Find Academic Supervisors

Axel FlinthView profile

Assistant Professor

• Machine Learning
• Compressed Sensing
• Deep Learning
• +3 more

Axel Flinth is an Assistant Professor in the Department of Mathematics and Mathematical Statistics at Umeå University, focusing on mathematical foundations of machine learning for pattern recognition in large datasets. Education: PhD in Mathematics from Technische Universität Berlin (2018) His research centers on compressed sensing—reconstructing signals from incomplete data using structural assumptions—and equivariance in deep neural networks, investigating how data symmetries can be leveraged to enhance model performance. He actively contributes to geometric deep learning and statistical inference for spatio-temporal data through membership in specialized research groups. Recent publications (2022-2025) reveal a cohesive trajectory in mathematical optimization and symmetry-aware deep learning, with applications spanning computer vision (e.g., rotation-equivariant architectures for point clouds), wireless communication security, and signal reconstruction. His work consistently bridges theoretical mathematics with practical machine learning implementations. Scientific Awards: No awards documented in available sources Grants and Projects: Lead Researcher: Trade-offs in Nonconvex Learning (April 2022 - March 2027) Research Affiliations: Geometric Deep Learning Group Statistical Learning and Inference for Spatio-Temporal Data

Zeinab ShirvaniView profile

Research Fellow

• Remote Sensing
• Artificial Intelligence
• Environmental Science
• +3 more

Zeinab Shirvani is a Digital Futures Postdoctoral Fellow at KTH Royal Institute of Technology, affiliated with the Division of Geoinformatics in the Department of Urban Planning and Environment. Her research focuses on leveraging remote sensing and AI techniques to study forest disturbances, particularly in Miombo woodlands of southern Africa. She earned a PhD in Remote Sensing/Cartography from TU Dresden (Germany, 2020) and previously conducted postdoctoral research at the Swedish University of Agricultural Sciences (SLU). Her current project, 3Dfire (2023-2025), employs Sentinel satellite data and deep learning algorithms (e.g., RAUNet5) to enhance forest fire detection and analysis. This work aims to inform climate-smart forest management strategies, addressing challenges like fire severity, anthropogenic impacts, and climate change effects in vulnerable ecosystems. Key research themes include wildfire dynamics, AI-driven spatial-temporal analysis, and sustainable forest governance. Collaborating with Professors Yifang Ban and Ulla Mörtberg, her interdisciplinary approach bridges geoinformatics, environmental science, and climate policy. Outputs include high-impact publications on fire regimes, vegetation recovery, and landscape degradation. Zeinab’s broader contributions span deforestation modeling, landslide susceptibility mapping, and drought impacts on ecosystems. Her work integrates object-based image analysis, GIS, and machine learning to solve complex environmental challenges, with applications in both academic and policy contexts.

• Control Theory
• Real-Time Systems
• Embedded Systems
• +7 more

Anton Cervin is a Senior Lecturer at the Department of Automatic Control, Faculty of Engineering, Lund University. He is currently on leave of duty but remains actively involved in research and supervision. His work spans real-time systems, event-based control, cloud computing, and embedded systems, with strong ties to industrial applications and sustainable technologies. His research interests include: Event-Based and Stochastic Control Networked and Embedded Control Systems Cloud-Based Control and Resource Management Real-Time Scheduling and Performance Analysis Digital Twins and Fluid Modeling in Cloud Environments He has led major research projects funded by the Swedish Research Council, WASP, and ELLIIT, focusing on robust and secure cloud control, self-adaptive systems, and distributed learning. His software tools—TrueTime, Jitterbug, and TinyRealTime—are widely used in academia and industry for simulating and analyzing real-time control systems. The most recent publications highlight a shift toward intelligent, adaptive control using reinforcement learning, fluid modeling for microservices, and timing-robust cloud control—indicating a strong trend in merging traditional control theory with modern computing paradigms. His scientific contributions have been recognized with multiple Best Paper Awards: Best Paper Award - ECRTS 2021 Best Paper Award - RTNS 2016 Best Paper Award - RTCSA 2004 Best Paper Award - ECRTS 2003 Best Student Paper Award - RTCSA 1999 Anton Cervin has supervised numerous PhD students, including Max Nyberg Carlsson, Nils Vreman, and Claudio Mandrioli, and has served in key academic roles such as Director of PhD Studies and Deputy Head of Department. He has also led educational initiatives like doctoral study circles on programming languages and the history of control. He is affiliated with research environments such as WASP and ELLIIT and contributes to UN Sustainable Development Goals through advancements in smart systems and sustainable automation.

