جست‌وجوی استاد و پژوهشگر

Joakim Andén-Panteraمشاهده پروفایل

دانشیار

• Signal Processing
• Statistical Data Analysis
• Machine Learning
• +۶ مورد دیگر

Joakim Andén-Pantera is an Associate Professor in the Division of Probability, Mathematical Physics and Statistics at the Department of Mathematics, KTH Royal Institute of Technology. His research spans signal processing, statistical data analysis, and machine learning with applications in cryo-electron microscopy, biomedical signal analysis, and audio classification. His educational background includes advanced training in mathematics and signal processing leading to his current academic position. Though specific degree details aren't provided in the text, his research profile indicates deep expertise in mathematical methods for signal representation. Professor Andén-Pantera's work focuses on developing mathematical frameworks that extract discriminative information from signals while remaining invariant to irrelevant variations like translation, frequency-shifting, and noise. His research bridges theoretical mathematics with practical applications across multiple domains: Developing wavelet scattering transforms for robust signal representation Applying these techniques to cryo-EM for molecular structure analysis Creating methods for audio and music classification through time-frequency analysis Designing algorithms for biomedical signal processing, particularly ECG analysis Contributing to computational methods for cosmological parameter estimation His publication record shows consistent contributions in both theoretical signal processing and practical implementations. The research trajectory demonstrates increasing sophistication in applying scattering transforms and deep learning to diverse signal processing challenges across biology, medicine, and acoustics. Notable scientific recognition includes: Best Paper Award (2nd Place) at IEEE International Workshop on Machine Learning for Signal Processing (2015) Best Paper Award (1st Place) at International Conference on Digital Audio Effects (2012) Best Paper Award at IPDPS for cuFINUFFT implementation Professor Andén-Pantera advises graduate students on degree projects in financial mathematics, mathematical statistics, and engineering mathematics. His research group develops computational tools including ASPIRE for cryo-EM and Kymatio for wavelet scattering transforms. He teaches courses in Applied Statistics, Probability Theory, and Statistical Learning at KTH. He leads the development of several influential open-source software projects that have become standard tools in their respective fields, with Kymatio particularly gaining widespread adoption across multiple research communities.

• Timber Engineering
• Wood Mechanics
• Structural Engineering
• +۵ مورد دیگر

Thomas K Bader is Professor in Building Technology and Head of the Department of Building Technology at Linnaeus University's Faculty of Technology. His academic career spans from doctoral research at Vienna University of Technology to his current leadership position, with expertise bridging theoretical mechanics and practical timber engineering applications. Professor in Building Technology since July 2019 Associate Professor in Building Technology (specialized in Structural Engineering) since September 2016 Habilitation in Mechanics of Materials and Structures from TU Wien Professor Bader's research focuses on the mechanical behavior of timber structures, with particular emphasis on wood mechanics across multiple length scales. His work integrates experimental characterization with numerical simulation to advance understanding of timber as a structural material. Key research areas include dowel-type connections in timber structures, cross-laminated timber systems, and the mechanical properties of wood under various loading conditions including compression perpendicular to grain and rolling shear. His publication record demonstrates consistent research productivity with numerous high-impact publications in leading engineering journals. The research trajectory shows increasing focus on sustainable timber construction, with recent work examining the relationship between structural engineering design and carbon footprint in multi-story CLT buildings. His research has evolved from fundamental wood mechanics to comprehensive structural systems analysis. Professor Bader leads multiple research groups including Composite Elements and Hybrid Structures, Connections in Timber Structures, Cross-Laminated Timber research, and the Linnaeus University Centre for Competitive Timber Structures. His current research projects address resource efficiency in timber construction, competitive advantages of CLT systems, and moisture-dependent properties of South Swedish hardwoods. As an educator, Professor Bader teaches advanced structural engineering courses, particularly Conceptual Structural Design in the Master's program in Sustainable Structural Engineering. He has supervised numerous bachelor's, master's, and doctoral students, with a teaching philosophy focused on increasing engineers' knowledge of timber as a structural material to fully exploit its potential in construction applications.

