جست‌وجوی استاد و پژوهشگر

Hanna Erikssonمشاهده پروفایل

دانشیار

• Cancer and Oncology
• Melanoma Research
• Translational Medicine
• +۴ مورد دیگر

Hanna Eriksson is an Associate Professor at the Department of Oncology-Pathology, Karolinska Institutet , where she leads the Clinical and Translational Melanoma Research Group. Her work bridges clinical decision-making and translational research, leveraging large-scale register data, artificial intelligence, and multi-omics analyses within clinical trials. Her research focuses on: Immune-tumor microenvironment interactions Overcoming immunotherapy resistance Real-world melanoma treatment outcomes Gender differences in immunotherapy side effects Development of quality of life modules for advanced melanoma Key trends in her 2025–2021 publications include tumor heterogeneity in brain metastases, adjuvant immunotherapy optimization, plasma proteomics, and biomarkers for immune checkpoint inhibitors. Her group explores AI integration in diagnostics and precision medicine strategies. She supervises PhD students and postdocs, and has secured grants from: Swedish Research Council Swedish Cancer Society Horizon 2020 (GenderNet-Plus) Radiumhemmet Research Funds Stockholm Regional Council Karolinska Institutet Translational Seed Funding Her team collaborates on trials like G-DEFINER (gender-specific immunotherapy toxicities) and AIDMel (AI in melanoma diagnostics), emphasizing real-world data and multi-omics approaches.

• Distributed Machine Learning
• Wireless Communications
• Stochastic Geometry
• +۷ مورد دیگر

Zheng Chen is an Associate Professor (Docent) at the Division of Communication Systems, Department of Electrical Engineering, Linköping University, Sweden. She has been with the university since 2017 and is actively involved in research, teaching, and academic leadership. B.Sc. in Optoelectronics, Huazhong University of Science and Technology (HUST), China (2011) M.Sc., CentraleSupélec, Université Paris-Saclay, France (2013) Ph.D., CentraleSupélec, Université Paris-Saclay, France (2016) Her research lies at the intersection of wireless communications and distributed machine learning, with a focus on stochastic geometry, decentralized intelligence, and information dissemination over complex networks. She investigates how to jointly optimize communication and computation in wireless systems to support efficient and reliable AI at the network edge. Her recent publications emphasize communication-efficient decentralized learning, over-the-air aggregation, and resource allocation in MIMO and device-to-device networks. These works reflect a strong trend toward enabling intelligent processing in future 6G systems with minimal latency and energy consumption. She has received prestigious recognition including the IEEE Communications Society Young Author Best Paper Award (2020) and serves as an Associate Editor for IEEE Transactions on Wireless Communications , IEEE Transactions on Communications , and IEEE Transactions on Green Communications and Networking . Zheng Chen leads multiple major research initiatives, including a Swedish Research Council (VR) Starting Grant, a WASP PhD project, a Zenith project grant, and a Knut and Alice Wallenberg research grant on 'Turning the air into an AI computer'. She supervises several Ph.D. students both as main and co-supervisor and teaches Master-level courses in Digital Communication and Information Networks. She is an active member of the Communication Systems (KS) division, collaborating with a broad team of researchers and students at Linköping University, including Professor Erik G. Larsson, with whom she frequently co-authors.

Eva Frühwald Hanssonمشاهده پروفایل

مدرس ارشد

• Timber Structures
• Biobased Materials
• Wood-Moisture Relations
• +۵ مورد دیگر

Eva Frühwald Hansson serves as Senior Lecturer and Head of Office in the Division of Structural Engineering at Lund University's Department of Building and Environmental Technology. She holds additional leadership roles as Deputy Head of Department and contributes to two LTH Profile Areas: Circular Building Sector and The Energy Transition. Lund University, Faculty of Engineering (LTH) Department of Building and Environmental Technology Division of Structural Engineering Her research centers on timber structures and biobased materials, with specialization in wood-moisture dynamics, durability prediction, and sustainable building envelopes. Key focus areas include probabilistic hygrothermal modeling, mold risk assessment, and circular construction methodologies. Her work directly supports UN Sustainable Development Goals through sustainable building technologies. Recent publications demonstrate strong trends in machine learning applications for mold prediction (with Random Forest algorithms), probabilistic service life modeling, and innovative use of hygroscopic materials for energy efficiency. Research spans building physics, structural engineering, and environmental sustainability with emphasis on practical implementation. Scientific recognition includes: Best Research into Practice Paper Prize (2019) She actively supervises doctoral students and leads significant research initiatives, including five major projects funded by Vinnova and industry partners. Her grant portfolio emphasizes biobased construction, industrialized building systems, and service life performance frameworks. Hansson co-manages two critical university infrastructures: the Civil Engineering Laboratory and the Structural Engineering Laboratory, supporting experimental research in timber structures and building materials.

