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Farzin GolzarView profile

Assistant Professor

• Sustainable Energy Systems
• Artificial Intelligence Applications in Environmental Science
• Circular Economy
• +3 more

Farzin Golzar is an Assistant Professor at KTH Royal Institute of Technology, affiliated with the Heat and Power Technology Division under the Digital Futures Faculty. His research focuses on sustainable energy systems, AI-driven environmental solutions, and circular economy strategies. He is part of a cross-disciplinary initiative at Digital Futures, addressing societal challenges through digital technology innovation. Key research interests include energy transition pathways for oil-dependent economies, optimization of renewable energy systems (e.g., solar-battery hybrids), and AI applications for CO₂ emission prediction. He also explores urban circularity through waste stream management and landfill site identification using GIS-AHP frameworks. His work spans technical-economic analyses of energy systems, battery degradation modeling, and symbiotic food-energy systems (e.g., biogas from manure for greenhouses). Recent studies emphasize climate policy implications and long-term sustainability in urban environments like Stockholm and Curitiba, Brazil. Advising and grants details are not explicitly provided in the text. He collaborates with institutions like RISE Research Institutes of Sweden and Stockholm University through Digital Futures. His lab/center, Digital Futures, is based at Osquars Backe 5, Stockholm.

Ming XiaoView profile

Associate Professor

• Wireless Communications
• Network Coding
• Machine Learning
• +10 more

Ming Xiao is an Associate Professor in the Division of Information Science and Engineering at KTH Royal Institute of Technology's School of Electrical Engineering and Computer Science (EECS). He is affiliated with the Digital Futures Faculty and leads research in wireless communications, machine learning, and network coding. His work focuses on 6G networks, distributed learning, and secure communications. Affiliations: KTH EECS, Digital Futures, Swedish Research Council, EU Horizon Europe projects Roles: Editor for IEEE Transactions on Wireless Communications, TPC Co-Chair for VTC Fall Research Interests: Dr. Xiao's expertise spans wireless communication systems (e.g., mmWave, NOMA), network coding, machine learning applications in communications, and energy-efficient distributed systems. He has pioneered work on intelligent reconfigurable surfaces (RIS), federated learning in edge computing, and integrated sensing-communications (ISAC). Projects: Ongoing EU-funded projects include ASCENT (autonomous vehicular networks) and COVER (unmanned aerial vehicles for emergency response). Past projects include 6G channel coding and intelligent energy management in smart communities. Grants: Over 12 active grants from VR, EU Horizon Europe, FORMAS, STINT, and VINNOVA Awards: IEEE Vehicular Technologies Society Best Paper Award (2023), World Top 2% Researcher (2020–2023), Highly Cited Researcher in Computer Science/Engineering. Labs/Teams: Leads the KTH Digital Futures initiative, collaborates with RISE Research Institutes, and manages a team of 12 PhD students/postdocs focusing on 6G, distributed ML, and secure IoT.

Reza SirjaniView profile

Senior Lecturer

• Energy Systems Optimization
• Renewable Energy Integration
• Power Electronics
• +9 more

Reza Sirjani is a Senior Lecturer in Electrical Engineering at Karlstad University. He has over 15 years of research experience in energy systems optimization, renewable energy, and power electronics. His academic journey includes roles at Cyprus International University (2013-2017) and Eastern Mediterranean University (2017-2022), where he became an Associate Professor and served as Vice Chair of the Department of Electrical and Electronics Engineering (2020-2022). Education: BSc in Electrical Engineering (Power Systems) from Khajeh Nasir Toosi University of Technology, Iran (2006) MSc in Electrical Engineering (Power Systems) from Tehran Science and Research University (2008) PhD in Electrical Engineering from National University of Malaysia (2013) Research interests include renewable energy integration, optimization techniques in energy systems, power electronics, FACTS devices, and smart grids. His work addresses challenges in power quality, energy storage systems, and distributed generation. Current projects: GränsENERGI (2025-2028): Innovative solutions for future energy supply LOKEN (2024-2027): Local Energy Management in Värmland Riskville power system expansion (2023-2024) Teaching: Courses include Electrical Power Systems Technology, Power Electronics, Electric Machines, and Renewable Energy Integration. Collaborations include Glava Energy Center, CSR, and Clear River Racing. Publications: Over 40 peer-reviewed articles focusing on optimization algorithms, energy storage, and renewable integration. Supervised 18 Master/PhD students.

