Find Academic Supervisors

• Spatial statistics
• Climate Science
• Environmental Sciences
• +3 more

Johan Lindström is a Senior Lecturer and Director of Studies in Mathematical Statistics at Lund University's Faculty of Engineering (LTH). He serves as a Researcher in MERGE (ModElling the Regional and Global Earth system), eSSENCE (e-Science Collaboration), and BECC (Biodiversity and Ecosystem services in a Changing Climate). He is also a Principal Investigator and Profile Area member in multiple initiatives, including LTH's Aerosols and LU's Nature-based Future Solutions. His research focuses on statistical methods for spatial data in climate and environmental applications, using Bayesian hierarchical models and computational techniques like MCMC, INLA, and Gaussian Markov Random Fields (GMRFs). Key areas of expertise include computational methods for large datasets, spatial statistics, and applications to environmental modeling. Lindström has been recognized with the Excellent Teaching Practitioner (ETP) award in 2018. He has supervised six students and contributed to over 60 research outputs, including work on wave energy converters, Holocene land cover change, and statistical modeling of ecological systems. Notable projects include the POLLENOMICS initiative, combining ancient DNA and paleo-pollen data to decode European farming history, and the ELLIIT Focus Period on Machine Learning for Climate Science. Lindström collaborates internationally and actively reviews PhD theses and organizes conferences, reflecting his leadership in both research and academic governance.

Oscar GustafssonView profile

Associate Professor

• Digital Circuit Design
• Field-Programmable Gate Array (FPGA)
• Application-Specific Integrated Circuit (ASIC)
• +7 more

Oscar Gustafsson is an Associate Professor at Linköping University, affiliated with the Department of Electrical Engineering (ISY) and the Division of Electronics and Computer Engineering (ELDA). His research focuses on energy-efficient computations in digital circuits, particularly in ASIC and FPGA architectures, with applications in communication systems and hardware acceleration for AI. The 2024 publications highlight his work on FPGA implementations for dynamic programming in hybrid vehicles, hardware-aware neural network optimizations, FFT architectures for MIMO systems, Python-based finite word-length effect simulations, and CPU-FPGA co-design strategies. These works intersect with Computer Engineering Artificial Intelligence Signal Processing Computer Arithmetic Embedded Systems domains. He leads the Division of Computer Engineering at Linköping University, emphasizing collaborative research including Swedish-Chinese 5G technology partnerships.

Claudio AltafiniView profile

Professor

• Nonlinear Dynamical Systems
• Automatic Control
• Complex Networks
• +7 more

Claudio Altafini is a Professor in the Department of Electrical Engineering at Linköping University, where he has been a faculty member since 2014. He leads research in nonlinear dynamical systems, with a focus on complex networks, systems biology, and quantum control. His work bridges engineering, biology, and social sciences through mathematical modeling and control theory. Research Interests: Modeling and control of nonlinear dynamical systems Complex networks and signed graphs Systems biology and biological network modeling Quantum control and open quantum systems Opinion dynamics and social networks Consensus algorithms and multi-agent systems His recent publications show a strong trend in the analysis of signed networks, opinion dynamics, and controllability of temporal networks, with applications in engineering, social systems, and biology. The keywords across his work include control theory, network science, dynamical systems, and interdisciplinary modeling. Scientific Awards and Honors: No specific awards mentioned in the provided text. Advising and Teaching: Claudio Altafini teaches the Control Theory (TSRT09) course since 2020 and previously taught Modeling and Simulation (TSRT62). He also leads the PhD course on Nonlinear Control and has taught specialized courses in Networked Dynamical Systems and Systems Biology. He advises numerous PhD and Master’s students in control theory and networked systems. Laboratories and Research Groups: Member of the Automatic Control (RT) division within the Department of Electrical Engineering (ISY) Active in the ELLIIT Focus Period on Network Dynamics and Control Collaborates on interdisciplinary projects involving climate negotiations, government formation modeling, and biological systems

• Topology Optimization
• Computational Mechanics
• Acoustics
• +3 more

Eddie Wadbro is a Visiting Professor at the Department of Computing Science , Umeå University. His research spans computational mechanics, acoustics, and machine learning, focusing on topology optimization and its applications in industrial design, forestry, and electromagnetics. He actively collaborates across disciplines to solve complex engineering challenges. Key Research Areas: Topology optimization, acoustic wave control, machine learning for industrial systems, fluid dynamics Recent Trends: Alzheimer's disease classification via morphological analysis, deep reinforcement learning for autonomous machinery, multiscale material design Collaborations: Works with experts in forestry engineering, electromagnetics, and biomedical imaging.

