جست‌وجوی استاد و پژوهشگر

He Guoمشاهده پروفایل

پژوهشگر

• Combinatorics
• Graph Theory
• Probabilistic Methods
• +۲ مورد دیگر

He Guo is a Researcher in the Department of Mathematics and Mathematical Statistics at Umeå University, Sweden. He holds a Ph.D. from Georgia Institute of Technology and a B.S. with distinction from Zhejiang University. His research focuses on combinatorics and graph theory, particularly probabilistic and topological methods. He has been affiliated with Technion – Israel Institute of Technology as a postdoctoral fellow, hosted by Ron Aharoni, and currently works under the mentorship of István Tomon. Key achievements include receiving the Top Graduate Student Award during his Ph.D., ranking first in his undergraduate class, and presenting at events like the European Conference on Combinatorics (Eurocomb'25). His work spans topics such as Ramsey theory, matroid intersections, rainbow cycles, and random graph structures. He maintains an active presence in the Discrete Mathematics research group and contributes to academic service and teaching.

• Spatial statistics
• Climate Science
• Environmental Sciences
• +۳ مورد دیگر

Johan Lindström is a Senior Lecturer and Director of Studies in Mathematical Statistics at Lund University's Faculty of Engineering (LTH). He serves as a Researcher in MERGE (ModElling the Regional and Global Earth system), eSSENCE (e-Science Collaboration), and BECC (Biodiversity and Ecosystem services in a Changing Climate). He is also a Principal Investigator and Profile Area member in multiple initiatives, including LTH's Aerosols and LU's Nature-based Future Solutions. His research focuses on statistical methods for spatial data in climate and environmental applications, using Bayesian hierarchical models and computational techniques like MCMC, INLA, and Gaussian Markov Random Fields (GMRFs). Key areas of expertise include computational methods for large datasets, spatial statistics, and applications to environmental modeling. Lindström has been recognized with the Excellent Teaching Practitioner (ETP) award in 2018. He has supervised six students and contributed to over 60 research outputs, including work on wave energy converters, Holocene land cover change, and statistical modeling of ecological systems. Notable projects include the POLLENOMICS initiative, combining ancient DNA and paleo-pollen data to decode European farming history, and the ELLIIT Focus Period on Machine Learning for Climate Science. Lindström collaborates internationally and actively reviews PhD theses and organizes conferences, reflecting his leadership in both research and academic governance.

• Neuroradiology
• Neuroimaging
• Nuclear Medicine
• +۷ مورد دیگر

Staffan Holmin is a researcher at Karolinska Institutet and Karolinska University Hospital , leading a group focused on clinical and translational research in neuroimaging, nuclear medicine, and vascular interventions. His work bridges technical innovation with medical applications, including pediatric neuroradiology, stroke thrombectomy, and radiotracer development. Collaborations : Royal Institute of Technology (KTH) for prototype design; Physics of Medical Imaging group for CT advancements. Research Highlights : Clinically relevant stroke models (M2CAO), vascular wall penetration catheters, endovascular biopsy techniques, and preclinical imaging at KERIC core facility. Key Trends in Publications : Holmin's recent articles emphasize thrombectomy outcomes, radiotracer validation, MRI/PET imaging, and endovascular device innovation for stroke and cardiovascular applications. Collaborative projects span 2016-2023, reflecting sustained interdisciplinary work. Laboratory & Facilities : His group utilizes state-of-the-art preclinical imaging (micro-PET, 9.4T MRI) and translational models for stroke, inflammation, and cancer research.

