جست‌وجوی استاد و پژوهشگر

Oskar Hanssonمشاهده پروفایل

استاد

• Alzheimer's Disease
• Parkinson's Disease
• Biomarker Development
• +۴ مورد دیگر

Professor Oskar Hansson is a senior consultant neurologist at Skåne University Hospital and full professor of neurology at Lund University, Sweden. He leads the Swedish BioFINDER studies, focusing on early diagnosis of Alzheimer's and Parkinson’s diseases through biomarker development. Co-director of Lund University's neuroscience research area, he also oversees clinical research at the Memory Clinic. Lund University (2017-present): Full Professor of Neurology Skåne University Hospital (2012-present): Senior Consultant Neurologist His research emphasizes clinical and translational studies, particularly in Neurodegenerative Diseases , Biomarker Development , and Neuroimaging . Key contributions include validating Tau PET imaging and blood-based biomarkers for early Alzheimer's detection. Recent publications highlight applications in Alzheimer's disease subtyping , polygenic risk scores , and Lewy body disorder biomarkers (2025 publications in Nature Communications , The Lancet , and Alzheimer's Research and Therapy ). Current projects include active research on amyloid immunotherapies and tau pathology biomarkers. 2024 : Torsten Söderberg Professorship 2023 : De Leon Prize, ERC Advanced Grant, NIH/NIA R01 grant 2023 : Elsa and Alfred Eriksson Award Hansson leads the Clinical Memory Research platform and oversees multiple initiatives including the MultiPark Parkinson's research program and Proactive Ageing profile area at Lund University.

• Image Analysis
• Artificial Intelligence
• Deep Learning
• +۵ مورد دیگر

Joakim Lindblad is a Professor at the Department of Information Technology, Uppsala University , and holds affiliated roles as Senior Research Associate at the Mathematical Institute of the Serbian Academy of Sciences and Arts, and Head of Research at Topgolf Sweden AB. With over two decades of expertise in image analysis and machine learning , his work bridges computational methods with biomedical applications. Key affiliations: Uppsala University, Serbian Academy of Sciences, Topgolf Sweden Specializations: Deep Learning, Multimodal Image Registration, Quantitative Microscopy His research focuses on reliable image processing frameworks that integrate intensity and spatial information , particularly for biomedical applications . Recent publications highlight innovations in autofluorescence-based cancer detection , self-supervised one-class learning for sparse instance identification, and rotation-equivariant CNNs for robust analysis of cytology images. Recent article trends demonstrate expertise in multimodal image analysis (2024: 3 papers), oral cancer detection (2025: 2 papers), and multiscale biomedical imaging . His 2025 work on the Uppsala Storytelling Dataset introduces novel frameworks for multimodal dataset creation in AI research. While no scientific awards are explicitly mentioned, his extensive publication record (2000-2025) across top venues like Pattern Recognition , PLOS ONE , and IEEE Transactions indicates significant academic impact. His methodological contributions span stochastic distance transforms , fuzzy set defuzzification , and multimodal image registration techniques. Collaborative work with researchers like Nataša Sladoje and interdisciplinary teams has produced innovations in automated cytology analysis , TEM image enhancement , and AI-driven medical diagnostics . His 2021-2022 projects introduced contrastive learning approaches for multimodal image registration and explainable AI frameworks for infant engagement analysis.

• Robotics
• Artificial Intelligence
• Human-Robot Interaction
• +۶ مورد دیگر

Thomas Hellstrom is a Professor at the Department of Computer Science , Umeå University, Sweden. He leads the Intelligent Robotics group and is affiliated with the Center for Transdisciplinary AI . His research spans human-robot interaction (HRI) , deep learning applications , robot ethics , and field robotics for agricultural and forestry automation. Coordinated EU projects: INTRO (FP7/ITN), SOCRATES (H2020), CROPS, SWEEPER Developed intelligent walker for stroke patients with CMTS/MT-FoU/Umeå Stroke Center Key contributions in robot learning , causal reasoning , and natural language understanding Research Focus : His work emphasizes understandability in robot behavior, including causal modeling , multi-modal communication , and ethical frameworks for autonomous systems. Current project ROCC (Swedish Research Council) explores robot causality, while SOCRATES addressed social robotics in eldercare. Scientific Awards : • Erdös-Bacon-Sabbath number ≤ 13 Grants & Funding : • ROCC (2023, 3.7M SEK, Principal Investigator) • SCAI (2022, 3.7M SEK, Co-Applicant) • VINNOVA (2019, 3.47M SEK, Co-Applicant)

• Machine Learning
• Natural Language Processing
• Predictive Maintenance
• +۴ مورد دیگر

