جست‌وجوی استاد و پژوهشگر

Johan Alenlövمشاهده پروفایل

مدرس

• Machine Learning
• Statistical Inference
• Particle Filtering
• +۵ مورد دیگر

Johan Alenlöv is a University Lecturer in the Statistics and Machine Learning (STIMA) group at the Department of Computer Science, Linköping University. His work is situated at the intersection of computational statistics and machine learning, with a focus on sequential Monte Carlo methods and state-space modeling. His research interests include Machine Learning , Statistical Inference , Particle Filtering , Sequential Monte Carlo , Bayesian Computation , and Data Science . These areas are central to modern data analysis and AI-driven modeling in dynamic systems. The recent publications highlight a consistent focus on online smoothing, marginal inference, and Hamiltonian Monte Carlo methods. Collectively, the articles reflect a strong trend in developing scalable, numerically stable algorithms for inference in complex, nonlinear state-space models—critical for applications in signal processing, robotics, and time-series analysis. Johan Alenlöv has not been mentioned as receiving any scientific awards in the provided text. There is no information available about students advised by Johan Alenlöv or any grants he may have received. However, his collaborative work with researchers like Jimmy Olsson and Fredrik Lindsten suggests active participation in the statistical learning research community. He is affiliated with the Statistics and Machine Learning (STIMA) research group at Linköping University, which conducts research in modern data analysis and hosts both a Bachelor's and an international Master's program in Statistics and Machine Learning.

Jun Yuمشاهده پروفایل

استاد

• Statistical Learning
• Spatiotemporal Data Analysis
• Compressive Sensing
• +۷ مورد دیگر

Jun Yu is a Full Professor in Mathematical Statistics at the Department of Mathematics and Mathematical Statistics, Umeå University, Sweden, where he also serves as Director of Doctoral Studies. His research focuses on Statistical Learning and Inference for Spatiotemporal Data, with applications in artificial intelligence and various scientific domains. Professor Yu leads a research group dedicated to tackling theoretical data science problems and developing statistical learning methods for solving real-world challenges across multiple disciplines. Professor Yu's primary research interests include statistical learning with sparsity, compressive sensing, mathematics of data science, hierarchical spatiotemporal modeling, nonparametric density/intensity estimation, statistical inference for hidden Markov models, and wavelet theory applied to signal and image analysis. His work spans numerous application areas including atmospheric icing, automobile industry, biomedical engineering, climate research, epidemiology, forestry, geochemistry, hydrology, radiation oncology, spatial ecology, sports science, and transportation. Professor Yu's recent publications demonstrate a strong trend toward interdisciplinary research at the intersection of statistical methodology and environmental science, medical imaging, and transportation systems. His work on tree-ring isotope analysis for climate reconstruction, compressive sensing for medical imaging, and statistical models for train delay prediction shows his ability to develop sophisticated statistical methods that address complex real-world problems across diverse domains. As Director of Doctoral Studies, Professor Yu oversees doctoral education in Mathematical Statistics and has supervised numerous PhD students. His teaching spans mathematical statistics at all levels, from basic education to postgraduate courses, for students in mathematics, statistics, biology, engineering, and forestry, delivered in English, Swedish, or Chinese. Professor Yu leads the research group on statistical learning and inference for spatiotemporal data at Umeå University. This group develops innovative approaches for analyzing complex spatiotemporal datasets using tools such as intelligent data sampling, large-scale environmental data modeling, multimodal image processing, and tree growth models, with applications across multiple scientific fields.

Isaac Skogمشاهده پروفایل

دانشیار

• Underwater Surveillance
• Signal Processing
• Navigation Systems
• +۵ مورد دیگر

Isaac Skog is an Associate Professor in Communication Systems at KTH Royal Institute of Technology, part of the Digital Futures Faculty. He also holds adjunct positions at Linköping University (Automatic Control) and the FOI Swedish Defence Research Agency. His research focuses on underwater surveillance, signal processing, navigation systems, and sensor fusion, with a particular emphasis on autonomous systems and acoustic environments. Education: BSc and MSc in Electrical Engineering, KTH Royal Institute of Technology (2003, 2005) PhD in Signal Processing, KTH Royal Institute of Technology (2010) Research Interests: Underwater acoustic surveillance and tracking Navigation systems using magnetic fields and inertial sensors Autonomous underwater vehicles and multi-agent systems Sensor calibration and fusion techniques Machine learning for sensor data analysis Recent Projects: COAST Project: Complex Acoustic Surveillance and Tracking Joint Sensing, Localization, and Communication for Next-Gen Autonomous Underwater Systems (WASP-funded) Tensor-Field Based Localization Electromagnetic Navigation for Smaller Unmanned Underwater Vehicles Grants & Collaborations: Funded by CENIIT, WASP Sweden, Swedish Research Council, Vinnova, and industry partners like Saab Dynamics. Active collaborations with FOI, Lund University, and Indian Institute of Science. Technical Demonstrators: Lyra platform for elevator condition monitoring OpenShoe experimental platform for foot-mounted inertial navigation Tactical Locator (TOR) for first-responder tracking Movelo AB spin-off for smartphone-based traffic monitoring

