جست‌وجوی استاد و پژوهشگر

• Sustainable Architecture
• Building Renovation
• Urban Planning
• +۷ مورد دیگر

Liane Thuvander is a Professor at the Department of Architecture and Civil Engineering, Chalmers University of Technology. Her research focuses on sustainable building practices, particularly the visualization of social and environmental aspects in existing buildings and the implementation of sustainable building knowledge in practice. She leads the Research Area 'Visualization and Auralization' in the Vinnova Competence Centre Digital Twin Cities Centre (DTCC) and coordinates the Centre for Sustainable Urban Futures (https://gmv.gu.se/urbanfutures/sv). Specializes in methodologies for spatial value mapping and visualization strategies Collaborates with municipalities, construction sector stakeholders, and interdisciplinary teams Key projects: Digital Twin Cities, Sustainable Sharing Economy, and Climate-Adaptive Building Renovation Her work integrates transdisciplinary approaches to support stakeholder dialogue and decision-making in urban and building stock transformations. She has published extensively in journals like Energy and Buildings , Journal of Cleaner Production , and IOP Conference Series . Current research explores digital twin applications for energy modeling, circular design in architecture, and climate adaptation strategies in Swedish multi-family residential buildings.

• Traffic Safety
• Driver Behavior
• Automated Vehicles
• +۴ مورد دیگر

Jonas Bärgman is a Professor at Chalmers University of Technology, leading the Safety Evaluation research group within the Division of Vehicle Safety at the Mechanics and Maritime Department . His work focuses on understanding traffic safety through interdisciplinary research involving driver behavior, vehicle automation, and environmental factors in pre-crash scenarios. Key research themes include: Quantifying driver comfort zone boundaries using naturalistic driving data Developing counterfactual simulation methods for safety impact assessment Analyzing crash causation mechanisms and driver response modeling Integrating human factors into automated vehicle development Recent publications demonstrate expertise in: Automated emergency braking algorithms Lane change dynamics and timing Crash scenario generation across severity ranges Bayesian modeling of driver behavior Validation of human benchmark models for AV approval He actively contributes to: Teaching Vehicle and Traffic Safety (TME202 masters course Lecturing on Active Safety systems International standardization through ISO participation Collaborative research projects like SENSI , V4SAFETY , and SHAPE-IT

• Bioinformatics and Computational Biology
• Computer Graphics and Computer Vision
• Medical Imaging

Kimmo Kartasalo is a Senior Research Specialist and Assistant Professor at the Department of Medical Epidemiology and Biostatistics , Karolinska Institutet , Sweden. His research focuses on the intersection of Artificial Intelligence , Bioinformatics , and Medical Imaging , particularly in cancer diagnostics. Current roles: Senior Research Specialist (2024-), Assistant Professor (2024-2030) Supervision: Sol Erika Boman (2023), Kelvin Szolnoky (2022) His work emphasizes deep learning for histopathology analysis, including prostate cancer grading, breast cancer spatial gene expression prediction, and multi-stain whole-slide imaging. He leads projects in 3D tissue reconstruction , virtual reality histology visualization , and AI-assisted diagnostic uncertainty estimation . Collaborations span international institutions and include algorithm benchmarking challenges like PANDA and ACROBAT. Key methodological contributions include physical color calibration for digital pathology scanners, conformal prediction frameworks for diagnostic reliability, and open-source tools like OpenPhi for whole-slide image processing. His publications appear in top journals including Nature Medicine , Cancer Research , and European Urology .

• Psychiatry
• Respiratory Medicine and Allergy
• Pediatric Epidemiology
• +۴ مورد دیگر