Tony Lindgren is an Associate Professor at the Department of Computer and Systems Science, Stockholm University, affiliated with the Data Science Research Group and Natural Language Processing Research Group. His work bridges data science and NLP , focusing on interpretable models, constraint programming, and predictive maintenance systems. Research interests include: Machine Learning for explainability and fairness Constraint Programming in maintenance optimization Natural Language Processing for risk analytics and troubleshooting Recent publications demonstrate trends in multi-objective optimization (2025 satellite scheduling), conformal prediction (2024 CoPAL), and fault detection (2024 Automotive Nowcasting). His work often integrates domain-specific constraints with scalable algorithms across applications like food safety and autonomous vehicles. Software tools developed by Lindgren include: Example-based Feature Tweaking Rule Indexing Frameworks His research groups focus on AI-driven decision support for high-stakes domains, combining technical innovation with societal impact considerations.

Zhou Zhouمشاهده پروفایل

استادیار

• Neurotrauma Prevention
• Biomechanical Modeling
• Medical Imaging
• +۵ مورد دیگر

Zhou Zhou is an Assistant Professor (tenure track) in Neuronic Engineering at KTH Royal Institute of Technology's Department of Biomedical Engineering and Health Systems. He is affiliated with the Digital Futures Faculty, a cross-disciplinary research center focusing on digital technologies for societal challenges. His research focuses on neurotrauma prevention, prediction, and protection through computational modeling, machine learning, and biomechanical validation. Dr. Zhou holds a PhD from KTH (2019) and completed postdoctoral training at Stanford University, specializing in sports safety and concussion prevention. His work is supported by VINNOVA, Swedish Research Council, and others. He received the 2025 Göran Gustafsson Prize for young researchers. Research interests include in silico modeling, fluid mechanics, and safety standardization for helmets. Zhou teaches courses such as Biomechanics and Neuronics (HL2035/CH213V) and leads labs in human and animal neurotrauma modeling. He seeks postdoctoral candidates for rat neurotrauma research. Education: PhD in Biomedical Engineering, KTH (2019); Postdoc at Stanford University (Sports Safety) Key Projects: InSilicoHealth ITN, helmet evaluation via virtual testing Labs/Teams: Neuronic Engineering Division, Digital Futures Collaboratory

• Voice Acoustics
• Choir Acoustics
• Voice Analysis
• +۵ مورد دیگر

Sten Ternström is a Professor at KTH Royal Institute of Technology's Division of Speech, Music and Hearing. He holds a MScEE (1982), PhD (1989), and has been a Professor since 2003. His research focuses on voice acoustics, particularly singing voice analysis and synthesis, with emphasis on clinical applications. Current interests include addressing voice variability, electroglottography, and audio technologies for music and voice clinics. He has led projects like FP7 EUNISON and SkAT-VG, and is a Fellow of the Acoustical Society of America. Education: All degrees at KTH—MScEE (1982), PhD (1989). Research Interests: Voice acoustics, choir acoustics, voice synthesis, biomedical signal processing. His work combines technical innovation with clinical relevance, e.g., developing FonaDyn software for real-time voice analysis. He explores how vocal fold dynamics influence voice quality and investigates non-invasive methods for voice assessment. Publications: Over 100 peer-reviewed articles, including recent work on WaveNet-based EGG prediction (2025), voice mapping post-thyroidectomy (2024), and pediatric voice analysis (2021). His research spans voice disorders, Parkinson’s disease voice therapy, and choral singing acoustics. Awards: Fellow of the Acoustical Society of America, Guest Editorships, and editorial roles in Acta Acustica. His contributions bridge engineering and clinical practice in voice science. Teaching: Leads courses in music acoustics, sound engineering, and supervises MSc projects on voice clinic software and singing synthesis. Active in outreach for choir acoustics and pedagogy. Labs/Teams: Involved in FonaDyn software development for voice analysis. Collaborates internationally on voice biomechanics and clinical voice measurement.

Kalle Åströmمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Geometry and Algebra of Multiple Views
• +۵ مورد دیگر