Paolo BientinesiView profile

Professor

• High-Performance Computing
• Numerical Linear Algebra
• Tensor Operations
• +8 more

Paolo Bientinesi is a Professor at the Department of Computing Science, Umeå University, and Director of the High Performance Computing Center North (HPC2N). His research bridges theoretical and applied computer science, focusing on optimizing computational workflows through domain-specific innovations. Research Interests: Core Areas: Automatic generation of algorithms and code, numerical linear algebra, tensor operations, performance modeling, and computer music. Interdisciplinary Applications: Materials science, molecular dynamics, computational chemistry, computational biology, and computational physics. His work emphasizes leveraging architecture-specific and problem-specific knowledge to develop high-performance solutions. Publication Trends (2022-2026): Recent articles demonstrate a focus on mixed-precision computing, tensor decompositions, linear algebra algorithms (e.g., FLOPs optimization, matrix chains), and music information retrieval (e.g., automatic drum transcription, DJ cue points). Work frequently intersects with parallel computing, performance diagnostics, and machine learning. Leadership: Heads the research group High-Performance and Automatic Computing , driving projects in algorithm automation and computational efficiency.

Roman IakymchukView profile

Associate Professor

• Numerical Linear Algebra
• Parallel Programming
• High Performance Computing
• +3 more

Roman Iakymchuk is an Associate Professor at the Department of Computing Science within Umeå University, Sweden. His work focuses on numerically reliable and sustainable algorithmic solutions, emphasizing accuracy, reproducibility, and energy efficiency in high-performance computing environments. Research Interests: Numerical linear algebra, parallel programming models, sustainable computing, and optimization of distributed systems. Teaching: Courses on scientific computing, CUDA/OpenMP/MPI-based parallel programming, and floating-point error correction. The articles highlight his contributions to mixed-precision computing, numerical reliability in Krylov solvers, and energy-efficient practices in HPC systems. His work spans algorithm design, tool-assisted implementation, and European collaborative projects like CEEC.

Mengchu ZhouView profile

Professor

• Petri Nets
• Automation
• Internet of Things
• +4 more

Mengchu Zhou is a Distinguished Professor at the New Jersey Institute of Technology within the Department of Electrical and Computer Engineering. With over 1200 publications, 31 patents, and numerous accolades, he is a leading figure in automation and intelligent systems. Education : Ph.D. from Rensselaer Polytechnic Institute (1990) Research Interests : Zhou specializes in Petri nets , automation , Internet of Things , and big data . His work focuses on modeling complex systems and optimizing industrial processes. Recent publications highlight advancements in scheduling algorithms, robotic grasp stability, and network throughput. Scientific Awards : Fellow, IEEE Fellow, IFAC Fellow, AAAS Fellow, CAA Fellow, NAI Collaborations : Zhou has collaborated with researchers across institutions, including Nanyang Technological University and The University of Hong Kong. He maintains an active network with 878 co-authors and over 1200 publications.