• Power Electronics
• Electric Vehicle Power Systems
• AC Motor Drives
• +6 more

Sobhi Barg serves as an Assistant Senior Lecturer in Power Electronics at Mälardalen University's Department of Computer and Electrical Engineering (DET) in Sundsvall, Sweden. His position within the STC Research Centre focuses on advanced power conversion systems and magnetic component design for electric vehicles and renewable energy applications. Education: PhD in Electrical Engineering from National Engineering School of Sfax (2018) Erasmus Master in Sustainable Transportation and Electrical Power Systems (University of Oviedo, University of Nottingham, Polytechnic Institute of Coimbra) (2015) Master in Electrical Systems from National Engineering School of Tunis (2010) Engineering Degree from National Engineering School of Tunis (2008) His research centers on multi-objective optimization of power electronic systems , with expertise in core loss modeling for high-frequency magnetics, wireless power transfer, and EV power conversion. Recent work demonstrates innovative approaches to planar magnetics using unbalanced-flux techniques and precise core loss calculation for non-sinusoidal waveforms, significantly advancing efficiency in power converter design. Publications since 2019 reveal strong focus on magnetic component optimization and loss modeling , with 50% appearing in IEEE Transactions journals. Key trends include development of empirical core loss models for complex waveforms (2025), novel unbalanced-flux approaches for planar magnetics (2023-2024), and multi-objective GA optimization techniques for flyback transformers (2019). Scientific Recognition: Erasmus Mundus Scholarship-Category A (48,000 Euro) for Master studies in Sustainable Transportation and Electrical Power Systems (2013-2015) Dr. Barg teaches Analog Electronics and Electric Circuits laboratory courses while contributing to the STORE (Electrical Energy Storage) and MDOBC research projects. His work at the STC Research Centre bridges theoretical modeling with practical applications in renewable energy systems and electric vehicle powertrains, emphasizing experimental validation through Seebeck effect measurements and FEA simulations. Current research activities include DC inductor design based on Ampere's Law (2024) and continued refinement of core loss models for next-generation power converters, with significant implications for high-efficiency energy conversion in sustainable transportation systems.

• Intelligent Transportation Systems
• Traffic Flow Modeling
• Linear Parameter Varying Systems
• +7 more

Professor Balázs Adam Kulcsár is a faculty member in the Automatic Control research group at the School of Electrical Engineering and Computer Science, Chalmers University of Technology. With 104 publications and involvement in 34 research projects, he is a prominent researcher in intelligent transportation systems. His work spans multiple domains within transportation engineering and control theory, with significant contributions to traffic flow modeling, electric vehicle routing, and advanced control systems. Professor Kulcsár's research primarily focuses on intelligent transportation systems design, traffic flow modeling for control, Linear Parameter Varying systems, and failure diagnostics. His work demonstrates a strong integration of control theory with practical transportation challenges, particularly in the context of electric mobility and sustainable transportation. Recent research shows a growing emphasis on machine learning applications for transportation optimization, electric vehicle infrastructure, and urban traffic management. Analysis of his recent publications reveals a clear trajectory toward sustainable transportation solutions, with electric vehicle charging infrastructure, fleet management, and public transit optimization as dominant themes. His work increasingly incorporates machine learning techniques, particularly graph neural networks and reinforcement learning, to address complex transportation challenges. The research demonstrates strong interdisciplinary collaboration across engineering disciplines, with a focus on practical implementation of theoretical advances. Professor Kulcsár leads and participates in numerous research projects focused on future transportation systems, including projects on electric mobility, traffic optimization, and intelligent transportation infrastructure. His research group collaborates extensively with industry partners like Volvo and Heart Aerospace, as well as with other academic institutions. Current projects include Rethinking the Sustainability of V2G, Quantum computing for future mobility solutions, and Digital Twin for Energy Prediction. His research group maintains strong connections with transportation industry stakeholders and contributes to major initiatives such as the Transport Area on Advance project, which aims to achieve leading competence in future green, safe, and efficient transport systems. The team operates at the intersection of theoretical control systems and practical transportation applications, with particular expertise in modeling complex traffic phenomena and developing implementable control solutions.

