جستجوی استاد راهنمای دانشگاهی

Danica Kragicمشاهده پروفایل

استاد

• Robotics
• Computer Vision
• Machine Learning
• +۳ مورد دیگر

Danica Kragic is a Professor of Computer Science at the School of Electrical Engineering and Computer Science at the Royal Institute of Technology (KTH) in Stockholm, Sweden. She serves as the Director of the Centre for Autonomous Systems and leads the Robotics, Perception and Learning Lab at KTH. Her research focuses on advancing robotics capabilities through computer vision and machine learning approaches. MSc in Mechanical Engineering from the Technical University of Rijeka, Croatia (1995) PhD in Computer Science from KTH (2001) Professor Kragic's research primarily centers on robotics, computer vision, and machine learning, with particular emphasis on robotic manipulation, grasp planning, and human-robot interaction. Her work bridges theoretical foundations with practical applications, exploring how robots can understand and interact with objects in complex environments. She investigates how visual and tactile sensing can be integrated to improve robotic perception and manipulation capabilities, with applications ranging from industrial automation to assistive robotics. Her recent publications demonstrate a strong focus on advanced grasp planning techniques, tactile sensing for manipulation, and mathematical representations for robotic control. Kragic's research shows increasing integration of machine learning approaches with traditional robotics frameworks, particularly in the areas of grasp synthesis, object recognition, and human-robot collaboration. Her work spans theoretical contributions in mathematical representations of grasps to practical implementations of robotic systems capable of adapting to novel objects and situations. 2007 IEEE Robotics and Automation Society Early Academic Career Award IEEE Fellow ERC Starting Grant (2012) Member of The Royal Swedish Academy of Sciences Member of The Royal Swedish Academy of Engineering Sciences Honorary Doctorate from Lappeenranta University of Technology Professor Kragic's research has been supported by major funding bodies including the EU, Knut and Alice Wallenberg Foundation, Swedish Foundation for Strategic Research, and Swedish Research Council. While specific student names aren't listed in the provided information, her publication record suggests extensive mentorship of PhD students and postdoctoral researchers in robotics and computer vision. Her lab, the Robotics, Perception and Learning Lab, serves as a hub for interdisciplinary research connecting computer science, engineering, and cognitive science perspectives on robotic systems. As Director of the Centre for Autonomous Systems at KTH, Kragic oversees a major research initiative focused on advancing autonomous technologies. Her Robotics, Perception and Learning Lab brings together researchers working on visual perception, machine learning, and robotic manipulation, with particular emphasis on developing systems that can understand and interact with objects in unstructured environments. The lab's work spans theoretical foundations of robotic manipulation to practical implementations of systems capable of learning from experience.

Xiaoming Huمشاهده پروفایل

استاد

• Multi-agent systems
• Nonlinear control systems
• Systems theory
• +۴ مورد دیگر

Xiaoming Hu is a Professor at the Division of Numerical Analysis, Optimization and Systems Theory within the Department of Mathematics at KTH Royal Institute of Technology (Kungliga Tekniska Högskolan) in Stockholm, Sweden. Born in Chengdu, China, he received his B.S. degree from University of Science and Technology of China in 1983, followed by M.S. and Ph.D. degrees from Arizona State University in 1986 and 1989 respectively. After serving as a research assistant at the Institute of Automation, Chinese Academy of Sciences (1983-1984), he was a Gustafsson Postdoctoral Fellow at KTH (1989-1990) before becoming a faculty member. His educational background includes: B.S. in Engineering, University of Science and Technology of China, 1983 M.S. in Engineering, Arizona State University, 1986 Ph.D. in Engineering, Arizona State University, 1989 Xiaoming Hu's research primarily focuses on multi-agent systems, nonlinear feedback stabilization, nonlinear observer design, and sensing and active perception. His work bridges theoretical control theory with practical applications in robotics and autonomous systems. He has made significant contributions to geometric control theory, mathematical systems theory, and nonlinear systems analysis and control. His research often involves developing theoretical frameworks for distributed control, formation control, and cooperative behavior in multi-robot systems. Professor Hu's publication record shows a consistent research trajectory with numerous high-impact publications in top-tier journals like Automatica, IEEE Transactions on Automatic Control, and Systems & Control Letters. His research has evolved from fundamental control theory to more applied problems in robotics and multi-agent systems, while maintaining strong mathematical foundations. Recent work shows increasing focus on safety-critical control, inverse problems in estimation, and networked systems. His scientific contributions include: Development of theoretical frameworks for multi-agent coordination and formation control Advances in nonlinear observer design for robotic systems Contributions to geometric control theory and systems theory Research on distributed estimation and control algorithms Applications of control theory to robotics and autonomous systems Professor Hu teaches several advanced courses including Mathematical Systems Theory, Geometric Control Theory, and Nonlinear Systems: Analysis and Control. He has supervised numerous degree projects at both undergraduate and graduate levels in mathematics, optimization, systems theory, and scientific computing. His teaching reflects his research expertise, providing students with both theoretical foundations and practical applications of control theory.