Kalle Åström is a Professor at Lund University's Centre for Mathematical Sciences within the Faculty of Engineering. He coordinates Lund University's Natural and Artificial Cognition profile area and the AI Lund network. His affiliations include ELLIIT (Linköping-Lund IT initiative), eSSENCE (e-Science Collaboration), Stroke Imaging Research group, and Computer Vision and Machine Learning research groups. His research spans computer vision, machine learning, and mathematical modeling with applications in medical imaging, autonomous systems, and cognitive vision. Key interests include geometry of multiple views, structure from motion using heterogeneous sensors, medical image analysis, and handwriting recognition. His work contributes to UN Sustainable Development Goals through AI applications in healthcare and engineering. Recent publications (2025) demonstrate strong trends in medical AI (Alzheimer's diagnostics, breast cancer classification) and autonomous systems (safety testing, sensor fusion). His work bridges theoretical mathematics with practical applications across healthcare and robotics domains. Best Nordic Ph.D. Thesis in Pattern Recognition (1995-1996) Innovation Cup 1991 for Autonomous Guided Vehicles EU IST Grand Prize 2003 (Decuma startup) Åström supervises graduate students and leads multiple active research projects including machine learning for Parkinson's disease analysis, audiovisual drone detection (Vinnova-funded), and Alzheimer's disease modeling. He co-founded startups Decuma (1999), Cognimatics (2003), Spiideo (2012), and Neuromathics (2015), and serves on boards of the Royal Swedish Physiographic Society and Swedish AI Society (SAIS). His research integrates mathematical rigor with real-world AI applications through extensive industry-academia collaborations.

Vladimir Vlassovمشاهده پروفایل

استاد

• Machine Learning
• Data-intensive computing
• Cloud computing
• +۷ مورد دیگر

Dr. Vladimir Vlassov is a full Professor in Computer Systems at the Division of Software and Computer Systems (SCS) , Department of Computer Science (CS) , School of Electrical Engineering and Computer Science (EECS) , KTH Royal Institute of Technology , Stockholm, Sweden. He leads the AVA project in ALEC2, an AI-powered system for mental health care. He is a member of the Distributed Computing research group (DC@KTH) . Education & Roles: Holds a PhD and is a member of ACM and IEEE. Previously visited MIT (1998) and UMass Amherst (2004). Teaches courses on Data Mining , Distributed Systems , and Concurrent Programming . Research Interests: Focus on scalable AI, Cloud computing, distributed systems, and NLP for mental health. Projects include ExtremeEarth (Copernicus data analytics) and EMJD-DC (distributed computing PhD program). Grants & Projects: Principal Investigator in ALEC2 (adaptive mental health care) and ExtremeEarth (EU H2020). Led EU projects like ENCORE (manycore systems) and PaPP (embedded systems). Labs & Teams: Directs the Distributed Computing group, contributing to Hopsworks (machine learning feature store) and Maggy (hyperparameter optimization).

• artificial intelligence
• image analysis
• deep learning
• +۸ مورد دیگر

Natasa Sladoje is a Professor in Computerized Image Analysis at the Department of Information Technology, Uppsala University. She is affiliated with the Vi3 and Image Analysis research group and leads the MIDA research group. Her work spans artificial intelligence, biomedical image analysis, deep learning, and algorithm development, with applications in medical imaging and life sciences. Her research focuses on developing advanced image analysis methods, particularly using machine and deep learning, to enable automated analysis of image data in science and everyday life. Key areas include medical image analysis, image registration, segmentation, pattern recognition, and discrete geometry. She applies these techniques to critical domains such as oral cancer detection, cytology, and multimodal imaging. The recent publications highlight a strong trend in AI-driven medical diagnostics, particularly in cancer detection using whole slide images, self-supervised learning for sparse instance detection, and contrastive learning for multimodal image registration. Her work also emphasizes reproducibility and benchmarking in bioimage analysis through frameworks like BIAFLOWS and public datasets like HISTOBREAST. She has no listed scientific awards in the provided text. Natasa Sladoje supervises research within the MIDA group and collaborates extensively on projects involving bioimage analysis, deep learning, and medical applications. While specific grant details are not mentioned, her leadership in collaborative frameworks and publication output suggests active involvement in funded research initiatives. She leads the MIDA (Medical Image Analysis) research group, which focuses on developing and applying novel image analysis tools for biomedical applications, particularly in cancer diagnostics and multimodal imaging.

Erchan Aptoulaمشاهده پروفایل

استاد

• Digital Image Analysis
• Computer Vision
• Remote Sensing
• +۳ مورد دیگر

Erchan Aptoula is a Professor of Computer Science at Sabanci University's Faculty of Engineering and Natural Sciences in Istanbul, Türkiye. He is affiliated with the Computer Vision and Pattern Analysis Laboratory (VPALab) and actively conducts research in digital image analysis, computer vision, and deep learning with a focus on remote sensing and (bio)medical data. University: Sabanci University School: Faculty of Engineering and Natural Sciences Academic Rank: Professor Email: erchan.aptoula@sabanciuniv.edu His research interests span domain generalization for remote sensing, explainable AI, medical image analysis, and precision agriculture applications. Recent work includes advancements in open-set domain generalization for hyperspectral classification, pollen classification with novel datasets, and domain adaptation techniques for SAR flood segmentation. Scientific contributions include 15+ recent publications addressing domain generalization, semantic segmentation, and uncertainty quantification in remote sensing and medical imaging. Awards include 2nd place at IEEE SIU'25 student paper awards. Projects involve international collaborations with institutions in Tunisia, Finland, and the UK, focusing on medical image understanding, crowd counting, and Ottoman document analysis.

