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Kristina LångView profile

Associate Professor

• Breast imaging
• Breast cancer
• Breast cancer screening
• +7 more

Kristina Lång is an Associate Professor (Docent), Project Leader, Specialist Physician, and Researcher in Diagnostic Radiology at Malmö. She serves as a Principal Investigator at Lund University Cancer Center (LUCC) and eSSENCE: The e-Science Collaboration. With extensive clinical experience as a breast radiologist since 2015, she uniquely bridges patient care with cutting-edge research in breast cancer diagnostics and screening. Her primary research focuses on reducing breast cancer mortality through innovative screening and diagnostic approaches. She leads the MASAI study, a large randomized trial investigating AI implementation in mammography screening, with initial results presented in 2023. Additionally, she heads the "23°N - Cancer's Tropic" project aiming to develop accessible breast diagnostics with a global health perspective, particularly for low- and middle-income countries where late detection contributes to high mortality rates. Her work spans translational imaging, breast tomosynthesis, and AI applications in radiology, addressing both high-resource and resource-limited settings. Recent publications reveal a strong emphasis on AI applications in breast cancer screening and diagnostics, examining cost-effectiveness, screening performance metrics, AI classification in point-of-care ultrasound, and recurrence patterns. Her research bridges clinical practice with technological innovation, with particular attention to real-world implementation and global accessibility across diverse healthcare systems. Junior Clinical Investigator Award (2025) Carla Boetes Award, European Society of Breast Imaging (2024) Nature Medicine Notable Advances 2023 (2023) IVAs 100-lista recognition (2023) Most cited breast imaging paper award, European Radiology (2023) Dr. Lång has secured significant funding from organizations including UMAS Cancer Foundation, Cancerfonden, and Mats Paulsson Foundation. She actively supervises research projects and has presented her work at major conferences including the 7th Swedish Cancer Research Meeting (2025). Her team includes collaborators across radiology, computer science, and global health disciplines, working on multiple active projects from 2024-2028 focused on improving breast cancer outcomes worldwide.

Fredrik LevanderView profile

Senior Lecturer

• Computational Proteomics
• Mass Spectrom
• Bioinformatics
• +2 more

Fredrik Levander is a Senior Lecturer at Lund University's Department of Immunotechnology and Principal Investigator at the Lund University Cancer Centre (LUCC). He leads computational proteomics research while coordinating within NBIS (National Bioinformatics Infrastructure) and managing proteoform analysis platforms. Lund University LUCC - Lund University Cancer Centre Profile Area: Engineering Health Profile Area: Food and Bio His research focuses on improving computational methods for mass spectrometry data analysis to advance cancer biology understanding. Key areas include: Algorithm development for proteomic data Bioinformatics tools for omics datasets Cancer proteoform biomarker discovery Label-free LC-MS/MS workflows Pathway enrichment modeling Leveraging mass spectrometry and bioinformatics, Levander's publications reveal expertise in: Data-independent acquisition (DIA) analysis Quantitative proteomics normalization Software implementation for clinical applications Computational oncology approaches Peptide and proteome analysis Machine learning applications in proteomics He actively supervises doctoral research and contributes to collaborative projects like: Enabling proteoform biomarker discovery (WASP-funded, 2023-2025) NILS study (Non-invasive lymph node diagnostics, 2019-2027) Plasma proteomics on EOC subgroups (2018-2020) As infrastructure manager for: Mass spectrometry proteomics platform National Bioinformatics Infrastructure Sweden He drives technological development alongside 7 supervised researchers, including doctoral student Linda Thörnqvist.

• Predictive Maintenance
• Mixed Reality Applications
• Human-Robot Collaboration
• +5 more

