جستجوی استاد راهنمای دانشگاهی

• Electromagnetic Structured Media
• Metamaterials
• Plasmonics
• +۴ مورد دیگر

Philippe Tassin is a Professor of Physics at Chalmers University, specializing in electromagnetic structured media and computational electrodynamics. He teaches optics, quantum mechanics, and computer science courses, earning recognition through the Golden Chalk award and Chalmers' Pedagogical Prize. M.Sc. and Ph.D. (summa cum laude) from Free University of Brussels Postdoctoral work at Iowa State University and Ames Laboratory (US DOE national lab) His research spans metamaterials, plasmonics, and nanophotonics, with significant contributions to inverse design methodologies using machine learning. He has authored influential papers in Science , Nature Photonics , and Physical Review Letters , and frequently presents at international conferences. Recent publications highlight advancements in liquid metal composites for electromagnetic absorption, AI-driven metasurface design, and adaptive meshing for photonic simulations. His work integrates computational physics with experimental validation across multiple electromagnetic domains. KAW Fellowship Swedish Research Council Grant IEEE & SPIE Fellowships BAEF Alumni Award Frans Van Cauwelaert Award (Royal Flemish Academy) As editor of Photonics and Nanostructures , member of the Young Academy of Sweden, and vice-chair of IEEE Photonics Sweden Chapter, he actively contributes to academic leadership and public science communication.

Sonakshi Gargمشاهده پروفایل

پژوهشگر ارشد

• Privacy
• Artificial Intelligence
• Synthetic Data
• +۳ مورد دیگر

Sonakshi Garg is a Research Fellow (Postdoctoral fellow) at the Department of Computing Science, Umeå University, affiliated with the NAUSICA research group (PrivAcy-AWare traNSparent deCIsions). Her office is located at MIT House, MIT.A.420 in Umeå. Dr. Garg's research focuses on the intersection of artificial intelligence and data privacy, with specific expertise in: Privacy-preserving techniques for high-dimensional data and foundation models Synthetic data generation and distribution learning Differential privacy implementations in machine learning Federated learning security and adversarial robustness Geometric and manifold-based privacy methods Her publication portfolio demonstrates significant contributions to privacy-enhancing technologies, particularly through innovative combinations of k-anonymity with differential privacy, federated learning security frameworks, and manifold-based data protection methods. Recent works explore applications in autonomous driving security, COVID-19 forecasting, and Large Language Model privacy preservation. She collaborates with international researchers across Europe and contributes to major conferences including ESORICS, SEC, and SECRYPT. Dr. Garg is an active member of the NAUSICA research group, advancing privacy-aware decision systems through theoretical and applied research.

• Machine Learning
• Natural Language Processing
• Predictive Maintenance
• +۴ مورد دیگر

Tony Lindgren is an Associate Professor at the Department of Computer and Systems Science, Stockholm University, affiliated with the Data Science Research Group and Natural Language Processing Research Group. His work bridges data science and NLP , focusing on interpretable models, constraint programming, and predictive maintenance systems. Research interests include: Machine Learning for explainability and fairness Constraint Programming in maintenance optimization Natural Language Processing for risk analytics and troubleshooting Recent publications demonstrate trends in multi-objective optimization (2025 satellite scheduling), conformal prediction (2024 CoPAL), and fault detection (2024 Automotive Nowcasting). His work often integrates domain-specific constraints with scalable algorithms across applications like food safety and autonomous vehicles. Software tools developed by Lindgren include: Example-based Feature Tweaking Rule Indexing Frameworks His research groups focus on AI-driven decision support for high-stakes domains, combining technical innovation with societal impact considerations.

Lars-Henrik Erikssonمشاهده پروفایل

مدرس ارشد

• Formal Methods
• Logic in Computer Science
• Programming Language Theory
• +۷ مورد دیگر

