جست‌وجوی استاد و پژوهشگر

• Machine Learning
• Artificial Intelligence
• Cyber-Physical Systems
• +۳ مورد دیگر

Ning Xiong is a Professor at Mälardalen University, affiliated with the School of Innovation, Design and Engineering and the Division of Intelligent Future Technologies. His research focuses on advanced artificial intelligence, machine learning, optimization algorithms, and cyber-physical systems. He explores applications ranging from digital twin frameworks in distributed systems to predictive maintenance using explainable AI and anomaly detection in timeseries data. His work integrates techniques like federated learning, Bayesian classifiers, and bio-inspired computing (e.g., membrane clustering) to address challenges in smart systems and data science. Key research areas include: Machine Learning & Deep Learning Cyber-Physical Systems Optimization Algorithms Smart Systems & IoT Data Science & Big Data Recent publications highlight advancements in digital twin frameworks for resilient distributed systems, ensemble learning for imbalanced data, and lightweight object detection methods for UAV imagery. His contributions emphasize practical applications in energy grids, predictive maintenance, and industrial automation while addressing theoretical challenges in model explainability and scalability. His research is characterized by interdisciplinary collaboration, combining software engineering, systems architecture, and domain-specific expertise to develop innovative solutions for dynamic environments.

• Smooth Transition Models
• State-Space Models
• Macroeconometrics
• +۸ مورد دیگر

Yukai Yang is a Senior Lecturer at the Department of Statistics, School of Economics and Business, Uppsala University. His research bridges econometric theory with applied statistical methods. Education : B.Eng, Shanghai Jiao Tong University Cand. Polit, University of Copenhagen PhD, Aarhus University (2012) His research spans smooth transition models , state-space models , Bayesian VAR , panel data analysis , and directional statistics , with recent interdisciplinary work in medical data science . Articles highlight methodological innovation in time series , machine learning , and mobile biometrics . Scientific Awards : None listed in provided data. His work includes software development (e.g., mfbvar package) and collaborations in multimedia systems and health economics .

Jerry Erikssonمشاهده پروفایل

دانشیار

• Computer Science
• Computational Science
• Medical Imaging
• +۵ مورد دیگر

Jerry Eriksson is an Associate Professor at the Department of Computer Science , Umeå University. He is also affiliated with the High Performance Computing Center North (HPC2N) at the same institution, focusing on computational methods and applications. Academic Rank : Associate Professor Primary Affiliation : Department of Computer Science, Umeå University Secondary Affiliation : High Performance Computing Center North (HPC2N) Email : jerry.eriksson@umu.se Dr. Eriksson's research spans interdisciplinary domains where computer science intersects with applied physics and network engineering . His work includes: Medical Imaging : Fluorescence optical tomography and electromagnetic shape tomography using advanced mathematical models Computational Methods : Implicit radial basis function techniques, Gauss-Newton optimization, and regularization schemes Network Engineering : Collision detection algorithms in wireless networks and peer-to-peer streaming protocols 3D Measurement : Bundle adjustment formulations for photogrammetry and robotics applications Article trends show a focus on applied inverse problems (2003-2017) with emphasis on: Medical imaging reconstruction algorithms Wireless network optimization techniques Geometric computing for 3D modeling Mathematical methods in computational engineering

• Signal Processing
• Machine Learning
• Statistical Signal Processing
• +۱۱ مورد دیگر

Saikat Chatterjee is a Professor in the Department of Information Science and Engineering at the School of Electrical Engineering and Computer Science, Royal Institute of Technology (KTH). He is also a Fellow of Digital Futures and maintains visiting researcher positions at Karolinska Institute, Karolinska Hospital (specializing in 'AI for Health Care'), and Oslo University Hospital in Norway. His primary research interests span Signal Processing and Machine Learning, with specific focus on signal modeling (sparsity, compressive sensing, dynamical systems), statistical signal processing, statistical machine learning, deep learning, speech/audio/image processing, medical data analytics, life science data analysis, perception for autonomous systems, distributed machine learning, and explainable AI (XAI). He particularly emphasizes explainable machine learning, having a strong background in signal processing and statistical machine learning, with growing passion for medical data analysis due to its societal importance. SSF - Swedish Foundation for Strategic Research Region Stockholm European Union Digital Futures Vinnova WASP Companies: Ericsson, Scania, Saab Professor Chatterjee is actively involved in teaching, serving as examiner and course responsible for various degree projects and courses including Machine Learning and Data Science, Pattern Recognition and Machine Learning, and Speech and Audio Processing. His research group has produced significant work across multiple domains, with notable publications in Bioinformatics and smart city applications, demonstrating the breadth of his research impact from healthcare to urban systems.