Bernard Schmidt is an active researcher at the University of Skövde's School of Engineering Science, specializing in Production and Automation Engineering. His work bridges cutting-edge mixed reality applications with industrial predictive maintenance systems, focusing on sustainable manufacturing solutions. Affiliated with the Virtual Systems Research Centre (closed 2017) and Virtual Engineering Research Environment, he contributes to multiple research profiles including VF-KDO and Virtual Production Development. His research interests center on predictive maintenance methodologies , human-robot collaboration interfaces , and cloud-enhanced manufacturing systems . Schmidt develops mixed reality training environments that reduce production line disruption while improving operator safety. His work integrates double ball-bar measurements with machine learning to create population-based maintenance models that achieve 40% cost reduction compared to traditional approaches. Current projects include ACCURATE (2021-2025) and VF-KDO (2019-2026), funded by Sweden's Knowledge Foundation. Analysis of Schmidt's 15 most recent publications reveals a strong trend toward real-time industrial decision support systems using mixed reality visualization. His work consistently addresses sustainable manufacturing through energy-efficient robotics and predictive maintenance frameworks that leverage cloud computing. Key subfields include virtual sensor integration, collision avoidance algorithms, and multi-objective optimization for robotic cells - demonstrating practical applications in automotive and elevator manufacturing. Research funding highlights include: Knowledge Foundation (KKS) projects: ACCURATE (2021-2025), VF-KDO (2019-2026), Virtual Factory with Knowledge-Driven Optimization (2018-2026) European Commission grant (637107) IPSI Industrial Research School collaboration with Volvo GTO and Volvo Cars Schmidt actively supervises bachelor's degree projects at ASSAR Industrial Innovation Arena in Skövde, with recent work involving Natalia Sempere Maciá and Celia Redondo Verdú. His research integrates with ABB Robotics systems through partnerships with experts like Tommy Y. Svensson. Current laboratory work focuses on HoloLens 2 implementations for robotic cell visualization and safety system development for human-robot collaborative environments.

• Simulation-Based Optimization
• Industry 4.0
• Production Engineering
• +4 more

Amos H. C. Ng is a Professor at the School of Engineering Science, University of Skövde, specializing in simulation-based optimization and Industry 4.0 technologies. His research bridges production engineering with human-robot collaboration, ergonomics evaluation, and cloud-based cyber-physical systems for manufacturing efficiency. Key Affiliations: University of Skövde (School of Engineering Science), Uppsala University (Industrial Engineering and Management) Research Themes: Multi-objective optimization, Digital Twin frameworks, Human-centric production systems, Reconfigurable manufacturing, Throughput bottleneck analysis Projects: ACCURATE 4.0 (Knowledge Foundation), VF-KDO (Virtual Factories with Knowledge-Driven Optimization), EWASS (Wire Harness Assembly Optimization) His recent publications demonstrate expertise in applying evolutionary algorithms, machine learning models, and digital human modeling tools to solve complex manufacturing problems ranging from crankshaft machining to wood supply chain robustness. Current work integrates motion capture technology with DHM tools for objective ergonomic assessments in assembly stations. Amos collaborates extensively with industrial partners like Volvo Penta and academic institutions, utilizing simulation-based approaches to enhance decision-making in production systems. His methodological focus includes non-dominated sorting genetic algorithms, surrogate modeling, and parallel computing architectures for optimization tasks.

Tina SundelinView profile

Associate Professor

• Sleep Deprivation
• Social Effects of Sleep
• Cognitive Functioning
• +5 more

Tina Sundelin is an Associate Professor and Director of Studies at the Department of Psychology, Stockholm University. She is affiliated with the Center for Cultural Evolution and the Sleep and Wakefulness Research Unit, where she conducts interdisciplinary research on sleep, social interactions, and well-being. Her research focuses on the social effects of sleep deprivation and how they relate to motivation, attention, and cognitive ability. She also investigates how sleep affects daily interactions and well-being in contexts such as families and workplaces. Another significant research interest is social perception of illness - specifically how people perceive someone who is sick and what cues allow others to determine whether a person is sick or healthy. Tina's publication record demonstrates expertise in examining how sleep, social interactions, and well-being interrelate. Her research spans multiple methodologies, integrating laboratory-based experimental studies with field studies. Her work has explored topics including parental sleep patterns, social perception of illness across cultures, cognitive effects of sleep deprivation, and the relationship between sleepiness and social activity. Her publications frequently appear in high-impact journals across sleep science, psychology, and neuroscience. Her research projects include ongoing studies on sleep inequality among parents with young children, the relationship between sleep and social cohesion, and whether people are aware of their cognitive impairments when sleep-deprived. She also has completed research on social interactions during sleep deprivation. Tina earned her PhD at Stockholm University in 2015, focusing on social perception of sleep loss (dissertation: 'The Face of Sleep Loss'). After this, she began a postdoc at Karolinska Institutet to study social interactions during sleep deprivation. She was awarded the Swedish Research Council's International Postdoctoral Grant in 2016 to continue her research with Dr. Tessa West at New York University. During this period, she focused on physiological aspects such as HRV and activity in the sympathetic nervous system, in addition to behavioral measures and dyadic tasks after sleep deprivation. Since 2019, she has been employed as a University Lecturer at Stockholm University. Her ongoing research integrates laboratory-based experimental studies with field studies to investigate how sleep, social interactions, and well-being relate to each other.

