Find Academic Supervisors

• Control Charts
• Measurement Error
• Performance Measure
• +5 more

Hamed Sabahno serves as a Senior Lecturer in the Department of Statistics at Lund University, Sweden. His academic profile is centered on advanced statistical methodologies with applications in engineering and quality control systems. His research spans statistical process control , speckle pattern analysis , and multivariate monitoring systems . Key focus areas include adaptive control charts, measurement error correction, and image-based displacement measurement techniques. His work bridges theoretical statistics with practical industrial applications, particularly in non-destructive testing and quality engineering. Recent publications demonstrate strong emphasis on Real-time process monitoring using optical methods Multivariate regression profile control Statistical refinement in speckle-based measurements Adaptive parameter systems for quality assurance His fingerprint analysis confirms dominance in Control Charts (100%) and Measurement Error (55%) research domains. Professional engagement includes ORCID registration (0000-0002-5618-887X) and active publication in high-impact journals including Scientific Reports and Quality and Reliability Engineering International . His research has attracted attention from 5 Mendeley readers and coverage in 1 news outlet.

Pelle SnickarsView profile

Professor

• Digital Cultures
• Media History
• Digital Humanities
• +4 more

Pelle Snickars is a Professor of Digital Cultures at Lund University's Department of Arts and Cultural Sciences within the Faculty of Humanities. His research sits at the intersection of media studies, media history, and digital humanities, with a particular focus on digital cultural heritage and the historical dimensions of digital media. Snickars has established himself as a leading scholar in Scandinavian media studies with significant contributions to understanding the evolution of media technologies and their societal impacts. His research interests span digital cultures, media history, digital humanities, cultural heritage, and digital archives. Snickars approaches these fields with both contemporary and historical perspectives, examining how digital technologies transform cultural heritage practices while also investigating the historical precedents for today's digital media landscape. His work often bridges theoretical frameworks with practical applications in digital archiving and cultural preservation. The trajectory of Snickars' recent publications reveals a growing emphasis on computational methods in media history research. His work increasingly incorporates digital tools for analyzing large historical datasets, as evidenced by projects like the Swedish Parliament Corpus and the Video Reuse Detector. While maintaining strong foundations in traditional media history, his research demonstrates a clear evolution toward methodologically innovative approaches that combine historical scholarship with digital analysis techniques. Snickars currently leads multiple significant research initiatives including the 'Modern Times 1936' project (funded by Riksbankens jubileumsfond) which uses digital methods to explore historical media materials in new ways, and he coordinates the national DIGARV research program (funded by the Swedish Research Council) focused on digitization and accessibility of cultural heritage collections. His work bridges academic research with practical applications in cultural heritage institutions. His laboratory and team work primarily through the Lund University research environment, collaborating with the Lund Centre for the History of Knowledge (LUCK) and other interdisciplinary research groups. These collaborations create a vibrant research ecosystem where media historians, computer scientists, and cultural heritage professionals work together to develop new approaches to digital cultural heritage.

Saghi HajisharifView profile

Researcher

• Visual Machine Learning
• HDR Imaging
• Photo-realistic Rendering
• +5 more

Saghi Hajisharif is a Researcher at Linköping University's Department of Science and Technology (ITN), affiliated with the Media and Information Technology (MIT) group. She holds a PhD in Visualization and Media Technology from Linköping University (2020), an MSc in Advanced Computer Graphics (2013), and a BSc in Computer Science from Amirkabir University (2009). Her work focuses on computational imaging, visual machine learning, HDR imaging, and light field technologies. She is a core member of the Computer Graphics and Image Processing research group. Research interests include sparse representation learning for computational imaging, synthetic data ethics, BRDF material modeling, and algorithmic fairness in AI. Her contributions span interdisciplinary projects recognized in IVA’s 100 List (2024), highlighting societal impact potential. She has co-authored influential studies on topics such as FROST-BRDF sampling techniques and metadata standards for GenAI synthetic data. Her articles reflect expertise in computer vision, graphics, and AI ethics, with key contributions to light field imaging, GAN fairness, and material modeling surveys. The IVA’s 100 List recognition underscores her innovative work’s societal relevance. She collaborates across disciplines to advance imaging technologies and ethical AI practices.

