Find Academic Supervisors

• Distributed Energy Resources
• Smart Grids
• Distribution Network Automation
• +5 more

Sami Repo is a Professor in Electrical Engineering, focusing on power distribution systems and smart energy technologies. His work spans distribution network automation, flexibility services, and integration of distributed energy resources. Doctor of Science (Technology) in Electrical Engineering (2001) Master of Science (Technology) in Electrical Engineering (1996) His research interests include smart grids, congestion management, and cyber-physical energy systems. Recent publications address challenges in electric vehicle charging, green hydrogen regulation, and photovoltaic revenue optimization. He serves as an examiner and doctoral dissertation opponent, contributing to academic evaluation in electrical engineering programs. Key subtopics include EV charging control, EU energy policy, and renewable energy integration. Examiner for Wenlong Liao (2023): Modelling and optimization of active distribution networks Opponent for Etherden Nicholas (2012): Distributed energy resource hosting capacity

Hamed BadihiView profile

Assistant Professor

• Condition Monitoring
• Fault-Tolerant Control
• Attack-Resilient Control
• +3 more

Hamed Badihi is an Assistant Professor in Automation Technology and Dependable Systems at Tampere University , part of the Faculty of Engineering and Natural Sciences. He leads the Dependability and Automation Research in Cyber-Physical Systems (DARES) Group within the Dependable Systems Cyber Laboratories . His research focuses on critical aspects of condition monitoring, fault-tolerant control, and attack-resilient control to advance sustainable, dependable cyber-physical systems. Research Interests include: Cybersecurity for industrial control systems Fault-tolerant control mechanisms Resilient control strategies for renewable energy systems Condition monitoring of wind turbines and microgrids Recent Contributions emphasize hybrid approaches combining machine learning and control theory for cyber-attack detection and system resilience in wind farms and microgrids. His work addresses challenges like real-time fault diagnosis and adaptive control under adversarial or environmental perturbations. Awards & Roles : Senior Member of IEEE, editor for International Transactions on Electrical Energy Systems , Advances in Fuzzy Systems , and Processes journals. Active in EU projects like StreamSTEP . Labs & Teams : Directs the DARES Group, collaborating on initiatives like the Dependable Systems Cyber Laboratories to pioneer innovations in cyber-physical system dependability.

• Power Electronics
• Switch Mode Power Supplies
• Electromagnetic Compatibility (EMC)
• +7 more

Jorma Kyyrä is a Professor at Aalto University's Department of Electrical Engineering and Automation. His primary research focuses on power electronics, wireless power transfer systems, and high-frequency converter design. He has contributed significantly to fields like switch mode power supplies, electromagnetic compatibility (EMC), and power factor correction. Key honors include the IEEE Education Society Chapter Achievement Award (2006) for Nordic Chapter. His work emphasizes practical applications in renewable energy integration, DC microgrids, and contactless charging systems. Recent research trends show strong focus on MHz-frequency wireless power transfer, model predictive control strategies for rectifiers, and high-efficiency DC-DC converter topologies. Publications span over 30 years, with notable contributions to the design of resonant converters, inductive power transfer systems, and energy storage solutions. Collaborative projects include investigations into multi-energy microgrids and adaptive control for industrial applications. His research often combines theoretical analysis with hardware implementation, evidenced by frequent conference participation at IEEE venues.

