Find Academic Supervisors

• Robotics
• Artificial Intelligence
• Human-Robot Interaction
• +6 more

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)

Kalle ÅströmView profile

Professor

• Computer Vision
• Machine Learning
• Geometry and Algebra of Multiple Views
• +5 more

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.

• Nonlinear Filtering
• Multi-Object Tracking
• Deep Machine Learning
• +3 more

Lennart Svensson is a Professor at Chalmers University of Technology in the Signal Processing research group. His work focuses on nonlinear filtering, multi-object tracking, Bayesian statistics, and deep machine learning with applications in autonomous systems and sensor fusion. Research Interests Nonlinear Filtering and Bayesian Inference Multi-Object Tracking and Sensor Fusion Deep Learning for Autonomous Systems Performance Metrics (GOSPA, T-GOSPA) Lidar-Camera Fusion and Radiance Fields 5G SLAM and mmWave Sensing Publications Trends Recent work emphasizes uncertainty-aware multi-object tracking metrics, trajectory estimation using Poisson Multi-Bernoulli Mixtures, and sensor fusion techniques for autonomous driving. His research integrates Bayesian methods with deep learning for applications in automotive radar, lidar, and 5G positioning systems. Contact Email: lennart.svensson@chalmers.se

Vladimir VlassovView profile

Professor

• Machine Learning
• Data-intensive computing
• Cloud computing
• +7 more

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
• Machine Learning
• Federated Learning
• +7 more

Shuguang Cui is the X.Q. Deng Presidential Chair Professor in the Department of Electrical and Computer Engineering at the School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen. He holds a PhD from Stanford University and has previously served on the faculties of the University of Arizona, Texas A&M University, and UC Davis. His research lies at the intersection of artificial intelligence and communication networks, particularly in AI+Networking , with major contributions in federated learning over wireless systems, intelligent reflecting surfaces, UAV trajectory optimization, and energy-efficient edge computing. His work bridges machine learning theory with practical communication system design. The recent publications reflect a strong trend toward integrating distributed AI with wireless network constraints, focusing on reducing communication overhead, optimizing resource allocation, and enhancing learning efficiency in edge environments. Key themes include federated learning convergence, hybrid VLC-RF systems, and intelligent surface-assisted communications. Award highlights include: IEEE Fellow (2013) IEEE Marconi Prize Paper Award (2023) Fellow of the Royal Society of Canada (2023) Fellow of the Canadian Academy of Engineering (2023) Multiple IEEE best paper awards (Globecom, WCNC, ICC) Thomson Reuters Highly Cited Researcher (2014) Prof. Cui has served in leadership roles such as Vice Chair of the IEEE VT Fellow Evaluation Committee, member of the IEEE ComSoc Award Committee, and currently as Editor-in-Chief of IEEE Transactions on Mobile Computing . He has secured significant research impact through high-visibility publications in top-tier journals and conferences, shaping the future of AI-driven wireless systems. He leads a research group focused on next-generation intelligent networks, supervising graduate students and collaborating across institutions. His team actively contributes to advancing the theoretical and practical foundations of AI-enabled communications.

• artificial intelligence
• image analysis
• deep learning
• +8 more

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.

• Quality of Experience
• Video Quality Assessment
• Augmented Reality
• +7 more

Kjell Brunnström serves as an Adjunct Professor in the Department of Computer and Electrical Engineering (DET) at Mid Sweden University, based in Sundsvall. He maintains active affiliation with the STC Research Centre, contributing to cutting-edge research in multimedia quality assessment and immersive technologies through extensive international collaborations. His research centers on Quality of Experience (QoE) with specialized focus on 3D video systems , virtual reality environments , and augmented teleoperation interfaces . He pioneers methodologies for subjective and objective video quality evaluation, particularly examining human factors in sports broadcasting (Video Assistant Referee systems), mining applications, and automotive displays. His work bridges theoretical psychophysical models with industrial implementations through standards development like ITU-T Rec. P.919. Analysis of his 15 most recent publications reveals consistent investigation into latency effects, distortion impacts, and viewing condition variables across emerging media formats. Key application domains include sports video refereeing, 360-degree video systems, VR simulators for remote machinery control, and adaptive streaming protocols, demonstrating strong industry-academia translation. Scientific Awards: No awards documented in source material. Advising and Grants: Student supervision and grant funding details were not disclosed in available documentation. Labs and Teams: Brunnström operates within Mid Sweden University's STC Research Centre framework, collaborating with global institutions including Royal Institute of Technology (Sweden), University of Surrey (UK), and multiple European research consortia. His work directly informs ITU standardization efforts and industrial implementations in broadcast and automotive sectors.

Erchan AptoulaView profile

Professor

• Digital Image Analysis
• Computer Vision
• Remote Sensing
• +3 more

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.

• Mechatronics
• Signal Processing
• Control Systems
• +7 more

Jonas Sjöberg is a Full Professor of Mechatronics at Chalmers University of Technology, where he leads the Mechatronic research group in the College of Engineering. His research spans multiple aspects of mechatronic systems with a strong focus on automotive applications. Sjöberg holds leadership roles in numerous research projects related to autonomous vehicles, vehicle control systems, and transportation safety. His research interests encompass a broad spectrum of mechatronics applications, with particular emphasis on model-based methods, signal processing, control systems, system identification, and optimization for design and product development of mechatronic systems. Sjöberg's work bridges theoretical control engineering with practical automotive applications, especially in the domains of Automotive Active Safety and Hybrid Electric Vehicles. Analysis of Sjöberg's recent publications reveals a strong research trajectory focused on autonomous vehicle technologies, with particular attention to vehicle dynamics control, intersection safety, road surface condition estimation, and optimization of vehicle maneuvers. His work demonstrates a consistent approach of applying advanced control theory to solve real-world transportation challenges, with increasing emphasis on machine learning techniques integrated with traditional control systems. Sjöberg actively supervises research and education at both undergraduate and graduate levels while leading multiple research projects funded by VINNOVA, the European Commission, and other organizations. His research group collaborates extensively with both academic institutions and industry partners in the automotive sector. His laboratory work focuses on mechatronic systems development, particularly for automotive applications including autonomous bicycles, bus docking systems, and vehicle control algorithms. The research group maintains strong connections with the automotive industry, particularly in Sweden's robust vehicle technology ecosystem.

