Find Academic Supervisors

• Healthcare Data Analysis
• Machine Learning
• Deep Learning
• +5 more

Awais Ashfaq is a Postdoctoral Researcher at Halmstad University's School of Information Technology, focusing on healthcare data analytics and AI applications in clinical decision-making. His work bridges the gap between advanced machine learning techniques and practical healthcare solutions, with particular emphasis on emergency department diagnostics and cardiovascular disease analysis. Dr. Ashfaq's research interests center around healthcare data analysis, applying statistics, traditional machine learning, deep learning, and language models to extract insights for informed clinical and management decisions. His work demonstrates strong expertise in interpretable AI models for medical diagnostics, particularly in emergency settings for conditions like dyspnea. He has made significant contributions to the development of evidence-based diagnostic tools that integrate seamlessly with clinical workflows. His publication record shows a consistent focus on leveraging complete healthcare system data to address real clinical challenges. The research trends indicate increasing sophistication in model interpretability while maintaining high diagnostic accuracy. His work spans multiple healthcare domains including emergency medicine, cardiology, and pediatric care, demonstrating the versatility of his methodological approaches. Dr. Ashfaq collaborates extensively with physicians across all care levels to ensure his research translates effectively to clinical practice and improves patient outcomes. His work on regional healthcare information platforms demonstrates commitment to building infrastructure that supports ongoing medical research and quality improvement initiatives.

Enayat RajabiView profile

Associate Professor

• Machine Learning
• Knowledge Engineering
• Data Analytics
• +4 more

Dr. Enayat Rajabi is an Associate Professor of Data Analytics at the Shannon School of Business , Cape Breton University , Canada. He also serves as an Adjunct Professor at Dalhousie University and is affiliated with Nova Scotia Health as a scientist. His academic journey included a Ph.D. in Information and Knowledge Engineering from the University of Alcalá, Spain, and he has contributed extensively to machine learning and semantic web domains. Education: Ph.D. in Information and Knowledge Engineering, University of Alcalá, Spain (2015) Master of Software Engineering, Ferdowsi University of Mashhad, Iran (2004) Bachelor of Software Engineering, Razi University, Iran (2001) Dr. Rajabi's research focuses on machine learning , knowledge engineering , and semantic web applications in healthcare and smart cities. His work explores explainable AI frameworks, knowledge graph construction, and data-driven solutions for sustainable transportation and clinical decision support systems. His recent publications highlight trends in knowledge graph integration with large language models for healthcare, graph neural networks , and predictive analytics in urban environments. He has secured significant grants, including the NSERC Discovery Grant and Mitacs Research Training Award , to advance these domains. Scientific Contributions: NSERC Discovery Grant (2020-2025) - Semantic Web Analysis over Nova Scotia Open Data ($156,000) New Health Investigator Grant (2022-2024) - Machine Learning for ALC Patients ($97,418) Mitacs Globalink ($4,250) - Graph Neural Networks CBU RISE grants for Explainable Clinical Decision Support Systems and Multi-Label Text Classification Dr. Rajabi has mentored numerous research assistants across projects and maintains active collaborations with institutions in Canada, Spain, and Iran. His technical expertise spans Python, Tableau, Databricks, and PySpark, with teaching responsibilities in Predictive Analytics , Data Visualization , and Quantitative Methods .

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.

• Anomaly Detection
• Topic Modeling
• Human-Computer Interaction
• +3 more

Tove Helldin is a Senior Lecturer at the School of Informatics, University of Skövde, Sweden. Her research focuses on anomaly detection , topic modeling , and human-computer interaction , particularly in telecommunications networks and decision support systems . She has led projects in AI for climate adaptation and future sepsis diagnostics , emphasizing team effectiveness and trust calibration in automated systems. PhD in Computer Science (2014), University of Skövde Licentiate in Computer Science (2012) MSc in Computer Science (2009) Her scientific work spans interactive machine learning , visualization of causal relationships , and transparency in military threat evaluation . She has contributed to automotive UI design and fighter aircraft automation , with publications in venues like ACM Computing Surveys and IEEE conferences. Notably, her work explores topic modeling applications in network monitoring and interpretable AI frameworks.