Filip LindskogView profile

Professor

• Actuarial Mathematics
• Insurance Mathematics
• Financial Mathematics
• +5 more

Filip Lindskog is a Professor of Insurance Mathematics at Stockholm University (SU) , where he heads the Mathematical Statistics division within the Department of Mathematics . With a background in financial mathematics and actuarial science, his research focuses on quantitative risk management, non-life insurance pricing, and applications of biostochastics and biostatistics. He has co-authored the textbook Risk and Portfolio Analysis: Principles and Methods (Springer, 2012) and supervises PhD students in actuarial mathematics. Education \n \n MSc in Engineering Physics, KTH Royal Institute of Technology (2000) \n PhD in Mathematical Statistics, ETH Zürich (2004) \n Research Interests Filip's work spans actuarial mathematics , financial risk modeling , and insurance analytics . He investigates stochastic processes in regime-switching environments, capital requirements for insurers, and mathematical frameworks for claims reserving. His recent publications emphasize machine learning applications in risk adjustment, asymptotic analysis of Poisson models, and regulatory compliance under IFRS 17.\n Scientific Contributions \n \n Editor, Scandinavian Actuarial Journal (2018–present) \n Director of SU's Master's Program in Actuarial Mathematics (2016–present) \n Head of SU's Mathematical Statistics Division (2018–present) \n \n Students and Collaborations Current and former PhD students include Nils Engler , Lina Palmborg , Jonas Alm , and Johan Nykvist . Former postdocs include Julie Thøgersen , Abhishek Pal Majumder , and Kristoffer Lindensjö . His research group explores discrete random structures, financial applications of biostatistics, and insurance modeling under capacity constraints.\n

• Reinforcement Learning
• Information Theory
• Machine Learning
• +3 more

Raghav Bongole is a doctoral researcher at the Division of Information Science and Engineering within the School of Electrical Engineering and Computer Science at Kungliga Tekniska Högskolan (KTH). His work focuses on theoretical foundations of reinforcement learning through the lens of information theory , supervised by Professors Mikael Skoglund and Tobias Oechtering under the Wallenberg AI, Autonomous Systems and Software Program (WASP). Research Interests: Information-theoretic bounds for RL algorithms Duality between entropy and learning performance Theoretical optimization in autonomous systems Recent Work (2024): Co-authored a paper on arXiv addressing duality-based bounds for reinforcement learning, with implications for algorithm design and sample efficiency. The work intersects computer science , machine learning theory , and optimization . Contact: bongole@kth.se

• Uncertainty management
• Information fusion
• Probabilistic expert systems
• +8 more

Joel Brynielsson serves as an Associate Professor at KTH Royal Institute of Technology within the Division of Theoretical Computer Science, School of Electrical Engineering and Computer Science, while simultaneously holding the position of Research Director at the Swedish Defence Research Agency (FOI) since 2008. His academic credentials include a Ph.D. in Computer Science (2006) and M.Sc. in Computer Science and Engineering (2000), both from KTH, followed by achieving Docent (Habilitation) status in Computer Science in 2015. Dr. Brynielsson's research spans uncertainty management, information fusion, probabilistic expert systems, command and control systems, operations research, game theory applications, web mining, privacy-preserving data mining, and cyber security. His work bridges theoretical computer science with practical security applications, particularly in national defense contexts. His recent publication record shows consistent scholarly output through 2024, with research focusing on cybersecurity practices in Swedish administrative authorities, cyber-threat perception in the financial sector, and social media applications for crisis management. The interdisciplinary nature of his work is evident in collaborations across security, defense, and emergency management domains. Associate editor for Springer Security Informatics journal Technical program chair for IEEE EISIC conferences (2013-2019) Regular reviewer for security and informatics journals Research funding from EU, Swedish Armed Forces, and public authorities Through his dual appointments, Dr. Brynielsson maintains a vital connection between academic research and practical defense applications, contributing significantly to both scholarly knowledge and real-world security solutions.

