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Torbjörn LarssonView profile

Professor

• Mathematical Optimization
• Operations Research
• Linear Programming
• +10 more

Torbjörn Larsson is a Professor in the Department of Mathematics at Linköping University, affiliated with the Division of Applied Mathematics (TIMA). His work bridges theoretical and applied optimization with significant impact in healthcare, logistics, and finance. His research interests include Mathematical Optimization , Operations Research , Brachytherapy Treatment Planning , Vehicle Routing , and Portfolio Optimization . He develops advanced algorithms such as Lagrangian heuristics, metaheuristics, and feasible direction methods to solve complex decision problems. The recent publications indicate a strong focus on developing bounding techniques and heuristic frameworks for discrete and multi-objective optimization, with applications ranging from radiation therapy to transportation logistics. His work emphasizes both theoretical rigor and practical implementation. Scientific Contributions: Development of novel optimization methods for brachytherapy treatment planning Advancement of Lagrangian and metaheuristic frameworks Application of optimization in finance (portfolio selection) and scheduling He collaborates extensively on research projects involving mathematical modeling and algorithm design. While specific advising roles are not listed, his co-authorship with junior researchers suggests mentorship activity. He has contributed to projects on decision support systems for scheduling and large-scale optimization in finance. Laboratories and Research Groups: Applied Mathematics (TIMA), Department of Mathematics, Linköping University Research environment focused on optimization and its applications in medicine and logistics

• Machine Learning
• Natural Language Processing
• Predictive Maintenance
• +4 more

Tony Lindgren is an Associate Professor at the Department of Computer and Systems Science, Stockholm University, affiliated with the Data Science Research Group and Natural Language Processing Research Group. His work bridges data science and NLP , focusing on interpretable models, constraint programming, and predictive maintenance systems. Research interests include: Machine Learning for explainability and fairness Constraint Programming in maintenance optimization Natural Language Processing for risk analytics and troubleshooting Recent publications demonstrate trends in multi-objective optimization (2025 satellite scheduling), conformal prediction (2024 CoPAL), and fault detection (2024 Automotive Nowcasting). His work often integrates domain-specific constraints with scalable algorithms across applications like food safety and autonomous vehicles. Software tools developed by Lindgren include: Example-based Feature Tweaking Rule Indexing Frameworks His research groups focus on AI-driven decision support for high-stakes domains, combining technical innovation with societal impact considerations.

• Vehicular Control Systems
• Electric and Hybrid Vehicle Technology
• Vehicle Dynamics
• +4 more

Jonas Fredriksson is a Professor in the Mechatronics research group at the Department of Systems and Control Engineering, Chalmers University of Technology. His work focuses on electric/hybrid vehicles, vehicle dynamics, active safety systems, and optimization-based coordination of automated vehicles. Academic Rank: Professor Affiliation: Chalmers University of Technology Department: Systems and Control Engineering Email: jonas.fredriksson@chalmers.se Research Themes: Powertrain control and energy management for electric/hybrid vehicles Advanced control strategies for heavy articulated vehicles Autonomous driving in confined environments Battery thermal management and charging optimization Vehicle stability and safety systems using Newtonian mechanics Article Trends: Recent publications emphasize 1) optimization algorithms for electric vehicle coordination, 2) aerodynamic modeling under crosswind conditions, 3) stochastic approaches to longitudinal vehicle dynamics, and 4) robust control systems for articulated heavy vehicles. The work combines classical mechanics with modern machine learning techniques. Teaching & Leadership: Supervises doctoral students and leads research projects in mechatronics. Manages the master's program in Systems, Control and Mechatronics. Teaches courses in mechatronics and vehicle control systems.

