جست‌وجوی استاد و پژوهشگر

• Spatio-temporal Data Mining
• Graph Neural Networks
• Causal Discovery
• +۳ مورد دیگر

Guojun Liang is a Postdoctoral Researcher at Halmstad University's School of Information Technology, actively contributing to the Cardiovascular health care project at the CAISR Health Center. He earned his Ph.D. in Artificial Intelligence from Macau University of Science and Technology in 2023, achieving the highest GPA in the Faculty of Computer Science and Engineering. His educational background includes doctoral studies at Macau University of Science and Technology (2023), where he received the Herry Fox Scholarship (2021) for academic excellence. His research career began with participation in China's National Artificial Intelligence and Industrial Robot Competition, where he secured a silver medal. Dr. Liang's research specializes in spatio-temporal data mining, with core expertise in Graph Neural Networks, causal discovery frameworks, diffusion modeling techniques, and time series analysis including prediction and imputation. His work bridges theoretical machine learning with practical applications in healthcare monitoring, transportation systems, and remote sensing. Recent publications demonstrate his focus on physics-informed architectures and causal inference mechanisms for spatio-temporal modeling. Analysis of his eight most recent publications (2021-2025) reveals a strong trend toward integrating physical constraints with deep learning architectures, particularly using graph-based approaches for spatio-temporal forecasting and imputation. His work increasingly incorporates causal discovery techniques and diffusion models to enhance model interpretability and probabilistic reasoning in time-series applications across healthcare and transportation domains. Herry Fox Scholarship (2021) Silver medal in National Artificial Intelligence and Industrial Robot Competition (China) As an educator, Dr. Liang has taught Analog/Digital Electronic Technology and Microcontroller Systems in China, and currently serves as Teaching Assistant for Artificial Intelligence, Applied Machine Learning, and Big Data Processing courses at Halmstad University. He maintains active research collaboration with the CAISR Health Center, focusing on cardiovascular health applications through advanced spatio-temporal data analysis.

• Gait analysis
• Accelerometer-based motion detection
• Embedded systems
• +۵ مورد دیگر

Nicholas Wickström is a Senior Lecturer at Halmstad University's School of Information Technology. His academic work spans multiple disciplines including computer science, biomedical engineering, and health technology, with a particular focus on motion analysis and sensor-based health monitoring systems. Dr. Wickström conducts his research within the Centre for Research on Embedded Systems (CERES) and the Intelligent Systems Laboratory/CAISR. His work bridges theoretical computer science with practical healthcare applications, particularly in the domain of Ambient Assisted Living systems. His primary research interests include: Gait analysis using inertial sensors and accelerometers Real-time motion detection and event recognition Embedded systems for health monitoring applications Time-frequency analysis of biomechanical signals Digital interventions for exercise motivation and health behavior Database-centric architectures for smart home environments Analysis of Dr. Wickström's publication record reveals a strong trajectory in motion analysis technology, particularly focused on gait analysis using wearable sensors. His work demonstrates a progression from fundamental signal processing techniques to practical healthcare applications. Recent publications show increasing interdisciplinary collaboration, merging computer science with health psychology and biomechanics to develop more effective ambient assisted living systems. Dr. Wickström currently supervises two PhD students in his research areas, indicating an active research group developing next-generation health monitoring technologies. His primary research environments include: Centre for Research on Embedded Systems (CERES) Intelligent Systems Laboratory/CAISR These research groups focus on developing innovative embedded and intelligent systems with applications in healthcare and assisted living environments.

• Supply Chain Integration
• Sales and Operations Planning
• Performance Measurement
• +۳ مورد دیگر

Hana Hulthén is a Senior Lecturer in Logistics and Supply Chain Management at the School of Business and Economics , Linnaeus University. She leads the master's program Business Process Control and Supply Chain Management and teaches courses on both undergraduate and advanced levels. Research Focus: Supply chain integration, sales and operations planning (S&OP), performance measurement systems, and Industry 4.0 technologies. Current Project: Performance Management and Fossil-Free Freight (2024–2026) funded by Triple F, involving partners like DHL Freight, Scania, and Systembolaget. Her recent publications explore topics such as: Proactive disruption identification in raw material supply chains Collaborative performance management for green transportation IT frameworks for modern S&OP processes Machine learning applications in supply forecasting Education: Licentiate Thesis (2013): A model of contextual factors and inter-organizational integration , Lund University Doctoral Thesis (2016): On understanding of external and internal integration in supply chains , Lund University Key Collaborations: Partnered with organizations like Bring, Elanders, and Lyreco for supply chain research and practice.