Jens Rydgrenمشاهده پروفایل

استاد

• Political Sociology
• Radical Right-wing Politics
• Social Networks
• +۴ مورد دیگر

Jens Rydgren is Professor of Sociology at Stockholm University, where he holds the Chair in Sociology since 2009. He is affiliated with the Department of Sociology within the Faculty of Social Sciences. Rydgren graduated from Stockholm University in 2002 and has been a visiting scholar at Columbia University, Cornell University, Harvard University, École des Hautes Études en Sciences Sociales in Paris, Copenhagen University, and Utrecht University. He is a fellow of the Royal Swedish Academy of Letters. Professor Rydgren's primary research interests lie in political sociology, particularly the study of radical right-wing parties and their voters, with a focus on explaining variation over time and across countries. He also has a longstanding interest in social networks and how relational and contextual factors influence individuals' beliefs, opportunities, and actions. His additional research interests include ethnic conflict, belief formation processes, collective memory, fanaticism, and sociology through literature. Much of his work combines theoretical insights with empirical analysis to understand complex social phenomena. His recent publications demonstrate a consistent focus on radical right-wing politics, xenophobia, nationalism, and social network analysis. Rydgren frequently employs comparative and multi-method approaches to examine political behavior and social phenomena. His work often intersects political sociology with social network theory to understand how contextual factors shape political attitudes and behaviors. He has made significant contributions to understanding how social networks influence various outcomes, from political attitudes to health behaviors. Fellow of the Royal Swedish Academy of Letters Rydgren is currently the Principal Investigator (PI) of the research project "Why do working class voters support the populist radical right?" funded by the Swedish Research Council. Previously, he was PI for the ERC Starting Grant project "Individual Life Chances in Social Context: A Longitudinal Multi-Methods Perspective on Social Constraints and Opportunities" (2011-2018), which combined survey data and register data to study the importance of egocentric networks for young adults' life chances. His research has important implications for understanding contemporary political challenges and developing evidence-based policy responses.

• Quantum Computing
• Superconducting Circuits
• Quantum Noise Reduction
• +۳ مورد دیگر

Jonas Bylander is a Professor at Chalmers University of Technology in the Department of Microtechnology and Nanoscience, specifically within the Quantum Technology division. He leads a research group focused on developing quantum computers using superconducting circuits.

Harald Rohracherمشاهده پروفایل

استاد

• Sociotechnical transitions
• Climate change
• Sustainable development
• +۹ مورد دیگر

Harald Rohracher is a Professor at the Department of Theme (TEMA) , Linköping University , affiliated with the Theme Technology and Social Change (THEME) . His research focuses on sociotechnical transitions for climate-neutral cities , examining energy systems, digitalization, and governance strategies. Grants: Formas, Horizon Europe, Vinnova Labs: STRIPE (Sociotechnical Research on Infrastructures, Politics, and Environment), Biogas Solutions Research Center Students: Adam Svensson, Stella Huang, Gavin Nilsson Lewis, Giorgi Kankia Rohracher investigates how households , social movements , and policy frameworks interact in transformative processes. His recent work explores smart grid politics , decentralized energy systems , and urban food digitalization , emphasizing power dynamics and governance experiments. He leads projects on transdisciplinary education for climate governance, positive energy districts , and collective experimentation across Sweden and Spain. His scientific grants highlight institutional support for sustainability research, while his research teams bridge academic, governmental, and civil society actors.