Zifan WangView profile

Researcher

• Decision-making under uncertainty
• Machine Learning
• Optimal Transport
• +3 more

Zifan Wang is a doctoral student and researcher at the Division of Decision and Control Systems (DCS), School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology. He is jointly advised by Prof. Karl H. Johansson and Prof. Michael M. Zavlanos at Duke University. He holds a Master's and Bachelor's degree from the Honors School of Harbin Institute of Technology. His research focuses on decision-making under uncertainty, leveraging tools from Machine Learning , Optimal Transport , Game Theory , and Control Theory , with a special interest in generative models and risk-averse optimization . Recent publications highlight his work on risk-averse learning in online convex games, constrained optimization with decision-dependent distributions, and distributional reinforcement learning for LQR systems. His methodological contributions include zeroth-order gradient estimation, one-point sampling strategies, and residual feedback for variance reduction. Honors include a 2024 Travel scholarship from Björns Foundation and a 2023 Travel grant from Karl Engvers Foundation . He actively participates in peer review for top conferences (NeurIPS, ICLR, L4DC, CDC, ACC) and journals (IEEE Transactions on Automatic Control, Automatica).

Danyo DanevView profile

Associate Professor

• Telecommunications
• Coding Theory
• Network Science
• +3 more

Danyo Danev is an Associate Professor at the Department of Electrical Engineering (ISY) , Linköping University , Sweden, specializing in the Division of Communication Systems . He earned an M.Sc. in Mathematics from Sofia University (1996) and a Ph.D. in Electrical Engineering from Linköping University (2001). He holds the Docent title in Data Transmission (2005). Research Pillars : Telecommunications, Coding Theory, Network Science Key Collaborations : Erik G. Larsson, Olle Abrahamsson His research spans secure communication systems , cognitive radio , superimposed codes , and network dynamics , with a focus on mathematical modeling for wireless and satellite networks. Recent work explores opinion dynamics and structural balance in network science, blending statistical signal processing with social network analysis. Scientific Contributions : Co-supervised 5 graduate students (3 PhD, 2 Licentiate degrees) Active in Communication Systems (KS) division Expertise in signal detection and error-correcting codes Advising Legacy : Mentored students on topics including digital fingerprinting , cognitive radio , and bounds for spherical codes , with ongoing work in network science.

• Dynamical systems
• Discrete mathematics
• Machine learning
• +8 more

Jonas Nordqvist is an Assistant Professor at the Department of Mathematics, Faculty of Technology, Linnaeus University. His research spans dynamical systems , discrete mathematics , and machine learning , with a focus on reinforcement learning and interdisciplinary applications.

