جست‌وجوی استاد و پژوهشگر

• Control Engineering
• Telecommunications
• Cloud Computing
• +۳ مورد دیگر

Johan Eker is a Professor at the Department of Automatic Control at Lund University and an Adjunct Professor at ELLIIT: the Linköping-Lund initiative on IT and mobile communication . He is also a member of the LTH Profile Area: AI and Digitalization and LU Profile Area: Natural and Artificial Cognition . His research focuses on control engineering, telecommunications, cloud computing, real-time systems, IoT, and anomaly detection. He actively contributes to UN Sustainable Development Goals through his work. He has received notable awards including the Best paper runner-up award at IEEE CloudNet 2023 , Best Paper Award at RTCSA 2004 , and Best Student Paper Award at RTCSA 1999 . Key projects include: AORTA: Advanced Offloading for Real-Time Applications (2023–2025) ICS: Industrial Cloud Sandbox (2019) AutoDC: Autonomous datacenter for long-term deployment (2018–2021) He has organized workshops such as the Real-Time Cloud Workshop and serves on the advisory board for Internet of Things and People (IoTaP) .

Markus H. Flierlمشاهده پروفایل

دانشیار

• Visual Computing and Communication
• Machine Learning
• Information Theory
• +۳ مورد دیگر

Markus H. Flierl is an Associate Professor at the School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology in Stockholm. He holds a PhD in Engineering from Friedrich Alexander University (2003). His career includes senior research roles at École Polytechnique Fédérale de Lausanne (2003-2005), leadership of the Max Planck Center for Visual Computing and Communication (2005-2008), and a Visiting Assistant Professorship at Stanford University (2000-2002). He has served as Program Director of the EIT Digital Master School’s 'Visual Computing and Communication' program and as Associate Editor for the IEEE Transactions on Circuits and Systems for Video Technology. Education: PhD in Engineering, Friedrich Alexander University, 2003 Studies at Friedrich Alexander University (1999-2002), EPF Lausanne (2003-2005), and Stanford University (2005-2008) Research Interests: Focused on visual computing, machine learning, and information theory. Key areas include distributed coding of dynamic scenes, multiview video coding, motion-compensated transforms, and applications in video compression and multimedia systems. His work explores efficient signal processing techniques for video and 3D visual search, with recent contributions in drone-based environmental monitoring and adversarial training methods. Awards: SPIE VCIP Young Investigator Award (2007) Teaching & Grants: Teaches courses on image/video processing, information theory, and multimedia systems. His research is supported by Ericsson, the Swedish Research Council, the European Commission, and KTH’s Digitalization Platform. He advises PhD and master’s students in visual computing and communication. Labs & Teams: Leads the Visual Computing and Communication research group at KTH, collaborating with industry partners like Ericsson and Qamcom. Former members include researchers now at Tobii AB and the University of Iceland.

• Neurology
• Neurosciences
• Radiology and Medical Imaging
• +۱۰ مورد دیگر

Anna Falk Delgado is an Associate Professor (Docent) in Radiology at Karolinska Institutet, affiliated with the Department of Clinical Neuroscience. She leads the Clinical Brain Imaging Methods research group and focuses on advanced neuroimaging techniques. University Medical Degree, Karolinska Institutet, 2009 Docent, Karolinska Institutet, 2021 Affiliated to Research, Department of Clinical Neuroscience, 2024-2027 Her research spans Neurology , Neurosciences , and Radiology and Medical Imaging , with expertise in PET/MR , Cone-Beam CT , Brain Tumor Imaging , and Stroke Radiology . She pioneered ultra-fast multi-contrast MRI protocols and dual-layer CT angiography for stroke evaluation. Recent publications emphasize technical innovations in hybrid imaging, stroke triage optimization, and pediatric neuroimaging safety. Her work integrates machine learning , radiation reduction , and clinical translation of imaging technologies.

