Taija Turunen is an Associate Professor at the Department of Management Studies, Aalto University. Her research focuses on service innovation and business models, strategic management, and organizational dynamics. She explores topics such as service platforms, competitive dynamics, and strategic renewal. Contact: taija.turunen@aalto.fi . Areas of expertise include Service Innovation and Business Models, Service Design, and Strategic Renewal. Her work emphasizes the intersection of technology, organizational behavior, and institutional challenges in business ecosystems. Recent research highlights include studies on moral dialogue in organizations, embodied identity work, and ecosystem-level business models. Key themes in her publications (2016–2025) include digital innovation barriers, transformative governance of ecosystems, and value creation in complex networks. She has contributed to understanding how organizations navigate institutional challenges and leverage technology for strategic advantage. No scientific awards are mentioned, but her work is widely cited in strategic management and service innovation literature. No advising or grant information is provided in the text. Her research often involves cross-disciplinary collaborations, particularly in analyzing service ecosystems and organizational resilience.
Professor Darla Hatton MacDonald is a Professor and Associate Head (Research & Performance) in the Department of Economics at the University of Tasmania's Tasmanian School of Business and Economics. Her research focuses on environmental and resource economics, with particular emphasis on ecosystem accounting, behavioral economics, and policy analysis. She leads projects addressing soil stewardship, Antarctic governance, marine plastic reduction, and energy transitions. Her work integrates interdisciplinary approaches to evaluate environmental policies, stakeholder preferences, and market mechanisms. Notable funded projects include studies on temperate saltwater marsh accounting (2024-2025), behavioral change in fisheries (2023-2024), and certification frameworks for soil stewardship (2022-2025). She has also contributed to policy-relevant research on Tasmania's hydrogen industry and groundwater management in mining regions. Her supervision spans doctoral projects on beetle conservation, blue economy hazards, and renewable energy transitions. Publications highlight her expertise in Antarctic ecosystem valuation, microplastic distribution, and consumer preferences for environmental policies. She engages in media outreach, notably critiquing the Murray-Darling Basin Plan's effectiveness. Her work bridges academic research with practical policy solutions for sustainable resource management.
Karen Butler-Purry is a Professor of Electrical & Computer Engineering at Texas A&M University, holding the Raytheon Company Professorship. She is affiliated with the Power System Automation Laboratory and the College of Engineering. Her research focuses on intelligent systems for power distribution automation, fault diagnosis, and renewable energy integration. She completed her B.S. at Southern University (1985), M.S. at UT Austin (1987), and Ph.D. at Howard University (1994). Her work emphasizes cyber-physical systems, smart grid security, and microgrid resilience. Recent research includes AI-driven grid restoration, cybersecurity frameworks for distribution systems, and self-healing shipboard power systems. She has pioneered methods for transformer lifespan prediction and unbalanced load management in distributed networks. Publications span over three decades, with notable contributions to IEEE journals and conferences. Her educational initiatives include the Texas A&M System AGEP Alliance and LSAMP programs, advancing underrepresented minority participation in STEM academia. She leads interdisciplinary projects at the intersection of power systems, machine learning, and equity-focused academic workforce development. Labs/Teams: Principal Investigator in the Power System Automation Laboratory, collaborating with industry partners like Raytheon. Active in curriculum development for digital systems education through initiatives like the Enrichment Experiences in Engineering (E³) program.
Per Printz Madsen is an Associate Professor in the Department of Computer Science at Aalborg University, under The Technical Faculty of IT and Design. His research focuses on distributed, embedded, and intelligent systems with applications in energy efficiency, smart buildings, and renewable energy integration. His research interests span energy engineering, home automation, learning algorithms, fuzzy systems, neural networks, and intelligent control systems. He applies computational intelligence to optimize energy consumption in buildings and district heating systems, with a strong emphasis on real-world implementation through digital twin technology and supervisory control. The recent publications highlight a consistent trend in energy management systems, particularly in forecasting, load control, and energy brokerage in smart grids and buildings. The work emphasizes the integration of renewable generation and storage, leveraging machine learning and distributed control architectures to improve energy efficiency and reduce CO 2 emissions. He has been involved in several major research projects including ENCOURAGE, Homeport, Flexnet, and Mediterranean Virtual University, funded by entities such as Artemis JU and the Danish Enterprise and Construction Authority. These projects demonstrate sustained external funding and collaborative research across Europe. Madsen contributes regularly through technical reports and conference proceedings, primarily through Aalborg University. His work is applied and systems-oriented, targeting practical deployment in energy infrastructure. He is affiliated with the Distributed, Embedded and Intelligent Systems research group at Aalborg University, where he conducts interdisciplinary research bridging computer science and energy engineering to develop intelligent solutions for sustainable energy systems.