Lars-Henrik Eriksson is a Senior Lecturer in the Department of Computer Science at Uppsala University, part of the Department of Information Technology. He holds a PhD and is recognized as an Excellent Teacher and educational mentor. He currently serves as the program director for the Master's program in Computer Science and has previously held leadership roles, including Head of the Department of Computer Science from 2004 to 2018. PhD in Computer Science Excellent Teacher Award Pedagogical Mentor at Uppsala University His research focuses on the applications of logic in computer science, particularly formal methods for software development. His work spans formal specification, verification, and synthesis, with strong emphasis on logic programming, interactive theorem proving, and logical frameworks. He has made significant contributions to the use of formal methods in safety-critical domains such as railway signaling. His current research includes modeling application domains within formal methods and formalizing concurrency theory using the Isabelle proof assistant. The recent publications reflect a sustained focus on modal logics for nominal transition systems, formal verification tools (e.g., GTO), and domain-specific applications of formal methods. These works demonstrate deep integration of theoretical logic and practical software engineering, especially in distributed and safety-critical systems. The recurring themes include concurrency, verification, and the use of domain models to enhance formal reasoning. Scientific Awards and Recognitions: Excellent Teacher Pedagogical Mentor Lars-Henrik Eriksson has advised numerous students through project supervision, thesis reviews, and course mentorship, though no formal list of advisees is provided. He has been involved in significant research management, including leading the department and directing a master’s program. He has also collaborated with industry, particularly in railway signaling, contributing to safety analysis and formal verification projects with Trafikverket and through companies he co-founded. He is a board member of Formal Methods Europe and served as program committee chair for the FME Symposium 2002 (part of FLoC’02). His work bridges academia and industry, especially in the application of formal methods to real-world engineering challenges.

• Internet of Things (IoT)
• Industrial IoT
• Edge Computing
• +۶ مورد دیگر

Stefan Forsström is an Associate Professor at Mid Sweden University, affiliated with the Department of Computer and Electrical Engineering (DET) and the STC Research Centre. His work focuses on distributed systems, IoT, and educational innovations for lifelong learning. Academic Rank: Associate Professor Department: Computer and Electrical Engineering (DET) Research Centres: STC Research Centre Location: Sundsvall, Sweden Research interests include IoT, edge computing, machine learning, and educational technology. He has led projects like DRIVEN, DAWN, and GRIT while exploring secure and scalable IoT architectures. Recent publications analyze trust in edge AI systems, task offloading in vehicular networks, and intrusion detection for IIoT. His work often involves real-world implementations and performance evaluations. Teaching initiatives emphasize flipped classroom models, MOOCs, and generative AI education for professionals.

• Optical Networks
• 6G Communications
• Quantum-Classical Networks
• +۷ مورد دیگر

Paolo Monti is a Professor and Head of the Optical Networks Unit at Chalmers University of Technology's Department of Communications, Antennas and Optical Networks. With extensive expertise in optical communication infrastructures, he leads research focusing on energy efficiency, network resiliency, programmability, automation, and techno-economics of optical networks. His work spans multiple international collaborations with funding from major research bodies across EU, USA, and Asia. Professor Monti's research interests center around next-generation optical networking technologies. His work explores the integration of artificial intelligence and machine learning with optical networks, quantum-classical network convergence, 6G infrastructure development, and network automation. His research addresses critical challenges in network energy consumption, reliability under failure conditions, and cost-effective deployment strategies for emerging communication technologies. The research group under his leadership develops frameworks for optical network monitoring, security, and resource optimization using advanced computational techniques. Analysis of his recent publications reveals a strong trend toward AI/ML integration with optical networking, with significant focus on quality of transmission estimation, network automation, and 6G readiness. His work increasingly combines quantum technologies with classical optical networks while addressing practical implementation challenges in multi-band elastic optical networks. The publications demonstrate a progression from theoretical network design to practical implementations with real-world validation. Professor Monti has received recognition as a Senior Member of IEEE, highlighting his contributions to the field of communications and networking. As an academic leader, Professor Monti has been involved as Principal Investigator, co-PI, and main technical leader in numerous national and international projects. His educational contributions include teaching courses at undergraduate, Master's, and PhD levels, as well as developing ICT-focused education programs. His research has been supported by major funding bodies including the European Commission, VINNOVA, and Wallenberg Centre for Quantum Technology. The Optical Networks Unit under Professor Monti's leadership operates as a vibrant research environment focusing on both theoretical and experimental aspects of next-generation optical communications. The unit maintains strong collaborations with industry partners and academic institutions worldwide, participating in multiple EU-funded projects and national initiatives focused on quantum communications and 6G infrastructure.

Pierre Nyquistمشاهده پروفایل

دانشیار

• Probability Theory
• Mathematical Statistics
• Applied Mathematics
• +۸ مورد دیگر

