جستجوی استاد راهنمای دانشگاهی

Joachim Toft is a Professor in the Department of Mathematics at Linnaeus University (formerly Växjö University), where he has been employed since February 2004. Prior to this position, he served as an assistant professor at Blekinge Institute of Technology in Karlskrona, Sweden. He completed his PhD in mathematics at Lund University in 1997 and was employed as a teaching assistant master at Kristianstad University from 1995 to 1998. Currently, he holds editorial positions for the Journal of Pseudo-Differential Operators and Applications and Annals of Functional Analysis, and serves as a member of the International Society of Analysis, its Applications and Computations (ISAAC). Professor Toft's research primarily focuses on pseudo-differential calculus and related mathematical fields. His work encompasses Fourier analysis, time-frequency analysis, harmonic analysis, basic operator theory, generalized functions (particularly Gelfand-Shilov spaces and Gevrey classes), and micro-local analysis. While these areas form the core of his theoretical work, he also explores applications to wave phenomena, including physics interpretations, non-stationary filters in signal analysis, and geophysics. His research approach emphasizes functional analysis, harmonic analysis, and basic operator theory more frequently than is typical within these specialized fields. The recent publication record demonstrates a strong focus on extending pseudo-differential operator theory to more generalized function spaces, including quasi-Banach spaces, Orlicz spaces, and Pilipović spaces. His work shows a clear progression toward developing more comprehensive frameworks for time-frequency analysis and operator theory, with particular attention to continuity properties, spectral invariance, and characterization of operators in various function spaces. The collaboration network spans multiple international institutions, reflecting the global nature of modern mathematical research. Professor Toft is actively involved in three major research groups at Linnaeus University: the International Center for Mathematical Modeling (ICMM), Scientific Computing and Partial Differential Equations, and Waves, Signals and Systems. His current research project focuses on developing Hörmander-Weyl calculus within the framework of ultra distributions, aiming to create calculi feasible for objects more complex than standard distributions.

• Pseudo-differential calculus
• Fourier analysis
• Time-frequency analysis
• +۹ مورد دیگر

Daniel Gnad is an Assistant Professor at Linköping University's Department of Computer Science (IDA), working within the Artificial Intelligence and Integrated Computing Systems (AIICS) division. He specializes in "Planning and model checking" within artificial intelligence, focusing on theoretical foundations and algorithmic approaches to complex planning problems. Dr. Gnad completed his Computer Science studies at Saarland University, earning his MSc before continuing as a PhD student under Prof. Jörg Hoffmann. In 2022, he joined the RLPLab at Linköping University as a postdoctoral researcher, becoming an assistant professor in 2023. His research primarily centers on AI planning, model checking, and decoupled state-space search methodologies. His recent publications demonstrate significant advancements in planning algorithms, pattern databases, and numeric planning techniques. Gnad's work bridges theoretical computer science with practical applications in artificial intelligence, particularly in developing more efficient search algorithms for complex planning problems. His research often involves transforming planning problems into more tractable forms through abstraction techniques and novel search topologies. Dr. Gnad has received numerous prestigious awards recognizing his contributions to the field: ICAPS 2022 Best Dissertation Award for "Star-Topology Decoupled State-Space Search in AI Planning and Model Checking" SoCS 2022 Best Paper Award for "Additive Pattern Databases for Decoupled Search" SPIN 2018 Best Paper Award for "Star-Topology Decoupling in SPIN" Dr.-Eduard-Martin-Preis for the best dissertation of the Faculty for Mathematics and Computer Science in 2021 Multiple recognitions at the International Planning Competition (IPC) 2018 for the "Saarplan" planning system As a researcher at AIICS, Dr. Gnad contributes to Linköping University's strong position in artificial intelligence research. His work is part of the broader research activities within the Wallenberg Autonomous Systems Program (WASP), one of Sweden's largest research initiatives. While specific details about his advisees aren't provided in the available information, his research group likely contributes to the department's doctoral programs in computer science. Dr. Gnad's laboratory work focuses on developing and analyzing planning algorithms, with applications ranging from theoretical computer science to practical AI systems. His recent integration of techniques like pattern databases with numeric planning represents an important direction in making planning algorithms more versatile for real-world applications.

