Find Academic Supervisors

• 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.

Ståle HolgersenView profile

Researcher

• Urban Planning and Policy
• Housing
• Class Analysis
• +5 more

Ståle Holgersen serves as a Researcher within the Department of Human Geography at Uppsala University, focusing on critical urban geography through the lens of Marxist theory. His work examines urban responses to economic and ecological crises, with particular emphasis on housing policy, class dynamics, and the political dimensions of sustainability in Scandinavian cities. He currently leads three major research initiatives: The Housing Question in Times of Crisis , Searching for sustainable alternatives in a time of crisis , and White skin, black fuel: Investigating the (anti-)climate politics of European right-wing populism . His research program centers on: Urban planning under austerity and Keynesian policies Housing crises and socially sustainable alternatives Class analysis in gentrification and urban policy Economic-ecological dual crisis management Climate change denial in right-wing populism Malmö as a critical case study for sustainable urban development Analysis of his recent publications reveals persistent engagement with capitalist crisis dynamics through urban geography. Key trends include deconstructing neoliberal 'solutions' to urban crises, examining Malmö's sustainability claims through political economy, and exposing contradictions in climate policies—particularly Norway's 'green neoliberalism'. His theoretical contributions connect Marxist dialectics to spatial planning while challenging conceptualizations of 'middle class' in gentrification studies. Holgersen's project leadership indicates active mentorship roles despite unlisted students. His research is institutionally funded through Uppsala University, with collaborative networks spanning Scandinavian human geography and political ecology. Current projects emphasize solution-oriented engagement with housing cooperatives and climate justice movements, reflecting his commitment to class-based alternatives to crisis management.

• Computer Architecture
• High-Performance Computing
• Memory Systems
• +3 more

Per Stenstrom is a Professor of Computer Engineering at Chalmers University of Technology, Sweden since 1995. His research focuses on computer architecture, particularly high-performance memory systems and energy-efficient computing. He has authored/co-authored four textbooks, over 200 publications, 20 patents, and supervised approximately 25 PhD students. Awarded ACM Fellow and IEEE Fellow. Member of Academia Europaea and the Royal Swedish Academy of Engineering Sciences. Co-founder of the HiPEAC Network of Excellence. Served as Editor or Associate Editor for journals like ACM Transactions on Architecture and Code Optimization and IEEE Transactions on Computers . Program Chair for major conferences including ISCA, HPCA, IPDPS, and ACM International Conference on Supercomputing. His research innovations include cache compression techniques, memory optimization strategies, and energy-aware resource management in multicore systems. Ongoing work emphasizes secure and scalable cache partitioning, hybrid memory systems, and defense mechanisms against microarchitectural attacks.

• 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) .

• Numerical Analysis
• High Performance Parallel Computing
• Algorithm Design
• +5 more

Carl C Kjelgaard Mikkelsen is an Associate Professor at the Department of Computing Science, Umeå University, Sweden. His research focuses on developing robust numerical algorithms that cannot fail, particularly in the context of finite precision arithmetic used by computers. His work bridges theoretical numerical analysis with practical high-performance computing applications. Dr. Mikkelsen's research interests center around numerical analysis and high performance parallel computing, with special emphasis on the worst case analysis of algorithms. He investigates the profound differences between exact arithmetic used by humans and finite precision arithmetic used by computers. His current projects include developing parallel constraint solvers for constrained molecular dynamics and parallel solvers for ultra-sparse linear systems. He is a co-author of the StarNEig library for solving dense nonsymmetric standard and generalized eigenvalue problems in parallel. His recent publications demonstrate a strong focus on accuracy requirements in numerical methods, particularly Newton's method, and the development of robust parallel algorithms for linear algebra problems. His work spans from theoretical analysis of numerical stability to practical implementation in high-performance computing environments. Dr. Mikkelsen teaches numerical analysis and scientific computing, emphasizing algorithm design, analysis, and implementation. He focuses on teaching students the critical differences between exact and finite precision arithmetic, and invests significant time in teaching software development practices and technical writing.

Ingo SanderView profile

Professor

• Embedded Systems Design
• Models of Computation (MoCs)
• Mixed-Criticality Systems
• +3 more

Ingo Sander is a Professor in Electronic Systems Design at KTH Royal Institute of Technology, affiliated with the Digital Futures Faculty and the Division of Electronics and Embedded Systems. He joined KTH in 1993 and has held his current professorship since 2018. His research focuses on formal system design methodologies like ForSyDe, emphasizing embedded systems, mixed-criticality applications, and design automation. He co-founded the cross-disciplinary Digital Futures research center, which addresses societal challenges through digital technology innovation. Education: MSc in Electrical Engineering (Technical University of Braunschweig, 1990), PhD and Docent at KTH (2003, 2009). Professional experience includes work at Ericsson (1991–1993). Research Interests: Design methodologies for embedded systems, models of computation (MoCs), formal verification, and cyber-physical systems. Key contributions include the ForSyDe framework and design space exploration techniques for multiprocessor platforms. His work bridges theoretical foundations with practical implementations, targeting safety-critical and high-performance embedded systems. Teaching: Ingo Sander supervises numerous master’s degree projects in computer engineering, electrical engineering, and ICT innovation. He leads courses on embedded software, systems design, and simulation. Labs & Projects: Digital Futures collaborates with Stockholm University and RISE, advancing innovations in digital technologies. Sander’s projects include the SAFEPOWER initiative for energy-efficient mixed-criticality systems and CONTREX for control systems design.

