Find Academic Supervisors

Evelyn Otero SolaView profile

Associate Professor

• Sustainable Aviation
• Air Traffic Management
• Emissions Reduction
• +5 more

Dr. Evelyn Otero Sola is an Associate Professor in Aeronautical Engineering at KTH Royal Institute of Technology, serving as Vice-Director of the Centre for Sustainable Aviation (CSA) and a member of the Executive Board of the Environmentally Compatible Air Transport System (ECATS) International Association. Her research focuses on sustainable aviation, particularly optimizing air traffic management (ATM) to reduce emissions (CO2 and non-CO2) and noise, with projects involving sustainable aviation fuels (SAF), hydrogen, AI, and drones. She leads the doctoral course FSD3831 Future Sustainable Aviation and the master’s course SD2830 Aircraft Performance and Air Traffic Management , emphasizing interdisciplinary approaches to sustainability. Key collaborations include the Swedish Aerospace Research Center (SARC) and initiatives like TREVOL, TRACE, CIDER, and KLIMAFLY. Her work bridges research, education, and industry to drive sustainable aviation solutions globally. Education details are not explicitly provided, but her academic contributions include over 15 publications since 2009, spanning CFD optimization, climate modeling, and aviation environmental impact. She advises 11 doctoral students in the first iteration of her course and plans to expand access internationally. Her initiatives aim to create a skilled workforce for sustainable aviation through holistic education and cross-sector networks.

• molecular processes in flow and wetting
• sampling of conformational transitions in biomolecules
• parallel algorithms for molecular dynamics simulation

Berk Hess is a Professor at the Royal Institute of Technology (KTH) in Stockholm. His research focuses on molecular processes in flow and wetting, conformational transitions in biomolecules, and parallel algorithms for molecular dynamics simulations. He coordinates and teaches advanced courses in molecular dynamics, biotechnology modeling, and simulation of biological systems. His work contributes to computational biophysics and high-performance computing in life sciences.

• X-ray Physics
• Synchrotron Radiation
• X-ray Free Electron Lasers
• +12 more

Pablo Villanueva Perez is a Senior Lecturer and Associate Senior Lecturer in the Department of Physics at Lund University’s Faculty of Engineering (LTH). He is a Principal Investigator in Synchrotron Radiation Research and actively contributes to key interdisciplinary initiatives including NanoLund, the LTH Profile Areas in Nanoscience and Photon Science, and the LU Light and Materials research environment. He is also affiliated with eSSENCE: The e-Science Collaboration, highlighting his role in computational and data-intensive science. His research focuses on developing novel X-ray imaging methods and instruments that leverage the unique properties of diffraction-limited synchrotron-radiation facilities and X-ray free-electron lasers, such as MAX IV Laboratory and the European XFEL. His work spans advanced imaging techniques including ptychography, phase contrast imaging, and crystal optics, with applications in materials science, nanotechnology, and structural biology. The research contributes to UN Sustainable Development Goals related to innovation, industry, and sustainable infrastructure. The recent publications highlight a strong trend in high-resolution, time-resolved, and multi-modal X-ray imaging, particularly using next-generation synchrotron and XFEL sources. These works emphasize technical innovation in instrumentation, reconstruction algorithms, and experimental design for in-situ and operando studies. Pablo Villanueva Perez is involved in significant research projects, including AI-Twin (funded by the Swedish Research Council and eSSENCE), which integrates physics-informed AI for imaging reconstructions, and XUVPM (funded by Horizon Europe), focusing on extreme ultraviolet ptychographic microscopy. These projects reflect his leadership in merging advanced computational methods with experimental physics. He supervises research students, including Gunnarsson J. K., and collaborates extensively with national and international teams. His work is supported by major grants from the Swedish Research Council, eSSENCE, and the European Commission, underscoring the strategic importance and impact of his research. He is part of collaborative networks involving institutions across Europe and contributes to the development of beamlines and imaging infrastructure. His role as a supervisor and co-investigator positions him at the forefront of next-generation X-ray science and technology.

• Embedded Systems
• Real-time Systems
• Distributed Real-time Systems
• +8 more

Hazem Ali is a Senior Lecturer at Halmstad University's School of Information Technology. He holds a Ph.D. in Electrical and Computer Engineering from Faculdade de Engenharia da Universidade do Porto (FEUP) and an M.Sc. in Computer Science and Engineering from Halmstad University. His research focuses on embedded systems, real-time systems, and dataflow programming models. He has expertise in hardware/software co-design, parallel computing, and optimization of real-time applications. Education: Ph.D. in Electrical and Computer Engineering (FEUP, Portugal) M.Sc. in Computer Science and Engineering (Halmstad University, Sweden) Recent publications highlight his work in cybersecurity for autonomous vehicles, GPU acceleration of MIMO systems, and optimization of dataflow models. His projects include ELLIIT B02 (Beyond 5G Wireless) and CyberInfra (Cybersecure Traffic Infrastructure). Proficiency in tools includes MATLAB, C/C++, Java, VHDL, and dataflow languages like CAL and Sigma-C, with extensive international experience in Sweden, Portugal, and Egypt.

