Amy Loutfi is a Professor of Information Technology at Örebro University, where she leads the AI, Robotics and Cybersecurity Center (ARC), and serves as Program Director for the Wallenberg AI, Autonomous Systems and Software Program (WASP) at Linköping University. Born in Saint John, Canada (1978), she earned her PhD in Computer Science from Örebro University (2006) and became a Docent (2011) and Professor (2015) in Information Technology there. Her research focuses on intelligent sensor systems, particularly their integration with artificial intelligence to convert raw data (e.g., from robotic olfaction or vision sensors) into human-interpretable semantic information. She explores human-robot interaction dynamics, emphasizing safety, collaboration, and usability across diverse environments like homes and industries. Key projects include BioLearning (water quality analysis), MicroSens (taste perception via microbiota), and TeamRob (robot teams working with humans). Amy’s work bridges technical innovation with societal impact, addressing challenges in healthcare, elder care, and smart home technologies. She advocates for Sweden’s national AI strategy and emphasizes ethical considerations in emerging robotics. Her research also involves collaboration with industry and public sectors, as seen in the E-care@home and Mednose initiatives. In 2020, she was elected to the Royal Swedish Academy of Engineering Sciences (IVA) for her contributions. Her grants and projects include strategic funding for AI applications and interdisciplinary collaborations. She collaborates with global teams and contributes to conferences like ICSR (Social Robotics) and ECAI (Artificial Intelligence).
جستوجوی استاد و پژوهشگر
استاد راهنما، استاد دانشگاه یا پژوهشگر مناسب برای مسیر پژوهشیتان را پیدا کنید و با او ارتباط بگیرید.
نمایش ۲۷۱-۲۸۵ از ۳۰۵ استاد راهنما
اطلاعات این پروفایلها از صفحات عمومی دانشگاهها جمعآوری شده و ممکن است بهروز نباشد.
Jonas Lundbergمشاهده پروفایل
مدرس ارشد
Jonas Lundberg is a Senior Lecturer in Computer Science at Linnaeus University since 2000, holding a Ph.D. in Theoretical Physics (Umeå University, 1994) and a second Ph.D. in Computer Science (Linnaeus University, 2012). He teaches foundational and advanced courses in programming, machine learning, and compiler design. His research focuses on data-intensive computing, program analysis, and cross-disciplinary projects like the Nordic Tweet Stream (NTS), analyzing social media data from Nordic countries. He leads the Computer Engineering program and participates in CDIO initiatives. Education: Ph.D. in Theoretical Physics, Umeå University (1994) Ph.D. in Computer Science, Linnaeus University (2012) Research interests include machine learning applications in software engineering, program analysis techniques for large codebases, and digital humanities projects leveraging big data. He co-founded the DISA research center (2016) to address challenges in data collection, analysis, and utilization. Recent work involves developing memory-efficient program analysis frameworks and detecting anti-democratic discourse in social media. Key projects include managing the Master's program in Computer Science and advancing self-adaptive systems engineering. His contributions span 70+ publications in venues like IEEE Transactions, Journal of Systems and Software, and conferences such as SAC and DHN. He is affiliated with research groups like Data Intensive Software Technologies (DISTA) and co-leads the Nordic Tweet Stream initiative. His work bridges computational methods with humanities, emphasizing real-time data analysis and cross-disciplinary collaboration.
Koraljka Golub is a Professor and Director of the iInstitute at Linnaeus University, leading the university's iSchool initiative and co-chairing the Digital Humanities Initiative. She is also Swedish DARIAH coordinator and a key figure in Huminfra, the national infrastructure for digital humanities. Her research focuses on knowledge organization systems, automated subject indexing, and LGBTQ+ literature accessibility. She teaches courses such as Knowledge Organization, Digital Humanities Methods, and Linked Data Structures, and actively develops interdisciplinary programs like the Digital Humanities Master's. Her leadership roles include Chair of iSchools Europe/Africa Region, EOSC representative, and ISKO STAC member. Notable projects include the DiMPAH digital methods platform, Queerlit database for LGBTQ+ literature, and the InKuiS cultural entrepreneurship project. She has pioneered research in metadata standards, social tagging integration, and AI's impact on cultural archives. Key achievements include over 100 peer-reviewed publications, book authorship (e.g., *Subject Access to Information*), and global conference presentations. Her work bridges library science, computational methods, and humanities scholarship to advance open science and cultural heritage preservation.