• Distributed Machine Learning
• Federated Learning
• Decentralized Optimization
• +۳ مورد دیگر

Hao Chen is a researcher at KTH Royal Institute of Technology, affiliated with the School of Electrical Engineering and Computer Science (EECS), specifically within the Computer Science department focusing on Communication Systems and the Optical Network Laboratory (ON Lab). He also contributes to research in Intelligent systems and Information Science and Engineering. Chen completed his doctoral dissertation titled 'Reliable and Efficient Distributed Machine Learning' in 2022, establishing himself as an emerging expert in distributed machine learning systems. Chen's research interests center around distributed and federated machine learning architectures, with particular emphasis on optimizing communication efficiency in decentralized systems. His work addresses critical challenges in distributed learning including communication bottlenecks, straggler nodes (devices with slow responses), and privacy preservation. His research spans applications in wireless IoT networks, satellite communications, and edge computing environments. His publication record shows a clear trajectory of increasingly sophisticated approaches to distributed machine learning. Starting with foundational work on coded stochastic ADMM methods in 2021, he progressed to developing asynchronous parallel algorithms (2023) and exploring applications in specialized domains like speech fatigue recognition (2024). A consistent theme across his work is the optimization of communication resources while maintaining learning performance, with particular attention to real-world constraints in wireless and satellite networks. Chen has collaborated extensively with researchers including Ming Xiao, Mikael Skoglund, Yu Ye, and others across multiple publications. His research has been supported by funding sources including the EU Horizon 2020 program (grant 825272) and the Swedish Foundation for Strategic Research (APR20-0023). His work appears in high-impact journals including IEEE Transactions on Big Data, IEEE Internet of Things Journal, and IEEE Wireless Communications. As a researcher at KTH, Chen contributes to cutting-edge work at the intersection of machine learning and communication systems, developing techniques that enable efficient distributed intelligence across networked devices while addressing practical constraints of real-world deployment.

• Computer Science
• Concurrency
• Programming Languages
• +۷ مورد دیگر

Konstantinos Sagonas is a Senior Lecturer/Associate Professor at Uppsala University's Department of Information Technology. His work bridges theoretical and practical aspects of computer science, focusing on concurrency, formal verification, and functional programming in Erlang. Despite his self-identification as a "terrible e-mail responder," he encourages phone contact for direct communication. University: Uppsala University Department: Department of Information Technology Academic Rank: Senior Lecturer/Associate Professor His research spans from stateless model checking and dynamic partial order reduction for concurrent systems to static analysis and type systems in functional programming. Recent work explores IoT protocol testing via fuzzing and symbolic execution, fine-grain memory coherence for security, and automated detection of state machine bugs in network protocols. Key publication trends include formal methods (2024: "Testing IoT Protocol Requirements"; 2023: "Tailoring Stateless Model Checking for Event-Driven Programs") and concurrent data structures (2021: "Lock-free Contention Adapting Search Trees"). Earlier contributions focus on Erlang optimization (2002-2018) and logic programming tabling (1999-2006).

• Quantum Computing
• Theoretical Physics
• Quantum Control
• +۳ مورد دیگر

Jorge Fernandez Pendas is a Researcher at the Applied Quantum Physics Division , Chalmers University of Technology , Sweden. He is affiliated with the Quantum Computer Theory Group at the Wallenberg Centre for Quantum Technology and participates in the European consortium OpenSuperQ for open superconducting quantum computers. His research focuses on modeling and optimal control of superconducting qubits , with expertise in quantum anomalies , quantum transport phenomena , and topological matter . He received his PhD in Theoretical Physics from the Universidad Autónoma de Madrid in 2019. Quantum Computing Theoretical Physics Quantum Control Topological Materials Quantum Transport Phenomena Computational Physics His recent work includes optimizing superconducting qubit gates , mitigating frequency collisions , and analyzing quantum dissipation effects . He collaborates with institutions like Volvo Group , Lund University , and RISE Research Institutes of Sweden .