Pedro NardelliView profile

Professor

• Cyber-physical Systems
• IoT in Energy Systems
• Smart Grids
• +5 more

Pedro Nardelli is a Full Professor (tenured) of IoT in Energy Systems at the LUT School of Energy Systems, Lappeenranta University of Technology (Finland). He holds a double doctoral degree in electrical engineering from the University of Campinas (Brazil) and communications engineering from the University of Oulu (Finland). As a Docent in Information Processing and Communications Strategies for Energy Systems, he leads research in cyber-physical systems, smart grids, and 6G-enabled energy networks. Research Interests : His work focuses on integrating IoT, AI, and communication technologies into energy systems. Key areas include cyber-physical systems, UAV-enabled networks, sustainable energy management, and cybersecurity in critical infrastructure. He emphasizes interdisciplinary approaches to address challenges in the green-digital transition. Projects & Leadership : He is Principal Investigator for projects such as 'Energy-Conscious Operation: Network Efficiency for Wireless Sustainability' (2024–2026) and coordinates the Finnish-Brazilian AI and 5G training program. Past roles include leadership in the 'Hydrogen and Carbon Value Chains in Green Electrification' initiative (2021–2024). Publications : Recent work explores topics like hybrid optimal power flow models, UAV-IRS NOMA systems, and energy-centric analysis. His research bridges theoretical frameworks with practical applications, emphasizing sustainability and resilience in energy networks. Labs & Teams : Leads the IoT Solutions group within LUT's MORE SIM research platform, focusing on simulation-driven innovation for energy systems.

Hossam H. H. MousaView profile

Researcher

• Electrical Power Engineering
• Renewable Energy Systems
• Power Systems
• +3 more

Hossam H. H. Mousa is a Doctoral Researcher at Aalto University's Department of Electrical Engineering and Automation, School of Electrical Engineering. He also serves as an Assistant Lecturer at South Valley University's Department of Electrical Engineering since 2020. B.Sc. in Electrical Engineering (2017), South Valley University M.Sc. in Electrical Power and Machines Engineering (2020), South Valley University His research focuses on electrical power engineering, including maximum power point tracking (MPPT) for renewable energy, power systems analysis, energy management, and machine learning applications in grid optimization. He has published extensively on topics like hosting capacity estimation, unbalanced microgrids, and hydrogen storage integration. The 15 most recent articles emphasize modern power systems optimization through machine learning (2025), smart inverter applications in renewable integration (2025), and hydrogen storage's role in cold climate energy management (2025). Earlier works include best practice studies on capacitor allocation (2024) and photovoltaic system controls (2024), earning him the 2024 Best Paper Award in the International Journal of Electrical Power & Energy Systems. Best Paper Award (2024), International Journal of Electrical Power & Energy Systems His scholarly activities span energy conversion, microgrid stability, and applied machine learning, contributing to sustainable energy transition solutions. He has collaborated on international research books addressing distribution network hosting capacity (2025) and future energy systems challenges.

Zhineng FeiView profile

Researcher

• Energy Systems
• Smart Grids
• Microgrids
• +3 more

Zhineng Fei is a Doctoral Researcher at the Department of Electrical Engineering and Automation , Aalto University, affiliated with the Multi-energy System Planning and Operation research group. Their work focuses on optimizing energy systems for maritime applications, integrating renewable energy, hydrogen technologies, and desalination processes. Research Areas: Multi-energy systems, smart grids, hydrogen energy, and stochastic optimization Recent publications highlight robust planning and coordinated operation of green ship microgrids , leveraging thermal inertia, noise control, and distributed stochastic methods. These works are aligned with advancing sustainable maritime energy solutions and hybrid power systems. Zhineng Fei collaborates with researchers such as Zhengmao Li, Josep M. Guerrero, and Yuehua Huang. Their findings are published in journals like IEEE Transactions on Smart Grid and conferences including the IEEE Power and Energy Society General Meeting .

• Automation Technology
• Mechanical Engineering
• Control Systems
• +3 more

Elham Kowsari is a Postdoctoral Researcher specializing in Automation Technology and Mechanical Engineering. Her research focuses on advanced control systems, particularly in forestry machinery automation and sway motion reduction in cranes. She explores nonlinear control strategies for DC microgrids and develops fault detection algorithms for electrical systems, including induction motors and continuous stirred-tank reactors. Her work integrates techniques like Kalman filtering, Gaussian processes, and model predictive control to address real-world engineering challenges. Key research areas include: Motion control and vibration suppression in forestry cranes Stabilization of DC microgrids with complex loads Fault diagnosis in electrical and mechanical systems Elham has collaborated internationally on sensor calibration (Star Tracker-Fiber Optic Gyroscope integration) and nonlinear system analysis. Her 12 peer-reviewed articles since 2014 demonstrate expertise in both theoretical control methodologies and practical industrial applications.