- Computer Graphics
- Real-Time Rendering
- Algorithms
- +۳ مورد دیگر
Tomas Akenine-Möller is a Professor in the Department of Computer Science at Lund University, affiliated with the Faculty of Engineering (LTH). He is also part of ELLIIT, the Linköping-Lund initiative on IT and mobile communication. His research focuses on real-time rendering, computer graphics algorithms, and human vision modeling, contributing to UN Sustainable Development Goals through technological advancements. Education details are not explicitly provided in the text, but his expertise stems from a strong academic background in computer science and engineering. His research interests include algorithms optimization, ray-tracing techniques, graphic hardware performance, and visual perception models. Recent work trends involve temporal edge detection in human vision, neural compression of material textures, and luminance-preservation in graphics. His projects emphasize practical applications of graphics algorithms in real-world scenarios. No scientific awards are explicitly listed. However, he has supervised doctoral research, including the project 'Evaluating and Improving Rendered Visual Experiences' (2019-2024), where he acted as an assistant supervisor. This project explores enhancing visual rendering quality and user experience. No specific lab or team affiliations are mentioned, but his collaborative networks span multiple countries, reflecting interdisciplinary research partnerships.
Faisal Qureshiمشاهده پروفایل
دانشیار
- Computer Science
- Computer Vision
- Machine Learning
- +۴ مورد دیگر
Prof. Faisal Qureshi is an Associate Professor of Computer Science at the University of Ontario Institute of Technology (OntarioTechU), where he directs the Visual Computing Lab. He is also a Guest Professor in Deep Learning/Machine Learning at the same institution and a Visiting Professor affiliated with Computer Engineering and Electronics. His research spans computer vision, embedded IoT systems, and video summarization. Ph.D. and M.Sc. in Computer Science from the University of Toronto M.Sc. in Electronics from Quaid-e-Azam University, Pakistan B.Sc. in Mathematics and Physics from Punjab University, Pakistan Research Focus: Faisal Qureshi's work emphasizes computer vision, camera networks, and data-intensive IoT applications. His projects include Embedded Deep Learning Systems (EDGY) , integrating machine learning with real-time embedded hardware. Scientific Awards: CRV 2017 Best Vision Paper award ACM/IEEE Distributed Smart Cameras 2007 Outstanding Paper award Professional Affiliations: Senior Member of IEEE, member of ACM, and CIPPRS. He has chaired workshops at CVPR (2011-13) and co-chaired CRV (2015/16).
Gunnar Mathiason is Associate Professor in Computer Science at the University of Skövde's School of Informatics. His research bridges artificial intelligence and industrial applications, particularly in manufacturing optimization and network systems. Current projects include ProcTwin (2025-2028) developing digital twins for steel production and ShiftLabs (2023-2025) supporting digital transformation in manufacturing. Mathiason's publications demonstrate expertise in applying machine learning techniques to industrial challenges, with consistent focus on predictive modeling and process optimization in manufacturing environments. Recent work shows evolution toward explainable AI and complex system analysis.