• Machine Learning
• Deep Learning
• Evolutionary Computation
• +۷ مورد دیگر

Mohammad Ghaith Altarabichi is a postdoctoral researcher at the School of Information Technology , Halmstad University. His research focuses on leveraging Evolutionary Computation (EC) to optimize Deep Learning (DL) models, covering feature selection, hyperparameter tuning, and architectural decisions like loss and activation functions. He has published in top-tier venues such as Expert Systems With Applications , Information Sciences , and conferences like GECCO and IEEE CEC . His work intersects Artificial Intelligence , Battery Technology , and Transportation Systems , particularly in optimizing DL for battery state-of-health estimation and fault detection in evolving environments. Recent publications explore Randomness Techniques in DNNs and Kolmogorov-Arnold Networks (KANs) for regularization and performance enhancement. Scientific Awards: 2nd place in ESREL 2020 AI competition Global Swede 2017 award He has contributed to advancing Evolutionary Deep Learning frameworks, with applications spanning Health Informatics , Computational Neuroscience , and Energy Systems . His research also includes Accessible Technology , such as a vision-based indoor navigation system for visually impaired individuals.

Håkan Sundellمشاهده پروفایل

استادیار

• Computer Science
• Artificial Intelligence
• Parallel and Distributed Systems
• +۷ مورد دیگر

Håkan Sundell is an Assistant Professor of Computer Science at the University of Borås , affiliated with the Faculty of Librarianship, Information, Education and IT and the Department of Information Technology . Since 2006, he has been active as a teacher in informatics and a research group leader for the CSL@BS research group , while also serving as program manager for the System Architecture Education with a Focus on Software Development . His research spans Artificial Intelligence , Parallel and Distributed Systems , Concurrent Programming , and Machine Learning Applications , with a particular emphasis on lock-free data structures and GPU algorithms . He has developed over 30 courses and supervised multiple doctoral students to completion. Scientific contributions include advancements in non-blocking algorithms, real-time systems, and AI-driven marketing tools. His article trends reflect interdisciplinary work in data science for fashion retail , district heating analytics , and marketing campaign optimization . Key Awards: Best Paper Award at IPDPS 2003 Recognized as a pioneer in Swedish computer game development As a main supervisor since 2012, he has driven externally funded research projects (VR, SSF, KKS, HUR) and served on university committees, including chairing the education committee from 2018–2022.

• Integrated Sensing and Communication
• Wireless Communications
• Signal Processing
• +۶ مورد دیگر

Dr. Fan Jiang serves as an Associate Senior Lecturer at Halmstad University's School of Information Technology, where he leads research at the intersection of wireless communications and localization technologies. His academic profile reflects strong technical expertise in next-generation wireless systems with practical applications in vehicular networks and satellite communications. His research program focuses on integrated sensing and communication (ISAC) frameworks, with particular emphasis on: Massive MIMO and millimeter wave system design Wireless localization/synchronization algorithms Integration of sensing functionalities within communication protocols Applications for vehicular positioning and satellite networks Analysis of his 15 publications from 2022-2024 reveals consistent output in high-impact venues, with growing emphasis on satellite communications and reconfigurable intelligent surfaces. His work demonstrates strong mathematical rigor in channel modeling and algorithm design while addressing practical implementation challenges like synchronization errors and hardware constraints. Dr. Jiang maintains active research collaboration through co-authorship networks and participates in experimental validation efforts, as evidenced by his 2022 field study on 5G mmWave positioning. His technical approach balances theoretical innovation with real-world applicability across multiple wireless domains.

Siyi Wangمشاهده پروفایل

پژوهشگر

• Value of information for control
• Stochastic control and optimization
• Online learning and control
• +۳ مورد دیگر

