Find Academic Supervisors

• Biostatistics
• Epidemiology
• Medical Statistics
• +7 more

Matteo Bottai is Professor of Biostatistics and Head of the Department of Biostatistics at Karolinska Institutet, where he also serves as Scientific Director of the Biostatistics Core Facility. He holds additional academic appointments as Visiting Professor in the Department of Mathematical Statistics at Stockholm University and Adjunct Professor in the Department of Biostatistics at the University of South Carolina. Education: Doctor of Science, Harvard University, USA His research focuses on advanced biostatistical methods, particularly in quantile regression, modeling of bounded and censored outcomes, longitudinal data analysis, and methods for handling missing data in distributed networks. His work bridges theoretical statistics and practical applications in epidemiology, environmental medicine, and public health. He has contributed to methodological developments in Laplace regression, parametric quantile modeling, and optimal probability weighting. His recent publications span a wide range of topics, including statistical methodology, cancer outcomes, respiratory health, cardiovascular disease, and mental health interventions. The articles reflect a strong emphasis on population-based cohort studies, clinical trials, and the use of complex statistical models to derive meaningful clinical and public health insights. Diagnostic guidelines for familial hemophagocytic lymphohistiocytosis Effect of internet-delivered cognitive behavioral therapy in pediatric OCD and Tourette syndrome Risk of thromboembolic events after cancer surgery Impact of air pollution on infant lung function Statistical methods for missing data and quantile regression Matteo Bottai actively collaborates across disciplines, working with researchers in oncology, neurology, cardiology, psychiatry, and environmental health. His role as Scientific Director of the Biostatistics Core Facility underscores his leadership in supporting high-quality statistical analysis in biomedical research. He has been involved in numerous clinical trials and large-scale cohort studies, contributing statistical expertise to study design, analysis, and interpretation. He leads the Biostatistics Unit at the Institute of Environmental Medicine, Karolinska Institutet, which provides statistical support and fosters methodological research in environmental and occupational health.

• Aircraft Propulsion Systems
• Aviation Noise Modelling
• Emissions Modelling
• +5 more

Xin Zhao is an Associate Professor at the Fluid Dynamics Division within the Department of Mechanics and Maritime Sciences at Chalmers University of Technology. His research focuses on multidisciplinary design and analysis of aircraft propulsion systems and air traffic management, including conventional, hybrid, electric, and hydrogen propulsion systems, aviation noise and emissions modeling, and thermal management systems for aero engine applications. Dr. Zhao's primary research interests include: Advanced propulsion technologies for sustainable aviation Noise prediction and abatement techniques Flight procedure optimization using meteorological data Boundary layer ingestion aerodynamics Environmental impact assessment of aviation systems Thermal management in aero engines His recent publications demonstrate a strong focus on sustainable aviation solutions, with emphasis on noise reduction techniques, flight procedure optimization, and hybrid-electric propulsion systems. The research consistently addresses both theoretical modeling and practical applications in aircraft design and operations. Dr. Zhao leads and contributes to numerous research projects funded by the European Commission, Swedish Transport Administration, and Swedish Energy Agency, including: HOPE (2023-2027): Hydrogen Optimized multi-fuel Propulsion system NEFAT (2023-2025): Noise Exposure in Future Air Traffic IMOTHEP (2020-2023): Hybrid Electric Propulsion technologies SUBLIME (2019-2022): Boundary Layer Ingesting Model Experiment STATMET (2019-2022): Statistical Meteorological database for flight procedures

Anna Sågvall HeinView profile

Professor

• Machine Translation
• Computational Linguistics
• Natural Language Processing
• +3 more

Anna Sågvall Hein is a Professor in Computational Linguistics at Uppsala University's Department of Linguistics and Philology. She concurrently serves as CEO and Head of the Board of Convertus AB, a Uppsala University spin-off specializing in machine translation technology since its 2006 founding. Her research centers on Machine Translation and Computational Linguistics , with significant contributions to Swedish-Turkish language pair systems, parallel corpus development, and grammar checking methodologies. Her work bridges theoretical linguistics with industrial applications, particularly in resource-scarce language scenarios and controlled language environments. Publication analysis reveals consistent focus on machine translation architecture evolution, parallel corpus construction for minority languages, and human-computer interaction in mobile text processing. Her MATS system development demonstrates a 'glass box' transparency approach to translation engineering. She leads major initiatives including the PLUG project (Parallel Corpora in Linköping, Uppsala, Göteborg) and the Supporting Research Environment for Swedish and Turkish. Her industry role at Convertus AB drives commercialization of academic language technology research through practical machine translation solutions.