Anna Hedman is a Senior Research Specialist and Lecturer at Karolinska Institutet's Department of Medical Epidemiology and Biostatistics (MEB), where she leads the Pediatric and respiratory epidemiology research group under Catarina Almqvist Malmros. Her primary affiliations include examiner and coordinator roles for medical degree projects at KI's medical program (semester 8), and she serves as Course Director for scientific writing and methodology courses. Education: Post-doc in Epidemiology, MEB, Karolinska Institutet (2015-2019) PhD in Neuroscience, Rudolf Magnus Brain Center, Utrecht University (2013) Her research integrates pediatric epidemiology with advanced methodologies including twin modeling, longitudinal data analysis, and biomarker studies (IgE, FeNO, cytokines, metabolic markers) to investigate atopic diseases and non-communicable conditions in children. Current work focuses on asthma endotypes, maternal-child health interactions, and genetic/environmental determinants of respiratory and psychiatric disorders through large-scale cohorts like STOPPA and Born into Life. Analysis of her 15 most recent publications reveals dominant themes in pediatric respiratory epidemiology (60%), psychiatric comorbidity research (25%), and methodological innovation in multi-omics integration (15%). Key trends include leveraging twin registries for causal inference, developing non-invasive biomarkers for asthma control, and exploring bidirectional relationships between mental health and immune disorders. Teaching & Mentorship: Examiner/coordinator for medical degree projects (2017–present) Lecturer in scientific writing (Basvetenskap 1, 2022–2023) Group leader for Medical Scientific Methodology courses (2017–2021) Supervisor for master's theses in medicine Her work operates within Karolinska Institutet's Pediatric and respiratory epidemiology research group, utilizing national registries, clinical cohorts, and biobanks including the Swedish Twin Registry. Current projects emphasize translational applications of inflammatory proteomics and epigenetic clocks for personalized pediatric care.

• Epidemiology
• Computational Pathology
• Deep Learning Applications in Medicine
• +۳ مورد دیگر

Bojing Liu is a Research Specialist at the Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, specializing in epidemiology and computational medicine. Their work bridges deep learning, histopathology, and neurodegenerative disease research. PhD in Medical Epidemiology and Biostatistics (2018), Karolinska Institutet Master of Medical Science (2012), Karolinska Institutet Liu's research focuses on applying deep learning to histopathology and genomics for cancer prognosis, including breast and prostate cancer. They also investigate neuroepidemiology , particularly Parkinson's disease risk factors linked to early-life conditions, surgical history, and gut-brain axis interactions. Their publications highlight expertise in computational pathology (e.g., spatial interpretation of weakly supervised models) and epidemiological methods using Swedish register-based datasets. Recent work includes self-supervised learning for colon cancer therapeutic strategies and AI-driven histological grading. Grants include funding from the Swedish Research Council for projects such as: CARE-B : AI in Registry-based Image Epidemiology for Breast Cancer (2024-2028) Deep-learning Models for Cancer Outcomes (2020-2022) Liu collaborates with the Predictive Medicine group led by Mattias Rantalainen, contributing to interdisciplinary research at the intersection of epidemiology , machine learning , and clinical medicine .

• Cancer and Oncology
• Medical Genetics and Genomics

Bram de Laere is a Research Fellow at the Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. His research focuses on translational cancer genomics with emphasis on liquid biopsy applications in prostate cancer, contributing to international clinical trials and collaborative projects aimed at advancing precision oncology for advanced prostate cancer patients. His primary research interests center on Cancer and Oncology and Medical Genetics and Genomics . He specializes in circulating tumor DNA (ctDNA) analysis for biomarker discovery in metastatic castration-resistant prostate cancer (mCRPC), investigating genomic alterations driving treatment resistance and disease heterogeneity through next-generation sequencing and computational approaches. Analysis of his publication record reveals consistent focus on liquid biopsy development, particularly ctDNA profiling for prognostic modeling and treatment selection. His work demonstrates strong interdisciplinary collaboration across oncology, genomics, and bioinformatics, with significant contributions to understanding androgen receptor signaling, DNA repair defects, and clinical implementation of genomic biomarkers in prostate cancer management.

• Privacy
• Information Theory
• Data Security
• +۳ مورد دیگر

Sara Saeidian is a researcher at KTH Royal Institute of Technology's School of Electrical Engineering and Computer Science (EECS), specifically within the Information Science and Engineering department under the Intelligent systems division. She completed her doctoral dissertation titled "Pointwise Maximal Leakage: Robust, Flexible and Explainable Privacy" in 2024, establishing herself as a promising researcher in information-theoretic privacy. Dr. Saeidian's research program centers on developing a comprehensive framework for privacy-preserving systems with three essential criteria: explainability (operationally meaningful privacy guarantees), robustness (resilience against diverse adversaries), and flexibility (applicability across contexts and data types). Her primary contribution is the development and analysis of pointwise maximal leakage (PML) as a privacy measure that quantifies information leakage about a secret variable to a publicly available related variable. Her publication record from 2021-2025 demonstrates a cohesive research trajectory examining PML's theoretical foundations, composition properties, and practical applications. She has established critical relationships between PML and existing privacy notions like differential privacy, while challenging misconceptions about the impossibility of meaningful inferential privacy guarantees. Her work spans theoretical investigations of optimal privacy mechanisms under leakage constraints to practical applications in privacy-preserving machine learning frameworks like PATE. Dr. Saeidian's research has been published in premier venues including IEEE Transactions on Information Theory, IEEE Transactions on Information Forensics and Security, and proceedings of the IEEE International Symposium on Information Theory, reflecting the significance and quality of her contributions to the field of data privacy.

