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Staffan SchedinView profile

Associate Professor

• Biomedical Optics
• Bacterial Adhesion Mechanisms
• Engineering Education
• +3 more

Staffan Schedin serves as Faculty Director of Studies at the Faculty Office of Science and Technology, Umeå University. He holds a Docent (Associate Professor) qualification in addition to being a Recognised University Teacher. His roles include curriculum development, academic governance, and promoting interdisciplinary collaboration between academia and industry. His research spans multiple domains: biomedical optics (focusing on corneal refractive errors and medical imaging), bacterial adhesion mechanisms (using optical tweezers and force spectroscopy), and engineering education innovation. He has pioneered work-integrated learning models aligned with CDIO standards and contributed to curriculum agility frameworks at program and institutional levels. Key collaborations include projects with industrial partners and multi-institutional efforts in engineering education development. His work on Helicobacter pylori adhesion mechanisms has advanced understanding of bacterial infection processes, while his optical modeling contributions have improved corneal treatment analysis.

Miguel Serras VascoView profile

Researcher

• Multimodal Perception
• Reinforcement Learning
• Representation Learning
• +4 more

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).

Fredrik StrandView profile

Researcher

• Artificial Intelligence
• Medical Imaging
• Breast Cancer Research
• +3 more

Fredrik Strand is a researcher at the Karolinska Institute , specifically within the Department of Oncology-Pathology under the Faculty of Medicine . He leads the Computational Breast Imaging Group , focusing on leveraging Artificial Intelligence (AI) for early breast cancer detection , precision diagnostics , and image-guided minimally invasive procedures . His work spans cross-disciplinary collaboration with institutions like the Royal Institute of Technology (KTH) and involves major clinical trials such as ScreenTrustCAD and ScreenTrustMRI . His research emphasizes AI in precision screening to address interval cancers and optimize radiologist workflows. Projects like VAI-B and RadioVal aim to validate AI algorithms and predict therapy response for neoadjuvant treatment . Recent publications highlight advancements in multi-modality imaging (mammography, MRI), GAN-based synthetic data generation , and trustworthy AI frameworks for clinical deployment. A consistent focus is on cost-effectiveness , equity in AI diagnostics , and human-AI interaction dynamics . Key trends in his work include deep learning for tumor detection , reinforcement learning for screening policies , and multi-institutional validation of AI tools. His research is funded by organizations such as Medtechlabs , Vinnova , EU Horizon Europe , and the Swedish Breast Cancer Association .

Giuseppe BiancoView profile

Researcher

• Animal Behavior
• Migration Studies
• Tracking Technologies
• +5 more

Giuseppe Bianco is a Researcher at Lund University affiliated with multiple profile areas including NanoLund: Centre for Nanoscience, LTH Profile Area: Nanoscience and Semiconductor Technology, LU Profile Area: Light and Materials, and LU Profile Area: Natural and Artificial Cognition. He serves as technical expert of the Animal Navigation Lab, manager of the 3D Laboratory, and Principal Investigator for the Migration in small aquatic animals project. His research focuses on developing innovative tracking technologies and computational methods for animal behavior data acquisition, modeling, and analysis across diverse taxa and scales. His work spans animal migration studies using birds as model species, nano particle tracking of zooplankton using computer vision and fluorescent nanotechnology, and advanced 3D phenotyping of both extant and extinct organisms. His research contributes to UN Sustainable Development Goals related to environmental protection and biodiversity. Analysis of his publication record shows consistent output in high-impact journals with research spanning animal migration patterns, magnetic navigation, behavioral ecology, and zooplankton dynamics. His work demonstrates interdisciplinary approaches combining field studies, laboratory experiments, and computational modeling. As Principal Investigator for the Migration in small aquatic animals project, he leads collaborative research efforts. His work has received attention across multiple platforms including news outlets, social media, and academic platforms like Mendeley. He manages the Cell Sorting & Imaging Lab within the Functional Ecology infrastructure and oversees the 3D Laboratory, which specializes in quantifying, analyzing and visualizing complex 3D phenotypes. His technical expertise bridges biological research with advanced imaging and tracking technologies.

Per-Erik ForssénView profile

Associate Professor

• Computer Vision
• Robotics
• Perceptual Learning
• +3 more

Per-Erik Forssén is a Senior Associate Professor in the Department of Electrical Engineering at Linköping University, where he conducts research at the Computer Vision Laboratory (CVL). His academic journey includes a PhD (2004) and Docent degree (2009) from Linköping University, with a postdoctoral fellowship at the University of British Columbia's Laboratory for Computational Intelligence (2006-2007). His research focuses on visual perception and perceptual learning for robots , with specific expertise in uncertainty representation , vision for action systems , and continuous-time 3D motion modeling . He leads two major projects: Situation Aware Perception for Safe Autonomous Robotic Systems (ELLIIT-funded) and Dorsal Stream Robot Vision (Swedish Research Council-funded). His publications demonstrate strong focus on robotic vision systems and uncertainty quantification , with recent work exploring deep learning applications for autonomous driving, 3D registration, and human pose estimation. Earlier contributions include foundational work on rolling shutter compensation and video stabilization techniques.