Gunnar Anderssonمشاهده پروفایل

استاد

• Demography
• Fertility
• Mortality
• +۶ مورد دیگر

Gunnar Andersson is a Professor of Demography at Stockholm University's Department of Sociology and Head of the Stockholm University Demography Unit (SUDA). He leads research on fertility, migration, mortality, and family dynamics in Nordic contexts, with a focus on register-based methodologies. His work explores socio-demographic shifts, including fertility decline, migrant health outcomes, and the intersection of welfare policies with family formation. University: Stockholm University Department: Department of Sociology Research Group: Stockholm University Demography Unit (SUDA) Research Interests: Gunnar’s work addresses fertility trends in Sweden and the Nordic region, migration’s impact on population health, and the role of socio-economic factors in family dynamics. Key projects include studies on fertility intentions post-2010, mortality disparities among migrants, and the societal implications of declining birth rates. His studies utilize advanced demographic techniques and longitudinal register data for policy-relevant insights. Recent Research Trends: Publications highlight analyses of Nordic fertility decline, socio-demographic risk factors for COVID-19 mortality, and cross-national comparisons of family policies. Collaborative projects with SUDA emphasize multi-generational and spatial demographic shifts, including residential segregation and commuter mobility in urban Sweden. Grants and Collaborations: Lead researcher in projects funded by Swedish Research Council and EU programs. Oversees the Swedish Generations and Gender Survey (GGS), a key infrastructure for demographic research. Directs the SUNDEM node, leveraging Sweden’s administrative registers for demographic innovation. Labs/Teams: Leads SUDA, a multidisciplinary group analyzing population dynamics. Collaborates with international partners in Denmark, Norway, and Germany for comparative studies.

• Bioinformatics
• Systems Biology
• Disease Module Identification
• +۶ مورد دیگر

Zelmina Lubovac is a Senior Lecturer in BioInformatics at the School of Bioscience, University of Skövde. She serves as both a Course Coordinator for multiple undergraduate and graduate courses in bioinformatics and a Programme Coordinator for Master's level programs. Her academic work focuses on the intersection of computational methods and biological applications, particularly in disease analysis and biomarker discovery. Dr. Lubovac's research spans several key areas in bioinformatics and systems biology: Disease module identification in complex biological networks Multi-omics integration (genomics, proteomics, metabolomics) for biomarker discovery Machine learning applications in RNA-seq and other high-throughput biological data Development of bioinformatics software tools for network analysis miRNA analysis in cancer and neurological disorders Her recent publications (2022-2024) demonstrate a strong focus on applying computational approaches to understand disease mechanisms, particularly in pancreatic cancer and multiple sclerosis. She has developed several widely-used bioinformatics tools including MODalyseR, MODifieR, and TFTenricher that facilitate disease module analysis and gene network interpretation. Her work often involves collaborative research with clinical teams to translate computational findings into potential diagnostic applications. Dr. Lubovac has been involved in significant research projects including: BIO-AID (Biomedical AI-driven data analytics): Oct 2020 - Sep 2024 Systems Biology DMDPipe: Mar 2018 - Feb 2021 She actively contributes to both undergraduate and graduate education at the University of Skövde, coordinating multiple courses and programs in bioinformatics and bioscience, with a clear emphasis on preparing students for careers at the intersection of biology and computational science.

• Control Technology
• Distributed Optimization
• Machine Learning
• +۴ مورد دیگر

Mikael Johansson is a Professor at Kungliga Tekniska Högskolan (KTH), specializing in Control Technology . He teaches and coordinates courses such as Distributed Optimization (FEL3311) and various advanced-level degree projects in computer science, electrical engineering, and systems engineering. His research spans Control Systems , Machine Learning , and Optimization , with a focus on asynchronous algorithms, federated learning, and applications in energy systems and construction. His work includes 15 recent publications on topics like neural networks, distributed optimization, and battery technology. Notable areas of contribution are in asynchronous learning, federated learning with privacy constraints, and quasi-Newton methods for optimization. His research bridges theoretical advancements with practical applications in urban design, healthcare, and autonomous systems.