Emre SürenView profile

Researcher

• LLM Security
• IoT Hacking
• Cyber Threat Intelligence
• +6 more

Dr. Emre Süren serves as a Researcher at KTH Royal Institute of Technology within the Division of Network and Systems Engineering and concurrently leads Cybercampus Sweden's Royal Hacking Lab as its Head. His dual roles position him at the forefront of national cybersecurity research with extensive collaborations across EU Horizon projects, Swedish government entities, and international academic institutions. His research spans critical domains including LLM security, IoT hacking, cyber threat intelligence, and digital forensics, with particular emphasis on AI-driven vulnerability research. Current work focuses on developing frameworks like CORTEX for AI-enhanced threat intelligence and Applied VORTEX for AI-based vulnerability discovery, supported by substantial grants from Vinnova, Swedish Research Council, and EU Horizon programs. Dr. Süren's publication portfolio demonstrates consistent innovation in security research, with recent articles exploring procedural generation for power systems security, penetration testing methodologies for connected households, and practical IoT threat research frameworks. His work increasingly integrates AI/ML techniques to address evolving cybersecurity challenges, particularly in zero-day exploit detection and IoT security. National Academic Infrastructure for Supercomputing grants (NAISS) for LLM-based threat intelligence EU Horizon funding for DFIRent (AI-assisted digital forensics) Swedish Research Council funding for Applied VORTEX (5.7M SEK) Digital Futures funding for CORTEX (3M SEK) Vinnova Cybercampus grant (17.5M SEK total) As an active educator, he supervises numerous PhD and Master's students on projects ranging from mobile application security to AI-powered honeypot architectures. His technical committee service includes NordSec, DFIR Review, and IEEE Security & Privacy, while his practical contributions include open-source tools like Memotopsy (memory forensics) and PatrIoT (IoT vulnerability research methodology).

• Marine Remote Sensing
• Bio-optics
• Baltic Sea Ecology
• +4 more

Susanne Kratzer is an Associate Professor in Marine Ecology at Stockholm University's Department of Ecology, Environment and Botany, specializing in marine remote sensing and bio-optics with focus on Baltic Sea ecosystems. She leads the 'Group Scratches' research group and coordinates the Nordic Network for Baltic Remote Sensing. Her work integrates satellite data, field measurements, and modeling for coastal zone management. Her educational background includes a German Diploma, Master's thesis in the School of Ocean Sciences (University of Wales, Bangor), and PhD in Biological Oceanography from the School of Ocean Sciences. She has been conducting marine remote sensing research at Stockholm University since 1997. Her research focuses on developing novel methods for ocean color remote sensing in optically complex waters, with particular emphasis on Baltic Sea applications. She has pioneered algorithms for retrieving suspended matter, Secchi depth, and chlorophyll-a from satellite data, addressing challenges posed by high CDOM concentrations. Her work bridges optical oceanography with practical coastal management needs, especially eutrophication assessment. Analysis of her recent publications reveals strong trends in satellite validation (particularly Sentinel-3 OLCI), integration of multi-source data (satellite, mooring, ship-based), and development of region-specific algorithms for Baltic Sea conditions. Her research increasingly addresses climate change impacts on coastal optical properties and monitoring requirements. National advisor for bio-optics in Swedish monitoring programs (SMHI, GU, UU, SU) Professional trainer for bio-optical measurements funded by Swedish Marine and Water Management Agency Contributor to 'The Colour of the Baltic Sea' film segment She actively mentors students through teaching marine remote sensing and bio-optics at all academic levels, and has coordinated numerous PhD courses through the Nordic Network. Her current projects include developing advanced methods for imaging biomolecules, improving national guidelines for marine bio-optical measurements, and long-term operation of the AERONET-OC station in Lake Vänern. Her research group maintains strong international collaborations with NASA, ESA, and European monitoring networks, contributing significantly to the integration of remote sensing into operational coastal zone management frameworks.

• Coefficient inverse problems (CIPs)
• Adaptive finite-element methods
• Globally convergent numerical methods
• +5 more