Pierre Nyquist is an Associate Professor and docent in the Department of Mathematical Sciences at Chalmers University of Technology and Gothenburg University. His research is sponsored by the Swedish Research Council, the Swedish e-science Research Center (SeRC), and the Wallenberg Artificial Intelligence, Autonomous Systems and Software Program (WASP). He is also an elected member of the Young Academy of Sweden for the period 2024-2029 and has served as a scientific ambassador for EURANDOM since November 2021. Dr. Nyquist's research interests lie at the intersection of probability theory, mathematical statistics, and applied mathematics. His main expertise is in probability theory, with a focus on large deviations theory and stochastic numerical methods. He has a general interest in all aspects of probability theory and much of what is categorized as applied mathematics, particularly questions related to partial differential equations, optimization, and stochastic optimal control. Recently, he has become increasingly interested in the mathematical foundations of complex data analysis and modeling, and the interplay with ideas from physics. His current research interests include large deviations, gradient flows and their generalizations, stochastic numerical methods, statistical learning theory, stochastic processes, and random dynamical systems. Pierre Nyquist has received research funding from several prestigious sources including the Swedish Research Council, the Swedish e-science Research Center (SeRC), and the Wallenberg Artificial Intelligence, Autonomous Systems and Software Program (WASP). His publications demonstrate a consistent focus on theoretical aspects of probability with applications to computational methods and data analysis, showing increasing integration with machine learning techniques in recent years. elected member of the Young Academy of Sweden (2024-2029) scientific ambassador for EURANDOM (since November 2021) Dr. Nyquist is actively involved in mentoring the next generation of researchers. He currently supervises several PhD students including Cinja Arndt (starting Aug. 2025), Niki Wilhemlson (started Aug. 2024), and Viktor Nilsson (started Aug. 2020). He has previously supervised successful PhD students such as Federica Milinanni (Aug. 2020-May 2025) and Carl Ringqvist (Aug 2015-June 2021). He regularly teaches graduate-level courses including "Modern methods of statistical learning" and has supervised numerous MSc theses on topics ranging from deep learning for time-series radar signals to neural network embedding in insurance pricing. His research group is active in both theoretical developments and practical applications, with current projects spanning from mathematical foundations of probability to applications in machine learning and data science. Dr. Nyquist maintains strong international collaborations, as evidenced by his frequent travel for conferences and research visits to institutions such as Brown University and TU Delft.

• Radio Astronomy
• Astrophysics
• Black Hole Imaging
• +۳ مورد دیگر

John Conway is a Professor of Radio Astronomy at Chalmers University of Technology , serving as Director of Onsala Space Observatory . His work spans multiple domains in observational astrophysics, focusing on: High-resolution VLBI imaging of black holes and AGN Instrumentation development for submillimeter telescopes Multiwavelength studies of M87 and Sgr A* black holes Large-scale radio surveys with LOFAR and SKA technologies As a key member of the Event Horizon Telescope collaboration, he contributes to polarization analysis and magnetic field studies around supermassive black holes. His instrumentation projects include work on the Onsala Twin Telescopes and SKA data stacking techniques. Current research involves black hole shadow characterization , jet dynamics , and machine learning applications for radio source detection. He collaborates extensively with international teams across projects like ALMA, LOFAR, and APEX.

Claes Lundströmمشاهده پروفایل

استاد مدعو

• Medical Imaging
• Artificial Intelligence
• Digital Pathology
• +۴ مورد دیگر

Claes Lundström is an Adjunct Professor at Linköping University's Department of Science and Technology (ITN), affiliated with the Media and Information Technology (MIT) school. His primary research focuses on medical imaging, integrating machine learning, visualization, and human-computer interaction in clinical settings. He leads the technical side of digital pathology research at the Center for Medical Image Science and Visualization (CMIV), directing national-scale grants and serving as Arena Director for the Analytic Imaging Diagnostics Arena (AIDA). Since 2010, he has held dual roles as Research Director at Sectra AB and academic researcher, driving innovations that translate into commercial healthcare solutions. Lundström's work spans AI-driven diagnostics, uncertainty visualization, and precision orthopedics, with contributions recognized through leadership in major projects like the €70M BIGPICTURE initiative. His academic credentials include a PhD (2007) and Docent degree (2014), alongside extensive industry collaboration. Education: PhD in 2007 and Docent degree in 2014 from Linköping University. PhD: 2007 Docent: 2014 Research interests emphasize AI integration in diagnostics, particularly digital pathology and medical imaging workflows. His work addresses challenges like domain adaptation in AI models, uncertainty quantification in segmentation tasks, and interactive visualization tools for clinicians. Recent projects include the VAI-B platform for AI validation in breast imaging and the BigPICTURE collaboration for global pathology data sharing. Key grants include leadership roles in national-scale projects, such as the AIDA arena and SCAPIS study-based AI lab. His contributions to AI ethics and clinical adoption include studies on human-AI collaboration and quality assurance frameworks. Lundström also oversees labs like CMIV and AIDA, fostering cross-disciplinary research in medical imaging and AI.