Wlodek KuleszaView profile

Professor

• Systems Engineering
• Measurement and Instrumentation
• Sensor Networks
• +3 more

Wlodek Kulesza is a Senior Professor in the Department of Mathematics and Natural Sciences at the Blekinge Institute of Technology in Karlskrona, Sweden. He has held academic positions since 2001, including a Professorship in Multi-sensor Systems since 2007. Research: Multisensory systems for health and security applications Teaching: Research Methodology, Philosophy of Science, and Sensors Signals and Systems Expertise: Systems engineering, sensor fusion, IoT, and measurement data handling Research Focus : His work in systems engineering emphasizes multisensor integration for applications spanning healthcare, security, wind energy safety, and subsea cable monitoring. He has pioneered approaches to stereovision calibration, localization algorithms, and real-time safety systems. Awards : Andy Chi Best Paper Award (2009, IEEE Transactions in Measurement and Instrumentation) Collaborations : Visiting Professor at Chinese and Polish universities, with extensive cross-border educational workshops and remote lab federations (e.g., PILAR/VISIR projects).

• Nuclear Engineering
• Thermal Hydraulics
• Two-Phase Flow Dynamics
• +3 more

Xiangyu Li is a doctoral student at the Department of Nuclear Science & Engineering, KTH Royal Institute of Technology. His research primarily focuses on nuclear engineering and thermal hydraulics, with a special emphasis on two-phase flow dynamics in porous media, machine learning applications, and reactor safety analysis. He has contributed to studies on debris bed remelting in reactor pressure vessels and optimization of heat exchangers using genetic algorithms. His work integrates experimental analysis with numerical simulations, addressing challenges such as interfacial drag forces and flow pattern identification through advanced signal processing and SVM classification techniques. While no academic awards are listed, his research has been published in peer-reviewed journals and conference proceedings since 2022.

Bestoun S. Ahmed Al-BeywaneeView profile

Professor

• Quality Assurance of Machine Learning Systems
• Software Testing
• Trustworthy AI Systems
• +5 more

Bestoun S. Ahmed Al-Beywanee is a Professor in AI and Software Engineering at the Department of Mathematics and Computer Science, Karlstad University, Sweden. He joined the university as a Senior Lecturer in 2019, was promoted to Associate Professor in 2020, and to Professor in 2023. His roles include teaching advanced courses such as AI Engineering, Automated Software Engineering, and Software Testing Fundamentals. His research focuses on software quality assurance, trustworthy AI systems, and MLOps, with a strong emphasis on combinatorial testing, IoT systems, and applied optimization techniques. Education: BSc (Electrical and Electronic Engineering, University of Salahaddin-Erbil, 2004); MSc (University Putra Malaysia, 2009); PhD (Software Engineering, University Sains Malaysia, 2012). Postdoctoral research at the Swiss AI Lab IDSIA (2015) and positions at Salahaddin University and Czech Technical University further enriched his expertise. Research interests span Quality Assurance of machine learning systems, software testing methodologies, trustworthy AI, IoT system reliability, and optimization algorithms. He has pioneered frameworks like PatrIoT for IoT testing and contributed to MLOps robustness. His work integrates machine learning with anomaly detection, adaptive systems, and industrial applications. Recent articles highlight advancements in data-driven heat pump management, MLOps robustness, and edge-cloud AR/VR optimization. Collaborations include projects on digital twins, smart manufacturing, and industrial IoT. His contributions bridge theoretical research with practical industrial solutions, emphasizing system reliability and AI ethics.

• Materials Science
• Mechanical Engineering
• Manufacturing Engineering
• +5 more

Rohollah Ghasemi is a Senior Lecturer in Mechanical Engineering at the School of Engineering Science , University of Skövde. His research focuses on materials science , manufacturing processes , and tribology , with particular emphasis on compacted/graphite iron microstructure analysis and laser welding optimization . His recent publications (2022–2025) explore advanced manufacturing techniques, including deep learning applications for welding quality prediction and multi-physics simulations for recoil force validation. Earlier works (2014–2019) investigate abrasion resistance , scratch mechanisms , and austempering variables in cast iron systems. Key Research Trends : Process-induced stress modeling, laser welding of dissimilar materials, microstructural engineering of CGI, tribological behavior under load, meta-model optimization Collaborations : Kent Salomonsson, Tobias J. Andersson, Anders E.W. Jarfors, Attila Dioszegi

Ali MansourianView profile

Professor

• Geospatial Artificial Intelligence
• Spatial Data Infrastructures
• Multi-Criteria Decision Analysis
• +7 more