Larisa Beilina is Full Professor at the Department of Applied Mathematics and Statistics, Chalmers University of Technology, Sweden. Her work sits at the intersection of numerical analysis, inverse problems, and computational electromagnetics, with recent extensions to biomedical modeling. Education and Career Milestones: PhD awarded in 2003 for pioneering an adaptive finite-element method for hyperbolic coefficient inverse problems. In 2008, co-developed—with Prof. M. V. Klibanov (UNC Charlotte)—a globally convergent numerical method for coefficient inverse problems. 2011: experimentally validated the globally convergent algorithm using blind field data from the US Army Research Laboratory’s Forward-Looking Radar. Research Interests Beilina’s research focuses on ill-posed and coefficient inverse problems governed by PDEs. She designs adaptive finite-element algorithms that deliver reliable reconstructions from limited, noisy data, and couples these with globally convergent optimization techniques to escape local minima. Application domains span microwave thermometry for hyperthermia monitoring , early-stage melanoma detection via RF/microwave imaging , subsurface imaging using ground-penetrating radar , and parameter identification in HIV dynamics models . Recent Publication Trends Over 2022-2024 her articles emphasize four themes: (1) stabilized and explicit finite-element schemes for Maxwell’s equations and coupled wave systems; (2) hybrid FEM/FDM domain-decomposition solvers for large-scale 3D electromagnetic inverse problems; (3) adaptive error control and a posteriori estimates ensuring quantitative accuracy in conductivity and permittivity reconstruction; and (4) biomedical applications —ranging from microwave melanoma detection to time-adaptive determination of drug efficacy in HIV infection models. Scientific Awards & Recognition Keynote speaker and session chair at multiple international conferences on inverse problems and computational electromagnetics. Serves on editorial boards and as guest editor for proceedings volumes such as Springer Proceedings in Mathematics & Statistics and Journal of Inverse and Ill-Posed Problems . Research Teams & Grants Leads a multidisciplinary group comprising graduate researchers and post-docs collaborating with partners including the US Army Research Laboratory, University of North Carolina at Charlotte, and medical institutions in Sweden. Funding sources encompass Swedish Research Council grants, EU Horizon programs, and industry contracts focused on microwave diagnostics and multiphase flow measurements. Laboratory & Infrastructure Heads the Computational Inverse Problems Laboratory at Chalmers, equipped with high-performance GPU clusters, anechoic chamber facilities for microwave experiments, and access to clinical data sets for melanoma and HIV projects.

Carolina BergelingView profile

Senior Lecturer

• Brain-Computer Interfaces
• Control Engineering
• Marine Engineering Systems
• +3 more

Carolina Bergeling is a Senior Lecturer in control engineering at the Department of Mathematics and Natural Sciences at Blekinge Institute of Technology (BTH), a position she has held since January 2022. Her research spans the interdisciplinary fields of Brain-Computer Interfaces, control engineering, and marine engineering systems. Dr. Bergeling's research interests focus on: Brain-Computer Interfaces and neural signal processing Control of complex systems, particularly in marine environments H-infinity optimal control theory and applications Systems engineering approaches to multi-agent and distributed systems Her recent publications demonstrate a strong focus on applying advanced control theory to Brain-Computer Interface technologies, with particular emphasis on tensor decomposition methods, domain adaptation techniques, and multi-armed bandit approaches for improving EEG signal processing and neural decoding. She also maintains an active research program in H-infinity optimal control, with contributions to theoretical foundations and practical applications in marine engineering contexts. Dr. Bergeling's scientific contributions include: Advancements in tensor decomposition techniques for transfer learning applications in BCI Novel methods for improving EEG-based decoding of auditory attention through domain adaptation Applications of multi-armed bandit theory to brain-computer interface calibration Significant contributions to H-infinity optimal control theory for various system classes She teaches courses in control engineering and robotics, and serves as a supervisor for projects in the Marine Engineering program at BTH. Her ongoing research project focuses on "Swarming technology for cooperative surface and underwater vehicles (drones)," demonstrating her commitment to applying theoretical control concepts to practical marine engineering challenges.

Robert BraunView profile

Associate Professor

• Distributed Simulation
• Parallel Computing
• Simulation-Based Optimization
• +3 more

Robert Braun is an Associate Professor and Docent at Linköping University, affiliated with the Department of Management and Engineering (IEI) within the Fluid and Mechatronic Systems (FLUMES) division. His research focuses on distributed simulation methods, including co-simulation, parallel computing, and simulation-based optimization. He holds a doctoral degree from Linköping University, defended in 2015, and his employment is shared with SICS, East Swedish ICT, and SKF. He teaches undergraduate courses in mechatronics and hydraulics and is a core developer of Hopsan, an open-source simulation tool. Education: Doctoral thesis in 2015 on 'Distributed system simulation methods: For Model-Based Product Development'. Research Interests: Co-simulation tool coupling, parallel simulation optimization, and simulation-based teaching. Active in projects like OpenCPS (EU-funded) for model-driven development. Publications: Recent works address transmission line modeling in co-simulation, morphing wing actuator systems, and interoperability in aircraft system development. His research emphasizes numerical robustness and scalability in multi-domain simulations. Teaching & Labs: Develops Hopsan for fluid and mechatronic systems simulation. Involved in organizing the Scandinavian International Conference on Fluid Power (SICFP’25).

