جستجوی استاد راهنمای دانشگاهی

Berkant Savasمشاهده پروفایل

دانشیار

• Scientific computing
• Numerical linear and multilinear algebra
• Large-scale graph/network computations
• +۳ مورد دیگر

Berkant Savas is an Associate Professor at Linköping University's Department of Science and Technology (ITN) within the Physics, Electronics and Mathematics school. His research focuses on scientific computing, numerical linear/multilinear algebra, large-scale graph/network computations, and tensor analysis. He has developed influential algorithms like Clustered Matrix Approximation (CMAPP) and Grassmann manifold computational tools. His work bridges theoretical foundations with practical applications in data science and engineering. Key contributions include scalable methods for massive graphs, low-rank tensor approximations, and optimization on manifolds. Savas' MATLAB-based tools (e.g., Grassmann classes, tensor approximation packages) are widely used in academic and industrial research. He has published extensively in top journals and conferences such as SIAM Journal on Matrix Analysis and IEEE transactions. Current research explores applications in link prediction, recommendation systems, and high-dimensional data analysis. Savas collaborates internationally, with notable co-authors like Inderjit Dhillon and Lars Eldén. His work emphasizes computational efficiency and memory optimization for large-scale problems arising in networks and information retrieval. Though no explicit awards are mentioned, his impactful contributions highlight recognition within the computational mathematics community.

• Condensed Matter Physics
• Spintronics
• Metamaterials Design
• +۳ مورد دیگر

Qichen Xu is a Research Fellow at the Department of PHYSICS OF LIGHT AND MATTER within the Royal Institute of Technology (KTH). His work focuses on advanced materials science and computational magnetism, with specializations in spintronics, metamaterials, and machine learning-driven materials discovery. He has developed tools like SpinView for visual analysis of magnetic systems and contributed to understanding exotic spin textures like skyrmions through algorithmic approaches. Research Interests include computational modeling of magnetic materials, optimization of energy systems via metamaterials, and applying machine learning to accelerate materials design. His recent work explores chemical disorder effects in FeCo alloys, skyrmion-based metamaterial design, and high-throughput screening of halide perovskites for energy applications. Notable contributions span 14+ peer-reviewed articles (2018–2024) addressing topics from near-field thermophotovoltaic systems to genetic-algorithm-driven energy optimization. While no formal awards are listed, his work demonstrates significant impact in interdisciplinary fields combining physics, materials science, and AI. Advising and grants sections are pending further information, though his involvement in collaborative projects like SpinView suggests active team-based research. Part of KTH’s cutting-edge Physics of Light and Matter group, his research bridges fundamental physics and applied technology development in energy and spintronics domains.

• Mathematical Statistics
• Machine Learning
• Artificial Intelligence
• +۴ مورد دیگر

Jimmy Olsson is a Professor in the Department of Mathematical Statistics at KTH Royal Institute of Technology. His research focuses on generative models, statistical computational methods, and dynamic systems analysis, particularly in the context of artificial intelligence and machine learning. He specializes in state-space models, sequential Monte Carlo methods, and Markov chain Monte Carlo techniques. His work emphasizes foundational contributions to hidden Markov models, computational algorithms for statistical inference, and the intersection of mathematical statistics with modern data science challenges. Key research areas include developing efficient computational tools for analyzing complex, time-dependent data streams, with applications in AI and machine learning. Olsson’s publications span advanced topics such as diffusion models, particle filtering, and online variational learning, reflecting his expertise in bridging theoretical statistics with practical computational methods. He collaborates with researchers across disciplines, including engineering, computer science, and applied mathematics.

