جست‌وجوی استاد و پژوهشگر

Natalya Pya Arnqvistمشاهده پروفایل

دانشیار

• Statistical Regression Modelling
• Functional Data Analysis
• Shape Preserving Smoothing
• +۷ مورد دیگر

Natalya Pya Arnqvist is an Associate Professor in Mathematical Statistics at Umeå University, Sweden. She is affiliated with the Department of Mathematics and Mathematical Statistics and has active roles in research groups focused on Functional Data Analysis, Semiparametric Regression, and Statistical Learning for Spatio-Temporal Data. Her career includes prior positions at Nazarbayev University (2015-2020), University of Bath (2011-2015), and KIMEP University (2000-2005). Education : PhD in Statistics (University of Bath, UK, 2007-2010), CSc in Physical and Mathematical Sciences (Institute for Mathematics, Kazakhstan, 2000-2005). Her research centers on statistical regression modelling and functional data analysis , with significant contributions to shape constrained additive models (SCAMs) and model-based functional clustering . She has developed R packages such as scam , fdaMocca , and nilde for these methodologies. Recent publications highlight applications in applied demography , defect detection , and nonlinear state space modeling , reflecting her focus on bridging statistical theory with industrial and ecological challenges. She teaches undergraduate and graduate courses in probability, regression, and statistical learning.

• Wave Energy Conversion
• Renewable Energy Systems
• Ocean Engineering
• +۱۰ مورد دیگر

Andrej Savin is a Senior Lecturer in the Department of Electrical Engineering at Uppsala University, Sweden, where he conducts research in marine renewable energy systems, particularly wave energy converters (WECs). He is based at the Ångström Laboratory and is actively involved in the Lysekil wave energy research project. Position: Senior Lecturer Institution: Uppsala University Department: Department of Electrical Engineering Email: andrej.savin@angstrom.uu.se His research focuses on the mechanical and electrical design, structural integrity, and performance optimization of point-absorbing wave energy converters. Key areas include mooring dynamics, power take-off systems (e.g., Low-RPM Torque Converter), ice interaction, and numerical modeling of WEC behavior under extreme and irregular sea conditions. He also explores the integration of machine learning for predicting WEC motion in hybrid offshore energy systems. The recent publications highlight a strong trend in experimental validation, structural analysis, and system-level optimization of wave energy technologies. His work spans hydrodynamics, mechanical design, thermal management, and data analysis, contributing to the advancement of reliable and efficient marine energy solutions. Andrej Savin has not received any explicitly mentioned scientific awards in the provided text. He has supervised or collaborated with multiple researchers and students, though no formal advisees are listed. His work is supported through institutional research funding at Uppsala University, particularly within the wave energy research group. He contributes to both experimental and theoretical aspects of offshore energy systems, with a focus on real-world deployment challenges in the Baltic Sea. He is part of the wave energy research team at Uppsala University, centered at the Lysekil research site, which conducts full-scale testing of wave energy converters. This team investigates power generation, survivability, sensor integration, and environmental interactions of marine energy devices.

• Machine Learning
• Artificial Intelligence
• Cybersecurity
• +۴ مورد دیگر

Zahra Taghiyarrenani is a postdoctoral researcher at Halmstad University , affiliated with the School of Information Technology and the IT - Computer Department . Her work bridges theoretical advancements in machine learning with practical applications in industrial and cybersecurity domains. Education : PhD in Machine Learning (2024, Halmstad University), Licentiate Thesis on Multi-Domain Learning (2022) Her research focuses on domain adaptation , federated learning , and explainable AI , with emphasis on handling data scarcity and heterogeneity. Key projects include fault identification in industrial systems, intrusion detection, and physics-informed models for diagnostics. Her publications highlight trends in cross-domain learning , combining privacy-preserving AI with edge computing , and addressing data efficiency through few-shot/supervised methods . Recent work explores Industry 5.0 applications of Explainable AI. Projects: EVE : Extending vehicle lifespan in electromobility Federated Learning for Well-being : Privacy and fairness in AI FeelAI : Edge-based predictive logistics models