Siyi Wang is a Postdoctoral Researcher at the Division of Decision and Control Systems (DCS) , KTH Royal Institute of Technology, under the mentorship of Prof. Karl Henrik Johansson. She earned her Ph.D. from Technical University of Munich (TUM) under Prof. Sandra Hirche and holds B.Eng and M.Eng degrees from Harbin Institute of Technology under Prof. Ligang Wu. Education: B.Eng & M.Eng in Electrical Engineering, Harbin Institute of Technology Ph.D. in Electrical Engineering, Technical University of Munich (TUM) Research Focus: She investigates the value of information for control , stochastic control and optimization , and online learning and control , addressing challenges in networked control systems (delays, packet loss) and developing risk-averse reinforcement learning frameworks. Key Publications: Her work spans networked control systems under cyber-physical threats, sliding mode control for multi-agent systems, and distributional reinforcement learning for LQR optimization. Recent papers emphasize adaptive event-triggered scheduling, disturbance handling, and safety-critical control strategies. Academic Contributions: Advising: Supervised master's (2022), bachelor's (2021), and research internships (2021–2022) on topics like event-triggered learning and information value simulations Professional Service: Reviewer for ISA Transactions , IEEE JSAC , IFAC , and IEEE CDC Laboratory Affiliation: Division of Decision and Control Systems, KTH Royal Institute of Technology, focusing on networked systems, cyber-physical security, and learning-based control methodologies.

• Bioinformatics
• Comparative Genomics
• Stochastic Models
• +۴ مورد دیگر

Marina Axelson-Fisk is a Professor in Mathematical Statistics at Chalmers University of Technology, working within the Department of Applied Mathematics and Statistics in the School of Engineering. She is an active researcher with publications spanning from 1998 to 2023, demonstrating sustained scholarly contribution to her field. Her primary research focus lies in bioinformatics, with particular expertise in stochastic models and algorithms for comparative genomics and cross-species gene finding. She has made significant contributions to the field through her work with the Mouse and Rat Sequencing Consortia, where she was involved in the initial comparative analyses of human, mouse and rat genomes. She is also an associate member of the Linnaeus Centre for Marine Evolutionary Biology (CeMEB), contributing bioinformatics expertise to the analysis of newly sequenced Swedish coastal organisms. Analysis of her 15 most recent publications reveals a consistent research trajectory at the intersection of mathematical statistics and biological applications. Her work demonstrates expertise in developing and applying statistical models to genomic data, with particular emphasis on gene prediction algorithms, comparative genomic analysis, and the application of hidden Markov models to biological sequence analysis. Her research spans both theoretical statistical development and practical biological applications. She has received research funding from the Swedish Research Council (VR) for projects including 'Statistical Significance of Biological Sequences' (2012-2015) and 'Next Generation Comparative Genomics' (2011-2013), indicating sustained recognition of her research contributions by national funding agencies.

• Distributed Machine Learning
• Federated Learning
• Decentralized Optimization
• +۳ مورد دیگر

Hao Chen is a researcher at KTH Royal Institute of Technology, affiliated with the School of Electrical Engineering and Computer Science (EECS), specifically within the Computer Science department focusing on Communication Systems and the Optical Network Laboratory (ON Lab). He also contributes to research in Intelligent systems and Information Science and Engineering. Chen completed his doctoral dissertation titled 'Reliable and Efficient Distributed Machine Learning' in 2022, establishing himself as an emerging expert in distributed machine learning systems. Chen's research interests center around distributed and federated machine learning architectures, with particular emphasis on optimizing communication efficiency in decentralized systems. His work addresses critical challenges in distributed learning including communication bottlenecks, straggler nodes (devices with slow responses), and privacy preservation. His research spans applications in wireless IoT networks, satellite communications, and edge computing environments. His publication record shows a clear trajectory of increasingly sophisticated approaches to distributed machine learning. Starting with foundational work on coded stochastic ADMM methods in 2021, he progressed to developing asynchronous parallel algorithms (2023) and exploring applications in specialized domains like speech fatigue recognition (2024). A consistent theme across his work is the optimization of communication resources while maintaining learning performance, with particular attention to real-world constraints in wireless and satellite networks. Chen has collaborated extensively with researchers including Ming Xiao, Mikael Skoglund, Yu Ye, and others across multiple publications. His research has been supported by funding sources including the EU Horizon 2020 program (grant 825272) and the Swedish Foundation for Strategic Research (APR20-0023). His work appears in high-impact journals including IEEE Transactions on Big Data, IEEE Internet of Things Journal, and IEEE Wireless Communications. As a researcher at KTH, Chen contributes to cutting-edge work at the intersection of machine learning and communication systems, developing techniques that enable efficient distributed intelligence across networked devices while addressing practical constraints of real-world deployment.