Patrick NormanView profile

Professor

• Theoretical Chemistry
• Molecular Response Theory
• Computational Spectroscopy
• +5 more

Patrick Norman is a Professor and Head of the Division of Theoretical Chemistry and Biology at KTH Royal Institute of Technology, Stockholm, Sweden. He also serves as Director of the PDC Center for High Performance Computing. His research focuses on advanced time-dependent molecular response theory methods, with applications in optical and X-ray spectroscopy. Coordinates VeloxChem and Gator HPC software projects Leads EU-EIC project OMICSENS (2023–2026) for lung cancer biosensors Principal Investigator in EU-ITN COSINE (2018–2021) and KAW network CoTXS (2014–2019) His theoretical work includes resonance convergent response theory via Liouville equations and Ehrenfest theorem, implemented in quantum chemistry software for linear/nonlinear spectroscopy. He co-authored the graduate textbook Principles and Practices of Molecular Properties (Wiley, 2018) and organizes international PhD schools on molecular response properties. Recent publications emphasize fragment state optimization, solvation effects, chiral luminescent materials, and amyloid ligand binding simulations. His work bridges quantum chemistry, HPC, and biomedical applications through tools like Gator and VeloxChem .

Berkant SavasView profile

Associate Professor

• Scientific computing
• Numerical linear and multilinear algebra
• Large-scale graph/network computations
• +3 more

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.

Fredrik LindstenView profile

Associate Professor

• Machine Learning
• Statistical Machine Learning
• Approximate Bayesian Inference
• +7 more

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.

Björn LisperView profile

Professor

• Computer Science
• Software Engineering
• Real-Time Systems
• +5 more

Björn Lisper is a Professor at Mälardalen University, Sweden, affiliated with the School of Innovation, Design and Engineering and the Division of Computer Science and Software Engineering. His research focuses on formal methods, real-time systems, embedded software, static analysis, and high-level synthesis. He has contributed to advancements in WCET analysis, machine learning applications in testing, and compiler optimization techniques. His work emphasizes practical industrial applications, particularly in automotive and multi-core systems. Research Interests: Real-Time Systems & WCET Analysis Static Program Analysis High-Level Synthesis for FPGAs Machine Learning in Software Testing Formal Methods & Verification Embedded Systems Design Publications span topics like neural network accelerators, automated testing frameworks, and compiler optimizations, reflecting a blend of theoretical and applied computer science. His work is characterized by collaboration between academia and industry to bridge gaps in embedded software predictability and performance.

• Theoretical Computer Science
• Distributed Computing
• Approximation Algorithms
• +4 more

Mohit Daga is a Researcher at KTH Royal Institute of Technology, Sweden, specializing in theoretical computer science, distributed computing, and algorithmic research. He holds an MS in Computer Science from IIT Madras (2018). His current role involves research engineering and supervising master thesis projects across Machine Learning, AI, and GPU Programming. Education: MS in Computer Science, Indian Institute of Technology Madras (2018) Research Interests: His work focuses on distributed algorithms, approximation methods, and dynamic systems. He has explored applications in network analysis, optimization, and medical imaging. Recent projects include developing consensus protocols for DAG-based systems and analyzing multi-shot broadcast stability in distributed networks. Awards: Google Summer of Code Scholarship (2020) Advising & Grants: Supervised 9 master’s theses since 2020. Research internships include Cystar Labs (IIT Madras), IMDEA Networks Lab, and Xerox Research. His work on resource allocation at Xerox led to a patented solution. Labs/Teams: Contributed to projects at KTH’s Department of Computer Science and collaborated with IMDEA Networks and Cystar Labs. Part of the team developing the AciSpace CubeSat payload integration system.

• Numerical Analysis
• High-Performance Computing
• Parallel Computing
• +5 more

Carl C. Kjelgaard Mikkelsen is an Associate Professor at the Department of Computing Science, Umeå University. He specializes in numerical analysis and high-performance parallel computing, focusing on robust algorithms that cannot fail. He co-authored the StarNEig library for nonsymmetric eigenvalue problems and works on parallel constraint solvers for molecular dynamics and ultra-sparse linear systems. Research Interests: Numerical analysis, parallel computing, eigenvalue problems, linear systems, algorithm design. His recent publications emphasize robust task-based algorithms, GPU acceleration, and precision requirements in Newton's method. He teaches numerical analysis and scientific computing, emphasizing finite precision arithmetic and software documentation.