Danyo Danevمشاهده پروفایل

دانشیار

• Telecommunications
• Coding Theory
• Network Science
• +۳ مورد دیگر

Danyo Danev is an Associate Professor at the Department of Electrical Engineering (ISY) , Linköping University , Sweden, specializing in the Division of Communication Systems . He earned an M.Sc. in Mathematics from Sofia University (1996) and a Ph.D. in Electrical Engineering from Linköping University (2001). He holds the Docent title in Data Transmission (2005). Research Pillars : Telecommunications, Coding Theory, Network Science Key Collaborations : Erik G. Larsson, Olle Abrahamsson His research spans secure communication systems , cognitive radio , superimposed codes , and network dynamics , with a focus on mathematical modeling for wireless and satellite networks. Recent work explores opinion dynamics and structural balance in network science, blending statistical signal processing with social network analysis. Scientific Contributions : Co-supervised 5 graduate students (3 PhD, 2 Licentiate degrees) Active in Communication Systems (KS) division Expertise in signal detection and error-correcting codes Advising Legacy : Mentored students on topics including digital fingerprinting , cognitive radio , and bounds for spherical codes , with ongoing work in network science.

• Computer Vision
• 3D Reconstruction
• Medical Image Analysis
• +۳ مورد دیگر

Christopher Zach is a Research Professor at Chalmers University of Technology, affiliated with the Signal Processing and Medical Technology department within the Digital Image Systems and Image Analysis research group . His work focuses on 3D reconstruction , real-time computer vision , and numerical optimization for machine learning. Develops 3D image understanding techniques Specializes in robust optimization for vision systems Leads research in medical image analysis Recent publications demonstrate expertise in low-light text enhancement , out-of-distribution detection , and domain adaptation for industrial applications. Active in Chalmers' Wallenberg AI and ÅForsk funded projects. Collaborates with researchers from Volvo Group , Volvo Cars , and SAFER Vehicle Safety initiatives.

• Quantum Physics
• Quantum Computing
• Trapped Ions
• +۲ مورد دیگر

Ivo Straka is a postdoctoral researcher at the Department of Physics at Stockholm University , specializing in Trapped Ion Quantum Technologies . His work focuses on advanced quantum systems and their applications. Current research includes the EU-funded BRISQ project (Brisk Rydberg Ions for Scalable Quantum Processors), which explores combining trapped ion technology with Rydberg interactions for scalable quantum computing. He also investigates rapid control of quantum motion using Rydberg ions, contributing to the development of quantum processors and quantum simulation technologies. Recent publications highlight expertise in photon statistics, quantum detection, and quantum optics. Key projects analyze quantum decoherence, single-photon detectors, and quantum non-Gaussian light states. His work spans theoretical and experimental quantum physics, with emphasis on practical quantum technologies and precise quantum state manipulation.

Jörgen Blomvallمشاهده پروفایل

دانشیار

• Financial Engineering
• Stochastic Programming
• Quantitative Finance
• +۳ مورد دیگر

Jörgen Blomvall is an Associate Professor at Linköping University's Department of Management and Engineering (IEI). His research focuses on optimal financial decision-making, accurate financial measurement, and stochastic optimization models applied to financial markets. He develops methods to enhance measurement accuracy for quantities like forward rates, default intensities, and local volatilities, which are critical for equity, interest, credit, and derivative markets. Blomvall's work integrates stochastic programming and dynamic programming to address portfolio optimization, risk management, and transaction cost reduction. He is affiliated with the Operations Management and Finance research group, exploring resource optimization in manufacturing and service industries. His key research areas include financial engineering, stochastic programming, and quantitative risk management. Recent work emphasizes improving dividend estimation from intraday quotes, reducing transaction costs in hedging strategies, and analyzing stage complexity in stochastic programming for portfolio choice. Blomvall's methodologies have advanced the modeling of systematic risks and optimal investment decisions under real-world market constraints. Blomvall has contributed extensively to the development of optimization-based frameworks for performance attribution and yield curve estimation. His articles frequently address practical applications of stochastic models in financial markets, balancing theoretical rigor with computational feasibility. Despite his prolific output, no specific academic awards or student advisees are documented in the provided texts. He is active in the Production Economics (PEK) research group at Linköping University.