• Artificial Intelligence
• AI Planning
• Model Checking
• +۳ مورد دیگر

Sebastian Dalleiger is an Assistant Professor at the Division of Theoretical Computer Science, Kungliga Tekniska Högskolan (KTH Royal Institute of Technology). His research focuses on theoretical foundations of machine learning, data mining, and graph theory, with particular expertise in matrix factorization, pattern discovery, and hypergraph analysis. Current affiliation: KTH Royal Institute of Technology Department: Theoretical Computer Science Email: sdall@kth.se His recent work explores federated learning architectures, non-negative matrix factorization, and structural analysis of stochastic block models across multiple graphs. He develops algorithms combining proximal optimization with privacy-preserving techniques, addressing challenges in distributed data analysis. Publications demonstrate interdisciplinary applications in network science, information theory, and computational geometry. Key contributions include novel frameworks for Ollivier-Ricci curvature in hypergraphs and sequential false discovery control for pattern mining.

• Machine Learning
• Data Mining
• Graph Theory
• +۳ مورد دیگر

Tianyi Zhou is a doctoral student and researcher at the Division of Theoretical Computer Science , KTH Royal Institute of Technology, Sweden. Supervised by Professor Aristides Gionis, his work is funded by the ERC project REBOUND . He is also affiliated with the Wallenberg AI, Autonomous Systems and Software Program (WASP). Research Interests Graph data mining Recent Article Trends His recent publications explore opinion dynamics through timeline algorithms and low-rank updates (2024), as well as heterogeneous information networks in social coding (2021). These works bridge graph theory, social network analysis, and computational modeling. Teaching Roles Assistant for Advanced Algorithms (DD2440) Assistant for Introduction to the Philosophy of Science and Research Methodology for Computer Scientists (DA2210) Assistant for Machine Learning (DD2421) Assistant for Software Engineering Fundamentals (DD2480) Grants Research supported by the ERC project REBOUND .

• Graph Data Mining

Sean Rudd is a Senior Researcher at Karolinska Institutet's Department of Oncology-Pathology, affiliated with the Nucleotide Metabolism and Molecular Therapeutics research group at SciLifeLab. His work focuses on molecular pharmacology and cancer therapy optimization through nucleotide metabolism studies.

• Nucleotide Metabolism
• Molecular Pharmacology
• Cancer Therapeutics
• +۴ مورد دیگر

Annica Gad is a Lecturer affiliated with the Department of Oncology-Pathology at Karolinska Institutet. She works within Katalin Dobra's research group focused on molecular pathology of the lung and pleura, combining biophysics and molecular biology to study cancer progression. Doctor of Philosophy (2005), Department of Laboratory Medicine, Karolinska Institutet Docent (2024), Karolinska Institutet Her research spans biophysics, cell biology, and oncology, focusing on cytoskeletal dynamics, vimentin regulation, oncogene effects on cell mechanics, and HDAC6 inhibition in cancer. She develops algorithms for filament network tracing and employs nanoscopy for subcellular analysis. Recent publications highlight her work on vimentin-targeting drugs, fibroblast migration, extracellular matrix mechanics, and cytoskeletal remodeling in cancer. Articles span high-impact journals in oncology, molecular biology, and biomedical engineering. Employment: Affiliated to Teaching/Tutoring (2024-2027), Lecturer (2025) Lab: Molecular pathology of the lung and pleura – Katalin Dobra's Group Research Themes: Cytoskeleton, mechanobiology, cancer metastasis, HDAC6, Rho GTPases