• Machine Learning
• Optimization Algorithms
• Optimal Transport
• +3 more

Jacob Lindbäck is a doctoral student at KTH Royal Institute of Technology , affiliated with the Division of Decision and Control Systems and the Wallenberg AI, Autonomous Systems and Software Program (WASP). His research focuses on the analysis and development of scalable optimization algorithms for machine learning, particularly computational optimal transport and its applications. Key research areas: Machine Learning Optimization Algorithms Optimal Transport Computational Mathematics Publications highlight advancements in GPU-accelerated splitting methods for optimal transport, novel algorithms, and computational efficiency. He also contributes as an assistant for the course Modelling of Dynamical Systems (EL2820) .

Adam FaragoView profile

Professor

• Financial Economics
• Asset Pricing
• Portfolio Choice
• +3 more

Adam Farago is a Professor of Financial Economics at the University of Gothenburg's Department of Economics. He holds a PhD in Finance from the Stockholm School of Economics (2014) and a Docent degree from the University of Gothenburg (2022). His research focuses on Financial Economics, Asset Pricing, Portfolio Choice, Behavioral Finance, and Financial Econometrics. He serves as Programme Coordinator for the MSc in Finance program and has held visiting lecturer positions at the Stockholm School of Economics. Farago has received several research grants and awards, including the Best Dissertation in Finance at SSE (2014) and multiple grants from institutions like VINNOVA and the Marianne and Marcus Wallenberg Foundation. His work frequently explores long-horizon returns, portfolio rebalancing strategies, and the behavioral aspects of financial decision-making. He has advised numerous PhD candidates and contributes to academic discussions through presentations at global conferences. Education: PhD in Finance (SSE, 2014), MA Economics (Central European University, 2009), MA Economics (University of Debrecen, 2007). Research Interests: Asset Pricing Dynamics Behavioral Portfolio Decisions Long-Term Investment Strategies Financial Market Efficiency Recent Work Trends: Focus on long-horizon stock return distributions, cognitive factors in fund management, and tax implications for wealth portfolios. Articles emphasize empirical methods and policy-relevant findings.

Konrad AbramowiczView profile

Associate Professor

• Functional Data Analysis
• Statistical Methods in Biomechanics
• Climate Research
• +4 more

Konrad Abramowicz is an Associate Professor and Head of the Department of Mathematics and Mathematical Statistics at Umeå University. His research focuses on functional data analysis, classification, and local inference procedures in the presence of misalignment, with applications in biomechanics, climate research, and forest growth analysis. He teaches stochastic simulations and parametric modeling methods, emphasizing student development and curiosity-driven learning. Education: University of Wrocław (Poland), with Erasmus exchange to Umeå University Research: Functional data analysis, interdisciplinary collaborations in biomechanics and climate science Awards: Teaching awards for dedication to student mentorship Internationalization: Postdoctoral work in Australia; collaborations with Italy and India; faculty-level international contact Konrad's research spans functional data analysis applied to knee kinematics after ACL injuries, climate reconstruction using sediment data, and statistical methods for forest growth analysis. He developed computational tools like the fiberLD R package for fiber length determination in forestry studies. His recent publications highlight interdisciplinary applications of statistical methods, with a focus on movement pattern analysis, climate modeling, and non-destructive forest assessment. Konrad advocates for international collaboration and adaptive academic practices to address global challenges. Konrad has received teaching awards and is recognized for his supportive approach to students. He actively participates in university initiatives for international student integration and served as international coordinator for academic exchange programs. As a key member of the IceLab interdisciplinary research hub, Konrad bridges statistical methods with applications in biomechanics and environmental science. His work emphasizes the importance of mathematical abstraction in solving real-world problems.