Markus Bohlinمشاهده پروفایل

استاد

• Applied Artificial Intelligence
• Railway Systems
• Transportation Engineering
• +۵ مورد دیگر

Markus Bohlin serves as Professor (on leave) at Mälardalen University within the School of Innovation, Design and Engineering's Division of Product Realisation. He concurrently holds leadership roles as Dean of the School of Business Society and Engineering, Division Manager for Product Realization, and School Director for the INDTECH Industrial Graduate School. His academic credentials include: Doctoral thesis defended at Mälardalen University (2009) Associate professorship (docent) granted at Mälardalen University (2013) Adjunct professorship in rail traffic systems analysis at KTH Royal Institute of Technology (2014) Full professorship in Computer Science with Applied AI focus at Mälardalen University (2019) Bohlin's research centers on Applied Artificial Intelligence across critical infrastructure domains. His primary focus areas include: Railway systems optimization and freight logistics Cyber-physical systems for construction automation Machine learning applications in manufacturing quality control Simulation-assisted decision support systems His work bridges theoretical AI with industrial implementation in transportation, production, and power systems. Recent publications demonstrate a clear trajectory toward integrating machine learning with simulation modeling for real-world problem solving, particularly in railway punctuality optimization, construction site autonomy, and manufacturing defect detection. This interdisciplinary approach consistently targets high-impact industrial applications. Bohlin has held significant leadership positions including: President of the Swedish Operations Research Association (2012-2016) Member of Trafikverket Board on Capacity in Railways Program chair for ICROMA 2019 and multiple national conferences His academic supervision includes 4 completed PhDs, 9 licentiate degrees, and 5 ongoing doctoral candidates. With 20+ years of experience managing organizations up to 60 employees and 40+ projects (including H2020 initiatives), he brings substantial operational expertise to research commercialization. Current leadership roles encompass the Product Realisation division and INDTECH Industrial Graduate School, driving industry-academia collaboration in innovation and engineering.

Niina Hernándezمشاهده پروفایل

مدرس

• Textile Engineering
• Garment Fit
• Virtual Fitting
• +۳ مورد دیگر

Niina Hernández is a University Lecturer at the Department of Textile Technology within the Academy of Textiles, Technology and Economics at the University of Borås. She has been affiliated with the university since 2011, initially serving as a training leader and teacher for Design Techniques while completing her doctoral studies. Education: Textile teacher's degree Licentiate in fit for deviant figures from the University of Gothenburg (Thesis: "Tailoring the Unique Figure") Doctoral thesis: "Does it really fit?" Research Interests: Hernández specializes in the mathematical, virtual, and physical aspects of garment fit. Her work spans systems for product development and production, software for custom-made clothing manufacturing, and virtual fit evaluation methodologies. Core research areas include Textile Engineering, Garment Fit, Virtual Fitting, Custom Clothing Production, Smart Textiles, and Wearable Technology, with emphasis on bridging theoretical models with practical applications. Publication Trends: Her 15 most recent publications (2014-2023) demonstrate concentrated expertise in garment fit prediction, smart textile sensors, and wearable health monitoring systems. Key themes include deep learning for textile degradation analysis, virtual sizing solutions, sensor integration for neurological disorders, and sustainable fashion technologies. The work consistently bridges textile engineering with computer science and healthcare applications, showing progression from theoretical fit modeling to real-world implementations in neurorehabilitation and UN collaborations. Research Projects and Funding: wearITmed: Funded by the Swedish Foundation for Strategic Research (SSF) for neurorehabilitation garments Digimode: Vinnova-funded project extending EC-supported "from Roll to Bag" circular fashion initiative SIIR: Industry collaboration projects for textile companies UN Collaboration (2024-2025): Development of guide clothing for UN headquarters in New York with design students Teaching and Advising: Hernández has instructed Design Techniques since 2011 and mentored students in high-impact projects like the UN guide clothing collection. Her teaching integrates industry experience from CAD/CAM systems development and women's apparel production. Laboratory and Team Involvement: She is a core member of the WearITmed Consortium developing sensor-based garments for neurological rehabilitation. Her work with SIIR facilitates industry-academia knowledge transfer, while the UN project demonstrates applied design leadership in international contexts.