• Biophysics
• Cell and Molecular Biology
• Medical Biotechnology
• +۳ مورد دیگر

Pierfrancesco Pagella is an Associate Professor (Docent) at Linköping University , affiliated with the Department of Physics, Chemistry and Biology (IFM) and the Biophysics and Bioengineering (BBIOBIO) division. His research focuses on unraveling mechanisms of organ and cell diversity to engineer 3D human organs with functional innervation, vascularization, and immune compartments, particularly in the oro/craniofacial complex (e.g., teeth, oral cancers). PhD in Molecular Life Sciences (2011–2015), University of Zurich/ETH Zurich Postdoctoral Fellow at Linköping University (2021–2023) and University of Zurich (2015–2017) His work integrates stem cell biology , tissue engineering , organ-on-chip systems , and genomics/proteomics to model developmental processes and diseases. Key contributions include a Tooth-On-Chip microfluidic device, studies on Wnt/β-catenin signaling in transcriptional regulation, and the role of Nogo-A in tooth enamel formation. Recent publications explore single-cell RNA sequencing in dental pulp remodeling, TBX3-Wnt interactions in colorectal cancer metastasis, and Notch signaling in tooth lineage specification. His research also develops CUT&RUN protocols for studying transcriptional co-factors and investigates tumor suppressor p53 in carcinoma regulation. He teaches Biotechnology Manufacturing , Production of Biopharmaceuticals , and Design of Biotechnical Processes courses. His laboratory affiliations include the Biophysics and Bioengineering (BBIOBIO) and Molecular Materials (BBMOLMAT) groups at Linköping University, focusing on multidisciplinary biomedical applications.

• Bioengineering
• Stem Cell Biology
• Tissue Engineering
• +۶ مورد دیگر

Sofia Eriksson is an Associate Professor in the Department of Mathematics within the Faculty of Technology at Linnaeus University. She specializes in numerical analysis and scientific computing, with a primary focus on developing finite difference methods for solving time-dependent partial differential equations. Her research has significant applications in fluid dynamics and aerodynamics. Dr. Eriksson's research interests span numerical analysis, scientific computing, finite difference methods, partial differential equations, and computational mathematics. She leads or participates in two major research groups: Computational Mathematics for Predictive Digital Twins (PreDiTwin) and Scientific Computing and Partial Differential Equations. Her work emphasizes developing stable numerical methods with proper boundary and interface treatments. Her publication record shows consistent output since 2007, with 15 recent publications spanning journals like Journal of Computational Physics, SIAM Journal on Numerical Analysis, and Foundations of Computational Mathematics. Her research shows a clear progression from foundational work on finite difference methods to more specialized applications in fluid dynamics and aerodynamics. The publications demonstrate expertise in developing stable numerical schemes, particularly for time-dependent problems with complex boundary conditions. Dr. Eriksson teaches courses in Numerical Methods, Linear Algebra, and Calculus, contributing to both undergraduate and graduate education in mathematics and computational science. Her teaching reflects her research expertise, providing students with practical knowledge of numerical methods applicable to real-world scientific and engineering problems.

• Numerical Analysis
• Scientific Computing
• Finite Difference Methods
• +۴ مورد دیگر

Peter Lind is an Associate Professor at the Department of Molecular Biology, Umeå University, Sweden. He leads the Lind Lab, focusing on experimental evolution and mathematical modeling of bacterial biofilm formation and antibiotic resistance, particularly in Pseudomonas and Acinetobacter species. His work bridges evolutionary biology with clinical applications to address antimicrobial resistance challenges. Contact: Email peter.lind@umu.se , Phone +46 90 786 89 80 Location: 6K/6L, Hospital Area, Umeå University Research Interests: The group studies evolutionary pathways in bacteria under stressors like antibiotics, aiming to predict and redirect evolution toward dead ends. Key areas include: Biofilm dynamics and extracellular matrix formation Antibiotic resistance mechanisms and multi-resistance evolution Mathematical modeling of molecular networks Clinical translation for infection biology and drug development Publication Trends: Over the past five years, his work spans hyperbaric oxygen therapy applications (for ARDS, long COVID, and ulcerative colitis), diving medicine (barotrauma, pulmonary edema), and advanced imaging techniques (PET, MRI, Doppler ultrasound). The research emphasizes interdisciplinary approaches combining clinical practice, computational modeling, and experimental evolution. Labs & Collaborations: Lind Lab collaborates with clinical teams at Umeå University Hospital, contributes to the UCMR program as education director, and develops open-source tools for diving-related medical diagnostics. Their work informs antibiotic stewardship and evolutionary prediction frameworks.

• Biofilm Formation
• Antibiotic Resistance
• Experimental Evolution
• +۳ مورد دیگر