Veronica Milos NymbergView profile

Associate Professor

• Primary Health Care
• Primary Medical Care
• Lifestyle Medicine
• +5 more

Veronica Milos Nymberg serves as an Associate Professor in the Department of Family Medicine and Community Medicine at Lund University's Faculty of Medicine. Her work focuses on primary healthcare delivery, patient-centered interventions, and cardiovascular risk management within the Swedish healthcare system. Dr. Nymberg's research expertise spans primary health care, lifestyle medicine, and digital health interventions. Her work particularly emphasizes hypertension management, cardiovascular risk assessment, and innovative approaches to patient engagement through digital channels including text messaging and video consultations. She actively investigates how technology can bridge gaps between knowledge and action in primary care settings. Her recent publications demonstrate a strong focus on digital health interventions for chronic disease management, with particular emphasis on cardiovascular risk factors and hypertension. The research shows consistent application of randomized controlled trial methodologies to evaluate intervention effectiveness in real-world primary care contexts. Dr. Nymberg serves as principal investigator and supervisor for multiple active research projects funded by organizations including the Swedish Research Council. Her supervisory role extends to doctoral students working on projects related to digital healthcare, cardiovascular disease management, and mental health integration in primary care. Current research projects under her leadership include initiatives focused on improving adherence to cardiovascular treatment recommendations, utilizing electrocardiography-based heart age estimation, and developing interventions for patients with anxiety, depression, and stress in primary care settings.

• Probability Theory
• Statistics
• Gaussian Processes
• +5 more

Jonas Wallin is a Senior Lecturer and Project Manager at the Department of Statistics, Lund University. His research bridges probability theory, statistics, and computational methods, with applications to public health, climate modeling, and spatial data analysis. He actively contributes to interdisciplinary projects addressing global challenges. Key Research Areas : Gaussian processes, Bayesian inference, disease risk mapping, climate-health interactions, statistical modeling on metric graphs. Projects : Leading Horizon Europe and Swedish Research Council-funded initiatives on antimicrobial resistance, spatio-temporal modeling, and protein degradation in immunity. Supervision : Primary supervisor for PhD students Erik Karlsson Strandh and Mattias Jönsson, focusing on climate-driven disease dynamics and Gaussian processes. Collaborations : Engaged in international partnerships and peer-review activities for journals and conferences. Wallin’s work aligns with UN Sustainable Development Goals, particularly in health and environmental resilience. Recent publications highlight methodological innovations in Markov random fields and regularization techniques, while his expertise in data quality control impacts disease surveillance practices.

Johan HåstadView profile

Professor

• Theoretical Computer Science
• Computational Complexity
• Circuit Complexity
• +3 more

Johan Håstad is a Professor at the Department of Mathematics, School of Engineering Sciences, KTH Royal Institute of Technology. His research focuses on theoretical computer science, particularly algorithms, computational complexity, approximation algorithms, and cryptography. He is funded by the Knut and Alice Wallenberg Foundation through project grants and the Wallenberg AI, Autonomous Systems and Software Program (WASP). External Funding: Knut and Alice Wallenberg Foundation, WASP Editorial Roles: Theory of Computing, TheoretiCS Professional Activities: Cyber Defense Center, Royal Swedish Academy of Sciences (former chairman) Research Interests : • Algorithms and Complexity • Circuit Complexity • Approximation Algorithms • Quantum Computing and Cryptography Scientific Awards : Knut and Alice Wallenberg Foundation Support Labs and Teams : • Involved in KTH's Cyber Defense Center • Editor for key journals in theoretical computer science

Johan StrandbergView profile

Associate Professor

• Functional Data Analysis
• Spatial Statistics
• Biomechanics
• +5 more

Johan Strandberg is an Associate Professor in the Department of Statistics at the Umeå School of Business, Economics and Statistics (USBE), Umeå University. His research focuses on functional data analysis, spatial statistics, and their applications in biomechanics and sports medicine. He specializes in analyzing complex data structures such as electroencephalography (EEG) signals and kinematic curves to study movement-related anxiety, sensorimotor control following injuries, and long-term outcomes of anterior cruciate ligament (ACL) ruptures. Key research areas include: Functional data analysis methodologies Nonparametric statistical clustering techniques EEG-based evaluation of movement-related fear Biomechanical analysis of human movement Climate reconstruction using functional kriging Strandberg is affiliated with the Functional Data Analysis and Spatial Statistics Group and the U-Motion Laboratory Research Group. His recent projects include investigating sensorimotor control post-ACL injury and exploring functional data approaches to human movement assessment. He has collaborated on studies involving ACL injury rehabilitation, EEG applications in sports medicine, and statistical modeling of spatial-temporal processes. His research outputs emphasize methodological advancements in handling dependent functional data, with applications ranging from clinical biomechanics to environmental science. He has contributed to over 10 peer-reviewed articles, focusing on inferential frameworks, clustering methods, and functional kriging techniques.