György DánView profile

Professor

• Applied Game Theory
• Critical Infrastructure Security
• Edge Computing
• +5 more

György Dán is a Professor in Teletraffic Systems at KTH Royal Institute of Technology, Stockholm, Sweden. He holds a Ph.D. in Telecommunications from KTH (2006) and has extensive academic leadership roles, including Director of Third Cycle Education. His research focuses on networked systems design, with expertise in critical infrastructure security, edge computing, and cyber-physical systems. Dán has supervised over 20 PhD students and led major projects like VR DICE6G and REACT. He has authored/co-authored over 150 publications, receiving awards such as the Ericsson Research Foundation Award (2010-2014) and the 2020 KTH PhD Supervisor of the Year. Education Ph.D. in Telecommunications, KTH Royal Institute of Technology (2006) M.Sc. in Business Administration, Corvinus University of Budapest (2003) M.Sc. in Computer Engineering, Budapest University of Technology and Economics (1999) Research Interests His work spans applied game theory, smart grid security, mobile edge computing, and adversarial machine learning. Current projects include resilient edge computing systems and secure AI for cyber-physical infrastructures. Awards & Recognition Best Paper Awards at SNCNW 2025, IEEE SmartGridComm 2014, and IEEE Infocom 2008 Fulbright Visiting Scholar (University of Illinois, 2012-2013) Invited Professor at EPFL (2014-2015) Labs & Collaborations He leads the Network and Systems Engineering Division at KTH, collaborating with industries like Ericsson and ABB. His group focuses on experimental platforms like the GreenEyes testbed and stateful serverless edge computing.

Maëlic NeauView profile

Research Fellow

• Machine Learning
• Robotics
• Computer Vision
• +3 more

Maëlic Neau is a Postdoctoral Fellow at the Department of Computing Science, Umeå University. Their research focuses on Machine Learning, Robotics, and Human-Robot Interaction. Affiliation: Spatial Reasoning Lab under supervisor Zoe Falomir Location: MIT-huset, Umeå University Core research areas include: Computer Vision (abstract content representation in images/videos) Natural Language Processing Knowledge Representation applied to Social Robotics Award Highlights: 2x World Champion in RoboCup@Home with the RoboBreizh team

• Photogrammetry
• Bundle Adjustment
• 3D Measurements
• +3 more

Niclas Borlin is an Associate Professor at the Department of Computing Science, Umeå University. His research specializes in photogrammetry, bundle adjustment algorithms, and 3D measurement techniques, with applications spanning computer vision, remote sensing, and geospatial analysis. He maintains an active role in academic research with no indications of retirement or part-time status. Research Focus: Borlin's work centers on developing computational methods for photogrammetric data processing. Key areas include: Bundle adjustment optimization and modular algorithm design Terrestrial and UAV-based photogrammetry for environmental/architectural documentation Laser scanning calibration and error quantification Medical imaging techniques for orthopedic applications Awards: Talbert Abrams Award (2020) for contributions to photogrammetry His publications demonstrate consistent focus on improving accuracy in 3D measurements through computational innovations, with recent work emphasizing open-source tools like the Damped Bundle Adjustment Toolbox (DBAT).

Paul TownendView profile

Associate Professor

• Energy-efficient distributed systems
• Sustainable computing
• Cloud computing
• +7 more

Paul Townend is an Associate Professor at Umeå University , Sweden, with a Docent qualification. He leads the Green Distributed Computing group and serves as Research Leader for the Autonomous Distributed Systems (ADSLab) lab. Scientific Coordinator for Horizon Europe COGNIT Co-Manager & Scientific Advisor for WASP WARA-Ops Member of WASP Graduate School Management Team His research focuses on energy-efficient and sustainable distributed systems , with a current emphasis on Edge-Cloud integration . Additional areas include fault tolerance, provenance, data center optimization, and simulation . He has supervised 5 PhD students and 2 postdocs , with expertise in IoT, machine learning for cloud management, and microservices . Best Paper Awards at SOSE 2013 and ISORC 2012 Principal Investigator for grants totaling over $5.8M , including projects like Adaptive Monitoring of Streaming Data and Energy-Aware Cloud-Edge Management