Ali Mansourian is a Professor of Geomatics at Lund University's Department of Physical Geography and Ecosystem Science, where he serves as Director of the Lund University GIS Centre and Coordinator of the GIS & RS Master Programme. He is actively involved with the United Nations Global Geospatial Information Management (UN-GGIM) Academic Network and previously served on the European Association of Geographic Information Laboratories in Europe (AGILE) council. His academic leadership spans large-scale international research initiatives and capacity-building projects funded by Erasmus+ and SIDA. Mansourian's research focuses on Geospatial Artificial Intelligence (GeoAI), Spatial Data Infrastructures (SDI), and Multi-Criteria Decision Analysis (MCDA) using multi-objective optimization techniques. His work extends to applying GIS in epidemiology and public health, disaster risk management, land-use planning, climate change, environmental management, and sustainability. His research portfolio demonstrates a strong interdisciplinary approach, bridging geospatial technology with critical societal challenges. His recent publications reveal a clear trend toward integrating advanced machine learning techniques with geospatial analysis, particularly in health applications, environmental monitoring, and climate change impacts. The research shows increasing emphasis on spatial ensemble learning, remote sensing applications, and the development of GeoAI tools that make geospatial analysis more accessible through natural language interfaces. His work spans multiple continents, with significant contributions in Africa, Europe, and Asia. Mansourian has extensive experience supervising PhD students and postdoctoral researchers, though specific student names aren't listed in the provided materials. He has coordinated numerous large-scale international projects including Geo-Academy, INTEGRAL, CADEO, and SWEMENA, demonstrating significant grant acquisition and management expertise. His leadership extends to evaluating proposals for major European research grant programs and serving as an invited evaluator for PhD theses. As Director of the Lund University GIS Centre and active member of multiple international networks, Mansourian leads a dynamic research environment focused on advancing geospatial technologies and their applications. His teams work at the intersection of traditional GIScience and emerging artificial intelligence approaches, creating innovative solutions for complex spatial problems across multiple domains including public health, environmental management, and sustainable development.

• Signal Processing
• Bayesian Statistics
• Deep Learning
• +4 more

Lars Hammarstrand is an Associate Professor at Chalmers University of Technology specializing in the Signal Processing research group. His work integrates model-based Bayesian statistics with deep machine learning for applications in visual localization, sensor fusion, and autonomous systems, with emphasis on robustness in real-world environments. His research focuses on bridging Bayesian inference and deep learning to solve challenges in autonomous vehicle perception. Key areas include visual localization under appearance changes, radar-camera sensor fusion, and out-of-distribution detection for safety-critical systems. Recent work explores neural radiance fields for radar, semi-supervised learning for mapping, and probabilistic hierarchical classification to address real-world uncertainties in autonomous driving. Analysis of his 2020-2025 publications reveals a trajectory toward unifying geometric and semantic understanding in autonomous systems. His work demonstrates increasing integration of neural radiance fields with traditional filtering techniques, while advancing open-set recognition capabilities. Notable contributions include road geometry estimation frameworks, extended object tracking with PHD filters, and methods to mitigate data leakage in localization benchmarks. No scientific awards were mentioned in the provided materials. Hammarstrand has contributed to academic supervision methodology through his publication on improving master's thesis supervision efficiency, though specific student names are not listed. The provided information contains no details about research grants or funding sources. He operates within Chalmers University's Signal Processing research group, which develops advanced algorithms for automotive perception systems, focusing on sensor fusion techniques that combine radar, camera, and motion data for robust environmental understanding in autonomous vehicles.

• Sustainable Mobility
• Electrification of Transport Systems
• Shared and Connected Mobility
• +5 more

Kun Gao is an Assistant Professor at the Department of Architecture and Civil Engineering at Chalmers University, leading the Urban Mobility Systems research group. His work bridges transportation engineering and data science to develop sustainable mobility solutions through electrification, shared systems, and connected infrastructure. Research Focus: Electric vehicle integration, charging infrastructure optimization, multimodal mobility systems Funding: Supported by JPI Urban Europe, FORMAS, Swedish Innovation Agency, Swedish Energy Agency, and Chalmers AoA Transport/Energy Methods: Machine learning, big data analytics, system optimization His recent publications emphasize autonomous vehicle safety , renewable energy integration , and equity in mobility systems . Current work explores AI-driven infrastructure planning and coupled transportation-energy systems.