Huizhong Caoمشاهده پروفایل

پژوهشگر

• Cognitive Ergonomics
• Mental Workload
• Manufacturing Digitalization Innovation
• +۵ مورد دیگر

Huizhong Cao is a researcher at Production Systems , Chalmers University of Technology. With a background in architecture and sustainable urban planning, she bridges design and technology through projects involving XR platforms , digital twins , and human-centered design . MSc in Sustainable Urban Planning (Chalmers) BSc in Architecture (Wuhan University) Her research focuses on mental workload assessment , XR interface design , and sustainable smart cities . Key projects include: RILIK - Immersive learning framework XR-TBP - Extended reality training for battery production TRIREME - Digital & Green Skills development DIGITALIS - Cognitive work optimization Recent publications analyze: VR interface design for collaborative manufacturing XR applications in environmental sustainability Digital twin integration with IoT Scientific recognition includes: IPOET Scholarship LEXUS Design Award Finalist KTH Innovative Incubator Program UIA-HYP Cup Third Prize She has contributed to UX design and XR development across multiple projects, often combining participatory design with game engine technology . Contact: huizhong@chalmers.se | huizhongcao1@gmail.com

Saghi Hajisharifمشاهده پروفایل

پژوهشگر

• Visual Machine Learning
• HDR Imaging
• Photo-realistic Rendering
• +۵ مورد دیگر

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.

• Scientific Visualization
• Data Analysis
• Medical Applications
• +۶ مورد دیگر

Ingrid Hotz is a Professor in Scientific Visualization at Linköping University, affiliated with the Department of Science and Technology (ITN) and the Center for Medical Image Science and Visualization (CMIV). She holds a Master's in Theoretical Physics from Ludwig Maximilian University (Munich) and a PhD in Computer Science from the University of Kaiserslautern. Her research focuses on data analysis and scientific visualization, spanning applications in fluid dynamics, medical imaging, and large-scale simulations. She has led research groups at the Zuse Institute Berlin (2006–2013) and the German Aerospace Center (DLR, 2013–2015). Her work integrates methods from computer graphics, computational geometry, and topology. Key research areas include multi-field visualization, topological data analysis, and scalable systems for ocean data exploration. She has contributed to software tools like VIAMD and pyParaOcean. Notable collaborations include material science research (e.g., beryllonitrene synthesis) and large-scale conference organization (Eurographics 2020, attracting 23,000 participants). Her research bridges theoretical foundations with practical applications in medicine, engineering, and environmental science. Publications highlight advancements in visualization techniques for molecular dynamics, medical imaging, and climate modeling. She actively participates in interdisciplinary projects, such as predicting liver steatosis dynamics and developing frameworks for analyzing brain activity via fMRI data. Her work emphasizes bridging gaps between computational methods and real-world scientific challenges.

Claes Lundströmمشاهده پروفایل

استاد مدعو

• Medical Imaging
• Artificial Intelligence
• Digital Pathology
• +۴ مورد دیگر

Claes Lundström is an Adjunct Professor at Linköping University's Department of Science and Technology (ITN), affiliated with the Media and Information Technology (MIT) school. His primary research focuses on medical imaging, integrating machine learning, visualization, and human-computer interaction in clinical settings. He leads the technical side of digital pathology research at the Center for Medical Image Science and Visualization (CMIV), directing national-scale grants and serving as Arena Director for the Analytic Imaging Diagnostics Arena (AIDA). Since 2010, he has held dual roles as Research Director at Sectra AB and academic researcher, driving innovations that translate into commercial healthcare solutions. Lundström's work spans AI-driven diagnostics, uncertainty visualization, and precision orthopedics, with contributions recognized through leadership in major projects like the €70M BIGPICTURE initiative. His academic credentials include a PhD (2007) and Docent degree (2014), alongside extensive industry collaboration. Education: PhD in 2007 and Docent degree in 2014 from Linköping University. PhD: 2007 Docent: 2014 Research interests emphasize AI integration in diagnostics, particularly digital pathology and medical imaging workflows. His work addresses challenges like domain adaptation in AI models, uncertainty quantification in segmentation tasks, and interactive visualization tools for clinicians. Recent projects include the VAI-B platform for AI validation in breast imaging and the BigPICTURE collaboration for global pathology data sharing. Key grants include leadership roles in national-scale projects, such as the AIDA arena and SCAPIS study-based AI lab. His contributions to AI ethics and clinical adoption include studies on human-AI collaboration and quality assurance frameworks. Lundström also oversees labs like CMIV and AIDA, fostering cross-disciplinary research in medical imaging and AI.