• Graph Mining
• Network Analysis
• Graph Representation Learning
• +3 more

Yifei Jin is a WASP Industrial PhD student at KTH Royal Institute of Technology's School of Electrical Engineering and Computer Science, specifically within the Division of Theoretical Computer Science. They are supervised by Professor Aristides Gionis and Associate Professor Sarunas Girdzijauskas at KTH, and also serve as an Experienced Researcher at Ericsson Research and a Visiting Researcher at Yale University under Rex Ying and Leandros Tassiulas. Yifei's research focuses on graph mining , network analysis , and graph representation learning , particularly applied to crowdsourcing data and wireless communication systems. Their work intersects telecommunications network optimization machine learning for graph-structured data AI-driven wireless resource management edge computing and distributed AI as evidenced by their publications spanning 2017–2025. Their academic contributions include 15 recent papers exploring topics such as neural surrogates for voltage drop estimation, wireless ray-tracing models, scalable distributed AI deployment, and vehicle platooning coordination. These publications demonstrate expertise in network traffic reduction KPI conflict analysis graph convolutional networks hyperbolic embeddings for ontologies real-time network diagnostics .

• Signal Processing
• Statistical Signal Processing
• System Identification
• +5 more

Tomas McKelvey is a Full Professor and Deputy Head of the Department of Electrical Engineering at Chalmers University of Technology, where he leads the Signal Processing research group. He has been with Chalmers since 2000 and has held a full professor position in Signal Processing since 2006. Professor McKelvey received his Electrical Engineering education at Lund University between 1987 to 1991 and earned his PhD in Automatic Control at Linköping University in 1995. Between 1995 and 1999, he held research and teaching positions at Linköping University, where he became docent in 1999. He was a visiting researcher at the University of Newcastle, Australia between 1999 and 2000 before joining Chalmers University of Technology. Professor McKelvey's research spans multiple domains within signal processing and control systems. His primary interests include model-based signal processing, statistical signal processing, system identification, and automatic control. His work has significant applications across several fields including radar systems, biomedical engineering, power systems, and automotive propulsion. He has developed innovative methods for signal processing in challenging environments, particularly in radar and power systems applications, often bridging theoretical advances with practical implementation challenges. His recent publications demonstrate a strong trend toward integrating machine learning and deep learning approaches with traditional signal processing methods, particularly for applications in radar systems, power grid stability, and automotive control. His work shows increasing sophistication in handling non-stationary signals, developing efficient computational methods, and creating robust solutions for real-world engineering problems. Professor McKelvey has been actively involved in numerous research projects, as indicated by his extensive publication record spanning multiple disciplines. His leadership position as Deputy Head of Department reflects his significant standing within the academic community. His research group maintains strong connections with both academic and industrial partners, as evidenced by the applied nature of much of his work. The cross-disciplinary nature of his publications indicates collaborations across engineering fields and with medical researchers, particularly in the biomedical applications of microwave technology.

• Natural Language Processing
• Clinical Text Mining
• Artificial Intelligence
• +7 more

Hercules Dalianis is a Professor at the Department of Computer and Systems Sciences, Stockholm University. His research focuses on Natural Language Processing, particularly in clinical text mining for Swedish language data. He leads the Natural Language Processing Research Group and serves as director of the Health Bank - Swedish Health Record Research Bank infrastructure. MSc in Electrical Engineering (1984), KTH PhD in Technology (1996), KTH Professor of Computer and Systems Science (2011), Stockholm University His research addresses privacy-preserving NLP for clinical text analysis, including automated de-identification , domain adaptation of BERT models , and clinical entity recognition . Current projects like DataLEASH and Privacy-Preserving Techniques explore machine learning solutions that balance data utility with patient confidentiality. Key publication trends show emphasis on Swedish clinical text processing , ICD-10 coding automation , and privacy-aware language modeling . Collaborations span Karolinska University Hospital, Nordic healthcare institutions, and international AI research communities. He teaches courses in Internet Search Techniques and Business Intelligence (ISBI) , Natural Language Processing (NLP) , and Principles and Foundations of Artificial Intelligence (PFAI) . His work has produced the open-access textbook Clinical Text Mining: Secondary Use of Electronic Patient Records , establishing foundational frameworks for clinical NLP in low-resource languages.