• Epigenetics
• Cancer Biology
• DNA Methylation
• +3 more

Mattias Aine is an affiliated researcher at Lund University Cancer Centre (LUCC) and a Postdoctoral fellow in the Division of Molecular Hematology (DMH). He holds roles as an Associate researcher in Breast/lung cancer and participates in research projects related to Lung Cancer. His work contributes to UN Sustainable Development Goals focused on good health and well-being. His research interests center on epigenetics, DNA methylation, and molecular characterization of cancers, particularly breast and lung cancers. Collaborations span international teams, with a focus on genomic and epigenomic profiling. Recent studies include analyzing tumor purity adjustments in DNA methylation data and developing automated image analysis tools for immune cell quantification in breast cancer. He has co-authored over 29 publications, with notable work in Nature Communications , Scientific Reports , and International Journal of Cancer . His projects are funded by institutions like Cancerfonden and Mrs. Berta Kamprad's Cancer Foundation. As an assistant supervisor, he mentors PhD students in cancer research.

• Deep Learning
• Robustness in Machine Learning
• Label Noise
• +4 more

Erik Englesson is a Researcher at the Division of Robotics, Perception and Learning at KTH Royal Institute of Technology. His work is supported by the Wallenberg AI, Autonomous Systems and Software Program (WASP-AI/MLX). He holds a PhD from KTH Royal Institute of Technology, focusing on label noise in image classification from an aleatoric uncertainty perspective. His research interests center on robustness and uncertainty quantification in deep learning, particularly addressing label noise and combining aleatoric and epistemic uncertainties. He teaches Probabilistic Graphical Models (DD2420) at KTH. Recent publications explore topics like gradient-based explanation methods, autoencoder parameterization, and noise-robust classification strategies. His work frequently intersects with computer vision and theoretical deep learning principles. Englesson collaborates with prominent researchers like Hossein Azizpour and receives funding from strategic initiatives in AI and autonomous systems. His research bridges foundational theory with practical applications in healthcare imaging and robust model design.

Alireza MohammadinodooshanView profile

Research Fellow

• Social Media Analysis
• User Engagement Dynamics
• Data Mining
• +3 more

Alireza Mohammadinodooshan is a Postdoctoral Fellow at Linköping University's Department of Computer Science (IDA), Sweden, working within the Database and Information Technology (ADIT) research group. He contributes to the Wallenberg AI, Autonomous Systems and Software Program (WASP) – Sweden's largest individual research initiative – focusing on data-driven analysis of social media engagement dynamics across Twitter, Facebook, and Instagram platforms. His research centers on quantifying how political bias, news reliability, and content-agnostic factors shape temporal user engagement patterns. Key interests include social media analysis, user engagement dynamics, data mining, information systems, network science, and artificial intelligence, with emphasis on cross-platform comparative studies and algorithmic amplification effects in news consumption. Analysis of his 15 most recent publications reveals consistent focus on temporal modeling of engagement decay, multi-format content interaction (photos/videos/albums), and the interplay between news source characteristics and user behavior. His methodological approach combines large-scale dataset analysis with network theory to identify virality predictors and platform-specific engagement mechanics. No scientific awards were documented in available sources. No advising roles or research grants were referenced in the provided materials. He operates within the ADIT research group at Linköping University, which specializes in advanced database and information systems for the digital society. This group forms part of IDA's broader WASP-affiliated ecosystem focused on AI-driven autonomous systems, enabling interdisciplinary collaboration on large-scale data challenges in social computing.

Thomas VakiliView profile

Researcher

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

Rickard PetterssonView profile

Senior Lecturer

• Glaciology
• Ice sheet dynamics
• Glacier mass balance
• +5 more

Rickard Pettersson is a Senior Lecturer in Physical Geography at Uppsala University's Department of Earth Sciences, where he works within the Program for Air, Water and Landscape Sciences. His research focuses on glaciology and the dynamics of ice sheets, with particular emphasis on the flow dynamics, stability, and climatic response of glaciers. Dr. Pettersson's research interests center on glacier mass balance, thermodynamics, and the coupling between dynamical and hydrological processes on glaciers and ice sheets. He specializes in collecting, processing, and interpreting geophysical and remote sensing data, and assimilating these data into glaciological models. His work spans multiple geographical regions including Svalbard, Greenland, Antarctica, and Sweden, with particular focus on Storglaciären in Sweden. Analysis of his recent publications reveals a strong focus on Arctic and Antarctic glacial systems, with particular attention to Svalbard. His research examines ice sheet hydrology (particularly firn aquifers), glacier mass balance, subglacial processes, and the impacts of climate change on glacial systems. Methodologically, his work combines field measurements, remote sensing techniques (especially ground-penetrating radar), and numerical modeling approaches. Dr. Pettersson maintains an active research profile with numerous collaborations across international institutions. His work appears in leading journals in glaciology and earth sciences including The Cryosphere, Journal of Glaciology, and Geophysical Research Letters. While specific grant information isn't provided in the available text, his extensive publication record spanning multiple continents suggests participation in significant research projects and collaborations. His research group appears to focus on physical geography with emphasis on glaciological fieldwork and data analysis. The Program for Air, Water and Landscape Sciences at Uppsala University likely provides the institutional framework for his research activities, facilitating studies of cryospheric processes in both Arctic and Antarctic environments.