Yudi Pawitanمشاهده پروفایل

استاد

• Bioinformatics
• Statistical Genetics
• Genomics
• +۲ مورد دیگر

Yudi Pawitan is Professor at the Department of Medical Epidemiology and Biostatistics at Karolinska Institutet, where he leads the research group on Statistical and Bioinformatics Analyses of High-Throughput Molecular Data. His work focuses on developing statistical methods for genomic studies including SNP/RNA arrays and next-generation sequencing. Education includes BSc in Statistics (Bogor Agriculture Institute, 1982), MSc in Statistics (UC Davis, 1984), and PhD in Statistics (UC Davis, 1987). Research interests span bioinformatics, cancer genomics, and statistical genetics with emphasis on high-dimensional data analysis, genetic correlations, and neural cell biology. His group addresses fundamental questions in genomic data interpretation and neurodegenerative processes. Publications demonstrate strong focus on genetic epidemiology, single-cell analytics, and statistical methodologies. Recent works explore machine learning applications in longitudinal data visualization, neural cell characterization, and cancer biomarker discovery. Awards: Not documented in provided texts. Supervises doctoral candidates including Linda Lindström and Ralf Kuja-Halkola. Manages the Live Imaging Facility at St. Vincent's Centre for Applied Medical Research. Research funded by Swedish Research Council and Swedish Cancer Society grants. Leads interdisciplinary collaborations through the Statistical and Bioinformatics research group, integrating computational biology with experimental neuroscience.

• Applied Machine Learning
• Microbiology
• Drinking Water Distribution Systems
• +۳ مورد دیگر

Niklas Gador is a Senior Lecturer in the Department of Computer Science at the Faculty of Natural Science. His research focuses on applying machine learning techniques to microbiological challenges, particularly in water distribution systems. He specializes in visualization, classification, anomaly detection, forecasting, and reinforcement learning, using Python as his primary tool. Current teaching responsibilities include courses in Discrete Mathematics, Statistics, Methods for Measuring Electricity, and Calculus and Algebra. Research collaborations include projects like EIVF-AI (Enhancing In Vitro Fertilization with AI) and studies on bacterial changes in drinking water systems post-monochloramine removal. He has participated in conferences such as the 3rd Annual RECS Workshop on AI and organized events on smart AI taxi systems. His work frequently involves flow cytometry and environmental data analysis. He serves as a deputy supervisor for a PhD project on bathing water quality using machine learning and DNA-based methods. Despite his active research profile, no scientific awards are explicitly mentioned in the provided texts.

Donald McMillanمشاهده پروفایل

دانشیار

• Human-Computer Interaction
• Conversational Interfaces
• Machine Learning Applications
• +۳ مورد دیگر

Donald McMillan is an Associate Professor (Docent) in Human-Computer Interaction at Stockholm University's Department of Computer and Systems Sciences. He is a core member of the Stockholm Technology & Interaction Research (STIR) group, which brings together technologists and social scientists to research and build new digital technologies within the Human-Computer Interaction field. His research focuses on the adoption, adaptation, and use of novel technology in everyday settings, with particular emphasis on expanding the interactional repertoire of conversational interfaces. McMillan investigates how insights from fluid human-human communication can inform the development of better human-computer interaction paradigms through machine learning applications. His work often examines the contextual and social dimensions of technology use in real-world environments. Currently, McMillan serves as Principal Investigator for the Advanced Adaptive Intelligent Systems project, which develops new ways for robots and intelligent agents to understand and adapt to human collaborative work, and the Designing New Speech Interfaces with Ambient Audio project. He is also involved in the Implicit Interaction project. Previous projects include Autonomous Last Mile (developing Extended Reality experiences for human intervention in autonomous vehicle scenarios) and From Tracking to Hacking (exploring how people interact with technology to hack traditional sleep schedules). Advanced Adaptive Intelligent Systems: Developing adaptive interaction frameworks for intelligent systems Designing New Speech Interfaces with Ambient Audio: Expanding conversational capabilities through environmental audio analysis Implicit Interaction: Exploring subtle, background interaction paradigms Autonomous Last Mile: Creating XR experiences for human-robot collaboration scenarios From Tracking to Hacking: Investigating technology adaptation for non-normative sleep patterns His extensive publication record demonstrates deep expertise across multiple HCI domains, with recent work focusing on conversational interfaces, ethical AI considerations, gaze patterns in speech interaction, and contextual wearable technology use. McMillan's research methodology often combines empirical studies of real-world technology use with design implications for future systems, resulting in practical contributions to the field of Human-Computer Interaction.

• Signal Processing
• Bayesian Statistics
• Deep Learning
• +۴ مورد دیگر

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.