• Electric Vehicle Engineering
• Battery System Design
• Autonomous Vehicle Optimization
• +5 more

Jelena Andric is an Associate Professor in the Vehicle Engineering and Autonomous Systems department at Chalmers University of Technology, working within the Mechanics and Maritime Sciences school. Her research spans multiple aspects of vehicle engineering with a particular focus on electric and autonomous vehicle technologies. Her research interests primarily center around electric vehicle engineering, battery system design, autonomous vehicle optimization, thermal management systems, computational fluid dynamics, reinforcement learning applications, and hardware-in-the-loop simulation. She has developed expertise in applying advanced computational methods to solve complex vehicle engineering problems, particularly in the areas of battery management and energy optimization. Analysis of her recent publications reveals a strong trend toward optimizing electric vehicle performance, particularly focusing on battery degradation mitigation, thermal management, and energy efficiency. Her work increasingly incorporates artificial intelligence techniques, especially reinforcement learning, to develop sophisticated optimization approaches for electric trucks and autonomous vehicles. The research spans from fundamental fluid dynamics studies to applied engineering solutions for real-world transportation challenges. Dr. Andric has been actively involved in numerous research projects funded by organizations including VINNOVA, the Swedish Energy Agency, CHAIR, and ÅForsk. These projects cover diverse areas such as battery aging prediction, vehicle management strategies for electrified vehicles, electric public transit systems, and virtual engine calibration. Her current research portfolio includes leadership in projects related to simulation-based evaluation of connected vehicles, battery aging prediction for electric autonomous vehicles, AI-cloud-based vehicle management strategies, and transition to electric public transit systems. These projects demonstrate her commitment to addressing critical challenges in sustainable transportation through advanced engineering solutions.

• Robotics
• Autonomous Systems
• Spatial Cognition
• +6 more

Patric Jensfelt is a full-time Professor at the Robotics, Perception and Learning (RPL) division within the School of Electrical Engineering and Computer Science (EECS) at KTH Royal Institute of Technology . He contributes to robotics and autonomous systems through research, teaching, and industry collaboration. Academic Rank: Professor Department: Robotics, Perception and Learning (RPL) School: School of Electrical Engineering and Computer Science (EECS) University: KTH Royal Institute of Technology His research focuses on robotics and autonomous systems , particularly spatial cognition for enabling robots to understand and model their environments. He integrates sensor data with high-level symbolic reasoning to advance navigation, mapping, and system integration. Recent work includes applications in service robotics, 3D foot scanning (via the company Volumental ), and drone-based perception. The 15 most recent articles highlight advancements in scene flow estimation, autonomous driving, LiDAR processing, and domain adaptation. They emphasize self-supervised learning , dynamic environment modeling , and robust spatial representations for autonomous systems. Patric supervises Master’s and PhD students, particularly in project courses like DD2410 Introduction to Robotics , DD2419 Project Course in Robotics and Autonomous Systems , and the doctoral course FDD3356 System Integration for Robotics . He is actively involved in the WASP Graduate School , teaching Autonomous Systems annually. He leads the Robotics, Perception and Learning (RPL) division, which fosters innovation in robotics through experimental projects and industry partnerships like Intelligent Machines . The division supports internships (unpaid) and encourages applicants to demonstrate prior engagement with his research.

Seif HaridiView profile

Professor

• Parallel Computing
• Distributed Computing
• Cloud Computing
• +4 more

Seif Haridi is a Professor at KTH Royal Institute of Technology in Stockholm, Sweden, specializing in parallel and distributed computing systems. He holds dual roles as Chair-Professor of Computer Systems and Chief Scientific Advisor at RISE SICS. His research integrates systems engineering with theoretical foundations, focusing on programming systems, distributed computing, and big data technologies. Key contributions include co-designing SICStus Prolog, the Mozart Programming System, and Apache Flink, as well as leading the development of HOPS, a European big data platform awarded the IEEE Scale Prize 2017. He has led major EU projects like EIT-Digital’s cloud computing initiative and co-founded startups such as LogicalClocks and HiveStreaming. His teaching includes courses on distributed algorithms and peer-to-peer computing at KTH. Notable awards include the European Data Science Technology Innovation 2019. His work spans systems like HOPS, Flink, and Kompics, emphasizing scalability and robustness in distributed environments. Current projects include CDA (Continuous Deep Analytics) and ExtremeEarth for geospatial data analysis. Research interests include distributed algorithms, consensus protocols, and cloud-native systems. His lab’s contributions to scalable storage (e.g., HopsFS) and stream processing (Apache Flink) highlight his impact on both academia and industry.

