributes to the Data-sharing SME, Last-Mile Delivery LivingLab initiative under Trafikverket's sustainable transport program, developing collaborative data platforms for urban logistics optimization while maintaining strong ties to Sweden's national transport research infrastructure.

Lisa DellmuthView profile

Professor

• Public Opinion
• Legitimacy
• Global Governance
• +7 more

Lisa Dellmuth is a Professor at the Department of Economic History and International Relations, Stockholm University. She co-directs the Stockholm Center for Global Cooperation (SCGG) and leads the PhD program in International Relations. Her work focuses on public opinion, legitimacy, and redistribution in global governance, employing experimental and quantitative methodologies. Education: PhD in Political Science (2011, University of Mannheim) Postdoctoral Fellowships: Stockholm Resilience Center (2015–2016), Stockholm University Political Science Department (2011–2014) Her research explores legitimacy politics, elite communication, and climate governance, with books such as Legitimacy Politics: Elite Communication and Public Opinion in Global Governance (Cambridge University Press, 2023) and Citizens, Elites, and the Legitimacy of Global Governance (Oxford University Press, 2022). She coordinates major projects funded by Formas, Mistra, EU, Riksbankens Jubileumsfond, and the Swedish Research Council. Recent publications address peer opinion, ideological divides, non-state actor participation, and climate adaptation. Awards include the Michael Endres Prize 2021 , Sage Award 2012 , and UACES Honorable Mention 2022 . Editorial Roles: European Journal of Political Research, Interest Groups & Advocacy Program Committees: Political Economy of International Organizations (PEIO) annual conferences

Berkant SavasView profile

Associate Professor

• Scientific computing
• Numerical linear and multilinear algebra
• Large-scale graph/network computations
• +3 more

Berkant Savas is an Associate Professor at Linköping University's Department of Science and Technology (ITN) within the Physics, Electronics and Mathematics school. His research focuses on scientific computing, numerical linear/multilinear algebra, large-scale graph/network computations, and tensor analysis. He has developed influential algorithms like Clustered Matrix Approximation (CMAPP) and Grassmann manifold computational tools. His work bridges theoretical foundations with practical applications in data science and engineering. Key contributions include scalable methods for massive graphs, low-rank tensor approximations, and optimization on manifolds. Savas' MATLAB-based tools (e.g., Grassmann classes, tensor approximation packages) are widely used in academic and industrial research. He has published extensively in top journals and conferences such as SIAM Journal on Matrix Analysis and IEEE transactions. Current research explores applications in link prediction, recommendation systems, and high-dimensional data analysis. Savas collaborates internationally, with notable co-authors like Inderjit Dhillon and Lars Eldén. His work emphasizes computational efficiency and memory optimization for large-scale problems arising in networks and information retrieval. Though no explicit awards are mentioned, his impactful contributions highlight recognition within the computational mathematics community.

Sonja AitsView profile

Senior Lecturer

• Artificial Intelligence in Medicine
• Cell Death Mechanisms
• Lysosomal Biology
• +8 more

Sonja Aits is an Associate Senior Lecturer and Research Team Manager at Lund University, focusing on interdisciplinary research at the intersection of Artificial Intelligence, Cell Biology, and Environmental Science. She leads studies on lysosomal biology, cell death mechanisms, and their implications for human health and environmental sustainability. Her work bridges computational methods with biomedical applications, including AI-driven image analysis and text mining in medicine. Affiliations: Lund University Cancer Centre (LUCC), BECC (Biodiversity & Ecosystems), and multiple AI-focused profile areas (e.g., AI and Digitalization). Research Themes: Lysosome regulation, environmental toxin effects, AI in microscopy, and sustainable healthcare. Her research interests emphasize leveraging AI to analyze large-scale biomedical datasets, develop predictive models for protein function, and integrate multi-omics data. Collaborations include NEUBIAS, TRANSAUTOPHAGY, and international networks like eSSENCE and EpiHealth. Publications highlight contributions to high-content microscopy datasets, AI ethics in healthcare, and interdisciplinary AI education. She actively participates in conferences and public outreach on AI’s societal impact, particularly in Sweden. Grants and Projects: Over 22 funded projects, including FORMAS-supported studies on environmental toxins and AI-based text mining for pandemic response. Supervised one doctoral student to date. Labs/Teams: Manages research teams in Cell Death studies and coordinates initiatives like the 'Cell Death Census' and OpenChart-SE medical data projects.

Maria LindénView profile

Professor

• Artificial Intelligence
• Wearable Sensors
• Healthcare Technology
• +4 more

Maria Lindén is a Professor at Mälardalen University's School of Innovation, Design and Engineering within the Division of Intelligent Future Technologies. Her research focuses on healthcare technology, wearable sensors, and artificial intelligence , with applications in medical imaging, chronic disease monitoring, and smart home systems. She collaborates extensively with industry and healthcare providers to develop practical solutions for non-invasive health monitoring and behavioral change interventions. Key research areas include machine learning for medical diagnostics (e.g., melanoma detection via deep learning), IoT-enabled health systems (e.g., smart home cognitive assessment), and signal processing algorithms for vital sign measurement (e.g., blood pressure estimation from PPG signals). Her work bridges clinical needs with engineering innovation, emphasizing interdisciplinary collaboration. Her publications (2021–2025) reflect a strong focus on wearable sensor systems , health technology adoption , and data-driven clinical decision support . Recent studies explore AI-driven tools for early disease detection, eHealth interventions for sedentary behavior, and novel algorithms for medical signal analysis. She has no listed awards but has led numerous research projects and co-authored over 50 peer-reviewed articles. Her team develops open-source tools like PPGFeat and collaborates on platforms for real-time patient monitoring. No student advisees are explicitly mentioned in the provided texts.

Nina KhairovaView profile

Visiting Professor

• Computational Linguistics
• Machine Learning for Text Mining
• Misinformation Detection
• +2 more

Nina Khairova is a Visiting Professor at the Department of Computing Science , Umeå University, specializing in computational linguistics and responsible AI. Her research focuses on misinformation detection, machine learning for text mining, and the ethical implications of large language models (LLMs). She actively contributes to projects analyzing war-related news reporting and social media credibility during the Russian invasion of Ukraine. Projects: Human-AI Method for Detecting Misinformation in News Reporting on Russia’s Invasion of Ukraine Building Trust in Social Media: Assessing Credibility of Ukrainian Migration Information Her work involves multilingual dataset creation, semantic labeling, and AI algorithm integration for text analysis. Contact: nina.khairova@umu.se .