Tino EbbersView profile

Professor

• Cardiovascular Imaging
• Blood Flow Dynamics
• Tissue Characterization
• +4 more

Tino Ebbers is a Professor in Physiological Measurements at Linköping University, affiliated with the Department of Health, Medicine and Caring Sciences and the Division of Diagnostics and Specialist Medicine. He holds leadership roles, including Chair of the Strategic Area Circulation and Metabolism (LiU-CircM) and membership on the Faculty of Medicine and Health Sciences Board. His research focuses on cardiovascular imaging and modeling, emphasizing non-invasive assessment of blood flow dynamics and tissue characteristics using MRI and CT technologies. Key projects include HEART4FLOW and the Center for Medical Image Science and Visualization (CMIV). Education: PhD (Biomedical Engineering, 2001), MSc (Electrical Engineering, 1996). Previous roles include Visiting Professor at UCSF (2015) and Predevelopment Engineer at Philips Medical Systems (2002–2004). Research interests span 4D flow MRI, computational fluid dynamics, and translational imaging techniques for cardiovascular disease diagnostics. He received the 2024 Onkel Adam Prize for his contributions to medicine-technology integration in cardiovascular research. Teaching roles include examiner for courses in physiological pressures, biomedical engineering, and electrical engineering. His work bridges clinical needs with technological innovation, aiming to improve diagnostic accuracy and patient care through advanced imaging and modeling.

Alexandros SopasakisView profile

Senior Lecturer

• Machine Learning
• Artificial Intelligence
• Computer Vision
• +9 more

Alexandros Sopasakis is a Senior Lecturer at the Department of Mathematics, Faculty of Engineering (LTH), Lund University. He is affiliated with multiple research initiatives including eSSENCE (the e-Science Collaboration), ELLIIT (Linköping-Lund IT and mobile communication initiative), and LU Profile Areas in Natural and Artificial Cognition. Senior Lecturer in Mathematics Principal Investigator for eSSENCE Active in LU Profile Areas: Climate, Health, and AI Research Interests: His work bridges machine learning, dynamical systems, and applied mathematics, focusing on: Stochastic modeling of complex systems Attention-based forecasting (Transformers) Diffusion models for synthetic data generation Graph neural networks in transportation and agriculture Anomaly detection in signal processing Climate modeling under non-Gaussian noise Scientific Awards: Recipient of three patents in: 5G/6G mobility devices Beam management measurement optimization Traffic network forecasting

Cresantus BiambaView profile

Senior Lecturer

• Educational Science
• Teacher Education
• Education for Sustainable Development
• +3 more

Cresantus Biamba is a Senior Lecturer at the University of Gävle, specializing in Educational Science. His research bridges education theory with technological advancements, focusing on teacher training, sustainability in education, and inclusive pedagogy. Researcher at University of Gävle (Education, Educational Science) Research interests include: Education for Sustainable Development (ESD) in global contexts Teacher education reform and policy analysis Inclusive classroom practices in the Global South Technological integration in educational systems Curriculum development for post-pandemic resilience Publication trends reveal interdisciplinary work combining AI, cloud computing, and IoT applications with educational challenges, particularly in African institutions. His articles address security optimization, healthcare technology, and sustainability frameworks. Academic activities involve collaborations with researchers in cybersecurity, AI, and energy systems, though specific grants or mentoring roles are not explicitly documented here.