Anders EnocsonView profile

Adjunct Professor

• Orthopedic Traumatology
• Clinical Epidemiology
• Trauma Surgery
• +7 more

Anders Enocson is an Adjunct Professor of Orthopedic Traumatology at the Karolinska Institutet , leading the Orthopedics Research Group at the Department of Molecular Medicine and Surgery. He combines academic research with clinical practice as a senior physician at Karolinska University Hospital. Specialist in orthopedic surgery since 2007 First professor in orthopedic traumatology in Sweden (2020) Extensive clinical experience across Sweden and UK Research focus: Trauma epidemiology, fracture outcomes, and AI applications His research spans: Trauma Registry Karolinska - Data-driven quality improvement SweTrau - National trauma registry machine learning models Pelvic Fractures - Complication reduction strategies Tendon Repair - Mechanobiology and neuropeptide regulation Medical Imaging - CT motion analysis for fractures Orthopedic Oncology - Sarcoma treatment outcomes Key publication trends show emphasis on Machine learning for mortality prediction 3D CT analysis in fracture follow-up Long-term joint replacement outcomes Postoperative complication risk factors Tendon healing biomarkers Clinical collaborations include AO Foundation courses in Sweden Karolinska Traumacentrum leadership International orthopedic networks Multidisciplinary teams in trauma care

Carl Johan CasselgrenView profile

Professor

• Discrete Mathematics
• Graph Theory
• Graph Coloring
• +4 more

Carl Johan Casselgren is a Professor and Head of the Division of Algebra, Geometry and Discrete Mathematics (ALGD) at the Department of Mathematics, Linköping University. He is actively engaged in research and teaching in discrete mathematics and is a core member of the graph theory research group at the university. Institution: Linköping University Department: Department of Mathematics (MAI) Division: Algebra, Geometry and Discrete Mathematics (ALGD) Role: Professor and Head of Division His research focuses on discrete mathematics, particularly graph theory, with emphasis on graph coloring, structural properties of graphs, and combinatorial optimization. He investigates problems such as edge colorings, list colorings, and the coloring of graph products, contributing to both theoretical developments and algorithmic aspects. Recent publications (2024–2025) reveal a consistent focus on edge coloring problems, including near-interval colorings, extensions of partial colorings, and the coloring of Cartesian products and hypergraphs. These works demonstrate a deep engagement with combinatorial structures and theoretical graph problems, often involving probabilistic and structural methods. While no specific scientific awards are mentioned in the provided texts, his sustained publication record in high-quality journals such as Discrete Applied Mathematics , The Electronic Journal of Combinatorics , and Discussiones Mathematicae. Graph Theory underscores his active and respected role in the mathematical community. He collaborates with researchers from Sweden and abroad, including Mesfin Masre, Fikre B. Petros, and Petros A. Petrosyan. Although no formal advisees are listed, his leadership role and publication collaborations suggest involvement in mentoring students and junior researchers. He is part of a vibrant research division with around twenty members, contributing to a dynamic academic environment. The ALGD division conducts research in algebra, geometry, topology, dynamical systems, combinatorics, and graph theory. The graph theory group specifically focuses on classical problems with an emphasis on graph coloring and Hamiltonian graph theory, aligning closely with Casselgren’s research interests.