• Secure Control Systems
• Scalable Control
• Positive Systems
• +۳ مورد دیگر

Sribalaji C. Anand is a postdoctoral researcher at KTH Royal Institute of Technology, Sweden, affiliated with the Division of Decision and Control Systems and the Department of Intelligent Systems. Hosted by Prof. Karl Henrik Johansson and Prof. Henrik Sandberg, his academic journey includes an M.Sc. in System and Control from Delft University of Technology (2019) and a Ph.D. in Automatic Control from Uppsala University (2024). Research Focus: Secure control systems, scalable control, positive systems, convex optimization applications, dissipative systems, and adaptive control. Grants: VR International Postdoctoral Grant (2024), STINT International Postdoctoral Scholarship (2024), and multiple travel scholarships from IEEE and Stenholm Wilgott. Publications: 15 recent articles covering security metrics, attack mitigation, and control system optimization.

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

Samuel Erickson Andersson is a doctoral student at the Royal Institute of Technology (KTH) in the Division of Decision and Control Systems. He is advised by Prof. Mikael Johansson and Prof. Cristian Rojas. Education: MSc in Engineering Mathematics from Linköping University (with a visiting graduate year at Stanford University) BSc in Applied Physics and Electrical Engineering from Linköping University BSc in Mathematics from Linköping University Research Focus: Samuel's work bridges machine learning and optimization , targeting applications in medicine and finance . His current emphasis on distributed learning and federated learning highlights his interest in decentralized data processing. Notable contributions include methodological advancements in statistical learning and high-dimensional statistics . Publications: Samuel's research trends include personalized federated learning and inverse covariance estimation , reflecting his expertise in handling complex, distributed datasets and statistical modeling. Professional Experience: Conducted master's thesis research at Lynx Asset Management (Spring 2024)

• System Identification
• Control Theory
• Bayesian Estimation
• +۵ مورد دیگر

Yue Ju is a Postdoctoral Fellow at the Royal Institute of Technology (KTH) in Stockholm, Sweden, specializing in system identification, control theory, and statistical learning methods. Their research focuses on regularization techniques, Bayesian approaches, and their applications in control systems engineering. Dr. Ju's research interests span theoretical aspects of system identification and control theory with a strong emphasis on regularization methods and statistical learning. Their work bridges the gap between theoretical analysis and practical implementation, particularly in addressing challenges related to hyper-parameter estimation, convergence properties, and efficient computational methods for complex control problems. Recent research has expanded into dynamic programming for exploration, neural network approaches for nonlinear system modeling, and spatial-temporal data analysis. The publication history demonstrates a consistent focus on regularization methods for system identification, with particular expertise in empirical Bayes approaches, hyper-parameter estimation, and convergence analysis across multiple theoretical frameworks. Their work shows strong mathematical foundations applied to practical engineering problems, particularly in control systems where statistical learning methods can enhance traditional approaches. Dr. Ju's research has significant implications for both theoretical understanding and practical implementation of control systems, with applications spanning various engineering domains where precise system modeling and control are critical.

Takumi Shinoharaمشاهده پروفایل

پژوهشگر

• Cybersecurity
• Control Systems
• Cyber-Physical Systems
• +۳ مورد دیگر

Takumi Shinohara is a postdoctoral researcher at the Division of Decision and Control Systems , KTH Royal Institute of Technology , under the supervision of Prof. Karl Henrik Johansson and Prof. Henrik Sandberg. He earned his B.E., M.E., and Ph.D. from Keio University in 2016, 2018, and 2024, respectively, advised by Prof. Toru Namerikawa.

• Machine Learning
• Light Field
• Digital Image Processing
• +۳ مورد دیگر

Ali Hassan is a Ph.D. student and Early Stage Researcher at Mid Sweden University , affiliated with the Department of Computer and Electrical Engineering (DET) . He is based at the Realistic3D lab under the Information Systems and Technology Department, working under supervisors Prof. Tingting Zhang and Prof. Mårten Sjöström. Education B.Sc. and M.Sc. in Computer Science from COMSATS University, Islamabad Research Interests span Machine Learning , Light Field , Digital Image Processing , Computer Vision , and Deep Learning , with a focus on mobile computational plenoptic and dimension reduction of deep networks . Publications highlight his work on neural architecture search for disparity estimation and light field compression. He contributes to projects like IMMERSE and PLENOPTIMA , exploring augmented reality and immersive imaging.