Idelfonso Nogueira is an Associate Professor in the Department of Chemical Engineering at NTNU. His research focuses on integrating artificial intelligence, advanced control systems, and digitalization to address industrial challenges, particularly in chemical processes and product development. He leads the AiP²S² framework, emphasizing synergies between AI and traditional engineering methods. Education: PhD in Chemical and Biological Engineering (University of Porto, 2018), Visiting Researcher at Tampere University of Technology (2016–2018), and multiple master’s degrees from Brazilian and Portuguese institutions. Research Interests: Process optimization using AI, hybrid modeling, digital twins, and Industry 4.0/5.0 applications. Collaborations span universities, institutes, and industries globally. Teaching focuses on preparing engineers for modern industrial demands. Publications: Over 60 manuscripts, with 70% in Q1 journals (e.g., Chemical Engineering Science , Computers and Chemical Engineering ). Key areas include pressure swing adsorption, machine learning for process control, and fragrance molecule design. Grants/Advising: Active in parameter estimation, process modeling, and renewable energy projects. No explicit student advising list provided. Labs/Teams: Engages in AI-driven process systems engineering, digital twin development, and sustainable technology initiatives.
Dr. Abhinav Kumar Singh is a Lecturer of Power Systems at the University of Southampton's School of Electronics and Computer Science since 2019. Previously, he held academic roles at the University of Lincoln (2017–2019) and Imperial College London (2015–2017 as Research Associate). His research focuses on real-time estimation and control of power systems, particularly addressing challenges posed by renewable energy integration. Key areas include power system dynamics, decentralized estimation/control methodologies, and modeling of renewable generation systems. Education: B.Tech from Indian Institute of Technology (IIT) New Delhi (2010), PhD in Electrical Engineering from Imperial College London (2015). He leads the 68-bus benchmark system development for IEEE PES and serves as editor for IEEE Transactions on Power Systems and Journal of Modern Power Systems and Clean Energy . Research Projects: PI of National Grid’s £480K 'Economic Ageing of Transformers' (2019–2021), Co-I on UK-China EPSRC-NSFC £780K 'Resilient Operation of Sustainable Energy Systems' (2020–2023). Teaching: Modules include Power System Dynamics, Power Electronics for DC Transmission, and Mathematics for Electrical Engineering. Awards: EPSRC Doctoral Prize (2015), IEEE PES Recognition Awards (2016, 2022), and The President’s Award for Outstanding Research Team (2016). Labs/Teams: Member of the Electrical Power Engineering group and Tony Davies High Voltage Laboratory. Active in IEEE PES Task Forces on Dynamic Estimation and Standard Test Cases.
Sancho Salcedo Sanz is a Full Professor at the Universidad de Alcalá, affiliated with the Signal Theory and Communications Department and the GHEODE Research Group. His work focuses on applying machine learning and optimization techniques to energy systems, climate science, and environmental modeling. He holds PhDs from Universidad Complutense de Madrid (2019) and Universidad Carlos III de Madrid (2002). Key research interests include deep learning for energy price prediction, spatio-temporal climate analysis, and hybrid models for renewable energy forecasting. His GHEODE group develops optimization algorithms for network design and distributed systems. Recent publications highlight advancements in extreme weather prediction, smart grid optimization, and explainable AI for environmental monitoring. He has pioneered methodologies like Autoencoder-based flow analogues for heatwave reconstruction and multi-method ensembles for energy demand modeling. Labs/Teams: Leader of the GHEODE Group, specializing in modern heuristics and network design. Collaborates extensively on interdisciplinary projects combining AI with environmental and engineering applications.
David Palma is an Associate Professor at the Department of Information Security and Communication Technology , Faculty of Information Technology and Electrical Engineering , Norwegian University of Science and Technology (NTNU). His research focuses on next-generation networking paradigms, including intent-based networking , knowledge-driven management , and IoT device automation . Key research interests include: Human-centric Internet of Things (IoT) Ontology-based network management Cloud/Edge/Fog computing for IoT Arctic and satellite communication 5G integration in smart grids His recent publications (2024-2019) highlight trends in knowledge graphs for network compliance, XR applications in critical sectors, UAV-based emergency networks , and 5G-enabled smart grid protection . Articles also explore Arctic connectivity via satellite swarms and energy-efficient IoT management.