• Artificial Intelligence and Law
• Legal Informatics
• Jurimetrics
• +5 more

Peter Wahlgren is a Professor of Law and Information Technology at the Faculty of Law, Department of Law, Stockholm University, appointed to this position in 2001. His academic career spans over three decades at the intersection of legal scholarship and technological innovation. His educational credentials include: LL.D. (1992) from Stockholm University with dissertation "Automation of Legal Reasoning: A Study on Artificial Intelligence and Law" (Kluwer) Docent (Habilitation) in Jurisprudence (Allmän rättslära) and Law and IT (Rättsinformatik), both awarded in 1994 Professor Wahlgren's research centers on legal methods, proactive law, legal risk analysis, and legislative techniques, with pioneering contributions to legal informatics and jurimetrics. His work examines how computational methods transform legal reasoning and law-making, particularly through artificial intelligence applications. He investigates the evolution from traditional "Lex Scripta" toward "Law 4.0" systems in the digital age, emphasizing risk-based approaches to legal design. His publication trends reveal a strategic progression from foundational AI-law studies toward contemporary regulatory frameworks for emerging technologies. Recent works focus on AI governance, legislative adaptation to technological complexity, and the societal implications of automated legal systems. His scholarship consistently appears in Scandinavian Studies in Law and specialized yearbooks addressing law and informatics. Scientific awards: No specific awards or honors are documented in the provided materials. He leads significant research initiatives including the Bridge Project (2015 deliverable on societal implications) and the Dual Twins project (2024), while serving as editor for the Scandinavian Studies in Law series. His grant portfolio demonstrates sustained funding for interdisciplinary technology-law research. Professor Wahlgren co-founded and actively participates in the Nordic Conference on Law and IT (upcoming 40th conference in 2024), serves within the Swedish Law and Informatics Research Institute (celebrating 50 years in 2018), and contributes to the Institute for Scandinavian Law's editorial board. His current projects address AI patent landscapes (2025) and regulatory complexity in digital governance.

• Wave Energy Conversion
• Renewable Energy Systems
• Ocean Engineering
• +10 more

Andrej Savin is a Senior Lecturer in the Department of Electrical Engineering at Uppsala University, Sweden, where he conducts research in marine renewable energy systems, particularly wave energy converters (WECs). He is based at the Ångström Laboratory and is actively involved in the Lysekil wave energy research project. Position: Senior Lecturer Institution: Uppsala University Department: Department of Electrical Engineering Email: andrej.savin@angstrom.uu.se His research focuses on the mechanical and electrical design, structural integrity, and performance optimization of point-absorbing wave energy converters. Key areas include mooring dynamics, power take-off systems (e.g., Low-RPM Torque Converter), ice interaction, and numerical modeling of WEC behavior under extreme and irregular sea conditions. He also explores the integration of machine learning for predicting WEC motion in hybrid offshore energy systems. The recent publications highlight a strong trend in experimental validation, structural analysis, and system-level optimization of wave energy technologies. His work spans hydrodynamics, mechanical design, thermal management, and data analysis, contributing to the advancement of reliable and efficient marine energy solutions. Andrej Savin has not received any explicitly mentioned scientific awards in the provided text. He has supervised or collaborated with multiple researchers and students, though no formal advisees are listed. His work is supported through institutional research funding at Uppsala University, particularly within the wave energy research group. He contributes to both experimental and theoretical aspects of offshore energy systems, with a focus on real-world deployment challenges in the Baltic Sea. He is part of the wave energy research team at Uppsala University, centered at the Lysekil research site, which conducts full-scale testing of wave energy converters. This team investigates power generation, survivability, sensor integration, and environmental interactions of marine energy devices.