Magnus WiktorssonView profile

Professor

• Smart Production Logistics
• Sustainability and Circular Economy
• Machine Learning Applications
• +7 more

Magnus Wiktorsson is a Professor at KTH Royal Institute of Technology's Department of Sustainable Production Development within the Digital Futures Faculty. His work focuses on smart production logistics, sustainability, and digital transformation in manufacturing systems. He explores applications of machine learning, IoT, blockchain, and digital twins to enhance supply chain visibility, optimize production processes, and achieve circular economy goals. His research integrates technical and socio-technical perspectives, addressing challenges in human-robot collaboration, data-driven decision-making, and Industry 4.0/5.0 transitions. Key themes include material efficiency, real-time data utilization, and participatory modeling in urban and industrial logistics. Recent work emphasizes frameworks for adaptive scheduling, explainable AI in logistics, and blockchain-based collaboration models. Wiktorsson collaborates with industry partners to validate frameworks through case studies, pilot projects, and simulation-based methodologies. His contributions span academic publications and contributions to strategic innovation programs like Produktion2030, focusing on sustainability and resilience in manufacturing systems.

Concetta BurgarellaView profile

Researcher

• Population Genetics
• Evolutionary Biology
• Genomics
• +3 more

Concetta Burgarella is a Researcher at the Department of Organismal Biology, Uppsala University, Sweden, affiliated with the Human Evolution Program and the Schlebusch lab, focusing on evolutionary genetics of human and plant systems. Her research employs population genetics to investigate how demographic history, mating systems, and selective pressures shape genetic diversity in wild and domesticated species. Funded by a Marie Skłodowska-Curie fellowship, her current work examines co-evolution between human populations and African cereals. Recent publications (2020-2025) reveal interdisciplinary expertise across human population history (Bantu expansion), plant evolution (wheat, yam, millet), and genomic methodologies, highlighting adaptation patterns, domestication processes, and diversification mechanisms. Scientific Awards: Marie Skłodowska-Curie fellowship Her primary active grant is the Marie Skłodowska-Curie fellowship supporting human-cereal co-evolution research. While no advisees are documented, she collaborates with international teams on population genomics projects. As a core member of the Schlebusch lab, she contributes to human evolutionary genomics research while advancing parallel studies on plant domestication and adaptation through cross-institutional partnerships.

Oskar KvimanView profile

Researcher

• Bayesian phylogenetics
• Probabilistic machine learning
• Variational inference
• +15 more

Oskar Kviman is a doctoral student at KTH Royal Institute of Technology working in the Lagergren Lab within the Division of Computational Science and Technology. His research bridges machine learning, statistics, and computational biology with a focus on developing and applying advanced probabilistic methods. His primary research interests include: Bayesian phylogenetics and probabilistic machine learning Variational inference, variational auto-encoders, and sequential Monte Carlo methods Generative AI techniques including flow matching, Schrödinger bridges, and diffusion models Computational cancer research focusing on differential expression testing and spatial transcriptomics Kviman's publication record demonstrates significant contributions to variational inference methodology, particularly in phylogenetics and generative modeling. His work spans top machine learning conferences including ICML, NeurIPS, and AISTATS, showing consistent development of techniques that improve efficiency and accuracy in probabilistic modeling. Recent publications focus on multi-marginal flow matching, variational resampling, and mixture learning in black-box variational inference. He has been recognized for his peer review contributions as a Top reviewer (10%) for AISTATS 2023. Kviman has supervised master's theses for Xindi Liu and Ricky Molén at KTH and serves as a lecturer for 'Statistical Methods in Applied Computer Science' since 2021, while previously working as a teaching assistant for 'Machine Learning, Advanced Course' and 'Deep Learning, Advanced Course'.