Marjan FirouzniaView profile

Researcher

• Medical Imaging
• Machine Learning
• Deep Learning
• +3 more

Marjan Firouznia is a Principal Research Engineer at Linköping University , affiliated with the Division of Diagnostics and Specialist Medicine (DISP) under the Faculty of Medicine and Health Sciences . With a PhD in Electrical Engineering from Amirkabir University of Technology and postdoctoral experience at institutions like Case Western Reserve University, she specializes in advancing machine learning models for precise segmentation of cardiac structures including the left atrium , epicardial fat , and fibrosis using CT and MRI scans. Her work aims to improve diagnostic accuracy and treatment planning in cardiovascular care. Marjan's research focuses on medical imaging , deep learning , and computational anatomy , with recent publications on FractalRG , FK-means , and Poincare-guided UNet for cardiac structure segmentation. Her academic contributions span 15 recent publications , emphasizing fractal geometry , chaos theory , and optimization algorithms in biomedical applications. She actively develops open-source datasets and tools, such as the FK-means codebase , to support reproducibility in medical AI research.

Wlodek KuleszaView profile

Professor

• Systems Engineering
• Measurement and Instrumentation
• Sensor Networks
• +3 more

Wlodek Kulesza is a Senior Professor in the Department of Mathematics and Natural Sciences at the Blekinge Institute of Technology in Karlskrona, Sweden. He has held academic positions since 2001, including a Professorship in Multi-sensor Systems since 2007. Research: Multisensory systems for health and security applications Teaching: Research Methodology, Philosophy of Science, and Sensors Signals and Systems Expertise: Systems engineering, sensor fusion, IoT, and measurement data handling Research Focus : His work in systems engineering emphasizes multisensor integration for applications spanning healthcare, security, wind energy safety, and subsea cable monitoring. He has pioneered approaches to stereovision calibration, localization algorithms, and real-time safety systems. Awards : Andy Chi Best Paper Award (2009, IEEE Transactions in Measurement and Instrumentation) Collaborations : Visiting Professor at Chinese and Polish universities, with extensive cross-border educational workshops and remote lab federations (e.g., PILAR/VISIR projects).

Leif NilssonView profile

Associate Professor

• Statistical Learning
• Defect Detection
• Occupational Health
• +5 more

Leif Nilsson is an Associate Professor at the Department of Mathematics and Mathematical Statistics, Umeå University. His research focuses on statistical learning methods for industrial quality control and occupational health risk assessment. Statistical learning for defect detection Exposure modeling for occupational hazards Biostatistical analysis of health outcomes Recent work applies probabilistic classifiers and spline smoothers to automate surface finish inspection. He also investigates exposure variability in hand-arm vibration and its health impacts through epidemiological studies. Nilsson collaborates with clinical teams on stress recovery interventions and contributes to methodological developments in biological monitoring. His statistical expertise spans Bayesian modeling, resampling techniques, and spatio-temporal data analysis.

Sonja BucheggerView profile

Professor

• Privacy-Preserving Technologies
• Data Security
• Decentralized Systems
• +3 more

Sonja Buchegger is a Professor at the Division of Theoretical and Computer Science at KTH Royal Institute of Technology. She serves as Co-PI of the DataLEASH research project (Learning and Sharing under Privacy constraints) and is affiliated with the Digital Futures Faculty, a cross-disciplinary center focused on digital technologies and societal challenges. Her work addresses privacy-preserving technologies in decentralized systems, IoT, and secure communication. Research Interests: Privacy-Preserving Technologies Data Security and Synthetic Data Generation Decentralized Systems and Cryptographic Protocols IoT Privacy and Ownership Management Secure Communication in Social Networks Key Projects: DataLEASH: Focuses on privacy constraints in data sharing and learning chownIoT: Enhances IoT privacy during ownership transfers Anonfaces: Anonymization of facial data with efficacy and security constraints Advising & Grants: As Co-PI of DataLEASH, she leads collaborative research on privacy challenges. Her work is supported by Digital Futures' interdisciplinary initiatives. Labs/Teams: Active contributor to Digital Futures' research on digital technologies and societal impact.

Markus H. FlierlView profile

Associate Professor

• Visual Computing and Communication
• Machine Learning
• Information Theory
• +3 more

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.

Emily BairdView profile

Professor

• Visual Ecology
• Insect Behavior
• Neuroethology
• +3 more

Emily Baird is a Professor at the Department of Zoology, Stockholm University , leading interdisciplinary research at the intersection of sensory ecology , neuroethology , and comparative morphology . Her work focuses on how insects like dung beetles and bumblebees process visual information to guide behavior in diverse environments. Key Collaborations : Dlife Project with University of Southern Denmark and University of Kiel (Human Frontiers Science Project) INVISMO Project with Lund University (Swedish Research Council) Research Themes : 3D micro-CT analysis of insect eyes Neural mechanisms for straight-line orientation Flight control in cluttered environments Mechanical principles of dung beetle ball rolling Techniques : Behavioral experiments, X-ray microtomography, computational modeling, ray-tracing simulations.