Jørn Vatn is a Professor in reliability, availability, maintainability, and safety at the Norwegian University of Science and Technology (NTNU) , specifically within the Faculty of Engineering and the Department of Mechanical and Industrial Engineering . His work focuses on integrating risk assessment and maintenance strategies for critical infrastructure. MSc courses: TPK4120 Safety and Reliability Analysis , RAMS optimization PhD course: PK8200 Risk Influence Modelling and Risk Indicators Continuing Education: Maintenance Optimization , Safety and Reliability in Railway Applications , Project Risk Management , Risk Reducing Measures Research Interests span maintenance management, reliability modeling, condition-based maintenance, risk influence analysis, and digital twin applications in infrastructure. His methods emphasize statistical analysis and Markov processes to model degradation and optimize maintenance scheduling. The 15 most recent articles (2025-2022) demonstrate trends in applying Markov models , digital twins , and data-driven optimization to offshore wind turbines, railways, hydrogen pipelines, and cyber-physical systems. Subfields include predictive maintenance, stochastic degradation modeling, and infrastructure resilience. He leads the ROSS Gemini Centre and the RAMS group , advising on risk modeling and maintenance strategies. Collaborations involve SINTEF, industry partners, and international researchers.
Dr. Cannon Dirk is a researcher affiliated with the University of Reading, as evidenced by his publications in the university's institutional repository, CentAUR. His work lies at the intersection of atmospheric science and renewable energy systems, with a focus on wind power forecasting and meteorological modeling. His research interests span Atmospheric Science , Renewable Energy , Wind Power Forecasting , Orographic Precipitation , Climate Modeling , and Energy Meteorology . These are reflected in his publications, which combine meteorological dynamics with practical energy system challenges. The recent articles show a strong trend toward probabilistic forecasting of wind power generation, quantification of extreme events using long-term reanalysis data, and the interaction between atmospheric processes and renewable energy infrastructure. His work often involves modeling frameworks applied to Great Britain's energy landscape. Dr. Cannon has collaborated with prominent researchers such as David Brayshaw, Joanne Methven, and Suzanne Gray, indicating integration within a well-established meteorological and energy research group at Reading. There are no listed scientific awards or honors in the provided data. Dr. Cannon has contributed to both journal articles and technical reports, including a MATLAB-based wind power model. While no formal advising or grant information is available, his repeated work on energy-meteorology integration suggests involvement in research projects related to sustainable energy systems. No lab or team name is explicitly mentioned, but his research aligns with atmospheric and renewable energy modeling groups, possibly within the Department of Meteorology at the University of Reading.
William Holderbaum is a Professor at the Department of Electrical Engineering, School of Engineering, University of Reading. His research spans control systems, energy management, functional electrical stimulation, robotics, and wireless power transfer, with over 95 publications since 2002. Research Interests: His work integrates theoretical control theory with practical applications in renewable energy, biomedical engineering, and smart systems. He has made significant contributions to microgrid protection, energy storage control, optimal power management for electric cranes, and FES for paraplegia rehabilitation. His recent work explores soft robotics using electroactive polymers and intelligent sensing for environmental and health monitoring. Publication Trends: His recent articles (2021–2025) reflect a multidisciplinary focus, combining engineering, materials science, and healthcare. Key themes include sustainable energy systems, intelligent control, wearable sensors, and novel computing paradigms using smart materials. Scientific Awards: No awards mentioned in the provided text. Advising and Grants: He has collaborated extensively with researchers such as F. Alasali, T. Yunusov, M. Alkowatly, and V. Becerra, suggesting a strong mentoring role. His work on energy storage, smart grids, and FES implies involvement in funded research projects, though specific grants are not listed. Labs and Teams: He is part of research teams focused on control systems and energy at the University of Reading, collaborating with the group led by B. Potter and V. Becerra. His work with biomedical applications suggests ties to interdisciplinary health-tech initiatives.
Jérôme Chenal is a Senior Scientist and Director of the Excellence in Africa Center at the École polytechnique fédérale de Lausanne (EPFL) , affiliated with the Institute of Environmental Engineering . He is also an Affiliate Professor at UM6P (Morocco). His work intersects urban and regional planning , environmental science , public health , and geospatial analysis , with a focus on informal settlements and sustainability in the Global South . Research Interests : Urban resilience, smart cities in Africa, ecosystem services, WASH services, urban health inequities, remote sensing for humanitarian response, and sustainable development in Sub-Saharan Africa. Advising : Supervised PhD students including Rim Mrani, Martí Bosch Padrós, Rémi Jaligot, and Andrea Salmi, with a focus on urban morphology, health accessibility, and digital urbanism. Scientific Networks : Collaborated with institutions in Switzerland, Kenya, Côte d'Ivoire, Cameroon, and Morocco. Published in journals like Water , Science of The Total Environment , and Sustainability , addressing topics such as water distribution, sanitation safety, and urban resilience. Teaching : Leads courses on Urban Planning in the South and contributes to doctoral programs, emphasizing interdisciplinary approaches to urban challenges.