• Automation Engineering
• Simulation-Based Optimization
• Reconfigurable Manufacturing Systems
• +۵ مورد دیگر

Tehseen Aslam is a Senior Lecturer in Automation Engineering at the School of Engineering Science , University of Skövde. As Deputy Vice-Chancellor for Industry Collaboration , they bridge academic research with industrial applications through projects like WISER (2021-2026) and Strategic Production Optimization (2017-2020). Research Interests : Simulation-based optimization, reconfigurable manufacturing systems, AI in industrial vision, digital twins, and sustainable production. Grants & Projects : Involved in Network City Skaraborg (2024), Digital Models for Energy Systems (2022), and Virtual Factory (2018-2020). Publications : 15+ works on multi-objective optimization, system dynamics, and digital manufacturing. Collaborations : Partnerships with Anna Syberfeldt, Amos H.C. Ng, and Gary Linnéusson.

• Molecular Imprinting
• Synthetic Polymers
• Computational Chemistry
• +۳ مورد دیگر

Jesper Wiklander is a Researcher at the Department of Chemistry and Biomedicine , Linnaeus University , affiliated with the Bioorganic and Biophysical Chemistry Laboratory (BBCL) and the Mindgap project. His work focuses on molecular imprinting, synthetic polymers, and computational chemistry, with applications in pharmaceuticals, biomolecule recognition, and analytical chemistry. Research Projects : Bridging the gap between mind, brain, and body (Mindgap) Labs : Bioorganic and Biophysical Chemistry Laboratory (BBCL) His research explores molecular imprinting mechanisms, computational strategies for polymer design, and bioorganic chemistry . Recent publications highlight applications in deuteration, oxytocin mimicry, and ligand recognition through advanced polymer systems. Key article trends include molecular imprinting for pharmaceuticals like paracetamol and oseltamivir, catalysis in indole functionalization, and sensor development using nanogels and QCM films. Techniques span NMR spectroscopy , molecular dynamics , and chemometric modeling .

• Machine Learning
• Data Science
• Digital Twin Technology
• +۳ مورد دیگر

Manoranjan Kumar is a researcher at Linnaeus University, affiliated with the Industrial Research School Data Intensive Applications (DIA) and the Data Intensive Software Technologies and Applications (DISTA) research group. His work focuses on data-driven methods like machine learning and artificial intelligence, particularly applied to industrial challenges at Volvo CE through digital twin development projects. Key Research Areas : Machine learning, digital twins, fatigue analysis, predictive maintenance, industrial automation. Recent Publications : 4 peer-reviewed works (2023-2025) spanning fatigue analysis in welded joints, wheel loader automation, driving pattern classification, and EGR clogging prediction. These highlight interdisciplinary applications across construction, automotive, and transportation sectors. Collaborations : Actively works with Volvo CE on industrial digital twin platforms, emphasizing customer problem-solving and operational optimization. Affiliated with the Linnaeus University Center for Data Intensive Sciences and Applications (DISA).

José Mairton Barros da Silva Júniorمشاهده پروفایل

استادیار

• Wireless Communications
• Machine Learning
• Distributed Optimization
• +۴ مورد دیگر

Dr. José Mairton Barros da Silva Júnior is an Assistant Professor at the Department of Information Technology , Uppsala University , Sweden. His work integrates Wireless Communications and Machine Learning to advance future network technologies. Ph.D. in Electrical Engineering and Computer Science, KTH Royal Institute of Technology (2019) M.Sc. and B.Sc. in Teleinformatics Engineering, Federal University of Ceará (2014, 2012) His research bridges distributed optimization and machine learning for wireless systems, focusing on federated learning , vehicular communications , and full-duplex architectures . He investigates communication efficiency via novel modulation techniques and privacy-preserving frameworks. Recent publications address over-the-air computation , resource-constrained federated learning , and differential privacy in multi-base station systems. His work spans 6G network design , IoT applications , and wireless sensor networks . Scientific Awards : Exemplary Reviewer , IEEE Open Journal of the Communications Society (2021) He supervises PhD and Master's students including Metehan Karatas and Saeed Razavikia . He organizes IEEE conferences and develops open-source MATLAB toolboxes for mmWave beamforming.