• Artificial Intelligence
• Machine Learning
• Data Mining
• +1 more

Farzaneh Etminani is a Senior Lecturer at the School of Information Technology, Halmstad University. Her research focuses on Artificial Intelligence , Machine Learning , and Healthcare Informatics , with specific projects including CAISR Health , Evaluation of deep learning for LBD diagnosis , AIR – Artificially Intelligent use of Registers , and iMedA – Improving Medication Adherence . Her recent work includes explainable AI frameworks, synthetic electronic health records evaluation, and temporal modeling of clinical data. Key collaborations span neuroimaging, chronic disease prediction, and human-centered AI systems. She contributes to interdisciplinary research bridging computer science and clinical applications. Her publications cover topics such as: 3D deep learning for dementia diagnosis Transfer learning in neuroimaging Temporal fidelity in synthetic medical data Graph neural networks for clinical risk prediction Behavior change strategies in digital health interventions

• Artificial Intelligence
• Machine Learning
• Anomaly Detection
• +6 more

Yuantao Fan is an Associate Senior Lecturer at Halmstad University's School of Information Technology, specializing in artificial intelligence and machine learning applications for industrial systems. His academic work bridges theoretical research with practical implementations in predictive maintenance and equipment monitoring. Dr. Fan's research focuses on Artificial Intelligence, Machine Learning, Anomaly Detection, and Data Mining , with particular emphasis on streaming data from real-world applications. His work spans equipment diagnosis and prognosis, predictive maintenance (PdM), and condition-based monitoring systems. His research integrates self-organizing models with expert knowledge to create robust diagnostic frameworks applicable to transportation systems and industrial equipment. Analysis of Dr. Fan's publication record from 2013-2025 reveals consistent specialization in predictive maintenance for transportation systems , particularly bus fleets and heavy vehicles. His work demonstrates progression from basic fault detection methods to sophisticated hybrid approaches combining physics-based models with deep learning. Recent publications increasingly address explainability in predictive maintenance systems and applications in healthcare analytics. Dr. Fan actively contributes to academic instruction as course responsible and examiner for Databases, Introduction to Linux and Python, and Linux administration system courses. He also teaches Artificial Intelligence, Data Mining, Perspectives on Computer Science and Engineering, Machine Learning for Predictive Maintenance, and AI Implementation courses. His technical expertise spans multiple domains including automotive systems diagnostics, battery health monitoring, acoustic signal processing, and healthcare prediction models. Dr. Fan's work demonstrates strong industry collaboration, particularly with Volvo and city bus fleet operators, translating academic research into practical industrial applications.

• Machine Learning
• Artificial Intelligence
• Cybersecurity
• +4 more

Zahra Taghiyarrenani is a postdoctoral researcher at Halmstad University , affiliated with the School of Information Technology and the IT - Computer Department . Her work bridges theoretical advancements in machine learning with practical applications in industrial and cybersecurity domains. Education : PhD in Machine Learning (2024, Halmstad University), Licentiate Thesis on Multi-Domain Learning (2022) Her research focuses on domain adaptation , federated learning , and explainable AI , with emphasis on handling data scarcity and heterogeneity. Key projects include fault identification in industrial systems, intrusion detection, and physics-informed models for diagnostics. Her publications highlight trends in cross-domain learning , combining privacy-preserving AI with edge computing , and addressing data efficiency through few-shot/supervised methods . Recent work explores Industry 5.0 applications of Explainable AI. Projects: EVE : Extending vehicle lifespan in electromobility Federated Learning for Well-being : Privacy and fairness in AI FeelAI : Edge-based predictive logistics models

Selpi SelpiView profile

Researcher

• Data-centric AI
• Multimodal Learning
• Unsupervised Learning
• +6 more

Selpi Selpi is a Researcher at the Department of Data Science and AI , Chalmers University of Technology. They lead Work Package 3 (Methodologies for Assessment) in the EU-funded PANACEA project , focusing on fit-to-drive assessments for professional drivers through data fusion and impairment detection. Supervision: Main advisor to doctoral students Amer Mustajbasic (2023-present) and Mengyu Huang (2024-present). Research Interests: Data-centric AI, multimodal learning, unsupervised learning, and interpretable AI applied to autonomous driving and traffic safety. Key contributions include algorithms for driver behavior analysis in mixed traffic, BEV data fusion techniques, and scenario-based risk assessment frameworks. Publications from 2025–2017 span journals like IEEE Transactions on Intelligent Transportation Systems and conference proceedings at ECML PKDD.