• Stochastic Processes
• Monte Carlo Simulation
• Mathematics of Data Science
• +۴ مورد دیگر

Henrik Hult is a Professor of Mathematical Statistics at KTH Royal Institute of Technology. He currently serves as director of Brummer and Partners MathDataLab at KTH. His academic appointments include Assistant Professor at Brown University (2006-2008) and Senior Lecturer at KTH (2008-2015) before becoming a full Professor in 2015. His research spans multiple domains with strong connections between theoretical probability and practical applications. Hult's work demonstrates significant contributions to stochastic processes , Monte Carlo simulation , mathematics of data science , mathematical finance , and insurance . His research methodology often bridges theoretical mathematics with computational techniques to solve complex real-world problems. The analysis of his recent publications reveals a strong interdisciplinary approach, with research spanning financial mathematics (algorithmic trading, risk management), probability theory (coalescent models, diffusion processes), computational statistics (rare event simulation, importance sampling), and medical applications (neuroimmunology, medical image analysis). His work demonstrates consistent focus on developing rigorous mathematical frameworks for practical applications across diverse fields. Hult teaches several advanced courses including Degree Project in Financial Mathematics, Foundations of Probability Theory, Modern Methods of Statistical Learning, Portfolio Theory and Risk Management, and Statistical Machine Learning, reflecting his broad expertise across theoretical and applied statistics. His collaborative research network is extensive, with frequent co-authorship with colleagues at KTH and international institutions. The diversity of his publication venues—from top probability journals like Electronic Journal of Probability to interdisciplinary journals like Proceedings of the National Academy of Sciences —demonstrates the wide applicability of his mathematical approaches.

Damiano Varagnoloمشاهده پروفایل

مدرس ارشد

• Distributed Optimization
• Control Theory
• Distributed Estimation
• +۶ مورد دیگر

Dr. Damiano Varagnolo is a Senior Lecturer at the Control Engineering Group, Luleå University of Technology, Sweden. His research focuses on distributed optimization, control systems for data centers and HVAC, and biomedical engineering applications. Current affiliation: Luleå University of Technology Primary research areas: Distributed optimization, Newton-Raphson Consensus methods, networked systems, energy-efficient control Selected applications: Smart building systems, biomedical modeling of pain responses, data center thermal management His work on distributed optimization explores asynchronous protocols and robustness in networked systems. Recent publications demonstrate applications in peer-to-peer networks, biomedical engineering, and energy-efficient computing infrastructure. Key collaborative projects include IEEE Transactions publications on data center cooling, European Control Conference contributions on sensor calibration, and IFAC symposium presentations on biomedical modeling. His distributed Gaussian regression methods appear in TPAMI publications.

Zheng Zhaoمشاهده پروفایل

استادیار

• Computational Statistics
• Machine Learning
• Generative Diffusion Models
• +۴ مورد دیگر

Zheng Zhao is an Assistant Professor in computational statistics and machine learning at Linköping University, leading a research team within the Division of Statistics and Machine Learning (STIMA) under the Department of Computer and Information Science (IDA). His work focuses on developing computationally and statistically efficient models for machine learning applications, particularly generative diffusion models and their use in signal/image processing. He also applies these methods to fields like astronomy (e.g., stellar magnetic field analysis) and climate science. His research team emphasizes practical applications of diffusion models and Bayesian methods, contributing to advancements in image restoration, sensor array analysis, and high-dimensional data modeling. Zhao collaborates with STIMA colleagues on interdisciplinary projects, leveraging statistical theory and computational techniques. Publications highlight his exploration of diffusion models, Bayesian neural networks, and probabilistic methods across domains like astrophysics and environmental science. His work bridges theory and application, addressing challenges in uncertainty quantification and overfitting mitigation. Zhao is actively involved in academic initiatives such as the international master's program in Statistics and Machine Learning (hosted by STIMA). His research has been applied in competitions like the NTIRE challenge, demonstrating real-world impact in image restoration.