• Computer Engineering
• Machine Learning
• Network Optimization
• +۲ مورد دیگر

Qing He is an Associate Professor at the Department of Computer and Electrical Engineering (DET) at Mid Sweden University, located in Sundsvall, and affiliated with the STC Research Centre. His research spans Computer Engineering with emphasis on Machine Learning, Network Optimization, Anomaly Detection, and Recommendation Systems. He develops optimization frameworks for content caching in wireless networks and applies sequential learning models to system log analysis for anomaly detection, demonstrating interdisciplinary integration of networking protocols and AI techniques. His 2022 publications reveal dual focus areas: wireless network optimization for personalized content delivery and machine learning-driven reliability analysis in system operations, highlighting practical applications in mobile networks and infrastructure monitoring. No scientific awards were mentioned in the provided text. No information regarding student advising or research grant management was provided in the text. He operates within the STC Research Centre at Mid Sweden University, contributing to technology-focused research initiatives in the Sundsvall campus environment.

Lucia Crevaniمشاهده پروفایل

استاد

• Organization
• Leadership
• New Technology Introduction
• +۵ مورد دیگر

Lucia Crevani is a Professor of Business Administration at Mälardalen University, specializing in Organization and Management within the Academy of Economics, Society and Technology. She holds a doctorate in Industrial Economics from KTH Royal Institute of Technology (2011) and integrates research, teaching, and academic leadership in collaborations with private and public organizations. Her research focuses on organizational processes, leadership practices, sociomateriality, and intersectionality, particularly examining how technology, diversity, and evolving workplaces shape organizational dynamics. Recent publications explore hybrid work, digitalization, and the ethical dimensions of leadership, reflecting her commitment to relational ontology and inclusive organizational practices. Key trends in her recent articles emphasize hybrid and postdigital work environments sociomaterial entanglements in leadership the interplay of technology and human interaction ethical responsibility in top management care practices in digitalized workplaces gender and diversity in organizational contexts Her work bridges theoretical innovation (e.g., performativity, intersectionality) with practical insights into contemporary organizational challenges.

• Air Quality Analysis
• Microdata Analysis
• Urban Pollution Modeling
• +۶ مورد دیگر

Yves Rybarczyk serves as Professor of Microdata Analysis at Dalarna University's Department of Data and Information Management. His academic profile spans computer science, environmental science, and healthcare technology applications, reflecting a highly interdisciplinary research approach. Currently teaching User-centered Design (GIK348), Data Warehousing (AIK232), and Bachelor's thesis in Microdata Analysis (GMI2C8), he maintains active engagement in both undergraduate education and research supervision. Professor Rybarczyk's research focuses primarily on air quality analysis, urban pollution modeling, and machine learning applications for environmental monitoring. His work demonstrates particular expertise in analyzing PM2.5 effects on urban environments, biosphere-atmosphere interactions, and the application of digital twin technology in home care settings. His research methodology frequently employs causal analysis, convergent cross mapping, and ensemble deep learning approaches to address complex environmental challenges. Analysis of his recent publication record reveals a strong trend toward interdisciplinary environmental data science, with approximately 60% of his work focusing on air quality and pollution modeling, 25% on healthcare technology applications including tele-rehabilitation systems, and 15% on broader data science applications. His publications appear in high-impact journals across multiple disciplines including environmental science, computer science, and public health, demonstrating his ability to bridge academic domains through data-driven approaches. While specific grant information isn't detailed in the available text, Professor Rybarczyk's collaborative publication pattern suggests involvement in international research projects, particularly with colleagues from South American institutions as evidenced by multiple studies focused on Quito, Ecuador. His research appears to receive support for environmental monitoring initiatives and digital health technology development.

• Variational inference
• Bayesian inference
• Deep learning
• +۴ مورد دیگر

Najmeh Abiri is a researcher with multiple affiliations at Lund University, holding positions as a Visiting Research Fellow at the Centre for Environmental and Climate Science (CEC), a Visiting Research Fellow at Computational Science for Health and Environment, a Postdoc at the Department of Statistics, and a Researcher at eSSENCE: The e-Science Collaboration. Her research focuses on the intersection of statistics and machine learning with practical applications. Key areas include: Variational inference Bayesian inference Deep learning architectures Missing data imputation techniques Computational biology applications Abiri's publication record shows a clear trajectory toward increasingly sophisticated applications of machine learning in scientific domains. Her recent work demonstrates expertise in diffusion models for time series analysis, AI-driven disease outbreak prediction, and quantum mechanical applications of deep learning. Her 2019 paper on denoising autoencoders for missing data problems has been particularly influential with 62 citation indexes. She has received notable attention for her interdisciplinary work, with publications featured in high-impact journals like The Lancet Regional Health - Europe and covered by multiple news outlets. Her research on West Nile virus prediction has garnered policy citations, indicating real-world impact. Abiri actively participates in the academic community through workshops and seminars, particularly those focused on AI technologies and their applications in scientific research. Her collaborative work spans multiple institutions and research domains, reflecting the interdisciplinary nature of her expertise.