• Information Network Analytics
• Decentralized Machine Learning
• Data Mining
• +۵ مورد دیگر

Sarunas Girdzijauskas is a Professor at the Department of Computer Science within KTH Royal Institute of Technology's School of Electrical Engineering and Computer Science (EECS), where he leads the Graph Analytics and Network Learning group (GALE) . He also holds a Senior Researcher position at RISE (Research Institutes of Sweden) and has served as Program Director for EECS's doctoral program in Information and Communication Technology (2019-2022) and Deputy Director of Third-Cycle Education at EECS. PhD from Ecole Polytechnique Fédérale de Lausanne (EPFL) in 2016 Docent in Computer Science at KTH (2016) Coordinated multiple large-scale projects: H2020 Marie Curie RAIS, Marie Curie ITN iSocial Research Focus : His work spans decentralized machine learning systems, graph analytics, and blockchain infrastructure. Key areas include: Information network analysis for social and technical systems Federated learning with adaptive personalization Graph partitioning and distributed algorithms Privacy-preserving models with differential privacy Knowledge distillation for evolving graph representations Scalable solutions for big data and IoT botnet detection Article Trends : Recent work demonstrates convergence of graph neural networks with federated learning, emphasizing privacy (differential privacy in 5/15 articles), robustness (out-of-distribution detection in 3 articles), and decentralized infrastructure (blockchain, gossip protocols, and P2P overlays). The articles frequently address temporal modeling, knowledge distillation, and heterogeneous graph representations. Scientific Leadership : Coordinated H2020 Marie Curie European Training Network 'RAIS' (2018-2023) Coordinated Marie Curie ITN 'iSocial' (2012-2017) Activity Leader for EIT Digital Innovation Activities Teaching & Advising : Has supervised/examined over 60 master theses and currently advises 8 PhD students, including Adam Orucu (Main Supervisor, 2024-), Filip Cornell (Main Supervisor, 2020-), and Ahmed Emad Samy Youssif (Main Supervisor, 2019-). Former supervisees include 9 PhDs like Dr. Edvin Listo Zec (2025) and Dr. Debaditya Roy (2024).

• Quantum Optics
• Quantum Information
• Quantum Machine Learning
• +۳ مورد دیگر

Anton Frisk Kockum is an Associate Professor in Applied Quantum Physics at Chalmers University of Technology. He holds a PhD in Physics from Chalmers (2014) and completed postdoctoral research at RIKEN in Japan (2014–2018), supported by a JSPS Fellowship. He joined the Wallenberg Centre for Quantum Technology in 2018, focusing on theoretical contributions to quantum computing and quantum program design. His research spans quantum optics, quantum information, and quantum machine learning, with emphasis on ultrastrong light-matter coupling, quantum acoustics, and circuit quantum electrodynamics. He explores quantum technologies for building scalable quantum computers and optimizing their applications. Recent work includes entanglement generation in hybrid systems, decoherence mitigation, and quantum process tomography advancements. His publications (67 total) address topics like giant atom dynamics, quantum amplification, and quantum algorithm optimization for real-world problems. He leads projects funded by EU, SSF, and VR, including studies on non-Hermitian topological phases and structured quantum environments.

Jonas Bjermoمشاهده پروفایل

پژوهشگر ارشد

• Optimal Design
• Psychometrics
• Educational Measurement
• +۳ مورد دیگر

Jonas Bjermo is a Research Fellow at Linköping University's Department of Computer and Information Science, specifically in the Division of Statistics and Machine Learning (STIMA). His work centers on statistical methodologies for educational testing and measurement. His research focuses on optimal design problems in achievement testing, particularly designing tests for measuring ability growth and optimizing examiner allocation during item calibration. Bjermo develops combinatorial optimization algorithms to solve these statistical challenges, advancing precision in educational assessment. His 2025 publications address calibration design under ability uncertainty and optimal test construction for mean ability growth estimation, demonstrating significant contributions to psychometrics and educational measurement through innovative statistical approaches. As part of STIMA, Bjermo contributes to a division dedicated to modern data analysis that hosts an international master's programme in Statistics and Machine Learning, fostering interdisciplinary research in data science.