• Algebra
• Discrete Mathematics
• Linear Algebra
• +۴ مورد دیگر

Göran Bergqvist is a Professor at Linköping University, affiliated with the Department of Mathematics and the division of Algebra, Geometry and Discrete Mathematics (ALGD). His work contributes to theoretical and applied aspects of algebra and discrete mathematics. His research interests include: Linear Algebra Tensor Analysis Matrix Theory Computational Mathematics Discrete Mathematics The recent publications indicate a strong focus on tensor decompositions, matrix spectra, and probabilistic aspects of tensor ranks. His work bridges pure mathematical theory with applications in signal processing and numerical analysis. There are no scientific awards listed in the provided information. He has advised no students listed in the available data and no information is available about grants or funding. No specific research team or laboratory is mentioned.

• Dynamical Systems
• Number Theory
• Machine Learning
• +۶ مورد دیگر

Karl-Olof Lindahl is a Professor in Mathematics at Linnaeus University , Sweden. He has held postdoctoral positions at Brown University, Pontífica Universidad Católica de Chile, and Universidad de Santiago de Chile, and was a guest researcher at Université de Picardie, Amiens. His research spans stochastic and deterministic dynamical systems , number theory , and machine learning , with a focus on reinforcement learning and optimization for AI applications in energy systems and agriculture. Current Teaching: Discrete Mathematics, Applied Probability and Statistics, Ordinary Differential Equations, Research Methodology Supervision: Head supervisor for PhD student Björn Lindenberg and Jonas Nordqvist (2020 graduate) Research Groups: AI and Machine Learning for Optimization , Computational Mathematics for Predictive Digital Twins (PreDiTwin) , and International Center for Mathematical Modeling (ICMM) His ongoing projects include reinforcement learning algorithms , climate-smart agriculture , and sustainable energy optimization , supported by collaborations with Kalmar Energi. International partners include Juan Rivera-Letelier (University of Rochester), Mike Zieve (University of Michigan), Charles Favre (École Polytechnique), and Youssef Fares (Université de Picardie). Key publications highlight advancements in p-adic dynamics , ergodicity , and reinforcement learning applications.

• Digital Human Modelling
• Ergonomics Evaluation
• Simulation-Based Optimization
• +۵ مورد دیگر

Aitor Iriondo Pascual is a researcher at the University of Skövde's School of Engineering Science , specializing in digital human modeling (DHM) and simulation-based multi-objective optimization (MBOO) for ergonomics and productivity in manufacturing systems. His work bridges industrial engineering with human factors research, focusing on automating ergonomic assessments using motion capture technology and machine learning algorithms. PhD in User-Centered Product Design (University of Skövde, 2023) Active in Virtual Production Development (VPD) and Synergy Virtual Ergonomics (SVE) research profiles Collaborator in international projects like MOSIM and VIVA Key contributor to DHM toolchain development with IPS IMMA and LUA scripting His research interests center on: Integrating motion capture with DHM for objective ergonomic evaluations Automated hand posture classification using random forest algorithms Concurrent optimization of productivity and worker well-being in factory layout design Standardization of 3D body shape prediction models for virtual simulations Machine learning applications in biomechanical exposure assessments Digital twin development for sustainable manufacturing systems Recent publications demonstrate his expertise in combining genetic algorithms with DHM tools for occupant packaging design and logistics area optimization. He has also developed open-source statistical body shape prediction models shared under MIT License on GitHub. Key collaborators include: Prof. Lars Hanson (ergonomics) Dr. Dan Högberg (simulation methods) Dr. Anna Syberfeldt (optimization frameworks) Dr. Erik Brolin (DHM tools) His methodological innovations include: Automating REBA / RULA assessments via motion capture data Developing Pugh's method implementation for DHM concept evaluation Creating cross-disciplinary optimization frameworks for production systems

• Software Security
• Formal Verification
• Cryptography
• +۲ مورد دیگر

Mats Näslund is an Adjunct Professor at the Division of Theoretical Computer Science within the School of Electrical Engineering and Computer Science (EECS) at KTH Royal Institute of Technology. He holds a part-time (20%) position focused on Secure Networks and Systems, building on his prior academic and industrial experience at Ericsson (1999–2017), where he worked on telecommunications security. His research spans Software Security Formal Verification of Protocols Cryptography 5G Network Security Lightweight Authentication He contributes to teaching courses such as Applied Cryptography (DD2520) and Computer Introduction (DD1301). Recent publications address topics like integrity protection, 5G security architectures, and cryptographically secure CRC for constrained devices. His work bridges academic research with industry applications in telecommunications.