• Manufacturing Engineering
• Laser Welding
• Digital Twin Technology
• +۶ مورد دیگر

Wang Wei is a researcher at the University of Skövde's School of Engineering Science. His work spans multiple domains including manufacturing processes, laser welding, digital twin applications, and sustainable supply chain strategies. Key affiliations: University of Skövde, The Virtual Systems Research Centre, SYMBIO-TIC project, VF-KDO project Collaborations with institutions: Nanjing University of Aeronautics and Astronautics, Innovation Fund Denmark, Vinnova Research interests focus on: Advanced manufacturing techniques (machining deformation prediction, thin-walled part optimization) Laser welding process improvement (porosity reduction, dissimilar material joints) Digital twin security frameworks for cyber-physical systems Green technology adoption and sustainable supply chain coordination Recent publications demonstrate expertise in: Neural operator applications for manufacturing Multi-objective optimization in machining Cybersecurity modeling for industrial systems Environmental economics and consumer behavior analysis Co-funded by: Knowledge Foundation (SIMPLE, VF-KDO projects) Vinnova (QWELD, LaserBATMAN projects) EU H2020 SYMBIO-TIC M-ERA.NET 3 call

• Applied IoT
• Edge-Cloud Computing
• Distributed Systems
• +۴ مورد دیگر

Neda Maleki is a Senior Lecturer at the Faculty of Technology, Department of Computer Science and Media Technology, Linnaeus University, starting in September 2024. Her research focuses on Applied IoT, Edge-Cloud Computing, Distributed Systems (Hadoop/Spark), Artificial Intelligence, and Machine Learning for data analysis. PhD in Computer Engineering (2014–2021), Science and Research Branch of Islamic Azad University, Tehran, Iran Master of Science in Computer Engineering (2009–2013), Ghazvin Islamic Azad University Bachelor of Science in Hardware Engineering (2004–2008), Ghazvin Islamic Azad University Her research explores IoT applications in energy forecasting, environmental conservation, and SME digital transformation. Key contributions include frameworks for power-aware Hadoop acceleration, energy-efficient IoT data formats, and predictive models for fuel consumption and city load forecasting. Recent publications highlight collaborations with industry partners in Sweden and international conferences across Qatar, Italy, Denmark, and the Netherlands. She teaches courses in Data Structures, Algorithms, IoT, and programming at the bachelor's and master's levels. 1DV018: Data Structures and Algorithms 1DV501: Introduction to Programming 4DV119: Applied IoT Competence (Master level) Final Thesis supervision