Sajjad Fattaheian DehkordiView profile

Researcher

• Power Systems Engineering
• Smart Grids
• Renewable Energy Integration
• +3 more

Sajjad Fattaheian Dehkordi is a Postdoctoral Researcher in the Department of Electrical Engineering and Automation at Aalto University, Espoo, Finland. His research focuses on advanced energy management systems for modern power grids, with emphasis on distributed and transactive control approaches for resilient and efficient grid operations. His core research interests include: Power system resilience under high renewable penetration Microgrid and energy community optimization Distributed energy resource integration Electric vehicle-grid interaction and V2G systems Real-time congestion and ramping management Analysis of his 2022-2024 publications reveals a dominant trend toward multi-agent transactive frameworks addressing grid stability challenges. His work consistently targets voltage regulation, asymmetrical power flows, and ramping events in distribution systems, leveraging optimization techniques like MILP while incorporating flexibility concepts for renewable integration. Key innovations include distance-driven P2P/P2G transactions and incentive-based congestion management. No scientific awards are documented in the available records. Information regarding student advising, research grants, or leadership roles is not provided in current documentation.

Kari AlanneView profile

Senior Lecturer

• Energy Efficiency
• Renewable Energy Systems
• Building Energy Technology
• +7 more

Kari Alanne is a Senior University Lecturer at the Department of Energy and Mechanical Engineering, LUT University. His research focuses on energy efficiency, renewable energy systems, smart buildings, and sustainable building technologies. He holds a doctoral degree in Engineering from Aalto University (formerly Helsinki University of Technology), a Licentiate from LUT University, and a Master's degree in Engineering from LUT. Key areas of expertise include building energy systems, machine learning applications in energy management, and geothermal technologies such as ground source heat pumps. Alanne has led projects like the Adaptive Multi-Energy Virtual Power Plant for building complexes, emphasizing smart grid integration and energy autonomy. He actively contributes to pedagogical innovation, particularly through gamified learning platforms for energy engineering education. His work aligns with UN Sustainable Development Goals (SDGs), promoting education and sustainable energy solutions. Alanne has reviewed over 20 doctoral theses and collaborated internationally, including visits to Addis Ababa Institute of Technology and KTH Royal Institute of Technology. His research portfolio spans 65+ publications, with recent emphasis on adaptive machine learning for building simulations and hybrid renewable energy systems.

• Power Engineering
• Power Systems
• Frequency Domain Analysis
• +7 more

Janne Seppänen is a Professor of Practice in the Department of Electrical Engineering and Automation at Aalto University, School of Electrical Engineering. His work focuses on modern power system challenges, especially stability, inertia, and integration of renewable energy sources. Diplomi-insinööri, Sähköjärjestelmät, Aalto University, 2011 Tekniikan kandidaatti, Sähkötekniikka, Helsinki University of Technology, 2009 His research centers on power engineering , with emphasis on frequency domain analysis , systems stability , modal identification , and renewable energy integration . He investigates dynamic behaviors in power systems, particularly forced oscillations and the interaction between nuclear and renewable energy sources. His work supports the transition to sustainable and resilient grids. The recent publications highlight a strong trend in data-driven modal identification , grid-forming inverter technologies , and stability assessment in low-inertia systems . Topics such as power system inertia, frequency response, and resonance due to forced oscillations are recurring themes, reflecting his focus on ensuring grid reliability amid increasing renewable penetration. While no formal scientific awards are listed, his work contributes to UN Sustainable Development Goals, particularly in energy and infrastructure resilience. He has supervised at least one doctoral thesis and served as an opponent in doctoral defenses, indicating active involvement in academic mentoring. He has led research projects like SINARP, focusing on the safe integration of nuclear and renewable energy, and has secured research funding for power system innovation. His collaborations span Nordic and European institutions. Janne Seppänen is involved in key research networks and projects such as STRONgrid and REAL SMART, contributing to international efforts in smart grid development and power system stability. His work combines theoretical analysis with practical measurement and system-level applications.