• Coding Theory
• Distributed Storage
• Caching Systems
• +۴ مورد دیگر

Alexandre Graell i Amat is a Professor in the Communication Systems group at Chalmers University of Technology's College of Engineering. His primary affiliation spans the Department of Electrical Engineering with strong cross-disciplinary ties to information theory and wireless communications research. His educational background includes: MSc in Telecommunications Engineering from Universitat Politècnica de Catalunya (2001) MSc in Electrical Engineering from Politecnico di Torino (2000) PhD in Electrical Engineering from Politecnico di Torino (2004) Graell i Amat's research focuses on modern coding theory with applications spanning distributed storage systems, caching architectures, distributed computing frameworks, and optical communication technologies. His work bridges theoretical information theory with practical implementations in next-generation communication systems, particularly addressing challenges in massive IoT networks, DNA data storage, and federated learning security. Recent publications demonstrate increasing emphasis on quantum error correction and privacy-preserving distributed learning. His publication trends reveal consistent contributions across three core domains: (1) novel LDPC code constructions for emerging storage paradigms, (2) privacy/security mechanisms in federated learning systems, and (3) performance optimization for wireless networks with energy constraints. The 15 most recent articles show strong industry relevance with 60% directly addressing 6G/IoT challenges and 30% targeting DNA storage applications. Key scientific recognition includes: 2010 IEEE ComSoc Best Young Researcher Award (EMEA region) Marie Curie Intra-European Fellowship (2006-2008) 2016 IEEE Sweden VT-COM-IT best student paper award As Editor at Large for IEEE Transactions on Communications, he previously served as Associate Editor for IEEE Transactions on Communications (2011-2016) and IEEE Communications Letters (2011-2013). He has chaired major conferences including the 7th International Symposium on Turbo Codes (2012), Swedish Communication Technologies Workshop (2013), and IEEE European School of Information Theory (2016). His research is supported by 16 active projects with significant European Commission and industry funding. He leads the Communication Systems research group which specializes in information-theoretic approaches to next-generation communication challenges, with particular strengths in coded distributed systems and optical networking.

Oskar Frånbergمشاهده پروفایل

مدرس ارشد

• Systems Engineering
• Hyperbaric Medicine
• Diving Physiology
• +۲ مورد دیگر

Oskar Frånberg is a Senior Lecturer at the Department of Mathematics and Natural Sciences at Blekinge Institute of Technology in Karlskrona, Sweden. His research focuses on systems engineering applied to hyperbaric medicine , diving physiology , and underwater equipment design . Frånberg's recent publications highlight his work on decompression algorithms for military diving, inert gas washout dynamics, and environmental impact assessment tools for offshore energy projects. His research spans both fundamental physiological studies and practical engineering solutions for diving safety hyperbaric therapy subsea infrastructure risk mitigation His team has developed advanced models for rebreather systems oxygen distribution regimens decompression table validation with significant contributions to the Swedish Armed Forces' diving protocols.

• Random Matrix Theory
• Probability Theory
• Mathematical Physics
• +۳ مورد دیگر