• Wireless Communication
• Ultra-Reliable Low-Latency Communication (URLLC)
• Channel Coding
• +4 more

Hasan Basri Celebi is a researcher at KTH Royal Institute of Technology's School of Electrical Engineering and Computer Science (EECS), active in the field of wireless communication systems. His work focuses on ultra-reliable low-latency communication (URLLC) for mission-critical IoT applications, with a particular emphasis on decoding complexity constraints and computational efficiency in next-generation networks. PhD in Electrical Engineering from KTH (2021) Key research areas: Channel coding, Finite blocklength regime, Industrial IoT, Signal processing, Biomedical sensor development Celebi's publications span telecommunications journals and conferences, addressing theoretical limits in low-latency communication and practical implementations for complexity-constrained receivers. His interdisciplinary work includes developing medical devices like transcutaneous bilirubinometers and optical probes for diffuse spectroscopy applications. A notable grant from the Swedish Foundation for Strategic Research (SSF) supported his work on low-complexity receivers.

• Smart Energy Systems
• Renewable Energy Integration
• Thermal Energy Storage
• +3 more

Amirmohammad Behzadi is a doctoral researcher at KTH Royal Institute of Technology, specializing in smart energy systems within the Fluid and Climate Technology group. His work focuses on designing, controlling, and optimizing sustainable heating, cooling, and electricity systems for buildings. His research spans Hydrogen-integrated microgrids Machine learning for energy management Thermal storage solutions Decarbonization of commercial/residential buildings Renewable energy hybridization and has been recognized in the World's Top 2% Scientists (2022-2024) by Stanford/Elsevier. Behzadi's peer-reviewed publications (15 most recent) demonstrate expertise in AI-driven optimization Fuel cell applications Hybrid ventilation systems Grid-connected renewable solutions Borehole thermal energy storage with a focus on cold climate adaptations. His scientific contributions include being selected as a top project by the Royal Swedish Academy of Engineering Sciences (IVA, 2024) and receiving the Best Researcher Award at the University of Tehran's 28th Research Festival (2021). His educational background includes an M.Sc. in Energy Conversion from the University of Tehran and a B.Sc. in Mechanical Engineering with top honors.

• Automation Engineering
• Production Simulation
• Multi-Objective Optimization
• +7 more

Amos H.C. Ng is a Professor of Automation Engineering at the School of Engineering Science, University West (Högskolan i Skövde). His academic qualifications include BEng, MPhil, and PhD degrees, complemented by professional certifications such as Chartered Engineer (UK) and membership in the Institution of Engineering and Technology (UK). His research focuses on production simulation, multi-objective optimization, simulation-based innovization, and digital human modeling, with applications in manufacturing systems, Industry 4.0, and smart manufacturing. Ng has contributed to over 150 publications since 2000, spanning topics like decision support systems, maintenance optimization, and reconfigurable manufacturing. His work integrates simulation, data mining, and evolutionary algorithms to address challenges in production systems, including bottleneck analysis, energy efficiency, and human-robot collaboration. Notable projects include developing the Mimer knowledge discovery tool and frameworks for digital twin applications in production lines. His research emphasizes practical industry applications, collaborating with organizations to improve production processes through advanced methods like trend mining and cloud-based optimization. Ng also serves as a course coordinator and contributes to educational initiatives in automation engineering.