Krzysztof Podgórskiمشاهده پروفایل

استاد

• Stochastic Processes
• Random Spatio-temporal Fields
• Non-Gaussian Stochastic Models
• +۵ مورد دیگر

Krzysztof Podgórski is a Professor and Head of the Department of Statistics at Lund University School of Economics and Management (LUSEM). His research spans applied probability, statistics, and interdisciplinary applications in engineering, finance, and environmental sciences. Key Research Areas: Multivariate non-Gaussian stochastic models Statistical analysis of spatio-temporal random fields Distributions at random crossing events Applications: Mechanical engineering (road modeling) Ocean engineering (wave/ship reliability) Financial econometrics (market linkages, risk analysis) Actuarial sciences (non-Gaussian claims) Methodological Contributions: Include ergodic theory of stochastic processes, extreme value theory, and computational statistics. His recent work explores functional data analysis with periodic splines and matrix variate distributions. Collaborative Networks: Extensive partnerships across theoretical and applied disciplines, with projects involving road dynamics, stochastic fields, and uncertainty quantification.

Lars Eklundhمشاهده پروفایل

استاد

• Remote Sensing
• Environmental Monitoring
• Vegetation Phenology
• +۵ مورد دیگر

Lars Eklundh is a Professor and Teaching Staff member at Lund University's Department of Physical Geography and Ecosystem Science. He serves as Principal Investigator for BECC (Biodiversity and Ecosystem services in a Changing Climate) and researcher at MERGE (ModElling the Regional and Global Earth system). He coordinates SITES Spectral, a Swedish infrastructure conducting continuous field sampling using PhenoCams and drone imagery. His research focuses on satellite-based environmental monitoring, particularly: Vegetation growth estimation using Landsat/Sentinel-2/MODIS data Carbon flux modelling across large areas Time-series analysis for vegetation seasonality studies Early-warning systems for boreal forest insect outbreaks Applications in forestry and precision agriculture Recent publications (2024-2025) demonstrate strong emphasis on: Vegetation phenology and productivity benchmarks Land-use constraints for bioenergy Machine learning applications in agriculture Climate adaptation strategies Landscape connectivity impacts He leads multiple research projects including: UPSCALE: Satellite-field biosignature integration (FORMAS-funded) CLIMB-FOREST: Climate mitigation pathways (Horizon Europe) HR-VPP2: Copernicus vegetation phenology monitoring and supervises BSc, MSc, and PhD students in remote sensing courses.

• Stochastic Numerical Analysis
• Stochastic Differential Equations
• Randomized Methods
• +۳ مورد دیگر

Monika Eisenmann is an Associate Senior Lecturer in the Department of Applied Mathematics at Lund University's Faculty of Engineering (LTH) and serves as a Principal Investigator at eSSENCE: The e-Science Collaboration. Her academic work bridges the fields of numerical analysis and probability theory, focusing on the development and analysis of stochastic numerical methods. Dr. Eisenmann's research centers on stochastic numerical analysis, investigating how randomness influences mathematical problems from multiple perspectives. Her work examines stochastic elements within models themselves (accounting for uncertainties in parameters, natural variability, or external noise) as well as randomness introduced through numerical methods. She develops algorithms for solving stochastic differential equations and creates randomized approaches for optimization problems, with applications ranging from groundwater flow to machine learning frameworks. Her research has significant implications for high-dimensional and low-regularity settings where traditional deterministic methods face challenges. By leveraging stochastic optimization techniques, her work helps avoid local minima while providing faster function evaluations in complex computational scenarios. Dr. Eisenmann actively supervises doctoral students and leads multiple research projects: As primary supervisor for Marvin Jans (2023-2028) As joint second supervisor for Måns Williamson (2019-2025) Principal Investigator for "Stochastic numerical analysis with applications in groundwater flow problems" (Swedish Research Council, 2024-2028) Principal Investigator for "eSSENCE@LU 9:6 - Flow problems in porous media" (eSSENCE, 2023-2024) Researcher on "Moving domain decomposition methods for parabolic PDEs" (Swedish Research Council, 2024-2028) Researcher on "Analysis of numerical methods for optimization problems arising in machine learning" (2019-2024) Her collaborative work extends across international boundaries, with research partnerships visible through her academic network. As part of eSSENCE: The e-Science Collaboration, she contributes to advancing computational science methodologies with practical applications in environmental modeling and scientific computing.