Alp YurtseverView profile

Assistant Professor

• Optimization
• Machine Learning
• Data Science
• +3 more

Alp Yurtsever serves as an Assistant Professor in the Department of Mathematics and Mathematical Statistics at Umeå University, Sweden, where his research pioneers end-to-end optimization frameworks bridging theoretical modeling and practical algorithm design for data science challenges. His work fundamentally rethinks traditional black-box system approaches by integrating problem formulation with solution methodologies. His academic journey includes: PhD in Computer and Communication Sciences from École Polytechnique Fédérale de Lausanne (EPFL) under Prof. Volkan Cevher Postdoctoral fellowship at MIT's Laboratory for Information and Decision Systems (LIDS) with Prof. Suvrit Sra Dual BSc in Electrical and Electronics Engineering and Physics from Middle East Technical University Yurtsever's research centers on optimization theory for machine learning, with groundbreaking contributions in federated learning systems, convex programming, and scalable semidefinite solvers. He champions a unified perspective where modeling and algorithmic development inform each other, yielding methods with proven theoretical guarantees and real-world efficiency. His work particularly addresses communication bottlenecks in distributed systems and non-convex landscapes in neural network training, with applications spanning privacy-preserving AI and edge computing. Analysis of his publication trajectory reveals dominant themes in federated optimization and Frank-Wolfe variants, where he consistently develops communication-efficient algorithms for heterogeneous device networks. His recent work demonstrates increasing sophistication in handling multi-tier architectures and personalized learning objectives, while maintaining rigorous convergence guarantees. The integration of quantum-classical hybrid approaches in his 2022 ECCV paper signals expanding methodological boundaries. His scientific recognition includes: Thesis Distinction for PhD dissertation "Scalable Convex Optimization Methods for Semidefinite Programming" (EDIC program committee) Yurtsever actively contributes to Umeå University's Mathematical Programming Group and Statistical Learning for Spatio-Temporal Data initiative, though specific grant awards remain undisclosed in available materials. His collaborative network spans EPFL, MIT, and multiple European institutions as evidenced by co-authorship patterns. While no formal advisees are listed, his publications show mentorship of junior researchers through joint conference presentations. His laboratory operations are embedded within Umeå University's Department of Mathematics and Mathematical Statistics in the MIT-huset building, leveraging institutional resources for high-performance optimization research while maintaining strong international connections through his post-PhD affiliations.

Masoumeh EbrahimiView profile

Associate Professor

• Computer Architecture
• Machine Learning
• Embedded Systems
• +6 more

Masoumeh Ebrahimi is an Associate Professor at KTH Royal Institute of Technology, Division of Electronics and Embedded Systems, and holds an Adjunct Professor position at the University of Turku, Finland. She leads research in hardware acceleration, neural architecture search, and fault-tolerant systems. Her work bridges machine learning, embedded systems, and network-on-chip (NoC) design. Research Interests: Hardware-Accelerated Machine Learning 6G Network Architectures Fault-Tolerant Computing High-Performance GPU Systems Network-on-Chip (NoC) Design Federated Learning Key Projects: Co-supervisor of Hui Chen’s postdoc project Generalizing hardware acceleration for nonlinear functions . Active in Digital Futures, a cross-disciplinary center focusing on societal challenges using digital tech. Collaborates on edge computing, 6G networks, and resilient embedded systems. Labs & Teams: Core member of KTH’s Digital Futures initiative, advancing AI accelerators and next-gen communication systems. Engaged in EU-funded projects on NoC reliability and federated learning frameworks.