Fredrik Lindstenمشاهده پروفایل

دانشیار

• Machine Learning
• Statistical Machine Learning
• Approximate Bayesian Inference
• +۷ مورد دیگر

Fredrik Lindsten is a Senior Associate Professor in Machine Learning and Head of the Division of Statistics and Machine Learning at Linköping University's Department of Computer and Information Science (IDA). His research focuses on statistical machine learning, emphasizing probabilistic modeling and uncertainty quantification in methods such as approximate Bayesian inference, representation learning, and graph-based approaches. Applications span weather forecasting, materials science, biochemistry, and automotive industry challenges. Education: MSc (2008), PhD (2013) in Automatic Control from Linköping University; Postdoctoral roles at University of Cambridge, UC Berkeley, and University of Oxford. Affiliations: WASP (Wallenberg AI, Autonomous Systems and Software Program), ELLIIT (Lab for Information and Communication Technology). Research Interests: Lindsten’s work bridges statistical methodology and machine learning, particularly in quantifying uncertainty in predictions. His team explores method development across diverse applications, including spatio-temporal models and graph-based techniques. Recent projects include probabilistic weather forecasting with graph neural networks and cryo-EM reconstruction techniques. Publications: Over 25+ peer-reviewed articles, with recent highlights in Nature Methods , NeurIPS , and Physical Review Materials , focusing on Bayesian methods, generative models, and computational statistics. Awards: Ingvar Carlsson Award (Swedish Foundation for Strategic Research), Benzelius Award (Royal Society of Sciences in Uppsala). Grants & Teams: Supervises 7+ PhD students; active in collaborative projects funded by WASP, ELLIIT, and VR (Swedish Research Council). Labs/Teams: Leads the Division of Statistics and Machine Learning (STIMA) at IDA, fostering interdisciplinary research in probabilistic machine learning.

Frank Millerمشاهده پروفایل

استاد

• Optimal experimental design
• Clinical trials
• Achievement tests
• +۴ مورد دیگر

Frank Miller is a Professor and Head of Unit at the Department of Computer and Information Science (IDA) at Linköping University, where he has been since 2022. Previously, he served as Associate Professor at Stockholm University (2013–2023) and worked in the pharmaceutical industry at AstraZeneca (2003–2012). He holds a Ph.D. in Statistics from the University of Karlsruhe, Germany (2002). His research focuses on innovative statistical methods for practical applications, including optimal experimental design, clinical trials, and achievement testing. Key research areas include active machine learning, adaptive designs, biostatistics, and optimization algorithms. He leads the Division of Statistics and Machine Learning (STIMA), which emphasizes modern data analysis and hosts an international master's program in Statistics and Machine Learning. Recent publications explore topics like robust experimental design and multidimensional achievement test calibration. While no specific awards are listed, his work contributes to advancing statistical methodologies in education and healthcare contexts.

• Autonomic Nervous System
• Psychoneuroimmunology
• Cancer Biology
• +۳ مورد دیگر

Marta Vigier is a Researcher at Linköping University, affiliated with the Department of Biomedical and Clinical Sciences (BKV) within the Division of Cell and Neurobiology (CNB). Her research focuses on autonomic nervous system dynamics in medical contexts, including doctor-patient interactions and psychophysiological responses to social and clinical environments. She investigates applications of machine learning in healthcare diagnostics and the interplay between psychological states and physiological biomarkers in chronic diseases. Key Research Areas: Autonomic nervous system responses, psychoneuroimmunology, cancer biology, and biomedical signal analysis. Recent Contributions: Published in Biological Psychology, Health Communication, and oncology-focused journals. Her work bridges clinical practice and basic science, with a particular emphasis on translational research in healthcare communication and disease progression mechanisms.