Martin Cooneyمشاهده پروفایل

مدرس ارشد

• Social Robotics
• Human-Robot Interaction
• Affective Robotics
• +۱۲ مورد دیگر

Martin Cooney is a Senior Lecturer at Halmstad University's School of Information Technology, where he serves as Programme Director for the Master's Programme in Information Technology (TAITE) and the Master's Programme in Embedded and Intelligent Systems (TAEIS). He holds the title of Docent (Associate Professor) in Sweden and maintains significant international collaborations, including his role as Visiting Associate Professor at Nara Women's University in Japan and Collaborative Researcher at the Deep Interaction Laboratory Group (DIL) at ATR in Kyoto. Dr. Cooney's research focuses on social robotics and human-robot interaction with applications for human well-being. His work explores how robots can recognize and respond to human emotions, support creative processes, and provide social assistance in various contexts. He has developed theoretical frameworks for designing robots capable of recognizing social touch behavior and generating appropriate responses that foster positive human-robot relationships. His research has evolved from foundational work on affectionate interaction with small humanoid robots to more complex systems addressing robot self-defense, ethical considerations in robotics, and applications in healthcare and education. His recent publications reveal a growing emphasis on robot ethics, particularly around self-defense capabilities and cultural implications of robots using force to protect humans. He has also expanded into educational applications of robotics, examining how AI and robotics can enhance teaching and learning experiences in the post-pandemic 'new world' of education. 10 awards in robotics competitions Excellent Teacher award at Halmstad University As an educator, Dr. Cooney has developed and taught courses on Design of Embedded and Intelligent Systems, User Experience Design for AI, Fundamentals of Computer Vision with Deep Learning, and Design for Extended Realities. He serves as a Responsible Examiner for several programs and has developed Robots and AI specifically for teaching purposes at his university's Pedagogical Center. His research is conducted through multiple international collaborations, most notably with the Deep Interaction Laboratory Group at ATR in Japan, where he contributes to cutting-edge research in human-robot interaction. He also serves as Local Coordinator for the EU Remind Project at Halmstad University, indicating active involvement in European research initiatives, and has previously led the KKS RELIFE project with visiting research positions in Norway, Spain, and Italy.

Erdal Aksoyمشاهده پروفایل

دانشیار

• Semantic Scene Perception
• Action Semantics
• Autonomous Systems
• +۹ مورد دیگر

Erdal Aksoy is an Associate Professor at Halmstad University 's School of Information Technology , with visiting scholar roles at Volvo Group Trucks Technology and Zenseact AB . His work focuses on semantic scene perception , action semantics , and environment understanding for autonomous systems including robots and unmanned vehicles. Education : PhD from University of Göttingen, postdoctoral research at Karlsruhe Institute of Technology (KIT) and University of Göttingen Research Interests : Semantic scene perception, action semantics, autonomous systems, human-robot interaction, LiDAR processing, and deep learning applications His recent publications demonstrate expertise in LiDAR data processing (SalsaNet, SalsaNext), semantic state estimation for robotic cloth manipulation, and multimodal failure detection systems. He has coordinated the HORIZON Europe ROADVIEW consortium with 15 partners. Scientific recognition includes Best Student Paper Award at IJCAI 2021 AI4AD workshop Best AI Master’s Thesis Award from Swedish AI Society (SAIS) Wimanska Prize for best bachelor thesis Distinguished Service Award as IEEE Robotics and Automation Letters Associate Editor His advising track record shows mentorship of award-winning students across bachelor, master, and PhD levels. Erdal Aksoy maintains active collaborations between academia and industry, particularly with automotive technology companies.

• 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.

• 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.

• FPGA-based Systems
• Real-Time Video Processing
• Wireless Vision Sensor Networks
• +۵ مورد دیگر

Najeem Lawal is a Senior Lecturer at the Department of Computer and Electrical Engineering (DET) at Mid Sweden University. His research focuses on FPGA-based systems, real-time video processing, and wireless vision sensor networks. He is affiliated with the STC Research Centre and has contributed extensively to hardware architecture optimization for smart cameras and volumetric surveillance systems. Doctoral Thesis: Memory Synthesis for FPGA Implementation of Real-Time Video Processing Systems (2009) Licentiate Thesis: Memory Synthesis for FPGA Implementation of Real-Time Video Processing Systems (2006) Lawal's research explores cost-effective multi-camera dome designs, real-time component labeling, and energy-efficient wireless vision sensor nodes. His work spans architecture exploration, hardware-software partitioning, and memory optimization for FPGA platforms. He has published in journals like IEEE Sensors Journal and conferences including the International Conference on Distributed Smart Cameras. Recent publications highlight advances in volumetric surveillance coverage, calibration of multi-camera systems, and low-power embedded vision architectures. Lawal's projects emphasize practical deployment of smart camera networks for outdoor monitoring applications. He has collaborated with researchers such as Muhammad Imran, Benny Thörnberg, and Mats O'Nils. His expertise includes designing hardware architectures for vision systems, optimizing processing pipelines, and modeling sensor network configurations.