• Autonomous Vehicles
• Vehicle Control Systems
• Traffic Safety
• +5 more

Jonas Sjoberg is a Professor of Mechatronics at Chalmers University of Technology and leader of the Mechatronics research group. His work spans over 30 years with 143 publications and leadership in 29 research projects. His current research focuses on autonomous vehicle systems, vehicle dynamics, and traffic safety applications. Dr. Sjoberg's research interests center on mechatronic systems for transportation applications. His work bridges theoretical control systems with practical automotive implementations, particularly in autonomous vehicle technology. His research group investigates vehicle dynamics, path planning, intersection management, and safety systems, with growing emphasis on micromobility applications including autonomous bicycles and pedestrian interaction. Recent work demonstrates strong focus on real-world applications of control theory to improve vehicle safety and performance. His publication record shows consistent output with 15+ papers annually, demonstrating sustained research activity. The work spans fundamental control theory (system identification, nonlinear control) to applied transportation problems (intersection management, road surface estimation, autonomous docking). Recent publications show increasing focus on micromobility applications, particularly autonomous bicycles, and integration of machine learning techniques with traditional control approaches. Dr. Sjoberg leads multiple significant research projects including MicroSIM (2026-2027), Mintox (2025-2026), and MicroITS, with funding from VINNOVA, EU, and industry partners. His projects address critical challenges in autonomous vehicle safety, micromobility integration, and traffic management. As leader of the Mechatronics research group, Dr. Sjoberg oversees research on vehicle control systems, autonomous driving technologies, and safety applications. His laboratory work focuses on practical implementation of theoretical control concepts, with recent emphasis on bicycle dynamics and micromobility safety systems.

• Signal Processing
• Bayesian Statistics
• Deep Learning
• +4 more

Lars Hammarstrand is an Associate Professor at Chalmers University of Technology specializing in the Signal Processing research group. His work integrates model-based Bayesian statistics with deep machine learning for applications in visual localization, sensor fusion, and autonomous systems, with emphasis on robustness in real-world environments. His research focuses on bridging Bayesian inference and deep learning to solve challenges in autonomous vehicle perception. Key areas include visual localization under appearance changes, radar-camera sensor fusion, and out-of-distribution detection for safety-critical systems. Recent work explores neural radiance fields for radar, semi-supervised learning for mapping, and probabilistic hierarchical classification to address real-world uncertainties in autonomous driving. Analysis of his 2020-2025 publications reveals a trajectory toward unifying geometric and semantic understanding in autonomous systems. His work demonstrates increasing integration of neural radiance fields with traditional filtering techniques, while advancing open-set recognition capabilities. Notable contributions include road geometry estimation frameworks, extended object tracking with PHD filters, and methods to mitigate data leakage in localization benchmarks. No scientific awards were mentioned in the provided materials. Hammarstrand has contributed to academic supervision methodology through his publication on improving master's thesis supervision efficiency, though specific student names are not listed. The provided information contains no details about research grants or funding sources. He operates within Chalmers University's Signal Processing research group, which develops advanced algorithms for automotive perception systems, focusing on sensor fusion techniques that combine radar, camera, and motion data for robust environmental understanding in autonomous vehicles.

Måns MagnussonView profile

Associate Professor

• Bayesian Statistics
• Probabilistic Machine Learning
• Statistical Inference from Textual Data
• +4 more

Måns Magnusson is an Associate Professor at the Department of Statistics, Uppsala University, with affiliations at the Institute for Analytical Sociology (Linköping University) and the Institute for Future Studies. His work bridges Bayesian statistics, probabilistic machine learning, and textual analysis, focusing on model evaluation, diagnostics, and inference algorithms. He contributes to computational social science and digital humanities through text-as-data methods. Research Themes: Bayesian inference, probabilistic machine learning, statistical inference from textual data Key Applications: Sociology, political science, law, education statistics, public health Current Projects: Improving probabilistic programming generalizability (Swedish Research Council grant), SWERIK research infrastructure (Riksbankens jubileumsfond) His recent publications emphasize text mining, model comparison, and legal data challenges. He has developed tools for national ID number validation, hate crime estimation, and parliamentary corpus construction. Notable awards include the Cramér Prize (2018), Statistician of the Year (2023), and membership in the Swedish Young Academy (2023) and ELLIS (2024). Scientific Contributions: Botten Ada Bayesian election model, 'loo' package for cross-validation Collaborative Work: AI4Research sabbatical (2024), Riksbankens jubileumsfond funding Industry Background: Statistician roles at Swedish Agency for Education, Crime Prevention, and Public Health