Peter Zsigmondمشاهده پروفایل

دانشیار مدعو

• Neuroscience
• Neurosurgery
• Biomedical Engineering
• +۳ مورد دیگر

Peter Zsigmond is an Adjunct Associate Professor and Docent at Linköping University's Department of Biomedical and Clinical Sciences, affiliated with the Division of Surgery, Orthopedics and Oncology. His work bridges clinical neurology, neurosurgery, and biomedical engineering, with a focus on advancing diagnostic and therapeutic interventions in neurological disorders. His research emphasizes cerebral microcirculation monitoring, deep brain stimulation (DBS) optimization, and outcomes of spinal surgeries. Notable areas include improving MRI safety in neurocritical care, analyzing fatty infiltration in chronic whiplash, and refining DBS simulations using patient-specific models. He collaborates across disciplines to integrate optical and electrical measurements for precise surgical guidance. Zsigmond's publications reflect a commitment to translational research, addressing clinical challenges in neurocritical care, chronic pain management, and neurodegenerative diseases. His work spans from fundamental studies on cerebral blood flow to applied clinical trials evaluating postoperative rehabilitation strategies.

Anders Franco-Cerecedaمشاهده پروفایل

استاد

• Cardiovascular Disease
• Thoracic Surgery
• Cardiology
• +۶ مورد دیگر

Anders Franco-Cereceda is a Professor/Senior Physician at Karolinska Institutet, leading the Thoracic Surgery research group within the Department of Molecular Medicine and Surgery. He specializes in cardiothoracic surgery and translational research, focusing on aortic valve and thoracic aortic diseases. His work spans molecular mechanisms, surgical techniques, and patient outcomes, with significant contributions to understanding aortic aneurysms and valve pathophysiology. Education & Career: MD from Karolinska Institutet (1992) PhD in Pharmacology (1988) Postdoc at Cleveland Clinic (1999–2000) Became a board-certified cardiothoracic surgeon (1997) and Professor (2005) Research Interests: Mechanisms of aortic valve degeneration and aneurysm formation Genetic and molecular drivers of cardiovascular diseases Surgical innovations and patient risk stratification Collaborations on international projects like FAD (EU), MIBAVA (Fondation Leducq), and NORCAAD Key Contributions: PI of ASAP, DAVAACA, and ARTIST studies analyzing 3,000+ surgical patients Over 300 publications, 17,000+ citations, and an H-index of 60 (2024) Recipient of awards including the Evarts A. Graham Memorial Fellowship Leadership: Head of Department of Molecular Medicine and Surgery (2021–2025) Guides a multidisciplinary team including PhD students and postdocs

Miia Kivipeltoمشاهده پروفایل

استاد

• Epidemiology
• Geriatrics
• Neurology
• +۳ مورد دیگر

Miia Kivipelto is a Professor of Clinical Geriatrics at Karolinska Institutet (KI) and Director for Research & Development of Theme Aging at Karolinska University Hospital. She holds a part-time Professor position at Imperial College London. Her research focuses on dementia prevention, Alzheimer’s disease, and cognitive impairment, leading the landmark FINGER trial and the World-Wide FINGERS network. Her team includes over 100 researchers across Sweden, Finland, and the UK. Educational background: MD from University of Kuopio (1999), PhD in Geriatric Epidemiology (2002), postdoc at KI (2002–2005). Awards include the Ryman Prize 2020 and Academy of Finland Award for Social Impact (2009). Research interests emphasize modifiable risk factors (e.g., lifestyle, vascular health), multidomain interventions, and biomarker development. Key projects include LiPiDiDiet, Nordic Brain Network collaborations, and biomarker studies in memory clinics. Her work spans translational research, clinical trials, and global partnerships. Grants and funding: Swedish Research Council, Alzheimerfonden, EU grants, and international foundations. Recent studies address eHealth tools, combination therapies, and FINGER model adaptations worldwide. Labs/teams: Nordic Brain Network, Alzheimer Research Center at KI, and collaborations with Imperial College London and University of Eastern Finland. Her research aims to transform dementia prevention into clinical practice.