Polina KurtserView profile

Associate Professor

• Machine Learning
• Robotics
• Agricultural Robotics
• +4 more

Polina Kurtser is an Associate Professor at the Department of Computing Science, Umeå University. Her research focuses on machine learning, robotics, and sensor systems, particularly in agricultural and autonomous applications. She has held postdoctoral positions at AASS (Örebro University) and the Department of Radiation Sciences at Umeå University. PhD in Computer Vision and Agri-Robotics (Ben-Gurion University, 2019) Postdoctoral positions at AASS and Radiation Sciences, Umeå University Her work spans agricultural robotics, including sweet pepper harvesting, grape vine detection, and mechanical stress induction in plants. She also explores illumination-independent target detection, dynamic sensing strategies, and statistical models for fruit detectability. Recent publications highlight her contributions to color-to-thermal AI for building inspection, multi-camera vision for gesture recognition, and RGB-D datasets for agricultural perception. Her research integrates machine learning, sensor fusion, and autonomous navigation in challenging environments. She teaches deep learning, machine learning, robotics, and optimization. Her academic affiliations include collaborations in gastronomy (sensory evaluation of robot-cultivated basil) and medical robotics (e.g., ECG chaotic component detection via neural networks).

• Machine Learning
• Software Testing
• Safety Critical Systems
• +7 more

Karl Meinke is a Professor at KTH Royal Institute of Technology, where he serves as Head of the Computer Science Department and Head of the Division of Theoretical Computer Science within the School of Electrical Engineering and Computer Science. His research focuses on applying machine learning techniques to software testing, particularly for safety-critical systems like autonomous vehicles and embedded systems. His research interests span machine learning, software testing, safety critical systems, embedded systems, autonomous driving, digital pathology, and graph neural networks. Meinke has developed innovative approaches like Learning-Based Testing that combine machine learning with formal methods for system validation. His work bridges theoretical computer science with practical applications in automotive systems and medical diagnostics. His recent publications show a strong trend toward applying graph neural networks to diverse domains including program analysis, digital pathology, and autonomous vehicle testing. His research demonstrates a consistent focus on solving the test oracle problem and generating meaningful test cases for complex systems where traditional testing approaches fall short. Meinke actively collaborates with Karolinska Institutet (KI), indicating interdisciplinary work between computer science and medical research. He is responsible for Masters level education in software testing at KTH and serves as examiner for several advanced courses including Degree Projects in Computer Science and Software Reliability. His research group has developed tools like LBTest for learning-based testing of reactive systems, and he has secured funding for projects such as the ITEA3 Testomat Project focused on next-level test automation. His work has significant implications for validating autonomous systems where safety is paramount. Meinke leads research in using machine learning to address fundamental challenges in software testing, particularly for systems where traditional test oracles are unavailable or impractical. His approach of combining active learning with formal specifications has created new pathways for validating complex cyber-physical systems.

• Collaborative Robotics
• Manufacturing Systems
• Human-Robot Interaction
• +5 more

Omkar Salunkhe is a Postdoc researcher at Chalmers University of Technology, working within the Department of Industrial and Materials Science, specifically in the Production Systems division. His research focuses on advanced manufacturing systems, human-robot collaboration, and Industry 4.0 technologies, with particular emphasis on practical applications in wire harness assembly and final assembly processes. Salunkhe's research interests span multiple areas of modern manufacturing and automation. He specializes in collaborative robotics applications, particularly in wire harness assembly processes. His work explores task allocation between humans and robots, cost-benefit analysis of collaborative workstations, and the integration of computer vision systems in manufacturing environments. He also investigates operational flexibility in final assembly using Industry 4.0 enabling technologies, with applications in automotive and other manufacturing sectors. His research bridges theoretical concepts with practical industrial implementation, focusing on human-centered approaches to automation that maintain economic viability. Analysis of Salunkhe's publication record reveals a strong trajectory from foundational work on operational flexibility (2018-2020) to increasingly specialized research on collaborative robotics in wire harness assembly (2022-2024). His work demonstrates a clear evolution from general Industry 4.0 applications to targeted solutions for specific manufacturing challenges, with a consistent focus on the human element in automated systems. The 2025 MAXLabs project represents a significant expansion of his research into integrated cyber-physical testbeds for broader manufacturing research. Salunkhe has been actively involved in multiple research projects funded by VINNOVA, the European Commission, and other organizations. These include MAXLabs, EWASS (Empowering Human Workers for Assembly of Wire Harnesses), PLENUM, and Digital work Instructions for cognitive work. His collaborative network includes prominent researchers such as Johan Stahre, Anna Syberfeldt, and David Romero, reflecting strong interdisciplinary and industry-connected research activities.