Naveen Kumar Dasanadoddi Venkategowdaمشاهده پروفایل

دانشیار

• Wireless Communications
• Internet-of-Things (IoT)
• Privacy and Security
• +۴ مورد دیگر

Naveen Kumar Dasanadoddi Venkategowda is an Associate Professor at Linköping University's Department of Science and Technology (ITN), affiliated with the Physics, Electronics and Mathematics (FEM) division. His research focuses on wireless communications, IoT, privacy/security in IoT systems, distributed learning, and optimization. He holds a Ph.D. from IIT Kanpur (2016) and a B.E. from Bangalore University (2008). Prior roles include postdoctoral researcher at NTNU (2017-2021) and Research Professor at Korea University (2016-2017). His work emphasizes modular low-power solutions for wireless sensor networks, high-data-rate communication, and antenna design. Recent publications address distributed algorithms, privacy-preserving techniques, and robust optimization in decentralized systems. Notable awards include the TCS Research Fellowship (2011-2015), ERCIM Alain Bensoussan Fellowship (2017), and a Best Paper Award (Fusion 2020). Education: Ph.D. in Electrical Engineering, Indian Institute of Technology Kanpur (2016) B.E. in Electronics and Communication Engineering, Bangalore University (2008) Awards: TCS Research Fellowship (2011-2015) ERCIM Alain Bensoussan Fellowship (2017) Runner-up Best Paper Award at Fusion 2020 His research spans distributed learning frameworks, IoT security, and signal processing for MIMO systems. Collaborations include work on hybrid beamforming, resilient consensus algorithms, and privacy-aware distributed systems.

• Mathematics
• Dynamical Systems
• Ergodic Theory
• +۴ مورد دیگر

Anders Johansson is a Lecturer at Gävle University, specializing in advanced mathematical research. His work focuses on dynamical systems, ergodic theory, and probability theory with applications in statistical mechanics and fractal geometry. His research explores topics such as transfer operators, g-measures, and phase transitions in complex systems. Key research interests include the mathematical analysis of Doeblin measures, Ising models, and Kusuoka measures. He has contributed to understanding the ergodic properties of non-uniformly hyperbolic systems and the construction of transport networks through stochastic processes. Recent work (2025) addresses continuous eigenfunctions of transfer operators in Dyson models and uniqueness criteria for Doeblin measures. Johansson’s publications span prestigious journals like Mathematische Zeitschrift and Ergodic Theory and Dynamical Systems. His earlier work (2003–2019) includes foundational studies on g-measures, graph theory, and combinatorial designs. His research often bridges pure mathematics with applications in theoretical biology and statistical physics. He has collaborated with notable researchers such as Anders Öberg and Mark Pollicott, producing influential papers on topics ranging from multifractal analysis to animal group size distributions. His work demonstrates a deep engagement with both theoretical rigor and interdisciplinary applications.