Ulf HolmbergView profile

Professor

• Control Theory
• Mechatronics
• Robotics
• +3 more

Ulf Holmberg is a Professor in Control Theory at Halmstad University's School of Information Technology. His research focuses on mechatronic systems, robotics, and advanced control methodologies with applications in automotive engineering and health technology. Research Areas: Control theory, mechatronics, combustion engine optimization, and assistive robotics Collaborations: Works with industrial partners in automotive and health technology sectors Teaching: Involvement in mechatronics curriculum development His publications highlight innovative sensor applications in combustion analysis and orthotic control systems. While no formal awards are documented in the source material, his work demonstrates cross-disciplinary impact in automotive engineering and rehabilitation robotics.

• Reconfigurable Manufacturing Systems
• Simulation-based Optimization
• Multi-Objective Optimization
• +4 more

Carlos Alberto Barrera Diaz is a researcher affiliated with the University of Skövde , specifically within the School of Engineering Science and its Production and Automation Engineering department. His work focuses on Reconfigurable Manufacturing Systems (RMS) , where he applies simulation-based multi-objective optimization (SMO) and knowledge discovery methods to address challenges in dynamic production environments. His research spans from 2020 to 2023 and involves collaborations with academics like Amir Nourmohammadi, Henrik Smedberg, and Amos H.C. Ng. Key themes include RMS availability analysis, buffer capacity allocation, task assignment to workstations, and knowledge-driven optimization techniques. Publications in journals such as Mathematics, IEEE Access, and MDPI highlight his technical expertise and contributions to industry-specific challenges like mass customization and Industry 5.0 sustainability goals. His findings demonstrate how SMO methods can enhance decision-making in RMS, particularly under fluctuating production volumes and capacity constraints. He has been funded by the Knowledge Foundation (KKS) through the Virtual Factories with Knowledge-Driven Optimization (VF-KDO) profile and acknowledged support from VINNOVA’s EXPLAIN project. While no direct student advising is mentioned, his work has significantly advanced RMS optimization frameworks through industrial case studies and algorithmic innovations.

Mariusz WzorekView profile

Assistant Professor

• Artificial Intelligence
• Robotics
• Autonomous Systems
• +4 more

Mariusz Wzorek is an Assistant Professor and Head of Unit at the Department of Computer and Information Science (IDA) at Linköping University, Sweden. He is affiliated with the Artificial Intelligence and Integrated Computer Systems (AIICS) division, which focuses on advancing research and education in artificial intelligence, robotics, and collaborative systems. Dr. Wzorek holds a PhD from Linköping University (2023) and has expertise in autonomous systems, unmanned aerial vehicles (UAVs), and robotics. His research emphasizes safe navigation, collaborative robotics, and applications in emergency response and public safety scenarios. Key areas include UAV-based collision avoidance, geolocation using aerial imagery, and distributed systems for multi-agent coordination. His recent work involves developing systems like the RGS⊕ (RDF Graph Synchronization) for collaborative robotics, secure remote ID protocols for UAVs, and algorithms for autonomous search and rescue missions. These projects address challenges in sensor fusion, real-time decision-making, and robust communication in dynamic environments. Publications highlight contributions to UAV navigation, wireless mesh networks in emergencies, and 3D reconstruction using heterogeneous UAV teams. His research bridges theoretical foundations in AI with practical applications in robotics and safety-critical systems. While no specific scientific awards are listed, his active role in the AIICS division and numerous peer-reviewed publications reflect his significant contributions to the field.