• Cancer Prevention
• Tumor Virology
• HPV Research
• +۵ مورد دیگر

Joakim Dillner is a Professor at Karolinska Institutet's Department of Clinical Science, Intervention and Technology, and serves as R&D Manager. He is also Head of Unit at the Karolinska University Hospital's Center for Cervix Cancer Elimination and Director of Research & Development at the Karolinska University Laboratory. His roles include overseeing Swedescreen, the first randomized HPV-screening trial, and managing national registries and international collaborations like the International Human Papillomavirus Reference Center. Education: - Dr. Med. Sc. (Ph.D.) in Tumor Biology, Karolinska Institutet (1986) - M.D., Karolinska Institutet (1995) - Post-doctoral fellowship at The Scripps Research Institute (1986-1988) - Docent in Virology (1990) Research Interests: His research focuses on cancer prevention, particularly human papillomavirus (HPV) biology, vaccination, and cervical cancer screening strategies. Key areas include molecular mechanisms of HPV-related cancers, clinical trial design for screening methods, and implementation science to improve global health outcomes. His work emphasizes translational research bridging laboratory science and public health policy. Article Trends: Recent publications highlight advancements in HPV testing standards, long-term vaccine efficacy, and risk-stratified screening approaches. Studies emphasize global collaboration (e.g., WHO standards, African vaccine trials) and innovative biomarkers (e.g., DNA methylation for triage). Findings underscore the need for tailored cervical cancer elimination strategies across demographic groups. Awards & Recognition: - 2018 Cancer Networker of the Year Award - Swedish Cancer Society Board Member (2009-2015) - Leadership in national and international quality registries Grants & Teams: Leads major projects including Swedescreen and Nordic HPV vaccination follow-ups. Collaborates with institutions like the University of Innsbruck and Tanzanian health systems. Oversees the Nordic eScience Center of Excellence and the eHealth Core Facility since 2018. Labs/Teams: Directs teams within CLINTEC and the Karolinska University Laboratory, focusing on cervical cancer elimination, HPV diagnostics, and implementation of precision medicine tools in clinical settings.

Peter Johanssonمشاهده پروفایل

استاد

• Digital Health
• Chronic Disease Management
• Mental Health
• +۳ مورد دیگر

Peter Johansson is a Professor at the Department of Nursing and Reproductive Health (Avdelningen för omvårdnad och reproduktiv hälsa), part of the Faculty of Medicine (Medicinska Fakulteten) at Linköping University. He also serves as Research Director at Närsjukvården i Öster, RegionÖstergötland. His roles combine academic leadership with clinical research and education. Education & Career: PhD in Medicine from Linköping University (2008) Guest Researcher at University Medical Center Groningen, Netherlands (2009) Docent in Medical and Health Sciences (2012) Associate Professor (2017) and Professor (2018) at Linköping University Research Interests: Johansson’s work focuses on digital interventions for chronic disease patients, particularly cardiovascular patients, and informal caregivers. His group, Gruppen för digital hälsa och vård , develops internet-based cognitive behavioral therapy (iCBT) programs tailored for depression management in heart disease patients. He also studies stress reduction strategies for healthcare workers via massage and mindfulness, and caregiver support systems leveraging digital tools. Key areas include: Psychological well-being in chronic illness Telemedicine and digital health Healthcare workforce resilience Public health innovations Key Projects: DOHART 2: Evaluates personalized iCBT for cardiac patients. INSERT: Designs support programs for kidney transplant candidates. IKSIT: Targets anxiety in non-cardiac chest pain patients. Digital Support Programs: Pilot studies for informal caregivers. Labs/Teams: Leads the transdisciplinary Forskningsgruppen för digital hälsa och vård , collaborating with nurses, psychologists, and clinicians to advance digital health solutions. Future Work: Expanding caregiver support programs, optimizing iCBT personalization, and exploring AI-driven health monitoring in primary care settings.

• Healthcare Logistics
• Operations Research
• Healthcare System Optimization
• +۶ مورد دیگر

Sebastiaan Meijer is a Professor of Health Care Logistics at KTH Royal Institute of Technology. His research focuses on applying logistics principles to healthcare systems, particularly in optimizing resource allocation, patient flow, and integrating technology like sensor data and simulations. He leads a team developing gaming simulations and interactive methods to improve healthcare efficiency, with a focus on smart cities and elderly care. Key areas include home health monitoring platforms, emergency department logistics, and medical waste management. His work addresses challenges in coordinating complex healthcare networks across institutions. Research Interests: Healthcare Logistics Simulation Modeling Patient Flow Optimization Telemedicine Reablement Programs for Older Adults Medical Waste Transportation Recent Articles: Focus on real-world evidence gaps in clinical trials, hospital overcrowding diagnostics, and hybrid modeling for complex systems. These studies highlight interdisciplinary approaches combining data science, simulation, and healthcare policy. Awards: Not explicitly mentioned in provided texts. Advising & Grants: Supervises 16+ students (listed in 'students'). Research projects include the ASSIST reablement program feasibility study and telemedicine evaluation in surgical settings. Labs/Teams: Leads a multidisciplinary team at KTH addressing healthcare logistics challenges through simulation games and data-driven methods.