Sandro Stuckiمشاهده پروفایل

مدرس

• Programming Languages
• Formal Methods
• Type Systems
• +۴ مورد دیگر

Sandro Stucki is a Lecturer in the Department of Computer Science and Engineering at Chalmers University of Technology. He has previously worked as an applied scientist at Amazon and as a postdoctoral researcher at Chalmers University of Technology and the University of Gothenburg. He completed his doctoral studies at the Programming Methods Laboratory (LAMP) at EPFL under the supervision of Professor Martin Odersky. His research focuses on programming languages, with particular interest in formal methods, type systems and theory, and the semantics and implementation of domain-specific languages. He applies formal methods and type theory to problems in privacy and security, investigates type safety of Scala and related type systems, and develops type soundness proofs using Agda. His work also includes designing domain-specific languages for modeling probabilistic and stochastic systems, especially biochemical systems. He has contributed to the Scala ecosystem by developing a GNU/Emacs mode for Kappa, a modeling language for systems biology. His recent publications demonstrate strong expertise across programming language theory, formal verification, privacy-preserving systems, and applications in systems biology. His work bridges theoretical foundations with practical applications, particularly in the Scala programming language ecosystem. Stucki is actively involved in academic service, having served on program committees for numerous conferences including ECAI, GPCE, NWPT, and as a steering committee member for the Scala Symposium series. He has also organized several academic events, including the Scala Symposium 2016. He currently teaches courses on Data Science and AI, Fundamentals of Program Development, and Neuro-symbolic AI at Chalmers/Gothenburg University, and has previously taught courses on Parallel Functional Programming, Principles of Concurrent Programming, and Types and Programming Languages.

• Control Systems
• Signal Processing
• Sensor Fusion
• +۷ مورد دیگر

Fredrik Gunnarsson is an Adjunct Professor at Linköping University's Department of Electrical Engineering (ISY), specializing in Automatic Control (RT). His research focuses on control systems, signal processing, and sensor fusion, with applications in positioning, navigation, and optimization for autonomous systems. University: Linköping University Department: Department of Electrical Engineering (ISY) Academic Rank: Adjunct Professor Research Interests Fredrik's work spans advanced signal processing, sensor fusion, and control algorithms. He contributes to 5G positioning, Gaussian process state-space models, and indoor localization. His research intersects with optimization-based control for autonomous systems and machine learning techniques in dynamic environments. Article Trends His recent publications emphasize wireless positioning, statistical filtering, and sensor fusion methodologies. Topics include Kalman filtering, Gaussian process modeling, particle methods for indoor navigation, and optimization algorithms for control systems. Keywords span Control Systems , Signal Processing , Machine Learning , and Wireless Communication . Labs and Collaborations LINK-SIC - Linköping Center for Sensor Informatics and Control Automotive Systems research SEDDIT competence center

Filip Strömbäckمشاهده پروفایل

دانشیار

• Computer Science Education
• Software Engineering
• Concurrent Programming
• +۳ مورد دیگر

Filip Strömbäck is an Associate Professor at Linköping University's Department of Computer and Information Science (IDA), part of the Software and Systems (SAS) division. His research focuses on computer science education, software engineering, and dynamic software systems. His work bridges pedagogical frameworks and technical systems, emphasizing teaching methodologies in programming and software maintenance. Key contributions include studies on abstraction skills development, human-machine teaching dynamics, and TA insights into complex CS concepts. Collaborating with colleagues like Daniel Varro and Pontus Haglund, his articles explore topics ranging from dynamic software updates to concurrent programming education. He contributes to the Graduate School in Computer Science (CUGS) and the SAS division’s research on software engineering and industry collaboration.

Björn Wallnerمشاهده پروفایل

استاد

• Computational Structural Bioinformatics
• Protein Structure Prediction
• Molecular Modelling
• +۳ مورد دیگر

Björn Wallner is a Professor and Head of Division at the Department of Physics, Chemistry and Biology (IFM) at Linköping University. His research focuses on computational structural bioinformatics, protein structure prediction, and dynamics, leveraging AI and machine learning. He is renowned for pioneering multiple sequence embeddings foundational to AlphaFold and contributions to the Rosetta software suite. His work integrates experimental data with computational methods to study proteins like MexR (antibiotic resistance regulator) and the disordered Myc protein-Pin1 interaction. Key innovations include error prediction tools for structural models and advancements in AlphaFold for large protein complexes. Research interests span protein modeling, AI-driven structural biology, and applications in drug discovery. Notable contributions include AFsample2 for ensemble predictions, MassiveFold for large protein structures, and integrating experimental constraints into AlphaFold. His methods have improved molecular replacement in crystallography and protein-protein docking. Wallner collaborates with SciLifeLab Linköping, advancing data-driven life science research. His recent work highlights advancements in multimeric complex modeling, structural variation analysis, and the mechanistic understanding of proteins involved in cancer and antibiotic resistance. These contributions underscore the intersection of computational methods and experimental biology in advancing structural biology's frontiers.