• Artificial Intelligence
• Natural Language Processing
• Machine Learning
• +۴ مورد دیگر

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

Sophie Isaksson Hallstedtمشاهده پروفایل

استاد

• Sustainable Product Development
• Strategic Sustainability
• Lifecycle Solutions
• +۴ مورد دیگر

Sophie Isaksson Hallstedt is a Full Professor at Chalmers University of Technology, conducting research in Sustainable Product Development (SPD) with a focus on strategic sustainability integration in product innovation. She holds appointments at both Chalmers and Blekinge Institute of Technology, and serves on international Design Society committees. Key Research Areas: Strategic socio-ecological sustainability, digital decision support tools, circular value chains, and sustainability in emerging technologies. Notable Projects: SUSTAIN (aerospace sustainability), Circular Design Nexus (user behavior analysis), and Digital Materials Ecosystems. Awards & Recognition: Featured in Royal Swedish Academy of Engineering Sciences (IVA) 100-list (2020, 2023). Publications: Over 80 peer-reviewed works demonstrating methods for sustainability maturity assessment, impact evaluation, and progress visualization. Teaching & Education: Developed master's programs and PhD courses in sustainability-driven product development. Collaborates extensively with industry partners like GKN Aerospace and VINNOVA. Current work examines predictive models for user behavior and sustainability implications of additive manufacturing.

Morteza Haghir Chehreghaniمشاهده پروفایل

استاد

• Machine Learning
• Decision Making
• Reinforcement Learning
• +۱۷ مورد دیگر

Morteza Haghir Chehreghani is a Professor of Artificial Intelligence and Machine Learning at the Data Science and AI Division of Chalmers University of Technology , Sweden. He leads the Machine Learning and Decision Making Lab and is affiliated with WASP , CHAIR , and ELLIS . Education : PhD in Computer Science (2014) from ETH Zurich under Prof. Dr. Joachim M. Buhmann Prior Roles : Staff Research Scientist at Naver Labs Europe (2014-2018) Research spans Interactive Machine Learning , Sequential Decision Making , Federated Learning , Efficient Deep Learning , and Graph-Based Learning . Key application areas include Transport , Autonomous Systems , Energy , Drug Discovery , and Computational Biology . Selected Publications (2020-2025) demonstrate expertise in Reinforcement Learning for drug design, Minimax Distance Measures for clustering, and Graph Neural Networks for trajectory analysis. Current work focuses on Combinatorial Bandits and Human-in-the-loop AI . Teaching includes graduate courses like Advanced Topics in Machine Learning (DAT441/DIT41), Algorithms for Machine Learning (TDA233/DIT382), and PhD-level Advanced Reinforcement Learning . He has also taught Statistical Methods for Data Science and Theoretical Foundations of ML . Patents include systems for Autonomous Vehicle Motion Control , K-NN Search via Minimax Distances , and Trip Prediction Algorithms . Collaborative projects involve Nature Communications (2022) and multiple ICML / CVPR publications.

Andreas Duitمشاهده پروفایل

استاد

• Comparative Environmental Politics
• Climate Policy
• Environmental Governance
• +۴ مورد دیگر