• Cardiology and Cardiovascular Disease
• Medical Materials
• Heart Failure
• +۳ مورد دیگر

Cecilia Linde is a Professor at the Department of Medicine, Solna, Karolinska Institutet. She holds a Docent degree from Karolinska Institutet (1997) and has been a faculty member since at least 2020, advancing to Senior Professor in 2025. Her research focuses on cardiovascular disease, particularly heart failure, cardiac resynchronization therapy (CRT), and clinical management of arrhythmias like atrial fibrillation. She is affiliated with the Endothelial dysfunction in atherosclerotic cardiovascular disease research group led by John Pernow. Education: Docent, Karolinska Institutet, 1997 Research Interests: Cardiac devices (CRT, pacemakers) Heart failure mechanisms and therapy Atrial fibrillation management Clinical trial design Cardiovascular biomarkers Publications Trends: Her work emphasizes translational cardiology, with recent focus on CRT efficacy across patient subgroups, device-guided AF management, and machine learning applications in predicting CRT outcomes. She collaborates globally on large-scale trials like REVERSE and NOAH-AFNET 6. Awards: No specific prizes mentioned, though her extensive editorial contributions to top journals like European Heart Journal and Circulation reflect peer recognition. Advising & Grants: While no grant details are provided, her involvement in multi-center clinical trials indicates significant research funding. She has not listed formal advisees but co-authors numerous international collaborators. Labs/Teams: Active in the John Pernow research group studying endothelial dysfunction and atherosclerosis. Collaborates with institutions globally on cardiovascular device trials and guideline development.

• Environmental Health
• Toxicology
• Epidemiology
• +۳ مورد دیگر

Marie Vahter is a Senior Professor at the Institute of Environmental Medicine, Karolinska Institutet, leading the Unit of Metals and Health. She specializes in environmental toxicology, focusing on the health impacts of trace metals and toxic elements like arsenic, cadmium, and fluoride. Her research bridges epidemiology, clinical studies, and molecular mechanisms, particularly in vulnerable populations such as pregnant women and children. Key affiliations include the Metal Toxicology research group led by Maria Kippler. Vahter’s work addresses how early-life exposures to environmental contaminants affect neurodevelopment, immune function, and metabolic health. She has collaborated on longitudinal cohort studies in Bangladesh, Sweden, and Uruguay, examining long-term consequences of metal exposure on birth outcomes, cognitive development, and chronic diseases. Her studies emphasize bidirectional relationships between environmental factors and health, integrating nutritional status (e.g., selenium, iodine) and genetic susceptibility. Funders include the Swedish Research Council, EU, and Formas. Vahter’s contributions have advanced understanding of metal toxicity mechanisms and informed public health interventions globally.

Gaurav Raiمشاهده پروفایل

پژوهشگر

• Computer Graphics
• Computer Vision
• Image Processing
• +۳ مورد دیگر

Gaurav Rai is a Ph.D. researcher at Indraprastha Institute of Information Technology Delhi (IIIT-Delhi), focusing on computer graphics, computer vision, and GPU computing. He has contributed to real-time animation frameworks and diffusion models for motion transfer. Ph.D. in Computer Science and Engineering (2020–present), IIIT Delhi B.Tech in Computer Science and Engineering (2016–2020), GKV Haridwar His research emphasizes creative algorithms for sketch-based animation and dynamic deformation. Recent work involves diffusion models with temporal consistency and motion-guided frameworks. Gaurav has published in top-tier conferences like SCA, GRAPP, and ICME, with a focus on computer graphics, deep learning, and multimedia. Earlier publications include applications in medical imaging and spam filtering. ACM Student Travel Grant for ICME 2025 CHANAKYA Doctoral Fellowship He has served as a teaching assistant for courses in Computer Graphics, Scientific Computing, and Linear Algebra at IIIT Delhi.