Niharika SinghView profile

Research Fellow

• Artificial Intelligence
• Reinforcement Learning
• Multi-agent Systems
• +3 more

Dr. Niharika Singh is a Postdoctoral Research Scientist at the University of Helsinki specializing in Artificial Intelligence and Reinforcement Learning. She concurrently serves as an Assistant Professor at Bennett University in Greater Noida, India since January 2022. Her research focuses on developing innovative algorithms for complex planning and decision-making problems with applications in smart cities, microgrids, and cloud computing security. Her educational background includes: PhD in Electrical and Electronics Communication (Artificial Intelligence) from Universiti Teknologi PETRONAS, Malaysia (2017-2021) M.Tech in Computer Engineering from University Institute of Engineering and Technology, Kurukshetra University, India (2014-2016) B.Tech in Computer Science and Engineering from Kurukshetra University, India (2010-2014) Dr. Singh's research centers on Artificial Intelligence with emphasis on Reinforcement Learning and multi-agent systems. She develops algorithms for smart microgrid communication networks and intelligent vehicle routing for urban environments. Her work bridges theoretical AI advancements with practical implementations in sustainability domains, particularly through her involvement with the Helsinki Institute of Sustainability Science and Helsinki Institute of Urban and Regional Studies. She has also made significant contributions to cloud computing security, developing protective layers for maintaining Multi-Version Concurrency Control (MVCC) in cloud environments. Her recent publications demonstrate a clear trend toward applying AI to urban sustainability challenges. The 2025 vehicle routing paper shows her focus on intelligent transportation systems, while the 2023 Cloud-HPA work addresses critical privacy concerns in data storage. These publications reflect her interdisciplinary approach combining computer science fundamentals with real-world applications in urban planning and cybersecurity. Dr. Singh has served as a reviewer for the Journal of Supercomputing and Behaviour and Information Technology. She has completed professional certifications from MongoDB and Elsevier, demonstrating commitment to scholarly excellence. As an academic leader, she organized a 5-day SERB Faculty Development Program on Natural Language Processing at Bennett University in 2022 and has delivered expert lectures on AI and machine learning at various institutions. She maintains active collaborations through the SMART research group at Universiti Teknologi PETRONAS, cybersecurity groups at NIT Kurukshetra, and serves as an AI Specialist at The DriveSales™ and Principal Consultant at 99Plays. Her IEEE membership since 2017 reflects her engagement with the broader academic community, participating in webinars on machine learning for biomedical applications and nanotechnology for renewable energy.

Pasi PeltoniemiView profile

Professor

• Electrical and Electronic Engineering
• Control and Systems Engineering
• Power Electronics
• +3 more

Pasi Peltoniemi is a Full Professor (tenured) in the Department of Electrical Engineering at Lappeenranta-Lahti University of Technology LUT. He is affiliated with the LUT School of Energy Systems, focusing on advanced research in Electrical and Electronic Engineering, Control Systems, and Power Electronics. His work emphasizes sustainable energy systems, particularly Low-Voltage Direct Current (LVDC) distribution networks and high-power converter technologies. His research spans power electronics, renewable energy integration, and grid-forming converter systems. Notable contributions include studies on multilevel inverters, SiC semiconductor applications, and energy management strategies for hybrid systems. He has pioneered LVDC distribution network designs, including fault protection, grounding schemes, and customer-end inverter optimization. Key research areas include: Advanced power electronics and converter topologies Grid stability and renewable energy integration Marine and industrial energy systems LVDC distribution infrastructure and standards His publications highlight trends in grid-forming voltage source converters, high-power charging systems, and microgrid control. Peltoniemi has contributed to international standards like LVDC rules and collaborated on projects such as the Congo Grand Inga hydroelectric evaluation. He leads research initiatives on energy storage systems, fault detection in power converters, and smart grid functionalities. His work integrates experimental validation (e.g., laboratory test setups) with theoretical models for system optimization. Labs/Teams: Active in LUT's LVDC research platform, focusing on utility network implementations and converter technology. Collaborates on projects like the On-Grid Hydrogen Production with Solar Energy in Nordic Conditions.