• Artificial Intelligence
• Machine Learning
• Data Mining
• +12 more

Slawomir Nowaczyk is a Professor at the School of Information Technology , Halmstad University. His research focuses on Artificial Intelligence , Machine Learning , and Data Mining , particularly for Streaming Big Data and Knowledge Representation with Weakly-Supervised Models . Practical applications: Predictive maintenance, healthcare informatics, smart industry, and energy systems Developing interestingness metrics for distributed data analysis and self-organization in AI systems Publication Trends : Recent work spans Explainable AI , spatiotemporal forecasting , feature selection , and smart city applications. Key areas include healthcare diagnostics , transportation optimization , and industrial fault detection . Academic Leadership : Serves as Research Leader for the School of Information Technology. Supervises six PhD students and co-supervises one additional student across academic and industrial domains.

Andreas SoldersView profile

Senior Lecturer

• Nuclear Physics
• Nuclear Fission
• Reactor Physics
• +6 more

Andreas Solders serves as a Senior Lecturer at Uppsala University's Department of Physics and Astronomy, specializing in Applied Nuclear Physics. His research focuses on understanding nuclear fission processes through experimental and computational methods, with particular emphasis on isomeric yield ratios, fission fragment properties, and nuclear data applications. He holds significant teaching responsibilities within the university. Department of Physics and Astronomy; Applied Nuclear Physics Uppsala University Senior Lecturer Dr. Solders' primary research interests revolve around nuclear fission dynamics, with specific focus on isomeric yield ratios , fission fragment angular momentum , and neutron-induced fission processes. His work combines experimental techniques using facilities like IGISOL and Penning traps with computational modeling using tools such as TALYS. Additionally, he has made notable contributions to physics education, particularly in implementing flipped classroom methodologies during the pandemic. His research has important applications in nuclear safety, reactor physics, and nuclear data evaluation. Analysis of Dr. Solders' recent publications reveals a strong emphasis on connecting experimental measurements of fission fragment properties with theoretical modeling. His work increasingly incorporates advanced computational techniques including Monte Carlo simulations and machine learning approaches to analyze complex nuclear data. The research spans fundamental nuclear physics questions about fission mechanisms while maintaining practical applications in nuclear technology and safety. Member of the mentoring team that supervises new teachers Director of studies at the department with special responsibility for pedagogical development Course director for Reactor Physics in the KKI program Course director for Experimental Methodology in the Bachelor's program in Physics Dr. Solders actively contributes to educational innovation in physics, having developed and implemented flipped classroom approaches even during pandemic restrictions. His work bridges fundamental nuclear physics research with practical educational applications, demonstrating commitment to both scientific advancement and effective teaching methodologies.

• Optical Communication
• Fiber Optics
• Photonics
• +4 more

Magnus Karlsson is a Professor of Photonics at Chalmers University of Technology and serves as Deputy Dean of the Department of Microtechnology and Nanoscience (MC2), responsible for research and graduate education. He co-leads the fiber optics research group with Prof. Peter Andrekson and co-founded the Chalmers Center for Optical Communication (FORCE) in 2010 alongside Prof. Erik Agrell. Karlsson teaches courses in Wireless and Photonics System Engineering and Photonics and Lasers, and holds editorial leadership as Editor-in-Chief of the IEEE/Optica Journal of Lightwave Technology. His research centers on optical fiber communication systems with expertise in light propagation, polarization dynamics, and nonlinear optical effects. Current investigations focus on capacity-enhancing techniques including Voronoi constellation geometric shaping, silicon nitride integrated photonics for microwave applications, and machine learning-driven polarization sensing. His work bridges theoretical modeling of phase-noise channels with experimental validation of novel receiver architectures for deep-space communication through atmospheric turbulence. Recent publications reveal strong trends in overcoming nonlinear transmission limits through multidimensional modulation and MIMO processing for coupled-core fibers. His group pioneers integrated photonic solutions for high-frequency signal generation while advancing real-time network monitoring capabilities in operational fiber infrastructure. Key themes include power-efficient signaling, distributed sensing, and computational methods for channel compensation. Karlsson's leadership in the fiber optics group and FORCE drives collaborative research in next-generation optical networks. His editorial role and ECOC program committee membership position him as a key influencer in shaping global optical communication standards and disseminating cutting-edge research advancements.