• Robotics
• Autonomous Systems
• Spatial Cognition
• +۶ مورد دیگر

Patric Jensfelt is a full-time Professor at the Robotics, Perception and Learning (RPL) division within the School of Electrical Engineering and Computer Science (EECS) at KTH Royal Institute of Technology . He contributes to robotics and autonomous systems through research, teaching, and industry collaboration. Academic Rank: Professor Department: Robotics, Perception and Learning (RPL) School: School of Electrical Engineering and Computer Science (EECS) University: KTH Royal Institute of Technology His research focuses on robotics and autonomous systems , particularly spatial cognition for enabling robots to understand and model their environments. He integrates sensor data with high-level symbolic reasoning to advance navigation, mapping, and system integration. Recent work includes applications in service robotics, 3D foot scanning (via the company Volumental ), and drone-based perception. The 15 most recent articles highlight advancements in scene flow estimation, autonomous driving, LiDAR processing, and domain adaptation. They emphasize self-supervised learning , dynamic environment modeling , and robust spatial representations for autonomous systems. Patric supervises Master’s and PhD students, particularly in project courses like DD2410 Introduction to Robotics , DD2419 Project Course in Robotics and Autonomous Systems , and the doctoral course FDD3356 System Integration for Robotics . He is actively involved in the WASP Graduate School , teaching Autonomous Systems annually. He leads the Robotics, Perception and Learning (RPL) division, which fosters innovation in robotics through experimental projects and industry partnerships like Intelligent Machines . The division supports internships (unpaid) and encourages applicants to demonstrate prior engagement with his research.

Ramin Karimمشاهده پروفایل

استاد

• Maintenance Engineering
• Cybersecurity
• Industrial Systems
• +۵ مورد دیگر

Ramin Karim is a Professor and Head of Subject in the Department of Civil, Environmental and Natural Resources Engineering at Luleå University of Technology. His research focuses on operation and maintenance technology, with expertise in railway systems, industrial cybersecurity, structural health monitoring, and the application of advanced analytics in asset management. He leads the Operation, Maintenance and Acoustics division, emphasizing interdisciplinary approaches to solving complex engineering challenges. Key research areas include predictive maintenance strategies for railway infrastructure, cybersecurity frameworks for Industry 5.0, and the integration of metaverse technologies in industrial contexts. His work often involves data-driven methodologies such as point-cloud processing, game theory for cyber threat modeling, and digital twin concepts. Recent publications highlight his contributions to railway maintenance policy optimization, health monitoring of ground support systems in mining, and cybersecurity challenges in industrial systems. He has co-authored over 50 peer-reviewed articles, many appearing in high-impact journals like International Journal of Systems Assurance Engineering and Management and Frontiers in Virtual Reality . Ramin Karim’s research also explores emerging technologies like federated learning for digital twins, blockchain applications in railways, and human-centric predictive health management systems. His work aligns with initiatives such as the Reality Lab Digital Railway, aimed at advancing sustainable and digitally enabled transportation solutions.

• Sustainable Manufacturing
• Production Systems
• Virtual Reality
• +۷ مورد دیگر

Björn Johansson is a Professor at Chalmers University of Technology, specializing in Production Systems. His research focuses on sustainability aspects of manufacturing through virtual tools, aiming to minimize environmental, social, and economic impacts. Key methodologies include flow simulation, dynamic environmental assessments, 3D visualization, and layout optimization. Primary research areas: Sustainable Manufacturing, Digital Twins, Environmental Impact Assessment Collaborations: Mélanie Despeisse, Henrik Söderlund, and others in automotive, battery production, and maritime industries His work emphasizes integrating digital technologies (e.g., VR, IoT) with sustainable practices, addressing challenges in supply chain resilience, human-robot collaboration, and circular economy models. Recent studies explore VR training environments, 5G-enabled manufacturing, and extended reality (XR) frameworks. Current projects involve 44 initiatives across battery systems, servitization, and digitalization for sustainability. Publications span 179 articles, including topics like digital twin implementation, ergonomic VR assessments, and hybrid simulation models for environmental analysis.

• Institutional Language Use
• Financial Economics
• Business Discourse
• +۵ مورد دیگر

Claes Ohlsson is an Associate Professor in Swedish Language at Linnaeus University, Sweden. His research examines institutional language use across financial economics, business discourse, and corporate responsibility, employing text/discourse analysis and computational methods. He leads projects on Market Language (2022-2025) and smaller studies on budget concepts and urban linguistic landscapes. Research Themes: Financial literacy rhetoric, corporate responsibility framing, market concept evolution, multilingual urban environments Affiliations: Linnaeus University (since 2017), University of Gothenburg, Chalmers University Recent Articles trace market terminology in Swedish parliamentary records (2022), analyze election campaign language (2023), and explore financial advertising history (2024). His work combines corpus linguistics, critical discourse analysis, and digital humanities approaches.