• Data-Intensive Technologies
• Software Engineering
• Machine Learning
• +5 more

Welf Löwe is a资深 researcher and faculty member at Linnaeus University's Faculty of Technology, Department of Computer Science and Media Technology, and also teaches at Linköping University's Department of Computer and Information Science. His research focuses on data-intensive technologies, software metrics, design pattern detection, and context-aware systems. He leads the Data Intensive Software Technologies and Applications (DISTA) group and contributes to the Linnaeus University Centre for Data Intensive Sciences and Applications (DISA). He actively collaborates on projects like the Data Intensive Applications (DIA) graduate school and the High-Performance Computing Center (HPCC). His work spans machine learning applications in healthcare, forestry, and industrial automation. Recent research includes feature engineering in medical data, skeleton avatar technology for aging studies, and AI-driven diagnostics. He has authored over 150 peer-reviewed publications and participates in interdisciplinary initiatives such as the iSchool project.

• Lean Product Development
• Systems Engineering
• Platform Development
• +3 more

Dag Henrik Bergsjö serves as an Assistant Professor in Product Development at Chalmers University of Technology. His academic work focuses on integrating lean methodologies with systems engineering approaches to optimize product development processes. Dr. Bergsjö's research spans several interconnected domains of engineering management and systems design. His primary interests include Lean Product Development, Systems Engineering, and Platform Development (covering technology, product, and production platforms). He is actively working to develop the research area of Platform-based Product Development and explores the combination of Lean Product Development with Product Data Management (PDM/PLM). His work particularly emphasizes Lean Product Development tools such as Pulse, visual planning, and checklists. His recent publications (2019-2025) demonstrate a consistent focus on human-centric manufacturing systems, knowledge management in engineering contexts, and the application of lean principles to complex product development challenges. The research trends show increasing attention to socio-technical aspects of digitalization in industrial engineering processes and the development of frameworks for designing human-centric manufacturing systems for novel products. Dr. Bergsjö has secured significant research funding through multiple projects, primarily supported by VINNOVA and other Swedish research agencies. His projects span various domains including digital product development centers, AI analysis in construction projects, knowledge sharing in digital collaborative projects, and sustainable manufacturing methods. He leads and participates in several research initiatives including the Wingquist Laboratory: Digital Center for Product Development (2024-2024), Smart Module for AI Analysis during the Design Phase in Safety-Critical Complex Construction Projects (2024-2025), and previously led projects such as KIDSAM: Knowledge and Information Sharing in Digital Collaborative Projects (2018-2021), TechMo: Technology Modeling of Sustainable Manufacturing Methods in the Electronics Industry (2017-2018), and several others focused on platform-based development, digitalization, and lean methodologies.

• Robotics
• Control Systems
• Kinematics
• +5 more

Shivesh Kumar is an Assistant Professor in the Dynamics Division of the Department of Mechanics and Maritime Sciences at Chalmers University of Technology. He also holds a part-time Senior Researcher position at the Robotics Innovation Center, German Research Center for Artificial Intelligence (DFKI) in Bremen, Germany. Assistant Professor at Chalmers Part-Time Senior Researcher at DFKI Co-Chair, IEEE-RAS Technical Committee on Model-Based Optimization for Robotics His research focuses on robotics , particularly in kinematics, dynamics, and control systems for developing animal-like physical/athletic intelligence in robots. Key areas include underactuated robotics, control algorithm benchmarking, and human-robot interaction. Recent publications (2023-2024) highlight work on: Parkour control for monoped hoppers Reinforcement learning for robotic athleticism Energy-efficient brachiation robots Robust co-design of underactuated systems His projects involve collaborations with Wallenberg AI, Autonomous Systems and Software Program and Chalmers' digitalization initiatives. He maintains advisory relationships with the Advanced AI Team: Team Mechanics & Control and Underactuated Robotics Lab at DFKI. Contact: shivesh.kumar@chalmers.se