• Industrial Digitalization
• Automation
• Human-Robot Collaboration
• +7 more

Professor Johan Stahre is affiliated with Chalmers University of Technology, where he leads the Division of Production Systems and serves as Assistant Head of Department. His expertise spans industrial digitalization, automation, and the human role in future manufacturing systems. Codirector of Produktion2030 (2013–present) Central figure in EIT Manufacturing development (2016–present) Chalmers representative in European Factories of the Future Research Organisation His research focuses on: Manufacturing resilience and uncertainty navigation Human-Robot Collaboration in restricted environments 5G-enabled smart maintenance systems Sustainability through digital servitization Skill gaps in Industry 4.0/5.0 Computer vision applications in assembly systems Recent publications highlight trends in immersive technologies for manufacturing and resilience frameworks. While no formal awards are listed, his leadership in national and European innovation programs underscores his impact.

• Computer Vision
• Biomedical Image Analysis
• Machine Learning
• +3 more

Kevin Smith is an Associate Professor at KTH Royal Institute of Technology, affiliated with the Division of Computational Science and Technology. His research focuses on applying machine learning and computer vision to biomedical problems, particularly in healthcare and medical imaging. Previously, he worked at ETH Zürich and EPFL. His work bridges AI, biology, and medicine, addressing challenges like disease diagnosis, risk prediction, and treatment optimization. Research Interests: Smith’s research spans computer vision, machine learning, and their applications in healthcare. Key areas include medical image analysis, AI-driven diagnostic tools, and privacy-preserving medical data processing. He explores topics such as tumor detection in ovarian ultrasound, breast cancer risk modeling, and MRI privacy through autoencoders. Publications & Trends: Recent work emphasizes AI-human collaboration frameworks, privacy in medical imaging, and domain adaptation for high-content data. His articles highlight advancements in transformer models for medical tasks and multicenter AI validation in breast imaging. Teaching & Roles: Smith teaches courses like Foundations of Machine Learning (DD1420) and oversees the Applied Programming and Computer Science program. He mentors MSc students in topics like medical image segmentation and risk prediction. Affiliations: He is part of the Science for Life Laboratory and contributes to KTH’s collaborative research ecosystem, including the VAI-B platform for AI validation in breast imaging.

Marcus LiwickiView profile

Professor

• Machine Learning
• Artificial Intelligence
• Pattern Recognition
• +4 more

Marcus Liwicki is a Chair Professor at Luleå University of Technology (LTU) and Senior Assistant Professor at the University of Fribourg, where he leads research in Machine Learning at the Department of Systems and Space Engineering. He serves on LTU's Vice-Chancellor's Council for Artificial Intelligence. Education: MS in Computer Science (Free University of Berlin, 2004) PhD (University of Bern, 2007) Habilitation (Kaiserslautern University of Technology, 2011) His research focuses on core machine learning (pattern recognition, neural networks) and applied AI in document analysis, quantum computing, and human-computer interaction. Recent publications demonstrate strong emphasis on generative AI models, multi-task learning optimization, and AI applications in physics/health domains. Awards: ICDAR Young Investigator Award (2015) for pattern recognition research He leads the Machine Learning Group at LTU's EISLAB, supervising multiple courses including Advanced Deep Learning, Neural Networks, and AI fundamentals.

• Image Processing and Computer Vision
• Machine Learning
• Evolutionary Computation
• +1 more