Maurice Duits is a Professor at the Department of Mathematics, KTH Royal Institute of Technology, Stockholm. His research focuses on probability theory, mathematical physics, and statistical mechanics, with a strong emphasis on random matrix theory, determinantal processes, and orthogonal polynomials. He is affiliated with the School of Engineering Sciences and holds roles such as examiner and teacher in courses like Degree Project in Mathematics and Ethics, Sustainability and Equity for Mathematicians. His work spans topics including lozenge tilings, Aztec diamond models, Ginibre ensembles, and fluctuation analysis in non-colliding particle systems. Notable contributions include studies on elliptic curves in tiling models and applications of non-Hermitian orthogonal polynomials. Recent research (2023–2025) explores Benford’s Law in matrix ensembles and q-Racah ensembles in hexagonal tilings. Publications frequently address asymptotic behavior of Toeplitz determinants, Painlevé kernels in Hermitian matrix models, and universality in multi-matrix models. No academic awards are explicitly listed in the provided texts. Grant details or lab affiliations are not documented here, but his collaborative projects (e.g., with T. Berggren, A. Borodin) highlight interdisciplinary work in mathematical physics and probability. His research bridges combinatorial structures and analytical methods, with applications to both theoretical and applied statistical systems.

Konrad Abramowiczمشاهده پروفایل

دانشیار

• Functional Data Analysis
• Statistical Methods in Biomechanics
• Climate Research
• +۴ مورد دیگر

Konrad Abramowicz is an Associate Professor and Head of the Department of Mathematics and Mathematical Statistics at Umeå University. His research focuses on functional data analysis, classification, and local inference procedures in the presence of misalignment, with applications in biomechanics, climate research, and forest growth analysis. He teaches stochastic simulations and parametric modeling methods, emphasizing student development and curiosity-driven learning. Education: University of Wrocław (Poland), with Erasmus exchange to Umeå University Research: Functional data analysis, interdisciplinary collaborations in biomechanics and climate science Awards: Teaching awards for dedication to student mentorship Internationalization: Postdoctoral work in Australia; collaborations with Italy and India; faculty-level international contact Konrad's research spans functional data analysis applied to knee kinematics after ACL injuries, climate reconstruction using sediment data, and statistical methods for forest growth analysis. He developed computational tools like the fiberLD R package for fiber length determination in forestry studies. His recent publications highlight interdisciplinary applications of statistical methods, with a focus on movement pattern analysis, climate modeling, and non-destructive forest assessment. Konrad advocates for international collaboration and adaptive academic practices to address global challenges. Konrad has received teaching awards and is recognized for his supportive approach to students. He actively participates in university initiatives for international student integration and served as international coordinator for academic exchange programs. As a key member of the IceLab interdisciplinary research hub, Konrad bridges statistical methods with applications in biomechanics and environmental science. His work emphasizes the importance of mathematical abstraction in solving real-world problems.

Per-Håkan Lundowمشاهده پروفایل

دانشیار

• Statistical Mechanics
• Spin Glasses
• Combinatorics
• +۳ مورد دیگر

Per-Håkan Lundow serves as an Associate Professor in the Department of Mathematics and Mathematical Statistics at Umeå University, Sweden, holding the Docent qualification. His office is located at MIT-huset, plan 3, Matematik och matematisk statistik, MIT.B.325, Umeå (901 87), with contact email per-hakan.lundow@umu.se and phone +46 90 786 79 93. Lundow's research spans: Statistical Mechanics of Ising models and spin glasses across dimensions 2-5 Computational combinatorics and optimization Finite-size scaling and universality classes Applications to Boson sampling and quantum computing His work combines theoretical analysis with advanced computational methods to investigate critical phenomena and phase transitions in complex systems. Publication trends reveal sustained focus on high-dimensional spin systems since 2002, with recent emphasis on five-dimensional Ising model behavior, damage spreading in random cluster models, and efficient permanent computation algorithms. Collaborations with Ian Campbell and Klas Markström dominate his output, appearing consistently across Nuclear Physics B, Physical Review E, and Journal of Computational Physics publications. Lundow participated in the "Three and four-dimensional tera-spin systems" research project (2016-2020) and is an active member of Umeå University's Computational Combinatorics and Optimization research group. No information regarding student advising or additional grant funding appears in the source material.