• Bioinformatics
• Systems Biology
• Computational Biology
• +7 more

Zelmina Lubovac-Pilav is a Senior Lecturer in Bioinformatics at the School of Bioscience, University of Skövde. She coordinates advanced and foundational courses in bioscience and serves as Programme Coordinator for Master's-level programs. Her research focuses on bioinformatics , systems biology , and disease module analysis , particularly in pancreatic cancer , multiple sclerosis , and breast cancer . Her work involves multi-omics data integration (genomics, proteomics, transcriptomics) and biomarker discovery , supported by projects like BIO-AID (AI-driven biomedical analytics) and DMDPipe (disease-causing protein modules in asthma). She develops computational tools such as MODalyseR and TFTenricher for disease module inference and transcription factor analysis. The 15 most recent articles highlight trends in latent space modeling , network biology , and clinical data analysis , with applications in pancreatic cancer early detection , multiple sclerosis biomarkers , and miRNA functional analysis . These studies emphasize algorithm development , data normalization , and community-driven network predictions . Contact: zelmina.lubovac@his.se

• Production Engineering
• Manufacturing Systems
• Simulation-Based Optimization
• +5 more

Kaveh Amouzgar is an Associate Professor in Production Engineering at the Department of Engineering, School of Engineering Science, University of Skövde. With a doctoral thesis completed in 2018 titled 'Metamodel Based Multi-Objective Optimization with Finite-Element Applications,' he has established himself as a significant contributor to the field of production engineering and optimization. Dr. Amouzgar's research primarily focuses on simulation-based optimization techniques, multi-objective optimization approaches, and the integration of augmented reality in manufacturing environments. His work bridges theoretical optimization methods with practical industrial applications, particularly in CNC machining, energy efficiency in manufacturing, and Industry 4.0 technologies. His research demonstrates a consistent trajectory of increasing complexity and industrial relevance since his doctoral work. His publication record reveals a strong trend toward interdisciplinary research at the intersection of manufacturing engineering, computer science, and human factors. Recent work (2023-2025) increasingly incorporates extended reality (XR) technologies, machine learning applications, and sustainability considerations into traditional manufacturing optimization problems. This evolution reflects the broader shift in production engineering toward digitalization and human-centered smart manufacturing systems. Dr. Amouzgar maintains extensive collaborations with researchers at the University of Skövde, particularly with Amos H.C. Ng, and has co-authored numerous publications with international partners. His work appears in reputable journals including Procedia CIRP, Journal of Manufacturing Systems, IEEE Access, and International Journal of Production Research, demonstrating both academic rigor and industrial relevance. At the University of Skövde, Dr. Amouzgar appears to be actively involved in research projects related to smart manufacturing, digital transformation of production systems, and optimization methodologies. His recent publications suggest involvement in learning factory environments and research initiatives focused on Industry 4.0 implementation in manufacturing contexts.

• Supply Chain Management
• Information Fusion in Manufacturing/Maintenance
• Multi-Agent Systems in Logistics
• +3 more

Sandor Ujvari is a Senior Lecturer in Logistics at the University of Skövde , associated with the School of Business and the Department of Commerce and Entrepreneurship. His research focuses on Supply Chain Management , Information Fusion in Manufacturing/Maintenance , Multi-Agent Systems (MAS) in Logistics , and AGV Modeling . His scholarly work spans interdisciplinary collaborations, particularly in healthcare quality improvement, where he explores Trust dynamics in management-initiated reforms System reliability in service delivery Cross-sectoral knowledge transfer He has published extensively on simulation-based optimization, stochastic transportation models, and institutional entrepreneurship. Recent trends in his publications highlight Integration of multi-agent systems for logistics Sustainability in supply chains Adaptation of industrial methods to healthcare His teaching involves Bachelor-level logistics courses and training programs, emphasizing ERP systems and digital transformation in supply chains. He currently leads the ongoing project "Labour Market Challenges of Green Transition in West Sweden" (2023–2029), analyzing societal impacts of automotive industry transformation. His co-authors include prominent researchers like Thomas Andersson , Nomie Eriksson , and Per Hilletofth . No scientific awards are explicitly mentioned in the provided texts.