Mikael AxinView profile

Associate Professor

• Fluid Power Systems
• Mechanical Engineering
• Hydraulic Systems
• +3 more

Mikael Axin is an Associate Professor at Linköping University's Department of Management and Engineering, specializing in Fluid and Mechatronic Systems. He teaches courses in machine elements, product development, and hydraulics, and supervises bachelor and master's theses. His research focuses on hydraulic systems for mobile machinery, energy efficiency, and system dynamics, with a particular emphasis on fluid power and mechanical design. Education: PhD in Fluid and Mechatronic Systems, Linköping University (2015) Licentiate of Engineering in Fluid and Mechatronic Systems, Linköping University (2013) Master of Science in Mechanical Engineering, Linköping University (2009) Research Interests: Dr. Axin explores hydraulic system optimization in mobile applications, including energy efficiency, pump control strategies, and damping design. His work bridges theoretical modeling with practical implementation, addressing challenges in industrial machinery and sustainable engineering solutions. Awards: Recipient of the Gyllene Skiftnyckeln award for Best Teacher (2013) Ingeströmska Stiftelsen Scholarship for overseas studies (2010) Teaching and Development: He actively contributes to pedagogical innovation, including digitalizing exams to enhance student accessibility and reduce environmental impact. His courses emphasize hands-on learning and real-world problem-solving in mechanical engineering. Lab/Team: His work is conducted within the Product Realisation (PROD) unit, collaborating with industry partners to advance fluid power systems and product development methodologies.

Johan PerssonView profile

Associate Professor

• Computer Aided Product Development
• Surrogate Models
• Finite Element Analysis (FEM)
• +4 more

Johan Persson is an Associate Professor and Head of Unit at Linköping University's Department of Management and Engineering (IEI). He leads the Product Realisation (PROD) division, focusing on advancing Computer Aided Product Development (CAD) and optimization techniques. His research emphasizes computationally efficient surrogate models to enhance FEM/CFD analyses and streamline product development processes. Key areas of interest include design automation frameworks, additive manufacturing integration, and multidisciplinary optimization strategies. He teaches courses such as Machine Elements (TMKT39), Design Optimization (TMKT48), and Collaborative Multidisciplinary Optimization (TMKT79). His work bridges academic research and industry applications, with notable contributions to frameworks supporting Additive Manufacturing (AM) and OpenMDAO-based optimization. Research trends in his publications highlight advancements in AM applications, design automation tools, and collaborative optimization methods. Notable projects include a framework for AM design integration and studies on hydraulic pump design using additive techniques. Johan supervises PhD students including Erik Gustafsson and Javier Villena Toro. His research is supported by collaborations with colleagues like Anton Wiberg and Johan Ölvander. The Automation Lab, a key platform for his work, pioneers innovations in CAD automation and digital design processes.

• Transportation Systems
• Electric Bus Optimization
• Energy Storage Scheduling
• +4 more

Ziling Zeng is a researcher at Chalmers University of Technology, Sweden, affiliated with the Mechanics and Maritime Sciences (M2) school and the Logistics and Transportation department. His work focuses on electric bus systems, energy storage optimization, and sustainable urban mobility. Research interests span public transit electrification , fleet management , battery degradation modeling , and multi-modal logistics . His 15 most recent publications emphasize optimization frameworks, cost analysis, and real-world implementation challenges for zero-emission transportation. Key projects include: ERGODIC (2023–2026) - VINNOVA & European Commission funding Accelerating transport electrification by machine learning (2021–2023) - European Commission Speed Control of Connected Vehicles (2020–2023) - VINNOVA His work addresses policy, technology, and infrastructure challenges in transitioning to sustainable transit systems.

• Robotics
• Machine Learning
• Tactile Sensing
• +4 more

Yasemin Bekiroglu is an Associate Professor at Chalmers University of Technology in the Control Engineering department. Her research focuses on data-efficient learning from multisensory data for robotics applications. PhD from Royal Institute of Technology (KTH) in 2012 Her work spans Robotics , including Grasp Planning , Adaptive Control , and Gaussian Processes . Recent publications emphasize Tactile Sensing and Multisensory Integration . Key trends in her research include Motion Planning , Safe Trajectory Generation , and Implicit Surface Representation for robotic systems. She has served as a reviewer and Associate Editor for leading robotics conferences and journals. Best Paper Award at RAHA 2016 Best Manipulation Paper Award at ICRA 2013 IROS CoTeSys Cognitive Robotics Best Paper Finalist in 2013 Yasemin contributes to projects like Dexterous Robot Assistant , advancing everyday object manipulation technologies.