Olle Gällmoمشاهده پروفایل

مدرس

• Machine Learning
• Artificial Intelligence
• Artificial Neural Networks
• +۲ مورد دیگر

Olle Gällmo is a Lecturer at Uppsala University's Department of Information Technology; Division of Computer Systems. He teaches advanced-level courses in Machine Learning and Artificial Intelligence, including guest lectures on AI in computer games. He serves as a student advisor for the bachelor programme in computer science and handles course prerequisites for the IT department. Teaches: Machine Learning, Artificial Intelligence Research: Natural computation methods (Artificial Neural Networks, Reinforcement Learning, Particle Swarm Optimization) Student Advisor: Computer Science bachelor programme (DVk) Bagpipe Expertise: Olle is a renowned figure in the revival of Swedish bagpipes. He contributes to the field as a performer, educator, and researcher, publishing works on instrument design, synthetic reeds, and historical evolution of the Swedish bagpipe tradition. Scientific Awards: No awards mentioned. Publications: Olle has authored 3 key works on Swedish bagpipes, focusing on their history, construction, and revival. His research spans synthetic reeds (2015), evolutionary history (2013), and traditional music documentation (1999).

Muhammad Qasimمشاهده پروفایل

مدرس

• Regularization methods
• Causal inference
• Mendelian randomization
• +۶ مورد دیگر

Muhammad Qasim is a Lecturer and Researcher at the Department of Statistics, Lund University. His work bridges theoretical and applied statistics, with a focus on causal inference, regularization methods, and high-dimensional data analysis. Current roles: Lecturer, Researcher Key research areas: Mendelian randomization, DEA, econometric techniques, sparse estimation Teaching expertise: High-dimensional data analysis, deep learning, regularization methods, and statistical theory His recent publications highlight advancements in instrumental variable selection, ridge regression, and addressing endogeneity in probit models. These contributions span econometrics, biostatistics, and energy sector applications. Collaborations with researchers like K. Månsson and N. Balakrishnan underscore his interdisciplinary approach.

• Corporate Sustainability
• Sustainability Reporting
• Accounting Information Systems
• +۴ مورد دیگر

Asif M Huq serves as a Senior Lecturer in Information Science at Dalarna University's School of Information and Engineering, where he coordinates the Data Analysis and Visualization course (AMI23A). His academic profile bridges computational methods with sustainability accounting, leveraging institutional partnerships including Trafikverket's sustainable transport initiatives. Dr. Huq's research centers on computational sustainability analysis , specializing in NLP-driven CSR report assessment , materiality measurement frameworks , and governance impacts on corporate sustainability . His methodology integrates text mining and quantitative disclosure analysis to evaluate greenhouse gas reporting quality, with particular focus on Swedish private firms navigating global crises like the pandemic and Ukraine war. Analysis of his 2021-2025 publications reveals three convergent research streams: (1) technical innovations in measuring accountable information through NLP techniques; (2) empirical investigations of how sustainability performance interacts with financial resilience during geopolitical disruptions; and (3) practical applications in sustainable logistics via LivingLab frameworks. His work consistently utilizes Swedish firm datasets to address real-world sustainability reporting challenges. Through the FEDeRATED project, Dr. Huq contributes to the Data-sharing SME, Last-Mile Delivery LivingLab initiative under Trafikverket's sustainable transport program, developing collaborative data platforms for urban logistics optimization while maintaining strong ties to Sweden's national transport research infrastructure.

Qinghua Wangمشاهده پروفایل

دانشیار

• Internet of Things (IoT)
• Wireless Sensor Networks
• Cyber Resilience
• +۷ مورد دیگر

Qinghua Wang is an Associate Professor in Computer Science at Kristianstad University, leading the Research Environment for Computer Science (RECS) from 2019 to 2024. They hold a PhD from Mid Sweden University (2017) and postdoctoral experience at Aalto University (2011–2013) and Norwegian University of Science and Technology (2010–2011). Research focuses on Internet of Things (IoT) , Wireless Sensor Networks , and Cyber Resilience , with applications to environmental monitoring (flood prediction, smart irrigation) and 6G communication security. Their work aligns with the UN SDG for Education/Academic Qualification . Cyberresiliens för AI-system (2025, PI) 6G-PHYSEC (2023–2027, Researcher) SmartVA (2021–2023, PI) Active in academic service: Chair of IEEE Sweden (since 2025), peer reviewer for Sensors, and committee examiner for PhD evaluations.