Sebastian Herold is an Associate Professor in Computer Science at Karlstad University, Sweden. His research focuses on software architecture, design, and quality, with an emphasis on mitigating architecture erosion and technical debt through empirical industry collaboration. He has held roles at RWTH Aachen University (2005 diploma/Master's equivalent), Clausthal University of Technology (PhD 2011), and Lero - The Irish Software Research Centre (2013–2015). Education: RWTH Aachen University of Technology, Germany: Diplom-Informatik (2005) Clausthal University of Technology, Germany: PhD in Computer Science (2011) Research interests span software architecture evolution, empirical studies in software engineering, privacy patterns (GDPR compliance), and usability evaluation in digital health. His work includes developing tools like InMap for automated code-to-architecture mapping and organizing workshops like SAEroCon on architectural consistency. Publications highlight trends in synthetic data for healthcare usability, machine learning for architecture mapping, and empirical validation of software practices. Recent work integrates AI methods for software engineering challenges and explores efficiency in digital health evaluations. Awards: None explicitly listed. Grants and advising: No specific grants or student advisees mentioned; collaborations with industry and Lero are emphasized. Labs/Teams: Active in Karlstad University's Department of Computer Science, with contributions to interdisciplinary projects like GDPR-focused MOOCs and healthcare software development studies.
Stefan Lindskog is a full Professor at the Department of Computer Science, Karlstad University, Sweden. His research focuses on tunable security services, network security protocols, intrusion detection systems, and encryption methodologies. He has held academic positions at Karlstad University since 1990 and holds a PhD in Computer Engineering from Chalmers University (2005) and a Docent degree in Computer Science from Karlstad University (2008). His work emphasizes practical cybersecurity solutions, including firewall usability, multipath TCP security, and privacy-preserving technologies. Lindskog has authored over 60 peer-reviewed publications, including studies on encrypted network traffic analysis, Tor anonymity vulnerabilities, and firewall rule optimization. He collaborates extensively with industry partners and academic networks, contributing to projects like the EU-funded NEWCOM++ initiative. Teaching: Real-Time Systems (7.5 ECTS) Key Research Themes: Security service tunability, adaptive intrusion detection, and secure communication protocols Collaborations: HITS project (High Quality Networked Services in a Mobile World) Publications span topics from malware detection in encrypted traffic to systematic reviews of firewall usability. Lindskog’s work bridges theoretical security models and operational system implementation, with a focus on real-world applicability in industries like offshore energy and mobile communications.
- Robotics
- Machine Learning
- Artificial Intelligence
- +۵ مورد دیگر
Florian T. Pokorny is an Associate Professor in Machine Learning at the Division of Robotics, Perception and Learning (RPL), Department of Electrical Engineering and Computer Science (EECS), KTH Royal Institute of Technology. He coordinates research projects such as the Horizon Europe-funded SoftEnable and the WASP-funded Intelligent Cloud Robotics for Real-Time Manipulation at Scale . His research focuses on data-driven methods for robotic manipulation, including transfer learning, cloud robotics, and caging-based manipulation of rigid and deformable objects. Education: PhD in Mathematics, University of Edinburgh (supervisor: Michael Singer) MSc in Advanced Study in Mathematics (Part III), University of Cambridge BSc in Mathematics, University of Edinburgh Research Interests: His work emphasizes scalable robotic manipulation, leveraging deep learning and geometric/topological methods. Key areas include training data requirements, transfer learning, and cloud robotics paradigms for manipulation at scale. Recent projects explore energy margin analysis, caging-guided morphology optimization, and robust policy learning. Publications: His recent work spans topics like caging-based manipulation, federated learning, and cloud robotics infrastructure. Notable contributions include CageCoOpt (manipulation robustness), CloudGripper (open-source testbed), and RealCraft (zero-shot video editing). Awards & Grants: Funded by WASP, Horizon Europe, and the Knut and Alice Wallenberg Foundation. His group hosts the CloudGripper platform and collaborates on benchmarks like DLO@Scale. Advising & Labs: Supervises multiple PhD students and research engineers. Alumni include Robert Gieselmann (Amazon Robotics), Yiannis Karayiannidis (Chalmers University), and Anastasiia Varava (Postdoc at KTH). Active in organizing workshops like RoDGE (IROS 2025) and ICRA 2025 robotics learning events.