Andreas Duit is a Professor of Political Science at Stockholm University and also serves as a Guest Professor at the Political Science Unit, Luleå University of Technology. His academic work focuses on comparative environmental politics with special emphasis on the role of the state in addressing environmental challenges. Department of Political Science, Stockholm University Guest Professor, Political Science Unit, Luleå University of Technology Former visiting scholar at the Workshop in Political Theory and Policy Analysis at Indiana University, Bloomington Former visiting scholar at the Department of Political Science, University of Washington Professor Duit's research centers on comparative environmental politics, particularly examining how states address environmental challenges. His work employs quantitative methods and cross-national panel data analysis, with additional engagement in complex system analysis and survey research. His primary research interests include climate policy, environmental governance, sustainability transitions, and the relationship between welfare states and environmental policy. Analysis of Professor Duit's recent publications reveals a strong focus on climate governance mechanisms, environmental state development, and the interplay between political institutions and environmental outcomes. His research spans multiple analytical levels from international climate agreements to domestic policy implementation, with particular attention to how different governance structures affect environmental performance across countries. GREENTRANS: The Great Green Transformation – Politics, Markets and Civil Society in the Anthropocene (funded by Marcus and Marianne Wallenberg Foundation) THERAPY: The Electoral Ramifications of Environmental Policy (funded by FORMAS) GLEAN: A Global Survey of Learning, Participation and Ecosystem Management in Biosphere Reserves GRACE: Governing the Anthropocene – Environmental Policy and Outcomes in a Comparative Perspective Professor Duit has secured significant research funding from major Swedish research organizations including FORMAS, MISTRA, the Swedish Research Council, and the Marcus and Marianne Wallenberg Foundation. His research portfolio demonstrates a consistent focus on understanding the political dimensions of environmental challenges and identifying effective governance approaches for sustainability transitions.

• Environmental Policy
• Conservation Biology
• Human-Wildlife Conflict
• +۷ مورد دیگر

Camilla Sandström is a Professor at Umeå University's Department of Political Science, Faculty of Social Sciences. Her research focuses on environmental policy, conservation, and governance, with a particular emphasis on human-wildlife conflict, forest governance, and adaptive management. She leads projects exploring biosphere reserves, climate policy equity, and the application of machine learning in environmental analysis. Research Interests: Conservation biology, sustainable development, political ecology, and the governance of socio-ecological systems. Key Projects: Contested Spaces: Bridging Protection and Development in a Globalizing World (VR-funded). GOVFORBIO: Governance Pathways for Sustainable Forest Use (Formas-funded). Her work bridges theory and practice, contributing to international guidelines like the IUCN's Human-Wildlife Conflict & Coexistence Framework. She collaborates with interdisciplinary teams to address challenges in multi-use landscapes, such as lion conservation in Tanzania and forest conflicts in Sweden. Publications highlight innovative methodologies, including machine learning for media analysis and spatial ecology for wildlife connectivity. She emphasizes participatory approaches to ensure equitable policy outcomes in climate and biodiversity crises.

Tommy Löfstedtمشاهده پروفایل

دانشیار

• Machine Learning
• Computer Vision
• Medical Image Analysis
• +۴ مورد دیگر

Tommy Löfstedt is an Associate Professor at Umeå University , affiliated with the Department of Computing Science and the Department of Mathematics and Mathematical Statistics. His research focuses on machine learning , computer vision , and medical image analysis , with applications in life sciences, radiation therapy, and biomedical imaging. He leads multiple research projects, including AI-driven delineation in radiation therapy, quantitative MRI for radiotherapy, and machine learning for plant nutrient uptake. Current research emphasizes structured regularization methods to improve model interpretability and robustness. Key applications include medical image segmentation , Alzheimer's classification , and uncertainty estimation in MRI . Recent publications highlight his work on morphological regularization , adversarial attack mitigation , and multi-task learning in medical imaging contexts. His projects span 2022–2026 with funding for pediatric oncology automation and gynecological cancer staging. Affiliated with both computing and mathematical departments, he bridges algorithm development with applied mathematical frameworks in medical and life science domains.

Anders Pousetteمشاهده پروفایل

مدرس

• Organizational Psychology
• Leadership Training
• Information Security
• +۴ مورد دیگر

Anders Pousette is a Lecturer at the University of Gothenburg, affiliated with the Department of Organization, Health and Addiction. His academic work focuses on organizational psychology, leadership training, information security, occupational safety, and psychosocial working conditions in high-risk industries such as healthcare and construction. His research explores the interplay between organizational support climates and workplace safety, with a particular emphasis on ethical value conflicts in healthcare, leadership practices in construction, and security culture in nuclear and transportation sectors. He employs mixed-methods approaches and longitudinal studies to evaluate interventions and workplace dynamics. Recent publications highlight his work on managerial training programs, cross-cultural comparisons of safety practices, and the impact of psychosocial factors on information security behavior. His collaborations span multiple disciplines and institutions, particularly with Marianne Törner and Martin Grill.