Mikael FridenfalkView profile

Senior Lecturer

• Artificial Intelligence
• Computer Graphics
• Game Design
• +4 more

Mikael Fridenfalk is a Senior lecturer at the Department of Game Design, Uppsala University, Campus Gotland. He received his PhD in robotics from Lund University in 2003 and completed a postdoctoral research associate position at MIT (2003-2004). Since then, he has been affiliated with Uppsala University Campus Gotland (formerly Gotland University), initially as a lecturer and from 2005 as an associate professor and researcher within computer engineering and AI. With over 40 years of experience in computer programming, primarily using C++, Fridenfalk specializes in artificial intelligence and computer graphics applications. His research spans multiple domains including game design, digital twins, cellular automata, and neural networks. His work demonstrates a consistent focus on the intersection of mathematical principles and practical applications in computer science. Fridenfalk's recent publications (2022-2025) show a shift toward applied research in digital twins for industrial metaverse applications, cloud-edge-terminal collaboration, and game environment design using modular architecture. This reflects an evolution from his earlier theoretical work to more industry-relevant applications while maintaining strong mathematical foundations. Scientific Contributions: Extensive research on cellular automata and modular spaces across numerous publications Development of analytical methods for neural network weight evaluation Innovative applications of L-systems in game environment and music generation Pioneering work on digital twin technology for industrial and marine applications Contributions to photogrammetry techniques for game environments Methods for examination problem distribution in e-learning environments Fridenfalk's work bridges theoretical computer science with practical applications across multiple domains, demonstrating remarkable versatility while maintaining mathematical rigor. His research has been cited in various contexts, including academic papers and Wikipedia references, indicating impact across disciplines.

Johan LundinView profile

Professor

• Medical Technology
• Artificial Intelligence in Healthcare
• Global Health
• +3 more

Johan Lundin is a Professor of Medical Technology at the Department of Global Public Health, Karolinska Institutet, and Research Director at the Institute for Molecular Medicine Finland (FIMM), University of Helsinki. His research focuses on digital diagnostics and artificial intelligence (AI) for improving patient care, particularly in resource-limited settings. Key areas include image-based diagnostics for cancer, infectious diseases, and burn injuries, with an emphasis on point-of-care solutions. He holds a Docent title from the University of Helsinki (2003). Lundin leads the MoMic Project, supported by a 10M SEK grant from the Erling-Persson Family Foundation, which develops AI-driven mobile microscopy for diagnostics in collaboration with Kenyan partners. His work includes validation studies in Kenya on cervical cancer screening, helminth infection monitoring, and malaria treatment efficacy. Lundin’s research group has pioneered cloud-based platforms like the WebMicroscope and mobile microscopy tools. He collaborates internationally, including with Uppsala University and Kinondo Kwetu Hospital in Kenya, under the GlobeLife initiative. His scientific contributions span over 150 peer-reviewed articles in journals like PLOS Digital Health, Nature, and Lancet Public Health. Awards include the Erling-Persson Family Foundation Grant (2021-2024), recognizing his innovation in global health diagnostics. His future work aims to scale AI diagnostics to enhance equity in healthcare access through sustainable technologies.

Konstantinos KaragiorgosView profile

Senior Lecturer

• Climate Risk Assessment
• Flood Risk Management
• Social Vulnerability
• +3 more

Konstantinos Karagiorgos is an Associate Senior Lecturer at Karlstad University, specializing in climate risk assessment and disaster modeling. His research focuses on flood exposure, social vulnerability indices, and urban resilience strategies. He has co-authored numerous peer-reviewed articles on topics such as FEMA's National Risk Index, institutional capacity in wildfire management, and the accuracy of global population datasets in flood risk analysis. His work integrates geospatial tools, case studies, and trans-disciplinary approaches to climate adaptation. Key research interests include: Flood Risk Management Social Vulnerability Indices Climate Policy and Institutional Capacity Urban Planning for Disaster Resilience Recent articles highlight advancements in cloudburst modeling, pluvial flood mitigation through Low-Impact Development, and the complexities of social vulnerability in Nordic contexts. His studies often emphasize practical applications, such as Sweden's municipal disaster preparedness and index-driven policy frameworks. As part of his role, he collaborates on open-source catastrophe models and explores innovative data sources like crowdsourced rain data to enhance urban flood risk management. His work bridges academic research with actionable strategies for communities and policymakers facing climate-related challenges.