Marian Codreanuمشاهده پروفایل

دانشیار

• Wireless Networking
• Signal Processing
• Machine Learning
• +۳ مورد دیگر

Marian Codreanu is a Senior Associate Professor at Linköping University's Department of Science and Technology (ITN), within the Communications and Transport Systems (KTS) division. He holds a M.Sc. from the University Politehnica of Bucharest (1998) and a Ph.D. from the University of Oulu, Finland (2007), where his thesis was recognized as the best in technical sciences. He has held roles including Marie Skłodowska-Curie Fellow (2019) and Academy of Finland Research Fellow (2013). His research focuses on information freshness optimization, sparse signal processing, and machine learning applications in wireless networking. Dr. Codreanu’s work spans wireless communications, statistical signal processing, and mathematical optimization. He co-founded the Nordic Workshop on System and Network Optimization for Wireless (SNOW) and served as IEEE Vice Chair (2011–2018). His contributions include over 100 journal/conference papers and co-authored books like Compressed Sensing with Applications in Wireless Networks (2019) and Weighted Sum-Rate Maximization in Wireless Networks (2012). His research group, Wireless Optimization and Analytics, develops models and algorithms for wireless network performance and data analytics. Key projects include B-PREPARED (disaster preparedness) and EAN (electric aircraft networks). His work integrates theoretical advancements with practical applications in smart cities, logistics, and air traffic management.

• Theoretical Physics
• Surface Science
• Heterogeneous Catalysis
• +۳ مورد دیگر

Anders Hellman is a Professor at the Department of Physics, Chalmers University of Technology, Sweden. He is also affiliated with the Competence Centre for Catalysis and serves as a senior strategic advisor to the Area of Advance (AoA) Energy at Chalmers. His research focuses on theoretical physics, surface science, heterogeneous catalysis, and materials for energy applications. He holds a PhD in theoretical physics from the University of Gothenburg (2003), followed by postdoctoral studies at Haldor Topsoe A/S, DTU Denmark, and Chalmers. His research interests include computational modeling of catalytic processes, energy materials design, and first-principles analysis of reaction mechanisms. Key areas of exploration involve chemical-looping combustion, catalyst screening for ethylene epoxidation, and the development of novel oxygen carriers for energy systems. He has contributed to advancements in understanding surface phenomena, nanoparticle behavior, and the interplay between material properties and catalytic performance. Dr. Hellman’s work bridges theory and experiment, with notable contributions to methane-to-methanol conversion, hydrogen evolution reactions, and photoelectrochemical systems for water splitting. His interdisciplinary approach integrates quantum chemistry, kinetic modeling, and materials discovery to address challenges in sustainable energy and catalytic innovation.

Daniel Jönssonمشاهده پروفایل

دانشیار

• Visualization
• Machine Learning
• Medical Imaging
• +۳ مورد دیگر

Associate Professor Daniel Jönsson works at the intersection of visualization and machine learning at Linköping University's Department of Science and Technology (ITN) and Media and Information Technology (MIT) division. He leads research in transforming complex datasets into intuitive visual representations, with applications in medical imaging and neuroscience. As a core developer of the Inviwo visualization software, he emphasizes interdisciplinary collaboration across fields like AI and radiology. His work includes developing tools like VisualNeuro for brain cohort studies and exploring neural network interpretability. Research focuses on visualization techniques for high-dimensional data, including fMRI analysis and neural network exploration. Notable achievements include an honorable mention at the IEEE VIS conference (2016) for brain imaging visualization work. Collaborations with CMIV (Center for Medical Image Science and Visualization) highlight his commitment to clinical and medical research applications. Publications emphasize combining visualization with machine learning, addressing challenges in data readiness for AI projects and improving neural network analysis. His contributions span software development, algorithm design, and cross-disciplinary methodological advancements in data-driven decision-making.