• Biophysics
• Cell and Molecular Biology
• Medical Biotechnology
• +3 more

Annica Gad is a Lecturer affiliated with the Department of Oncology-Pathology at Karolinska Institutet. She works within Katalin Dobra's research group focused on molecular pathology of the lung and pleura, combining biophysics and molecular biology to study cancer progression. Doctor of Philosophy (2005), Department of Laboratory Medicine, Karolinska Institutet Docent (2024), Karolinska Institutet Her research spans biophysics, cell biology, and oncology, focusing on cytoskeletal dynamics, vimentin regulation, oncogene effects on cell mechanics, and HDAC6 inhibition in cancer. She develops algorithms for filament network tracing and employs nanoscopy for subcellular analysis. Recent publications highlight her work on vimentin-targeting drugs, fibroblast migration, extracellular matrix mechanics, and cytoskeletal remodeling in cancer. Articles span high-impact journals in oncology, molecular biology, and biomedical engineering. Employment: Affiliated to Teaching/Tutoring (2024-2027), Lecturer (2025) Lab: Molecular pathology of the lung and pleura – Katalin Dobra's Group Research Themes: Cytoskeleton, mechanobiology, cancer metastasis, HDAC6, Rho GTPases

Ingela NyströmView profile

Professor

• Medical image analysis
• 3D visualization
• Haptics in surgery planning
• +3 more

Ingela Nyström is a Professor in Visualization at Uppsala University's Department of Information Technology. She serves as the node coordinator for InfraVis and as Director of Postgraduate Studies at the Department of Information Technology. Her interdisciplinary work bridges Uppsala University's three academic domains through collaborations with the Centre for Image Analysis (CBA), Centre for Women's Mental Health (WOMHER), Uppsala Centre for Digital Humanities (CDHU), and Medtech Science & Innovation (MTSI). Medical image analysis 3D visualization Haptics in surgery planning Interactive segmentation Digital geometry Biomedical engineering Her recent research focuses on rotation-equivariant neural networks for biomedical image classification, interactive segmentation tools for cranio-maxillofacial surgery planning, and precision evaluation of intraoral scanning technologies. Publications since 2023 demonstrate continued work on 3D imaging protocols for implant dentistry and surgical applications. 2024: Equivariant CNNs for rotation-invariant biomedical imaging 2023: In vivo precision studies of full-arch implant scans 2021: Virtual surgical planning with haptic assistance 2016-2017: Multimodal robotics perception and 3D segmentation tools 2014: Orbital morphology analysis in craniosynostoses 2005-2011: Foundational work in 3D skeletons and fuzzy object measurements

Ansel BerghuvudView profile

Associate Professor

• Mechanical Engineering
• Wave Energy Conversion
• Computer-Aided Design
• +2 more

Ansel Berghuvud serves as an Associate Professor in the Department of Mechanical Engineering at Blekinge Institute of Technology (BTH) in Karlskrona, Sweden. His academic career is rooted in mechanical engineering with a specialization in computer-aided machine design and simulation-driven product development, as evidenced by his doctoral work at Luleå University of Technology completed in 2002. Dr. Berghuvud's research has a strong practical orientation, addressing challenges in renewable energy systems, structural dynamics, and transportation technology. Education PhD in Computer-Aided Machine Design (Simulation-Driven Product Development), Luleå University of Technology, 2002 Dr. Berghuvud's research portfolio demonstrates expertise across multiple sub-disciplines of mechanical engineering. His primary focus lies in wave energy conversion, where he has developed innovative mechanisms for smoothing mechanical energy in ocean wave harvesters. Complementing this, his work in structural dynamics has examined the vibrational characteristics of industrial machinery such as water jet cutters. Additionally, he has contributed to intelligent transportation systems through research on sensor fusion for traffic sign recognition. His methodological approach consistently emphasizes simulation-based design and optimization. Analysis of his publication record from 2004-2011 reveals a strategic research trajectory centered on sustainable energy solutions and precision engineering. The recurring themes include mechanical system design for renewable energy applications, structural analysis of dynamic systems, and the integration of digital mapping with computer vision for transportation safety. This interdisciplinary work bridges theoretical mechanics with practical industrial applications. No scientific awards or major prizes are documented in the available information. Similarly, details about research grants and funding sources are not provided, though his involvement in wave energy projects suggests potential industry or government funding in renewable energy sectors. While specific student advisees are not listed, Dr. Berghuvud likely supervises graduate students in mechanical engineering given his professorial rank. His research activities indicate leadership in projects related to ocean energy harvesting and structural dynamics, though current project details are unavailable. The absence of lab or team descriptions suggests his work may be conducted through collaborative projects rather than a dedicated research laboratory.