Pekka ToivanenView profile

Professor

• Artificial Intelligence
• Healthcare Systems
• IoT and Smart Grids
• +3 more

Pekka Toivanen is a Professor at the School of Computing, Faculty of Science, Forestry and Technology at the University of Eastern Finland. His research focuses on artificial intelligence, healthcare systems, IoT, cybersecurity, and machine learning. He has contributed extensively to fields like medical image analysis, smart grids, and robotics, with over 100 peer-reviewed publications. His work bridges theoretical advancements and practical applications, such as improving healthcare diagnostics via AI, enhancing IoT security, and optimizing energy management in microgrids. Research Interests: Dr. Toivanen’s primary areas include AI-driven healthcare solutions, cybersecurity for IoT devices, deep learning applications in medical imaging, and energy-efficient smart grid systems. He has pioneered methods for tuberculosis risk prediction and developed novel encryption algorithms for Bluetooth security. Publications: His recent work emphasizes AI in healthcare (e.g., tuberculosis prediction models) and energy systems (e.g., reinforcement learning for microgrid management). He also explores vulnerabilities in wireless technologies like Bluetooth and ZigBee, proposing robust countermeasures. Grants/Teams: Leads projects on AI distribution platforms for healthcare and secure IoT architectures. Collaborates with interdisciplinary teams in medical and engineering domains.

Antti HildénView profile

Researcher

• Power Quality
• Smart Energy Systems
• Photovoltaic Systems
• +3 more

Antti Hildén is a Doctoral Researcher in the field of Electrical Engineering, focusing on advanced research in power systems and smart energy applications. His work contributes to the UN Sustainable Development Goals related to affordable and clean energy (SDG 7) and industry innovation (SDG 9). Research Interests: Hildén’s research spans power quality analysis, renewable energy integration, and smart grid technologies. He explores topics such as electric vehicle charging infrastructure, microgrid stability, and the impact of photovoltaic systems on distribution networks. His work combines data-driven approaches with machine learning to innovate solutions for modern energy challenges. Key Contributions: His publications address critical areas like automated load control detection, energy communities, and the optimization of groundwood lines for grid stabilization. Notable projects include a data collection platform for smart energy applications in modern buildings, enhancing energy efficiency and grid reliability.

Alireza OlamaView profile

Researcher

• Parallel Computing
• Distributed Computing
• Machine Learning
• +3 more

Alireza Olama is a Postdoctoral Researcher in the Department of Information Technology at Åbo Akademi University's Faculty of Science and Engineering. His work bridges parallel and distributed computing with machine learning and numerical optimization, contributing to the UN Sustainable Development Goals through algorithmic advancements. Doctoral research: Distributed framework for sparse convex optimization (2019, Universidade Federal de Santa Catarina) Master's thesis: Lyapunov Based Hybrid Model Predictive Control (2017) Research spans: Developing novel algorithms for distributed convex optimization (e.g., ADMM variants, Augmented Lagrangian methods) Creating software tools for sparse convex programming Applying optimization techniques to energy management systems Advancing GPU-accelerated machine learning frameworks Recent publications focus on ℓ0 sparsity, distributed consensus optimization, and hybrid control systems. Active in the academic community through conference presentations and collaborations with institutions like Norwegian University of Science and Technology (NTNU) and KTH Royal Institute of Technology.