Ehsan Doostmohammadiمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Natural Language Processing (NLP)
• Language Models
• +۵ مورد دیگر

Ehsan Doostmohammadi is a Researcher at the Artificial Intelligence and Integrated Computing Systems (AIICS) department of Linköping University . His work focuses on Natural Language Processing (NLP) and Language Model Optimization , with particular interest in Retrieval-Augmented Models , Multimodal Learning , and Low-Resource Language Processing for Persian and Swedish. Research Interests : Retrieval-Augmented Language Models Swedish Language Processing Persian Language Technology Medical Text Analysis Cross-lingual Transfer Learning Publications : 15 recent articles spanning 2018–2025 Key themes: AI , NLP , Language Model Efficiency

• Signal Processing
• Bayesian Statistics
• Deep Learning
• +۴ مورد دیگر

Lars Hammarstrand is an Associate Professor at Chalmers University of Technology specializing in the Signal Processing research group. His work integrates model-based Bayesian statistics with deep machine learning for applications in visual localization, sensor fusion, and autonomous systems, with emphasis on robustness in real-world environments. His research focuses on bridging Bayesian inference and deep learning to solve challenges in autonomous vehicle perception. Key areas include visual localization under appearance changes, radar-camera sensor fusion, and out-of-distribution detection for safety-critical systems. Recent work explores neural radiance fields for radar, semi-supervised learning for mapping, and probabilistic hierarchical classification to address real-world uncertainties in autonomous driving. Analysis of his 2020-2025 publications reveals a trajectory toward unifying geometric and semantic understanding in autonomous systems. His work demonstrates increasing integration of neural radiance fields with traditional filtering techniques, while advancing open-set recognition capabilities. Notable contributions include road geometry estimation frameworks, extended object tracking with PHD filters, and methods to mitigate data leakage in localization benchmarks. No scientific awards were mentioned in the provided materials. Hammarstrand has contributed to academic supervision methodology through his publication on improving master's thesis supervision efficiency, though specific student names are not listed. The provided information contains no details about research grants or funding sources. He operates within Chalmers University's Signal Processing research group, which develops advanced algorithms for automotive perception systems, focusing on sensor fusion techniques that combine radar, camera, and motion data for robust environmental understanding in autonomous vehicles.

• Robotics
• Autonomous Systems
• Spatial Cognition
• +۶ مورد دیگر

Patric Jensfelt is a full-time Professor at the Robotics, Perception and Learning (RPL) division within the School of Electrical Engineering and Computer Science (EECS) at KTH Royal Institute of Technology . He contributes to robotics and autonomous systems through research, teaching, and industry collaboration. Academic Rank: Professor Department: Robotics, Perception and Learning (RPL) School: School of Electrical Engineering and Computer Science (EECS) University: KTH Royal Institute of Technology His research focuses on robotics and autonomous systems , particularly spatial cognition for enabling robots to understand and model their environments. He integrates sensor data with high-level symbolic reasoning to advance navigation, mapping, and system integration. Recent work includes applications in service robotics, 3D foot scanning (via the company Volumental ), and drone-based perception. The 15 most recent articles highlight advancements in scene flow estimation, autonomous driving, LiDAR processing, and domain adaptation. They emphasize self-supervised learning , dynamic environment modeling , and robust spatial representations for autonomous systems. Patric supervises Master’s and PhD students, particularly in project courses like DD2410 Introduction to Robotics , DD2419 Project Course in Robotics and Autonomous Systems , and the doctoral course FDD3356 System Integration for Robotics . He is actively involved in the WASP Graduate School , teaching Autonomous Systems annually. He leads the Robotics, Perception and Learning (RPL) division, which fosters innovation in robotics through experimental projects and industry partnerships like Intelligent Machines . The division supports internships (unpaid) and encourages applicants to demonstrate prior engagement with his research.