Andreas Tillmarمشاهده پروفایل

دانشیار مدعو

• Forensic Genetics
• DNA-Based Genealogy
• Population Genetics
• +۵ مورد دیگر

Andreas Tillmar is an Adjunct Associate Professor and Docent at Linköping University, affiliated with the Department of Biomedical and Clinical Sciences (BKV) and the Faculty of Medicine and Health Sciences (MEDFAK). He is part of the Strategic Research Area in Forensic Sciences, a collaboration between Linköping University and the National Board of Forensic Medicine in Sweden. His research focuses on advancing DNA methods for forensic applications, including distant kinship analysis, identification of human remains, and predicting phenotypic traits from DNA. Key achievements include developing the FORCE method for sensitive DNA analysis and solving the 2004 Linköping double murder case using forensic genetic genealogy. Research Interests Enhanced DNA Analyses (e.g., FORCE method) Forensic Investigative Genetic Genealogy (FIGG) Population Genetics (e.g., Swedish and northern Swedish populations) Facial Prediction from DNA Media & Outreach Featured in SVT and Universitetsläraren for contributions to forensic DNA techniques and solving high-profile cases. Project on predicting facial images from DNA, supported by the Strategic Research Area. Grants & Collaborations Active in interdisciplinary projects, including international collaborations for mass victim identification and forensic method development. His work bridges academic research and practical crime-solving applications. Labs/Teams Associated with the Division of Molecular Medicine and Virology (MMV) and the Department of Biomedical and Clinical Sciences (BKV) at Linköping University.

Graham Hillمشاهده پروفایل

پژوهشگر

• Geophysics
• Volcanology
• Magmatic Systems
• +۴ مورد دیگر

Dr. Graham Hill is a Senior Research Scientist and leads the Electromagnetics Research Group at the Institute of Geophysics, Czech Academy of Sciences. His expertise lies in electromagnetic induction theory, inverse theory, and the application of magnetotelluric (MT) methods to study magmatic systems, volcanism, and geothermal systems. He has pioneered MT applications in Antarctic tectonics and volcanic monitoring. Current research focuses on temporal MT for volcano and geothermal monitoring, and the mechanisms of andesite melt generation. Education: Ph.D. in Geophysics (not specified further in text). Awards: New Zealand Geophysics Prize, Antarctic Science Medal, Lumina Quaeruntur Fellow. Research Interests: Hill's work bridges geophysics and volcanology, emphasizing: Electromagnetic imaging of magmatic systems Volcanic hazard assessment via MT Crustal-scale tectonic processes in Antarctica and New Zealand Geothermal reservoir characterization Publications: Over 10 significant contributions since 2009, including groundbreaking work on Tongariro eruption mechanics and Antarctic crustal uplift. His 2020 review in Surveys in Geophysics highlights polar geophysical imaging advancements. Advising & Grants: Leads large-scale MT projects (e.g., Antarctic studies with University of Utah). Collaborates internationally on volcano monitoring and subduction zone dynamics. Labs/Teams: Electromagnetics Research Group at the Institute of Geophysics, Czech Academy of Sciences.

Per Anderssonمشاهده پروفایل

مدرس ارشد

• Embedded Systems
• Code Generation
• Optimization
• +۷ مورد دیگر

Per Andersson is a Senior Lecturer at the Department of Computer Science, Lund University. He serves as Director of first and second cycle studies and holds additional roles at ELLIIT (Linköping-Lund initiative on IT and mobile communication) and the Parallel Systems group. His research focuses on embedded systems, code generation, optimization, and reconfigurable hardware design. He contributes to UN Sustainable Development Goals through technology advancements. Professional roles include leadership in academic program administration and participation in major initiatives like the EASE project (Embedded Applications Software Engineering, 2008-2018). His work bridges computer science and education policy, particularly in recognition of prior learning (RPL) and higher education accreditation processes. Research interests span technical domains such as parallel architectures, reconfigurable systems, and medical informatics, alongside educational topics like lifelong learning systems. His 20+ publications reflect interdisciplinary engagement, with notable contributions in IEEE conferences and journals like Haematologica and International Journal of Lifelong Education. He collaborates extensively with industry and academia, evidenced by participation in projects involving software engineering, radio standard integration, and clinical trial analyses. His work emphasizes practical applications of theoretical advancements in both technical and educational fields.