• Functional Data Analysis
• Spatial Statistics
• Applied Statistics
• +۴ مورد دیگر

Sara Sjöstedt de Luna is a Professor in Mathematical Statistics at Umeå University's Department of Mathematics and Mathematical Statistics. She is a board member of The Wallenberg AI, Autonomous Systems & Software Program (WASP) and holds the title of Docent. Her research spans functional data analysis, spatial statistics, and their applications in climatology, forestry, and human motor control. Affiliation : Umeå University Department : Mathematics and Mathematical Statistics Research Focus : Functional data analysis, spatial statistics, and interdisciplinary applications in environmental and biomedical domains Her recent work involves developing statistical tools like the fdaMocca and fiberLD R packages, advancing methodologies for clustering functional data with covariates, and refining kriging approaches for spatiotemporal predictions. She has contributed to climate reconstruction using varved lake sediment data and biomechanical analysis of knee kinematics post-injury. Applications of her research include: Climate modeling using historical sediment records Wood fiber length determination for forestry studies Biostatistical analysis of human motor control Sjöstedt de Luna employs both parametric and nonparametric statistical techniques, with a focus on handling dependent data structures, censoring challenges, and algorithm design for complex datasets.

Romaric Duvignauمشاهده پروفایل

دانشیار

• Distributed Systems
• Network Security
• IoT/CPS Applications
• +۷ مورد دیگر

Romaric Duvignau is an Associate Professor at Chalmers University of Technology since 2024, affiliated with the Department of Computer Science and Engineering within the IT Faculty. His primary research focuses on distributed systems, network security, and cyber-physical systems (CPS), with an emphasis on optimizing communication and computational efficiency in distributed applications. Key projects include the READY project for continuous monitoring systems and contributions to P2P energy sharing optimization via the DEEP and HyperP2P initiatives. He has been at Chalmers since 2017 and also holds teaching responsibilities in computer networks and data-driven systems. Research Interests: Distributed Algorithms & Optimization Cybersecurity & Privacy Peer-to-Peer Energy Sharing Vehicular Networks Edge Computing & Streaming Analytics Notable Articles: His recent works include advancements in P2P energy matching algorithms (2025), Byzantine fault-tolerant consensus (2025), and vehicular data localization (2021). Earlier contributions include impactful studies on vehicular networks (DRIVEN framework, 2020) and network traffic analysis (2019). Teaching & Grants: Responsible for courses like Computer Communication (EDA343) and Data-driven Support for CPS. Active in educational innovation, exploring AI impacts on teaching and active learning tools like Kahoot/Mentimeter. Labs/Teams: Member of the Distributed Computing and Systems group at Chalmers Networks and Systems division, collaborating with researchers like Marina Papatriantafilou and Vincenzo Gulisano on energy systems and distributed algorithms.

Ole Sönnerbornمشاهده پروفایل

مدرس

• Quantum Mechanics
• Mathematical Physics
• Topology and Geometry in Physics
• +۳ مورد دیگر

Ole Sönnerborn is a Lecturer in Mathematics at Karlstad University, focusing on the intersection of topology, geometry, and quantum mechanics. His research explores quantum systems with topological/geometric origins, including holonomy, geometric phases, quantum speed limits, and quantum information theory. He teaches advanced mathematics courses for engineering and mathematics education programs. Key research areas include: Quantum holonomy and its applications in computation Time constraints for quantum processes (quantum speed limits) Self-testing quantum systems and Bell inequality violations Geometric uncertainty principles Symmetric informationally complete measurements (SICs) Recent work (2023-2025) emphasizes iso-holonomic inequalities, tight quantum speed limits, and non-adiabatic holonomic computation. Over 20 peer-reviewed articles span topics from quantum control to topological systems. Teaching responsibilities include: Mathematics for Engineers I/II Mathematical relationships in single-variable calculus Discrete Mathematics and Algebraic Structures Linear Algebra II Supervises bachelor/master thesis projects in mathematical physics and quantum theory. No awards explicitly listed in provided materials.