Johan Strandbergمشاهده پروفایل

دانشیار

• Functional Data Analysis
• Spatial Statistics
• Biomechanics
• +۵ مورد دیگر

Johan Strandberg is an Associate Professor in the Department of Statistics at the Umeå School of Business, Economics and Statistics (USBE), Umeå University. His research focuses on functional data analysis, spatial statistics, and their applications in biomechanics and sports medicine. He specializes in analyzing complex data structures such as electroencephalography (EEG) signals and kinematic curves to study movement-related anxiety, sensorimotor control following injuries, and long-term outcomes of anterior cruciate ligament (ACL) ruptures. Key research areas include: Functional data analysis methodologies Nonparametric statistical clustering techniques EEG-based evaluation of movement-related fear Biomechanical analysis of human movement Climate reconstruction using functional kriging Strandberg is affiliated with the Functional Data Analysis and Spatial Statistics Group and the U-Motion Laboratory Research Group. His recent projects include investigating sensorimotor control post-ACL injury and exploring functional data approaches to human movement assessment. He has collaborated on studies involving ACL injury rehabilitation, EEG applications in sports medicine, and statistical modeling of spatial-temporal processes. His research outputs emphasize methodological advancements in handling dependent functional data, with applications ranging from clinical biomechanics to environmental science. He has contributed to over 10 peer-reviewed articles, focusing on inferential frameworks, clustering methods, and functional kriging techniques.

Vladimir Carli and Gergö Hadlaczkyمشاهده پروفایل

پژوهشگر

• Suicide prevention
• Mental health epidemiology
• Psychiatric genetics
• +۳ مورد دیگر

Vladimir Carli and Gergö Hadlaczky are researchers at the National Centre for Suicide Research and Prevention of Mental Health (NASP) within Karolinska Institutet's Department of Learning, Informatics, Management and Ethics. They lead interdisciplinary research on suicide determinants and mental health interventions. Research Focus: Their work spans Biological, psychological, and social suicide risk factors Digital mental health systems (e.g., VR, eHealth) Genomic studies of suicidality Population-level mental health epidemiology Key Projects: Forte-funded suicide process study (SEK 5.995M) Tim Bergling Foundation digital training initiative EXPERIENCE project (VR + XAI for depression) Publication Trends: Recent articles analyze Genetic interactions with childhood trauma (P2RX7, CLOCK genes) Screen time and sleep pathways Body odor's catalytic role in anxiety treatment Population-level suicide pattern analysis

• Electron Microscopy
• Magnetism
• Materials Theory
• +۵ مورد دیگر

Jan Rusz is Professor at the Department of Physics and Astronomy, Uppsala University, specializing in Materials Theory. His research focuses on theoretical aspects of electron microscopy, magnetism, and electronic structure of materials, with particular emphasis on advanced spectroscopic techniques and their theoretical foundations. His primary research interests include: Theory of inelastic electron scattering and dynamical electron diffraction Electron energy loss near-edge structure (ELNES) and electron magnetic circular dichroism (EMCD) X-ray and optical spectroscopies including XMCD, Kerr effect, and Faraday rotation Electronic structure calculations of strongly correlated electron systems Magnetism, magneto-crystalline anisotropy, and exchange interactions Theory of two-dimensional angular correlation of electron-positron annihilation radiation Professor Rusz's recent publications demonstrate a strong focus on advancing electron microscopy techniques for atomic-scale magnetic and vibrational measurements. His work bridges theoretical modeling with experimental applications, particularly in developing methods to visualize magnetic moments and phonon modes at unprecedented resolution. His research has appeared in top-tier journals including Nature, Nature Materials, and Physical Review Letters, reflecting significant impact in the field of materials physics and electron microscopy. His research group appears to collaborate extensively with experimentalists to develop and validate new methodologies in electron microscopy, particularly for measuring magnetic properties at the nanoscale and atomic scale. The work has implications for developing new magnetic materials and understanding fundamental aspects of magnetism in condensed matter systems.