• Maritime Safety
• Search and Rescue Operations
• Human Factors in Naval Operations
• +۳ مورد دیگر

Fredrik Forsman is an Instructor at the Maritime Human Factors Unit of Chalmers University of Technology. He also serves as Head of Training at the Swedish Sea Rescue Society, where he develops training frameworks for maritime rescue crews. University: Chalmers University of Technology School: School of Maritime Studies Role: Instructor (Faculty), Head of Training (Swedish Sea Rescue Society) Research Focus includes maritime safety, human factors in extreme environments, and adaptive training methodologies. His work addresses: High-speed navigation and cognitive performance Team coordination in search and rescue Information architecture for fast response crafts Emergency response systems in Arctic conditions Simulation technology for maritime training Interoperability in littoral operations Notable Projects include: Eyes on Scene 2 (2023–2025): Drone-assisted sea rescue PICASSO (2016–2018): Safer ships on oceans SMACS (2012–2014): Arctic emergency response Publications span maritime ergonomics, command systems, and training innovations, with recent trends focusing on AI integration and sustainability in maritime operations.

• Optical Networks
• Network Automation
• Spectrally-Spatially Flexible Optical Networks
• +۲ مورد دیگر

Piotr Lechowicz is a postdoctoral researcher at the Optical Networks (ON) Unit within the Department of Electrical Engineering at Chalmers University of Technology . He holds an M.Sc. (2016) and Ph.D. (2019) in Information and Communications Technology from the Wrocław University of Science and Technology in Poland. A member of IEEE since 2020, his work focuses on the modeling and optimization of optical networks, particularly in network automation, spectrally-spatially flexible optical networks, and network fragmentation. Education: M.Sc. in Information and Communications Technology (2016), Wrocław University of Science and Technology Ph.D. in Technical Information and Communications Technology (2019), Wrocław University of Science and Technology Research Interests: Lechowicz specializes in optical network modeling, optimization, and automation. His work addresses challenges in spectrally-spatially flexible and elastic optical networks, with a focus on time-varying traffic, resource allocation, and network fragmentation. He integrates machine learning techniques, including transfer learning and graph-based methods, to enhance network performance and reliability. Publications and Collaborations: He has co-authored 35 publications (10 JCR-indexed, 25 conference papers), often collaborating with researchers like Carlos Natalino Da Silva, Aleksandra Knapinska, and Paolo Monti. His projects involve time-varying traffic analysis, energy efficiency in multilayer networks, and explainable AI for network optimization. Professional Contributions: Lechowicz has reviewed over 60 scientific papers and participated in six projects funded by the Polish National Science Center and National Center for Research and Development. His methodological innovations include the Traffic Weaver tool for generating semi-synthetic network traffic data.

• Robotics
• Human-Robot Interaction
• Motion Prediction
• +۵ مورد دیگر

Emmanuel Dean is a Senior Researcher in the Automation research group at Chalmers University of Technology, School of Electrical Engineering. His work focuses on robotics, human-robot interaction, and assistive technologies. Dean's research interests span multiple areas of robotics and human-computer interaction: Robotics and autonomous navigation systems Human-robot collaboration and interaction Tactile sensing and physical interaction Motion prediction and obstacle avoidance Brain-computer interfaces for robotic control Assistive robotics for physical therapy His recent publications show a strong focus on developing advanced navigation systems for mobile robots that can predict human motion and avoid collisions in dynamic environments. Dean has also made significant contributions to tactile-based interaction methods, particularly for assistive robotics applications in physical therapy contexts. Dean has received research funding for the CRAFT (Collaborative-Robot Assistant for Technicians) project (2021-2023), which focused on developing robotic assistants for industrial technicians. His work demonstrates strong interdisciplinary collaboration, with publications spanning robotics, neuroscience, and rehabilitation engineering.