Miguel Serras Vasco is a postdoctoral researcher at KTH Royal Institute of Technology in Stockholm, Sweden, affiliated with the Robotics, Perception and Learning (RPL) division. He holds a PhD from Instituto Superior Técnico, University of Lisbon (2023), where his work earned the Best PhD Thesis in AI in Portugal award. His research focuses on multimodal perception, reinforcement learning, and aligning artificial agents with human perception. He previously worked as an RSS Pioneer and research intern at Sony AI. Education: PhD in Artificial Intelligence, Instituto Superior Técnico, University of Lisbon (2023) Research Interests: Vasco’s work bridges robotics, neuroscience, and AI, emphasizing embodied agents that co-exist with humans. He explores representation learning, human-aligned image models, and sample-efficient reinforcement learning. His recent projects include super-human autonomous racing agents and olfactory perception modeling with transformers. Key Contributions: Vasco co-developed the GT Sophy racing agent, achieved outstanding results in visual decoding from brain activity, and proposed methods like FLoRA for preference-based RL. His work on NeuralSolver advances algorithm extrapolation in reinforcement learning. Awards: Best PhD Thesis in AI in Portugal (APPIA, 2023) Outstanding Paper Award at RLC 2024 (for autonomous racing research) Grants/Advising: Vasco advises students on multimodal and reinforcement learning topics. He actively organizes conferences like the Reinforcement Learning and Video Games Workshop (RLVG) at RLC 2025 and collaborates with institutions like INESC-ID (Lisbon). Labs/Teams: Associated with the Collaborative Autonomous Systems unit at KTH and previously with the GAIPS Lab (INESC-ID, Lisbon).
Anastasiia Varava is an affiliated faculty member and PhD student at KTH Royal Institute of Technology, working within the Division of Robotics, Perception and Learning. Her research focuses on computational geometry and topology applied to robotic manipulation, particularly caging theory and its practical implementations for handling rigid and deformable objects. She collaborates with advisors Florian Pokorny and Danica Kragic on theoretical and applied robotics challenges. Her work integrates robotics with interdisciplinary fields such as topology, machine learning, and computer vision. Key topics include path planning, deformable object manipulation, and geometric algorithms for motion verification. Recent contributions emphasize data-driven approaches for latent space modeling and visual action planning in robotics systems. Varava has authored or co-authored over 30 peer-reviewed articles and conference papers, covering topics from caging-based motion planning to molecular screening algorithms inspired by robotics. Her publications span journals like IEEE Transactions on Robotics and conferences such as IROS and ICRA, reflecting a strong focus on practical robotic systems with theoretical rigor. Her doctoral thesis, Path-Connectivity of the Free Space: Caging and Path Existence (2019), established foundational work on caging and path non-existence in robotics. Current projects involve advancing geometric evaluation techniques for data representations and exploring diffusion models for robotic skill learning.
Yuchong Zhangمشاهده پروفایل
پژوهشگر ارشد
Yuchong Zhang is a Postdoc Fellow at KTH Royal Institute of Technology's Division of Robotics, Perception and Learning, supervised by Prof. Danica Kragic. He completed his Ph.D. at Chalmers University of Technology (2018-2023) and holds an M.Sc. from Nanyang Technological University (2016-2017) and B.Eng. from Hangzhou Dianzi University (2012-2016). Ph.D. in Interaction Design, Chalmers University of Technology, Sweden (2018-2023) M.Sc. in Computer Science, Nanyang Technological University, Singapore (2016-2017) B.Eng. in Computer Engineering, Hangzhou Dianzi University, China (2012-2016) His research centers on human-robot interaction through human-centered design, with expertise in augmented/mixed reality , interactive AI , and affective computing . He bridges cognitive science and engineering to develop intuitive interfaces for industrial tomography and wearable robotics, emphasizing perceptual accuracy and user experience in immersive systems. Publications from 2019-2025 reveal evolving focus from industrial AR visualization (2019-2021) to brain-robot interfaces (2025) and wearable robotics (2024). Key trends include multimodal feedback in AR, EEG-based control systems, and social drone design, consistently applying human-centered methodologies across HCI, robotics, and sensory augmentation domains. He supervises master's projects in interaction design and mobile development while contributing to major initiatives like BOS (Body-centric Operating System) and ELSA (European Lighthouse on Secure AI). His work integrates hardware prototyping with user studies for industrial and assistive applications. As part of KTH's Robotics, Perception and Learning group, he collaborates on projects including TOMOCON (industrial tomography sensors) and European AI safety frameworks, leveraging his background in cross-cultural academic environments (China, Singapore, Sweden).