• Suicidology
• Transcultural Psychiatry
• Migration and Mental Health
• +3 more

Marie Dahlin serves as Associate Professor (Docent) and Senior Lecturer in Psychiatry at Karolinska Institutet's Department of Clinical Neuroscience, concurrently holding the position of Director of KI's Medical Program. Her clinical practice operates through Norra Stockholms Psykiatri at S:t Göran's Hospital in Stockholm, where she functions as a Senior Physician. With over two decades of academic engagement since her 1994 medical degree, she maintains dual commitments to psychiatric research and medical education within Sweden's national healthcare framework. Education: Docent (Associate Professor), Karolinska Institutet, 2017 Doctor of Philosophy, Department of Clinical Neuroscience, Karolinska Institutet, 2007 University Medical Degree, Karolinska Institutet, 1994 Research Focus: Her work concentrates on suicidology , particularly violence-related suicide risk in youth and university students, using national register data to identify predictors like psychosis comorbidity and violent attempt methods. Simultaneously, she leads transcultural psychiatry research examining migration's impact on mental health, including culturally sensitive diagnostics for eating disorders, asylum seeker suicide interventions, and barriers to child psychiatric services for migrant parents. Current projects address intergenerational trauma in Eritrean-Swedish communities and health communication during crises. Publication Trends: Recent output (2024-2025) reveals heavy emphasis on suicide prevention tools (ASSIP, C-SSRS) and migration health disparities . She combines register epidemiology with qualitative fieldwork to analyze risk stratification in psychiatric emergencies and service access barriers for non-Swedish speakers. Key themes include gender differences in suicidal behavior, cultural adaptation of DSM-5 frameworks, and pandemic-era communication failures targeting vulnerable migrant populations in Järva/Södertälje. Scientific Awards: No specific awards, fellowships, or medals were documented in source materials Advising & Grants: As Medical Program Director, she oversees curriculum development without named graduate students. Her primary current grant is the Swedish Research Council-funded project (2022-2025) "Enhanced preparedness in a societal crisis" analyzing intercultural communication during COVID-19. The group participates in EPiCSS's TINA project for newly arrived children and collaborates with Lund University on ASSIP trials, though leadership roles in grants remain unspecified. Labs & Teams: She heads the "Suicidology and transcultural psychiatry" research group physically based at Norra Stockholms Psykiatri's psychiatric emergency department. The team maintains strong ties to Region Stockholm's Transcultural Centre and Uppsala University, while operating under KI's Department of Clinical Neuroscience. Their unique position within an emergency facility enables near-patient research on acute suicide risk, complemented by qualitative work on migration health through Stockholm's Knowledge Centre in Transcultural Psychiatry.

Philipp HallerView profile

Associate Professor

• Programming Languages
• Type Systems
• Concurrency
• +11 more

Philipp Haller is an Associate Professor in the Department of Computer Science at KTH Royal Institute of Technology, affiliated with the Theoretical Computer Science Group, Digital Futures, and WASP. He holds a PhD from EPFL (2010) and has held research positions at Stanford and EPFL, and industry roles at Typesafe Inc. Research Interests: His work centers on programming language design, concurrency (especially actor and futures models), distributed programming, type systems, and serverless computing. He is a leading contributor to the Scala language, having co-created its async/await syntax and futures library. His recent research includes the Portals framework for stateful serverless applications and formal models for consistency and concurrency safety. Publication Trends: His recent work (2020–2024) shows a strong focus on safe and scalable concurrency, stateful serverless systems, formal verification of language features, and distributed data consistency. He frequently publishes in top venues like ECOOP, OOPSLA, and PLDI, often combining theory with practical implementations in Scala. 2019 ACM SIGPLAN Programming Languages Software Award (for Scala) 2017 Teacher of the Year, EECS, KTH Advising and Grants: He supervises PhD students including Jonas Spenger, Xin Zhao, and Viktor Palmkvist. He led the Software Development Academy, a reskilling program for immigrants funded by the Wallenberg Foundations. He actively participates in academic service, co-chairing workshops like PLACES and AGERE!, and serving on program committees for OOPSLA, ECOOP, and POPL. Labs and Teams: He is part of the Theoretical Computer Science Group at KTH and leads research related to the Portals framework, which unifies dataflow and actor models for edge and serverless computing.

• Functional Programming
• Domain-Specific Languages
• Climate Impact Research
• +7 more

Patrik Jansson is a Professor of Computer Science at Chalmers University of Technology since 2011, with joint affiliation to Gothenburg University. His work bridges Functional Programming, Domain-Specific Languages (DSLs), and applications to Climate Impact Research, Fusion, and Physics.