• Hyperspectral Imaging
• Computer Vision
• Sensor Technology
• +۶ مورد دیگر

Benny Thörnberg is an Associate Professor (Docent) at Mid Sweden University, working in the Department of Computer and Electrical Engineering (DET). He is employed in the Electronics subject area and is affiliated with the STC Research Centre. His office is located in room L204b in Sundsvall, and he can be contacted at benny.thornberg@miun.se. Dr. Thörnberg completed his Licentiate thesis in 2004 and his Doctoral thesis in 2006, both from Mid Sweden University, focusing on "Memory modeling and synthesis for real-time video processing systems." His academic journey has centered around hardware and software solutions for imaging and sensor systems. Thörnberg's primary research interests span hyperspectral imaging , computer vision , sensor technology , and FPGA-based real-time processing systems . His work bridges theoretical development with practical applications, particularly in material analysis, environmental monitoring, and transportation safety. He has made significant contributions to short-wave infrared imaging, 3D reconstruction techniques, and specialized instrumentation for challenging environments like icing conditions on wind turbines and roads. His recent publications demonstrate a clear trend toward practical applications of advanced imaging technologies, with focus areas including material classification, atmospheric icing measurement, anti-icing agent detection, and autonomous systems. Thörnberg has developed innovative solutions such as the Material Imaging Analyzer (MIA) and cost-optimized multi-camera dome systems for volumetric surveillance, showing his ability to translate theoretical concepts into deployable technologies. Thörnberg leads or contributes to several research projects including MEQAL (Detect methane leaks with new technology) and SENAVIS (Sensor technology for smart de-icing). His completed projects span diverse areas from autonomous friction measurement and airport monitoring to wood disintegration processes and rail pollution detection. These projects highlight his interdisciplinary approach and ability to address real-world challenges through advanced engineering solutions. As part of the STC Research Centre, Thörnberg collaborates with a multidisciplinary team focused on sensor technology and imaging systems. His work often involves partnerships across different sectors, including transportation, energy, and materials science, demonstrating the broad applicability of his research. His recent patent for an "Imaging Material Analyzer" underscores his commitment to developing practical tools that bridge laboratory research and field applications.

• 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 .

• infinite-dimensional differential geometry
• geometric hydrodynamics
• shape analysis
• +۳ مورد دیگر

Klas Modin is an Assistant Professor at the Department of Applied Mathematics and Statistics within the Department of Mathematical Sciences at Chalmers University of Technology in Gothenburg, Sweden. His research spans multiple disciplines at the intersection of mathematics, physics, and computer science, with a particular focus on geometric approaches to complex systems. Dr. Modin's research interests encompass a broad spectrum of mathematical and computational fields. His primary focus lies in infinite-dimensional differential geometry , geometric hydrodynamics , and shape analysis , which he applies to problems in computational mathematics , deep learning , and medical image analysis . His work often bridges theoretical mathematics with practical computational applications, particularly in fluid dynamics and image processing. The geometric perspective he brings to these fields has yielded novel insights into the mathematical structures underlying complex physical and computational systems. His recent publications demonstrate a strong trend toward interdisciplinary research that combines geometric mechanics with modern computational techniques. Many of his papers focus on optimal transport theory , geometric integration methods , and computational anatomy , with applications ranging from fluid dynamics to medical imaging. His work often develops novel mathematical frameworks that preserve important geometric structures in numerical simulations. Dr. Modin has received several prestigious awards for his research: Ingvar Carlsson Award from SSF (2013) Marie Sklodovska-Curie Individual Fellowship from EU's Horizon 2020 (2015) Starting Project from the Swedish Research Council (2017) Wallenberg Academy Fellow (WAF) by the Royal Swedish Academy of Sciences (2019) His research is supported by multiple grants from major funding organizations, including the Swedish Research Council, the Wallenberg Academy, and the European Union. Dr. Modin leads several research projects, including "Infinite-dimensional Lie groups in hydrodynamics and beyond" (2024) and "Långtidsbeteende för tvådimensionella inkompressibla flöden via kvantisering" (2023-2026), which explore fundamental mathematical structures in fluid dynamics and computational methods. Dr. Modin is actively involved in the geometric mechanics and computational mathematics research communities, collaborating with researchers across Europe and beyond. His work has significant implications for both theoretical mathematics and practical applications in medical imaging and fluid dynamics simulation.