Kalle Malmberg is a Professor at the Department of Medicine, Huddinge, Karolinska Institutet , with dual affiliation to Oslo University Hospital . His research focuses on the molecular mechanisms of Natural Killer (NK) cell differentiation , KIR receptor dynamics , and HLA-E interactions in health and disease. Key translational efforts include off-the-shelf NK cell therapy platforms and clinical trials for refractory malignancies Funding sources: Cancerfonden, EU Horizon 2020, Vetenskapsrådet Research Trends : Recent publications highlight genetic engineering of NK cells to overcome immune suppression in cancer, KIR-HLA polymorphism in transplantation outcomes, and single-cell technologies for repertoire analysis. Students : Hannah Cuthbertson Husbyn, Herman Krogstad Netskar Collaborative Networks : IMTAC, Theme Center for Regenerative Medicine
Sándor Darányi is a Professor at the Swedish School of Library and Information Science, University of Borås (since 2011), with a career spanning over four decades in information science, digital libraries, and cultural heritage preservation. He also holds an Honorary Professorship at Szeged University, Hungary. His research focuses on advanced access to digital libraries, automatic indexing, information visualization, digital preservation, narrative genomics, and quantum interaction models for semantics. Education: Candidate of Science (CSc) in Ethnography, Hungarian Academy of Sciences (1994) PhD in Information Science, Eötvös Loránd University (1989) MA in Library and Information Science, Eötvös Loránd University (1985) MSc in Agricultural Sciences, University of Agriculture (1975) Research Interests: Darányi’s work bridges computational methods with cultural studies, including: - Formalization of folk narratives for big data analysis - Quantum-inspired models of semantic change - Haptic interfaces for accessibility in museums - Evolving semantics in digital ecosystems - Cultural heritage digitization projects Professional Activities: Editorial Board Member of Journal of Information Science Education Organized major conferences such as SEMANTiCS, IC-ININFO, and AMICUS workshops Supervised doctoral students in computer science and information studies Co-developed the PERICLES project on digital preservation Labs/Teams: His research groups have pioneered projects like the MuseIT inclusive museum initiative and the SHAMAN digital preservation framework, leveraging high-performance computing and cloud technologies.
Shahina Begum is a Professor in Artificial Intelligence at Mälardalen University (MDU), leading the Artificial Intelligence and Intelligent Systems group. She focuses on developing intelligent systems for medical and industrial applications, with expertise in multimodal machine learning, explainable AI, and decision support systems. She holds a PhD in AI from MDU (2011) and has led numerous research projects, including initiatives funded by the Swedish Knowledge Foundation and KK-Stiftelsen. Her teaching roles include designing and leading over 21 courses/learning modules in AI and machine learning, both on-campus and distance-based. She is the main responsible for AI content in MDU's 'Bachelor program in Applied AI' and has spearheaded MOOC courses like 'Basic Knowledge on ML' and AIClass. She also leads industrial collaboration projects such as IntoDeep, KIT, and PROMPT, addressing Industry 4.0 challenges. Research interests span sustainable AI development, trustworthy AI systems, and AI for manufacturing/predictive maintenance. She has received notable recognition, including the Prospect grant (2011) and inclusion in the Royal Swedish Academy's top 100 sustainable AI researchers (2020). Her work emphasizes bridging academic research with industry needs through collaborative initiatives. She has published extensively on multimodal learning, explainable AI, and applications in transportation, healthcare, and manufacturing. Her awards include the Best Paper Award at the 2024 conference for work on second-order learning. She actively contributes to academic governance, serving on evaluation committees, conference organizing committees, and funding review boards.