• Machine Learning Theory
• Statistical Physics
• Neuroscience
• +۵ مورد دیگر

Stefano Sarao Mannelli is a tenure-track Assistant Professor in the Department of Computer Science and Engineering at Chalmers University of Technology and University of Gothenburg. He also holds a Visiting Lecturer position at the University of the Witwatersrand. His research group focuses on fundamental aspects of learning in biological and artificial systems, with emphasis on bias generation, optimization dynamics, and comparative neuroscience. Education: Ph.D. in Theoretical Physics, Université Paris-Saclay (2020) M.Sc. in Electronic Engineering, Politecnico di Torino (2017) M.Sc. in Physics of Complex Systems, Politecnico di Torino/SISSA (2016) M2 in Physique Théorique, Paris Diderot/UPMC/ENS Cachan (2016) B.Sc. in Mathematics for Engineering, Politecnico di Torino (2014) Research: Dr. Mannelli develops model-based approaches to reduce complex machine learning problems into analytically tractable frameworks. His core interests include: 1) Bias amplification mechanisms in AI systems, 2) Learning differences between biological and artificial neural networks (continual/transfer/curriculum learning), and 3) Optimization in high-dimensional landscapes. His work bridges statistical physics, neuroscience, and deep learning theory. Publication Trends: Recent articles (2024-2025) predominantly analyze curriculum learning dynamics, bias propagation in optimization, and theoretical comparisons between biological and artificial learning systems. Methodologically, they combine statistical physics frameworks with control theory and high-dimensional analysis. Awards: Academic Grant (CM Lerici Foundation, 2025) Travel Grants (Guarantor of Brains, G-Research 2024) UK–IT Trustworthy AI Exchange Programme (Alan Turing Institute, 2023) SCGB Conference Award (Simons Foundation, 2023) Ph.D. Scholarship (CEA, 2017-2020) Team & Funding: Leads a research group with 2 PhD students and 1 postdoc. Secured significant funding for international workshops including Analytical Connectionism (£42K, 2023; $152K, 2024) and High-Dimensional Methods (135,500 SEK, 2025).

• Machine Learning
• Health Informatics
• Disease Surveillance
• +۳ مورد دیگر

Atiye Sadat Hashemi is a Research Fellow at the Academy of Information Technology, Halmstad University. Her work bridges machine learning and healthcare innovation, with a focus on developing secure and interpretable AI models for medical applications. She maintains an active research profile through publications and collaborative projects in data-driven healthcare solutions. Research Focus: Her expertise spans machine learning applications in disease surveillance, precision medicine, and adversarial robustness. Key areas include: Optimizing ML for real-time disease outbreak detection using anomaly detection Advancing personalized treatment strategies through data-driven models Enhancing model security against adversarial attacks in critical domains Developing privacy-preserving synthetic health data generation techniques Publication Trends (2022-2024): Her recent works demonstrate a consistent focus on healthcare-AI integration, with emphasis on: Anomaly detection systems for epidemiological monitoring Privacy-enhancing technologies for medical data Explainable AI methods for clinical decision support Robustness improvements for safety-critical ML applications Methodologies frequently involve generative adversarial networks, graph neural networks, and time-series analysis.

Yuan Yaoمشاهده پروفایل

استادیار

• Computer Architecture
• Parallel Computing
• Power and Thermal Management
• +۷ مورد دیگر

Yuan Yao serves as an Assistant Professor in the Department of Information Technology at Uppsala University, Sweden. His academic role spans teaching and research within the Computer Systems division, focusing on cutting-edge computer architecture and parallel computing systems. He maintains active collaborations across international institutions, particularly in energy-efficient hardware design and emerging computing paradigms. His educational journey includes: B.S. in Micro-electronics from Northwestern Polytechnical University, China (2009) M.S. in System-on-Chip Design from KTH Royal Institute of Technology, Sweden (2014) Ph.D. in Electrical Engineering and Computer Science from KTH Royal Institute of Technology (2019) Yao's research centers on power and thermal management for chip multi-processors, Network-on-Chips (NoCs), and GPUs. He pioneers hardware/software co-design for high-performance computing, coherency mechanisms for emerging memory technologies, and performance analysis of on-chip networks. Recent work expands into neural network acceleration and battery-less Internet of Things architectures, reflecting a trajectory toward energy-constrained specialized systems. His methodology integrates formal modeling with practical implementation for real-world impact. Publication trends reveal consistent innovation in energy efficiency across parallel architectures. From foundational DVFS techniques for NoCs (2016-2018) to recent breakthroughs in battery-less IoT (2023-2024), his work demonstrates evolutionary progression toward novel computing domains. Key thematic threads include thermal-aware optimization, memory consistency protocols, and hardware acceleration for AI workloads, with applications spanning data centers to embedded systems. Scientific recognition includes: Best paper candidate at IEEE International Symposium on High Performance Computer Architecture (HPCA) 2018 for in-network packet generation research Yao actively supervises graduate researchers and leads collaborative projects in computer architecture. His grant portfolio supports work on battery-less IoT systems and neural network accelerators, though specific funding details aren't publicly enumerated. Current projects emphasize sustainable computing through novel architectures for energy-harvesting environments. He operates within Uppsala University's Computer Systems division, contributing to research groups focused on hardware acceleration, embedded systems, and networked architectures. His lab environment fosters interdisciplinary work bridging computer architecture, energy harvesting, and machine learning for next-generation computing platforms.