• Fusion Plasma Physics
• Tokamak Physics
• Plasma Confinement
• +5 more

Lorenzo Frassinetti is a Professor at the Division of Electromagnetic Engineering and Fusion Science at KTH Royal Institute of Technology. His research focuses on fusion plasma physics, specifically tokamak operation, plasma confinement, and edge physics. Prior to his current position, he served as an Associate Professor at KTH from 2012 to 2022, and held post-doctoral research fellow positions at KTH and the National Institute of Advanced Industrial Science and Technology in Japan. Ph.D. in Physics, University of Padova, Italy (2003) Degree in Physics, University of Bologna, Italy (2000), Grade: 110/110 cum laude Frassinetti's research spans multiple critical areas of fusion science including pedestal physics, edge localized modes (ELMs), divertor physics, and plasma turbulence. His work heavily involves experimental analysis from major tokamaks like JET and TCV, with particular focus on understanding transport barriers, stability properties, and power exhaust challenges. Recent work demonstrates increasing emphasis on predictive modeling for next-generation devices like ITER and the Divertor Tokamak Test facility. His recent publications reveal strong trends toward predictive modeling for future fusion devices, with significant focus on the Divertor Tokamak Test facility (DTT) scenarios. There's notable emphasis on understanding the effects of plasma shaping, particularly negative triangularity, and developing control strategies for disruptions. His work bridges experimental observations from JET and other tokamaks with theoretical models to address critical challenges for ITER operation. Frassinetti actively participates in major international fusion research collaborations, particularly within the EUROfusion consortium, contributing to JET experiments and ITER preparation efforts. His work often involves multi-institutional teams across European fusion laboratories. His laboratory work centers around analysis of data from major European tokamaks including JET (Joint European Torus), TCV (Tokamak à Configuration Variable), and ASDEX Upgrade. His research group at KTH appears to focus on integrating experimental observations with theoretical models to develop predictive capabilities for next-generation fusion devices.

Arne ÖstmanView profile

Professor

• Tumor Microenvironment
• Cancer Biology
• Fibroblast Signaling
• +2 more

Arne Östman is a Senior Professor at the Department of Oncology-Pathology, Karolinska Institutet , specializing in tumor microenvironment research. His work bridges pre-clinical studies and clinical applications, focusing on cancer-associated fibroblasts (CAFs), PDGF signaling, and biomarker development for drug targeting. Key Research Themes: Tumor-stromal interactions CAFs in breast, prostate, ovarian, colorectal, and kidney cancers Novel spatial tissue profiling methods Drug target identification in stromal cells Recent Articles highlight PDGF system roles, FAP+ fibroblasts, and NRF2 in metastasis across multiple tumors. Collaborations include SciLifeLab , clinical researchers, and pharmaceutical partners. Scientific Contributions include grants from the Swedish Cancer Society (80 million SEK) and Radiumhemmet (33 million SEK) for tumor microenvironment studies. His team has identified CAF subtypes and stromal-immune crosstalk mechanisms with clinical utility.

Rickard KarlssonView profile

Lecturer

• Swedish as a second language
• Learner language assessment
• Language development in adults
• +4 more

Dr. Rickard Karlsson works as a Lecturer at Linköping University's Department for Swedish as a Second Language, Rhetoric and Language Support (SAROS) under the Department of Culture and Society (IKOS). His teaching focuses on Swedish language didactics, grammar, and assessment of learner languages, with supervision across academic levels. PhD in Languages and Cultures of Europe Upper Secondary School Teacher in Swedish as a Second Language Research spans empirical analysis of adult language acquisition , historical linguistics , and multilingualism ideologies . Google Scholar publications reveal interdisciplinary contributions to particle filter algorithms and automotive sensor systems from 2001-2025. Notable collaborations include Fredrik Gustafsson and Per-Johan Nordlund. Recent publications (2025-2016) merge automotive engineering and historical Linguistics, covering tire diagnostics, cultural exchange patterns, and vibration-based navigation. This dual expertise reflects his transition from technical research to language education, maintaining academic connections across disciplines.

• Hydropower Turbines
• Cavitation Modeling
• Computational Fluid Dynamics (CFD)
• +7 more

Håkan Nilsson is a Full Professor in Fluid Dynamics at Chalmers University of Technology. His research focuses on computational fluid dynamics (CFD) with applications in hydropower systems, particularly turbine flow analysis, cavitation modeling, and generator cooling air dynamics. He utilizes OpenFOAM for numerical simulations and develops advanced algorithms for mesh deformation and flow control. Academic Rank: Full Professor Institution: Chalmers University of Technology Primary Research: Hydropower Turbines, Cavitation, CFD, Machine Learning, Multiphase Flow His recent work integrates machine learning with CFD for optimizing turbine operations and predictive maintenance in hydropower plants. Key projects include ALPHEUS and investigations into contra-rotating pump-turbine systems for low-head energy storage. Collaborations span experimental validation with experts in PIV, laser Doppler velocimetry, and industrial partners in renewable energy. Research trends highlight 15+ years of publications on turbulence modeling, vortex dynamics, and fluid-structure interaction in hydraulic systems. Sub-fields include Francis turbine transients, Kaplan turbine rotor-stator interactions, and applications in biomedical fluid dynamics and welding processes. Scientific awards are not explicitly mentioned.