Yi WangView profile

Professor

• Embedded Systems
• Real-Time Systems
• Real-Time Scheduling
• +7 more

Yi Wang is a Professor of Embedded Systems at the Department of Information Technology, Uppsala University , Sweden. He leads research in real-time and embedded systems with a focus on modeling, analysis, and implementation of safety-critical applications. He is affiliated with the Embedded Systems Group and serves as a Principal Investigator (PI) in major research centers such as UPMARC and projects like CUSTOMER (ERC Advanced Grant), CoDeR-MP, and CERTAINTY. Research Interests: Yi Wang’s work centers on Embedded Systems Design, Real-Time Scheduling, Multicore Programming, and Model-Checking of Real-Time Systems . His research addresses fundamental challenges in timing predictability, schedulability analysis, and the verification of complex real-time systems. He has made significant contributions to the digraph real-time task model, mixed-criticality systems, and timing analysis of ROS 2 systems. His work bridges theory and practice, often resulting in deployable tools and formal methods for industrial applications. Recent Research Trends: His most recent publications (2023–2025) focus on optimizing real-time performance in ROS 2, managing parallel task graphs with resource contention, improving GPU-based inference on embedded platforms, and enhancing timing predictability in multithreaded executors. These works reflect a strong trend toward applying formal real-time theory to modern robotics, AI integration, and multicore embedded architectures. Scientific Tools and Leadership: He is a key contributor to foundational tools in real-time systems: UPPAAL – Model checking for timed automata TIMES – Schedulability analysis and code generation CATS – Compositional analysis of timed systems TIMES-Pro – Based on the digraph real-time task model Advising and Research Funding: Yi Wang has supervised numerous PhD students and postdocs. He has led or participated in multiple large-scale funded projects supported by the Swedish Research Council (VR), the Swedish Foundation for Strategic Research (SSF), and the European Commission (FP7, ERC). These include UPMARC (10-year Linnaeus center), CoDeR-MP (with ABB and SAAB), SAVE++ (with VOLVO), and CREDO. Laboratories and Research Groups: He is a core member of the Embedded Systems Group at Uppsala University and leads research within the UPMARC center, which focuses on programming models and analysis techniques for multicore architectures. His lab develops formal methods and tools to ensure correctness and timing guarantees in embedded and cyber-physical systems.

Håkan SundellView profile

Assistant Professor

• Computer Science
• Artificial Intelligence
• Parallel and Distributed Systems
• +7 more

Håkan Sundell is an Assistant Professor of Computer Science at the University of Borås , affiliated with the Faculty of Librarianship, Information, Education and IT and the Department of Information Technology . Since 2006, he has been active as a teacher in informatics and a research group leader for the CSL@BS research group , while also serving as program manager for the System Architecture Education with a Focus on Software Development . His research spans Artificial Intelligence , Parallel and Distributed Systems , Concurrent Programming , and Machine Learning Applications , with a particular emphasis on lock-free data structures and GPU algorithms . He has developed over 30 courses and supervised multiple doctoral students to completion. Scientific contributions include advancements in non-blocking algorithms, real-time systems, and AI-driven marketing tools. His article trends reflect interdisciplinary work in data science for fashion retail , district heating analytics , and marketing campaign optimization . Key Awards: Best Paper Award at IPDPS 2003 Recognized as a pioneer in Swedish computer game development As a main supervisor since 2012, he has driven externally funded research projects (VR, SSF, KKS, HUR) and served on university committees, including chairing the education committee from 2018–2022.

• Artificial Intelligence
• Machine Learning
• Data Science
• +5 more

Amira Soliman is a Senior Lecturer at Halmstad University's School of Information Technology, specializing in artificial intelligence and machine learning with applications in healthcare. Her work bridges the gap between advanced computational techniques and practical healthcare solutions, focusing on improving patient outcomes through data-driven approaches. Dr. Soliman's research interests span a wide range of areas in AI and data science: Artificial Intelligence and Machine Learning for healthcare applications Graph analytics and network analysis in healthcare systems Federated learning for privacy-preserving healthcare analytics Healthcare informatics and clinical decision support systems Social network analysis for public health applications Distributed systems for large-scale healthcare data processing Her recent publications demonstrate a strong focus on applying advanced machine learning techniques to healthcare challenges, particularly in the areas of heart failure prediction, dementia diagnosis, and synthetic health data generation. Her work often combines clinical expertise with cutting-edge AI methodologies, resulting in practical tools that can be implemented in real-world healthcare settings. A notable trend in her research is the emphasis on explainability and interpretability of AI models in medical contexts, ensuring that clinicians can understand and trust the recommendations made by these systems. As an educator, Dr. Soliman teaches courses including Artificial Intelligence for healthcare, Smart Healthcare with applications, Big-data Parallel Programming, and Applied Data Mining. She also provides thesis supervision for both MSc and PhD students. Her teaching approach emphasizes the practical application of theoretical concepts, preparing students to tackle real-world challenges in healthcare AI. Dr. Soliman has served as a peer reviewer for prestigious journals and conferences including IEEE Internet Computing, Data Mining and Knowledge Discovery, Journal of Social Network Analysis and Mining, Journal of Future Generation Computer Systems, IEEE/WIC/ACM International Conference on Web Intelligence, and European Conference on Parallel and Distributed Computing. Her research has been supported through projects such as HaRP (Heart failure Readmission Prediction) and PadAI (AI for better mental health in young people).