• Wireless Communications
• Radio Propagation
• Millimeter-wave (mmWave) Channels
• +۷ مورد دیگر

Xuesong Cai is an Associate Professor (Docent) in the Department of Electrical and Information Technology at Lund University, Sweden. He serves as an Associate Senior Lecturer and holds roles in ELLIIT and the NEXTG2COM Competence Centre. His research focuses on radio propagation, millimeter-wave (mmWave), and terahertz (THz) channel modeling for 5G and beyond wireless systems. He has published over 70 peer-reviewed papers and leads projects like 'Breaking the Barriers of Terahertz Communications.' Education: B.S. and Ph.D. (Hons.) from Tongji University (2013, 2018), with internships at Huawei and postdocs at Aalborg University, Nokia Bell Labs, and Lund University. Recognized with awards including the EU Marie Skłodowska-Curie Fellowship and Swedish Research Council grants. Key research interests include high-resolution parameter estimation, channel emulation, and radio-based localization. He is an Associate Editor for IEEE journals and a Senior IEEE Member. Projects span distributed MIMO, channel sounding technologies, and 6G initiatives. Supervised research students include Xu Y. and Al-Ameri A. His work contributes to UN SDGs related to innovation and infrastructure. Current grants include funding from the Crafoord Foundation and Swedish Research Council.

Martin Skoglundمشاهده پروفایل

دانشیار مدعو

• Signal Processing
• Auditory Attention Decoding
• EEG-based Speech Tracking
• +۷ مورد دیگر

Martin Skoglund is an Adjunct Associate Professor at Linköping University, affiliated with the Department of Electrical Engineering and the Automatic Control (RT) research group. His work bridges engineering and neuroscience, focusing on advanced signal processing for hearing-impaired listeners. Institution: Linköping University Department: Department of Electrical Engineering (ISY) Research Group: Automatic Control (RT) Email: martin.skoglund@liu.se His research interests lie at the intersection of biomedical signal processing and cognitive neuroscience, particularly in decoding auditory attention using EEG signals. He specializes in enhancing speech tracking for hearing aid users through deep learning, contrastive learning, and nonlinear signal compensation techniques. His work addresses real-world challenges such as noise interference and hardware-induced nonlinearities in neural recordings. The recent publications highlight a strong trend in developing machine learning models that improve brain-computer interfaces for hearing assistance. These studies predominantly apply deep learning and statistical methods to EEG data, aiming to decode which speaker a listener is focusing on in complex acoustic environments. The research is highly interdisciplinary, combining elements of electrical engineering, neuroscience, and clinical audiology. Martin Skoglund actively collaborates with experts in auditory neuroscience and control systems. While no formal awards or students are listed in the provided texts, his contributions appear in high-impact journals such as Journal of Neural Engineering and eNeuro , as well as top-tier conferences like ICASSP. He is involved in research projects related to auditory attention decoding, EEG-based speech tracking, and signal enhancement for hearing-impaired individuals. These efforts are likely part of broader initiatives in assistive neurotechnology and intelligent hearing systems at Linköping University.

• Nuclear Engineering
• Monte Carlo Simulations
• Neutron Transport
• +۶ مورد دیگر

Jan Dufek is an Associate Professor in the Department of Nuclear Science & Engineering at KTH Royal Institute of Technology. His research focuses on advancing numerical methods for Monte Carlo neutron transport simulations, with applications in nuclear reactor design and safety. Key areas include coupled simulations of thermal-hydraulic feedback, Monte Carlo burnup methods, fission source convergence acceleration, and fission matrix-based techniques. He also develops deterministic nodal nuclear data models using polynomial regression to handle multi-dimensional state variables efficiently. He teaches courses such as Monte Carlo Methods and Simulations in Nuclear Technology and Nuclear Reactor Physics , serving as an examiner and course responsible. His work contributes to projects like the McSAFE initiative, aiming to enhance high-performance Monte Carlo methods for reactor safety. Collaborations include studies on deep learning for nuclear fuel composition prediction and transient analysis using hybrid stochastic-deterministic approaches. His research bridges computational efficiency with practical reactor engineering challenges. Dr. Dufek is affiliated with the NRT (Nuclear Reactor Technology) group at KTH, fostering interdisciplinary advancements in reactor physics and computational methodologies.