• Mathematical Statistics
• Machine Learning
• Artificial Intelligence
• +۴ مورد دیگر

Jimmy Olsson is a Professor in the Department of Mathematical Statistics at KTH Royal Institute of Technology. His research focuses on generative models, statistical computational methods, and dynamic systems analysis, particularly in the context of artificial intelligence and machine learning. He specializes in state-space models, sequential Monte Carlo methods, and Markov chain Monte Carlo techniques. His work emphasizes foundational contributions to hidden Markov models, computational algorithms for statistical inference, and the intersection of mathematical statistics with modern data science challenges. Key research areas include developing efficient computational tools for analyzing complex, time-dependent data streams, with applications in AI and machine learning. Olsson’s publications span advanced topics such as diffusion models, particle filtering, and online variational learning, reflecting his expertise in bridging theoretical statistics with practical computational methods. He collaborates with researchers across disciplines, including engineering, computer science, and applied mathematics.

• Non-equilibrium statistical physics
• Theoretical cell biology
• Theoretical computer science (unconventional computing)
• +۴ مورد دیگر

Zoran Konkoli is an Associate Professor at Chalmers University of Technology, affiliated with the Department of Microtechnology and Nanoscience (MC2) within the Electronic Materials division. His research spans interdisciplinary areas including non-equilibrium statistical physics, theoretical cell biology, and unconventional computing. He holds a PhD in Theoretical Chemistry from Gothenburg University and has held postdoctoral positions at Chalmers, NORDITA, and the Applied Physics Department at Chalmers. His academic journey includes roles such as assistant professor in Biological Physics (2002–2006) and a permanent position as Associate Professor since 2007. Konkoli’s research focuses on diffusion-controlled reactions, theoretical cell biology (e.g., HIV latency mechanisms), and computational models like cellular automata and memristor networks. His work intersects condensed matter theory, statistical physics, and computer science. Key projects include the EU-funded RECORD-IT (2015–2018) and SYMONE (2012–2015), exploring reservoir computing and biomimetic systems. He collaborates widely, emphasizing interdisciplinary networks to advance theoretical ideas into practical applications. His expertise includes mathematical logic, measure theory, and approximation theorems, reflecting his broad academic interests.

Fredrik Lindstenمشاهده پروفایل

دانشیار

• Machine Learning
• Statistical Machine Learning
• Approximate Bayesian Inference
• +۷ مورد دیگر

Fredrik Lindsten is a Senior Associate Professor in Machine Learning and Head of the Division of Statistics and Machine Learning at Linköping University's Department of Computer and Information Science (IDA). His research focuses on statistical machine learning, emphasizing probabilistic modeling and uncertainty quantification in methods such as approximate Bayesian inference, representation learning, and graph-based approaches. Applications span weather forecasting, materials science, biochemistry, and automotive industry challenges. Education: MSc (2008), PhD (2013) in Automatic Control from Linköping University; Postdoctoral roles at University of Cambridge, UC Berkeley, and University of Oxford. Affiliations: WASP (Wallenberg AI, Autonomous Systems and Software Program), ELLIIT (Lab for Information and Communication Technology). Research Interests: Lindsten’s work bridges statistical methodology and machine learning, particularly in quantifying uncertainty in predictions. His team explores method development across diverse applications, including spatio-temporal models and graph-based techniques. Recent projects include probabilistic weather forecasting with graph neural networks and cryo-EM reconstruction techniques. Publications: Over 25+ peer-reviewed articles, with recent highlights in Nature Methods , NeurIPS , and Physical Review Materials , focusing on Bayesian methods, generative models, and computational statistics. Awards: Ingvar Carlsson Award (Swedish Foundation for Strategic Research), Benzelius Award (Royal Society of Sciences in Uppsala). Grants & Teams: Supervises 7+ PhD students; active in collaborative projects funded by WASP, ELLIIT, and VR (Swedish Research Council). Labs/Teams: Leads the Division of Statistics and Machine Learning (STIMA) at IDA, fostering interdisciplinary research in probabilistic machine learning.

• Computer Networking
• Low Latency Communication
• Multi-Path Systems
• +۳ مورد دیگر

Johan Garcia is an Associate Professor of Computer Science at Karlstad University, Sweden, where he earned his Ph.D. in 2005. His research focuses on computer networking, particularly low-latency communication in multi-path systems, Starlink satellite performance, and deterministic network emulation using tools like KauNetEm. He has led projects such as the 8-year DRIVE initiative and the HITS project, collaborating with industry partners like Icomera. His work includes evaluating cellular and satellite connectivity for trains, as well as machine learning explainability and network protocol optimization. Education: Ph.D. in Computer Science (Karlstad University, 2005). Key projects include KauNetEm development, mobility testing across the Arctic Circle for Starlink/5G, and sectorization-based localization algorithms for eNodeBs. His research spans wireless networking, protocol evaluation, and algorithmic optimization for network reliability and performance. Research interests include: satellite communication performance, cellular networks in high-speed mobility, deterministic network emulation, ML model interpretability, and protocol efficiency in heterogeneous networks. He has advised multiple master’s and bachelor’s projects and contributed to over 100 publications since 2000. Notable collaborations include the EU FP6 Newcom Network of Excellence and the Swedish Knowledge Foundation-funded 'Reliable and Secure Communication in SCTP' project. Current roles include sub-project manager in DRIVE and co-leader of wireless network modeling work packages.