Nina KhairovaView profile

Visiting Professor

• Computational Linguistics
• Machine Learning for Text Mining
• Misinformation Detection
• +2 more

Nina Khairova is a Visiting Professor at the Department of Computing Science , Umeå University, specializing in computational linguistics and responsible AI. Her research focuses on misinformation detection, machine learning for text mining, and the ethical implications of large language models (LLMs). She actively contributes to projects analyzing war-related news reporting and social media credibility during the Russian invasion of Ukraine. Projects: Human-AI Method for Detecting Misinformation in News Reporting on Russia’s Invasion of Ukraine Building Trust in Social Media: Assessing Credibility of Ukrainian Migration Information Her work involves multilingual dataset creation, semantic labeling, and AI algorithm integration for text analysis. Contact: nina.khairova@umu.se .

Stefan JohanssonView profile

Associate Professor

• Visualization
• Numerical Linear Algebra
• Computer Graphics
• +4 more

Stefan Johansson is an Associate Professor at Umeå University's Department of Computing Science, where he also serves as Deputy Head of Department overseeing finance and personnel. He maintains a part-time research affiliation with Curiosum Science Center, leading technical development for exhibitions and dome theaters. His research focuses on visualization , numerical linear algebra , and interactive educational tools . Current projects include VisA, which develops dome-theater visualizations of atmospheric data for science education. His work bridges computer graphics, software engineering, and didactic methods. Johansson's publications reflect dual expertise: earlier works emphasize matrix computations and control theory (2004-2017), while recent research shifts toward scientific visualization applications (2020-2025), particularly in geosciences and educational technology. Awards & Leadership Recognised University Teacher (Meritorious Teacher) 2021 Program Director, MSc in Computing Science (2022-2023) Deputy Director, Curiosum Science Center (2019-2021) He contributes to research groups in Matrix Computations with Applications and Parallel and Scientific Computing , and develops STEM curricula for primary/secondary schools.

• Ergonomics
• Digital Human Modeling
• Multi-Objective Optimization
• +7 more

Professor Dan Högberg is affiliated with the University of Skövde , specifically the School of Engineering Science . His work bridges ergonomics , digital human modeling (DHM) , and multi-objective optimization in industrial contexts. Research Interests: Human-centric manufacturing systems Simulation-based design optimization Remote collaboration tools Machine learning for ergonomic evaluation Recent publications focus on virtual design reviews , automated layout planning , and hand posture classification , integrating NSGA-II algorithms and digital twins . His projects include PLENUM (funded by Vinnova) and VF-KDO (Knowledge Foundation). He collaborates with researchers like Francisco Garcia Rivera , Aitor Iriondo Pascual , and Lars Hanson , contributing to decision support tools for factory ergonomics and worker well-being . Key labs include the Interaction Lab (ILAB) , with methods aligned with Industry 4.0/5.0 principles.

David GranlundView profile

Associate Professor

• Health Economics
• Industrial Organization
• Pharmaceutical Market Competition
• +3 more

David Granlund is an Associate Professor at Umeå School of Business, Economics and Statistics (USBE), Umeå University, specializing in health economics and industrial organization . His research explores pharmaceutical market competition, collusion detection, and public policy impacts. Contact: david.granlund@umu.se . Research Focus : Pharmaceutical price competition, parallel imports, therapeutic alternatives, antibiotic resistance, and collusion analysis. Current Projects : Leading studies on collusion mechanisms (Swedish Research Council), public sector efficiency (Jan Wallander/Tore Browaldhs Foundations), and pharmaceutical market collusion (Swedish Competition Authority). Granlund's recent work investigates price discrimination in pharmaceutical supply chains, zero-price effects in drug choice, and collusion probability estimation . His empirical studies span 1303 Swedish pharmaceutical markets and cross-border healthcare economics. Scientific Awards & Grants : Multiple competitive grants, including SEK 4.07M (2025–2027) and SEK 2.79M (2025–2028). Granlund is a member of the Economic Policy Network , contributing to cross-disciplinary research on healthcare reforms and market dynamics.

Henrik ErikssonView profile

Professor

• Artificial Intelligence
• Epidemiology
• Data Science
• +3 more

Henrik Eriksson is a Professor and Head of the Department of Computer and Information Science (IDA) at Linköping University. His research focuses on epidemiological modeling, societal resilience, and semantic web technologies. He has contributed to projects analyzing infectious disease transmission dynamics and pandemic response strategies, leveraging data science and computational methods. Key affiliations include the IDA divisions of Artificial Intelligence and Integrated Computer Systems (AIICS), and his work intersects with public health informatics, urban resilience, and high-performance computing. Recent publications address real-time forecasting of disease outbreaks and guidelines for resilient urban systems. He leads interdisciplinary initiatives at LiU, fostering collaborations in AI-driven solutions for societal challenges.