Seyed Jalaleddin Mousavirad (Jalal) serves as a Postdoctoral researcher at Mid Sweden University in Sundsvall, Sweden, within the Department of Computer and Electrical Engineering (DET) and affiliated with the STC Research Centre. His research focuses on advancing AI-driven solutions for sustainable technologies and complex optimization problems. He earned his PhD in Computer Engineering specializing in Artificial Intelligence from the University of Kashan, Iran. Previous academic appointments include Assistant Professor at Hakim Sabzevari University (Iran), instructor roles at the University of Tehran (2018-2019) and Azad University (2019-2020), and a Research Fellow position at the University of Beira Interior (Portugal) where he contributed to the European GreenStamp project on sustainable Android applications. Dr. Mousavirad's research spans Image Processing and Computer Vision, Machine Learning, Evolutionary Computation, and Applied Artificial Intelligence, with significant contributions in pattern recognition, metaheuristic algorithms, and neural network optimization. His work demonstrates strong interdisciplinary applications in healthcare diagnostics, power systems, and medical imaging. Recent publications reveal a pronounced trend toward federated learning frameworks for privacy-preserving medical analysis, adversarial robustness in diffusion models, and hybrid optimization techniques for ECG classification and brain tumor detection. This reflects a strategic focus on translating AI innovations into practical healthcare and sustainability solutions. He actively contributes to the academic community as a guest editor for journals including Computational Intelligence and Neuroscience, Entropy, and Mathematical Biosciences and Engineering. His editorial leadership extends to organizing special sessions at IEEE CEC and EvoApplications conferences. Dr. Mousavirad maintains extensive peer-review commitments across 50+ prestigious venues including IEEE Transactions on Evolutionary Computation and IEEE Transactions on Cybernetics. His collaborative research includes international engagements at Xi'an Jiaotong-Liverpool University (China) and current work within Mid Sweden University's STC Research Centre on energy-aware computing and neural network optimization.

• Informatics
• Digital Technologies
• Sustainable Digitalization
• +6 more

Arianit Kurti is a full professor of informatics at Linnaeus University, where he serves as Head of both the Informatics Department and the Computer Science and Media Technology Department. With over 20 years of international academic experience across Sweden, Kosovo, and North Macedonia, Prof. Kurti brings extensive expertise in digital technologies and sustainable innovation. He previously held research leadership positions at RISE Research Institutes of Sweden. Prof. Kurti's research focuses on data-driven approaches for business innovation to achieve sustainable digitalization. His work spans multiple domains including forestry (Forest 4.0), furniture industry digital transformation (APPEND, STELLA), education technology (Edutain, IGNITE), and social inclusion (STEFORA). He leads the Data-driven Business Innovation research group and is active in the Interaction Design Research Group, DISA research center, and LNU Systems Community. His recent publications reveal a strong emphasis on practical applications of AI, IoT, and data analytics across diverse sectors. The research trajectory shows progression from mobile learning and context-aware systems in early career to current focus on sustainable digitalization, business model innovation, and domain-specific applications in forestry and manufacturing. Key themes include human-centered technology design, data ecosystems, and bridging academic research with real-world industry challenges. Prof. Kurti has secured approximately €8.5 million in research funding from prestigious sources including EU EACEA, EU H2020, Swedish Knowledge Foundation, Vinnova, Wallenberg Foundation, and FORMAS. He currently leads two major EU Horizon projects focused on AI and IoT technologies for forestry management. He has published over 70 peer-reviewed works spanning journals, conferences, and books, with recent output showing strong international collaboration patterns. His research groups maintain active partnerships with industry and academic institutions across Europe, with particular strength in Scandinavian countries and the Balkans.

• Computer Vision
• Artificial Intelligence
• Machine Learning
• +5 more

Fahad Khan is an Associate Professor (Docent) at Linköping University, affiliated with the Department of Electrical Engineering (ISY) and the Computer Vision Laboratory (CVL). He is a core faculty member within the Faculty of Science and Engineering, contributing to research and academic activities in computer vision and related domains. His research interests lie primarily in computer vision , machine learning , and image processing , with applications likely spanning robotics, automation, and intelligent systems. As a member of CVL, his work aligns with cutting-edge developments in visual data analysis and AI-driven perception. While specific scientific awards and publications are not detailed in the provided text, his position as Associate Professor and Docent indicates a strong research track record and academic recognition. He advises students and contributes to the academic community through research supervision and collaboration. Fahad Khan is actively involved in the research ecosystem at Linköping University, particularly in the field of computer vision. His work benefits from institutional support and infrastructure, including access to advanced computing resources and interdisciplinary networks. He collaborates within the Computer Vision Laboratory and contributes to the broader mission of the Department of Electrical Engineering in advancing intelligent systems and technologies.