• Machine Learning
• Artificial Intelligence
• Cyber-Physical Systems
• +3 more

Ning Xiong is a Professor at Mälardalen University, affiliated with the School of Innovation, Design and Engineering and the Division of Intelligent Future Technologies. His research focuses on advanced artificial intelligence, machine learning, optimization algorithms, and cyber-physical systems. He explores applications ranging from digital twin frameworks in distributed systems to predictive maintenance using explainable AI and anomaly detection in timeseries data. His work integrates techniques like federated learning, Bayesian classifiers, and bio-inspired computing (e.g., membrane clustering) to address challenges in smart systems and data science. Key research areas include: Machine Learning & Deep Learning Cyber-Physical Systems Optimization Algorithms Smart Systems & IoT Data Science & Big Data Recent publications highlight advancements in digital twin frameworks for resilient distributed systems, ensemble learning for imbalanced data, and lightweight object detection methods for UAV imagery. His contributions emphasize practical applications in energy grids, predictive maintenance, and industrial automation while addressing theoretical challenges in model explainability and scalability. His research is characterized by interdisciplinary collaboration, combining software engineering, systems architecture, and domain-specific expertise to develop innovative solutions for dynamic environments.

Mobyen Uddin AhmedView profile

Professor

• Artificial Intelligence
• Computer Science
• Embedded Sensor Systems
• +5 more

Mobyen Uddin Ahmed is a Professor in Artificial Intelligence and Computer Science at Mälardalen University (MDU), affiliated with the School of Innovation, Design and Engineering’s Division of Computer Science and Software Engineering. He leads the Artificial Intelligence and Intelligent Systems group and contributes to the ESS-H research profile (Embedded Sensor Systems for Health). Education: M.Sc. in Computer Engineering (Dalarna University, Sweden), PhD in Computer Science (Mälardalen University, 2011), and postdoctoral research at Örebro University’s Center for Applied Autonomous Sensor Systems (2012–2014). Research focuses on AI-driven healthcare solutions, IoT technologies, sensor systems, and machine learning applications. He leads major projects like H2020 ‘SimuSafe’, ‘BrainSafeDrive’, and national initiatives such as InVIP. He co-organized international conferences like HealthyIoT (2016–2017), ICCBR2018, and pHealth2015. Teaching responsibilities include courses like Applied Artificial Intelligence, Machine Learning with Big Data, and Deep Learning for Industrial Imaging. He develops distance education programs for industrial professionals via projects like Into DeeP. Labs/Teams: Principal Investigator of ESS-H, involved in research groups focused on predictive analytics and intelligent embedded systems.

• Molecular Computer Simulations
• Force Field Development
• Coarse-Grained Modeling
• +7 more

Alexander Lyubartsev is a Professor at the Department of Chemistry in Stockholm University, specializing in Molecular Computer Simulations . His research bridges fundamental physical chemistry with applied computational modeling, focusing on biomembranes, DNA-chromatin structures, inorganic surfaces, and multiscale method development. He teaches courses like Physical Chemistry (15 hp) and Chemical Modeling (7.5 hp) , mentoring students in computational approaches for molecular systems. Education: PhD in Molecular Biophysics (1988), St. Petersburg State University Academic Positions: 1988-1992: Researcher, St. Petersburg State University 1993-2001: Research Associate, Stockholm University 2001-2007: Associate Professor, Stockholm University 2007-present: Professor, Stockholm University His research interests center on developing computational methods for molecular systems. Key areas include: Force Field Development : Slipids for lipids, inorganic surfaces (TiO2, ZnO), zeolite interactions Coarse-Grained Modeling : DNA, nucleosome core particles, and multiscale approaches using inverse Monte Carlo Advanced Sampling : Expanded ensemble techniques, metadynamics for free energy calculations Software Development : MDynamix for molecular dynamics, MagiC for coarse-graining The articles reflect his focus on: Modeling G-quadruplex elasticity under variable ionic conditions (2017) Extending Slipids force field to polyunsaturated lipids (2016) Analyzing polyelectrolyte conformational transitions with Monte Carlo methods (2017) Investigating ion-DNA binding specificity through molecular dynamics (2016) Developing transferable potentials for gas adsorption in zeolites (2016) Studying TiO2 nanoparticle wetting mechanisms via ab initio simulations (2016) His methodological contributions include inverse Monte Carlo coarse-graining, systematic force field optimization, and multiscale approaches for chromatin components. The research directly impacts drug design, nanotoxicity modeling, and materials science applications.