• Phonetics
• Prosody
• Multimodal Communication
• +۴ مورد دیگر

Gilbert Ambrazaitis is a Senior Lecturer in Swedish as a second language and a phonetics researcher at Linnaeus University, Department of Swedish Language. He holds a PhD in Phonetics (2010) and an M.A. in Phonetics and Digital Speech Processing (2004). His research focuses on prosody, particularly the multimodal interaction between speech and bodily movements, and children's acquisition of contrastive intonation. He leads an externally funded project on contrastive focus production in children and collaborates with the Linnaeus University Centre for Intermedial and Multimodal Studies (IMS) and the Educational Linguistics research group. Educations: PhD in Phonetics (2010), M.A. in Phonetics (2004) Affiliations: Linnaeus University Centre for Intermedial and Multimodal Studies (IMS), Educational Linguistics, LiLa Lab (Language, Cognition & Culture Lab) Research Interests: Prosody, multimodal communication, intonation acquisition, Swedish phonetics, speech-gesture interaction. His work bridges phonetics, linguistics, and education, with projects like 'Learning to Focus' exploring children’s intonation development across dialects. He also investigates the role of gestures and head movements in Swedish news presentations. Key Projects: Leading 'Learning to Focus' (Riksbankens Jubileumsfond) and collaborating on 'Alternative Media Logic' (Swedish Research Council). Current studies include embodied pronunciation training and media production analysis. Labs/Teams: Linnaeus University Language, Cognition and Culture Lab (LiLa Lab), part of interdisciplinary networks like Nordic Research Network for Educational Linguistics (NordEdLing).

Gustaf Hendebyمشاهده پروفایل

دانشیار

• Statistical Sensor Fusion
• Model-Based Sensor Fusion
• Target Tracking
• +۴ مورد دیگر

Gustaf Hendeby is an Associate Professor and Docent in Automatic Control at Linköping University's Department of Electrical Engineering (ISY). His career spans academia and defense research, including a part-time role at the university and prior positions at the German Research Institute for Artificial Intelligence (DFKI) and the Swedish Defence Research Agency (FOI). Dr. Hendeby’s research focuses on statistical and model-based sensor fusion , particularly in target tracking, SLAM, positioning, and nonlinear estimation. He has contributed to Kalman filter approximations (EKF, UKF) and particle filter methodologies, aiming to enhance sensor data utilization and algorithm accessibility for non-experts. His recent publications address magnetometer-IMU calibration, magnetic-field SLAM, DVB-T signal localization, and adaptive basis function selection for efficient predictions. Collaborations include researchers like Isaac Skog and Chuan Huang, with applications in autonomous systems and sensor networks. Teaching : Lectures on Sensor Fusion (TSRT14) and supervises Master’s theses. Projects : Technical coordinator for EU’s COGNITO project; software integration for Trivisio GmbH’s Colibri IMUs.

Arunava Nahaمشاهده پروفایل

استادیار

• Control Systems
• Cyber-Physical Systems Security
• Reinforcement Learning
• +۶ مورد دیگر

Arunava Naha is an Assistant Professor in the Department of Electrical Engineering at Linköping University, Sweden, affiliated with the Information Coding (ICG) research group. His work bridges control theory, cybersecurity, and machine learning, focusing on secure and reliable operation of networked and cyber-physical systems. His research interests include: Optimal and risk-constrained control under uncertainty Reinforcement learning for control systems Detection of deception and replay attacks in cyber-physical systems Physical watermarking for active intrusion detection Sequential change detection and statistical monitoring The recent publications show a strong trend in applying machine learning and stochastic control techniques to enhance the security and resilience of networked control systems. His work often involves probabilistic constraints, model-free methods, and quickest detection frameworks, contributing to both theoretical advances and practical solutions in critical infrastructure protection. Scientific awards: No awards mentioned in the provided text. Advising and grants: No information available on students, advising, or funded grants. Labs, teams, and research groups: Arunava Naha is part of the Information Coding (ICG) group within the Department of Electrical Engineering at Linköping University, which conducts research in communication, signal processing, and control systems with applications in secure and reliable information transmission and system operation.