Kivanc Tatarمشاهده پروفایل

استادیار

• Musical Artificial Intelligence
• Machine Learning
• Interactive Arts
• +۴ مورد دیگر

Kıvanç Tatar is an Assistant Professor in Interactive AI at Chalmers University of Technology, Sweden. His work bridges Music, Artificial Intelligence, Interactive Arts, and Virtual Reality, creating multimodal AI systems for performances, immersive environments, and generative art. Exhibited globally at events like Ars Electronica (2017, 2020) and Rio Olympics 2016 Founder of the AI in Computational Arts, Music, and Games research group Projects integrate AI with movement computation, VR, and livecoding His research focuses on musical AI , interactive systems , and creative algorithms , with applications in real-time performance, environmental art, and robotics. Recent projects include Koami 2025 (upcoming livecoding), Latent Timbre Synthesis (2021), and Brush of AI (2020 NFT series). Key Collaborations : A.I.D (Istanbul), Gold Saucer (Vancouver) Exhibition Highlights : CHI 2018, Mutek Montreal 2018, Contemporary Istanbul PlugIn 2019

Luis Velez Quinteroمشاهده پروفایل

استادیار

• Data Science
• Extended Reality (XR)
• Machine Learning
• +۴ مورد دیگر

Luis Velez Quintero is an Assistant Professor at Stockholm University within the Department of Computer and Systems Sciences (DSV) . He is affiliated with the Data Science Research Group and the Stockholm Technology & Interaction Research (STIR) group, focusing on Human–Computer Interaction. His research spans adaptive immersive systems, affective computing, and physiological signal analysis in extended reality (XR) environments. Education & Background : Holds a PhD in Computer and Systems Sciences from Stockholm University (2023), MSc in Health Informatics from Karolinska Institutet (2019), and BSc in Electronics Engineering from the National University of Colombia (2015). He has led the startup PortalSense since 2018, developing VR solutions for real estate visualization. Research Interests : Combines data science and ML with XR technologies to create context-aware systems for healthcare, education, and professional training. Key themes include: Adaptive VR/AR systems using real-time physiological and behavioral signals Biosignal integration for personalized user experiences Immersive technologies for cognitive assessment and skill development Publications : Over 20 peer-reviewed articles, including work on affective databases (AVDOS-VR), biosignal frameworks (Excite-O-Meter), and XR applications in cybersecurity education. Recent efforts explore third-person locomotion in VR and early-stage Alzheimer’s detection via spatial navigation tasks. Awards & Grants : Wallenberg Foundation Grant (2023-2025): Analyzing non-verbal communication in psychotherapy Swedish Institute Scholarship (2017-2019): Fully funded Master’s and PhD studies Seed funding for PortalSense from Fondo Emprender SENA Colombia (2022-2023) Advising & Projects : Lead researcher in projects like AVDOS-VR and CS:NO . Co-designed the Excite-O-Meter open-source plugin for real-time physiological analysis in VR. Active in industry collaborations for scalable health interventions and immersive training systems. Labs & Teams : Collaborates with multidisciplinary teams at STIR and DSV, advancing human-centered AI and adaptive XR technologies.

• Human-Robot Interaction
• Machine Learning
• Robotics
• +۳ مورد دیگر

Parag Khanna is a Doctoral Student at the Division of Robotics, Perception and Learning , KTH Royal Institute of Technology. His work focuses on Human-Robot Interaction (HRI) , particularly in developing adaptive techniques for robot-human handovers. University: KTH Royal Institute of Technology Department: Robotics, Perception and Learning Khanna's research explores physical and social aspects of HRI . He studies human-human handovers to improve robotic grip release strategies and object weight adaptation. His work also delves into non-touch modalities like EEG and gaze tracking for intention detection. His recent publications highlight advancements in data-driven control , failure explanations , and multimodal datasets for HRI. Notable works include the REFLEX Dataset and studies on adaptive grip release in human-robot collaborations. Key Research Areas: Human-Robot Handovers Machine Learning for Robotics EEG and Gaze-Based Intention Detection Failure Communication in Social HRI Khanna is affiliated with the Digital Futures - Advanced Adaptive Intelligent Systems project, aiming to empower elderly and disabled individuals through autonomous robotics.

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.