Fabio Favoino is an Associate Professor at the Department of Energy (DENERG) at the Polytechnic of Turin, Italy, and a member of the FULL Interdepartmental Center - Future Urban Legacy Lab. His academic career focuses on building physics and energy systems, with particular expertise in building envelope technologies, energy efficiency, and sustainable building design. Research Interests Professor Favoino's research spans multiple areas of building science and technology, with a strong emphasis on energy performance and sustainable design. His primary research interests include building energy performance and nearly zero-energy buildings, advanced building envelope systems and facade technologies, building insulation materials and responsive building elements, smart glazing and electrochromic window systems, double-skin facades with integrated thermal storage, building simulation and performance assessment methodologies, integration of renewable energy systems in buildings, and thermal comfort and indoor environmental quality. Publication Trends Professor Favoino's recent publications demonstrate a clear focus on advanced building envelope technologies, particularly responsive and adaptive systems. His work increasingly integrates multi-domain analysis, combining thermal, acoustic, and daylight performance assessment. There is a strong emphasis on experimental validation of novel technologies like electrochromic windows, double-skin facades with phase change materials, and smart ventilation systems. His research also shows growing interest in living lab methodologies, sensor networks for building performance monitoring, and the integration of IoT infrastructure for building management systems. Professional Recognition Editorial Board Member for Building and Environment (2022-present) Editorial Board Member for Glass Structures & Engineering (2018-present) Effective Member of the Italian Thermotechnical Association (2018-present) Effective Member of CIBSE, United Kingdom (2016-present) Founding Partner of IBPSA Italy (2012-present) Effective Member of REHVA, European (2011-present) Effective Member of AICARR, Italy (2011-present) Research Leadership Professor Favoino actively supervises PhD students working on cutting-edge building technologies and leads several significant research projects including MIRABLE (2023-2025) on measurement infrastructure for healthy and zero-energy buildings, and the PRIN-funded iclimabuilt project (2021-2025) on functional and advanced insulating materials for climate adaptive building envelopes. He has also led commercial research projects on high-performance glazing systems and participated in the Cost Action TU1403 - Adaptive Facade Network (2014-2018) as coordinator. Research Infrastructure Professor Favoino's work with the FULL Interdepartmental Center - Future Urban Legacy Lab and involvement with the HIEQLab facility provide platforms for interdisciplinary research on sustainable urban development, building technologies, and human-centered environmental quality assessment.
Paolo Marocco is a Fixed-term Assistant Professor at the Department of Energy (DENERG) of Politecnico di Torino, Italy. His work focuses on hydrogen-based mobility solutions, renewable energy systems, and techno-economic/environmental sustainability analysis. He serves on teaching committees for Electrical and Energy Engineering, Mechanical Engineering, and Aerospace Engineering programs. Academic Role: Assistant Professor (Fixed-term) Department: Department of Energy (DENERG) Teaching: Hydrogen Laboratory, Electrical Energy Storage Systems, Applied Thermodynamics Research spans four key areas: Hydrogen Infrastructure : Green hydrogen production for ammonia synthesis, aircraft propulsion, and rail transport decarbonization. Projects include SOFFHICE (SOFC hybridization in maritime engines) and PNRR initiatives for industrial decarbonization. Energy Storage Systems : Hydrogen-battery hybrid storage, virtual thermal inertia storage, and optimal dispatch models for wind-electrolysis systems. Industrial Decarbonization : Semiconductor manufacturing, steel industry high-temperature heat substitution, and biogas carbon recovery. Policy Integration : Bridging technical research with European green policy targets through Italian energy modeling. He supervises 7 PhD students across Energetics, Mechanical Engineering, and Energy/Nuclear Engineering programs. Current projects include SOFFHICE (2023-2026) and PNRR-funded industrial furnace decarbonization (2023). Recent publications analyze hydrogen train feasibility, aviation fuel cells, and storage system optimization.
Professor Zhe Chen is a distinguished academic at Aalborg University's Faculty of Engineering and Science, where he leads research in Electric Power Systems and Microgrids within the Intelligent Energy Systems and Flexible Markets department. With an extensive publication record spanning over two decades and more than 1,200 publications, he has established himself as a leading expert in power engineering and renewable energy systems. Professor Chen's research focuses on Wind Turbine Engineering, Power Engineering, Control Strategy, Wind Power Engineering, Energy Engineering, and Reinforcement Learning. His work bridges theoretical advancements with practical applications in smart grid technology and microgrid systems. His research interests center around developing innovative solutions for renewable energy integration, power system stability, and efficient energy management. His recent publications demonstrate a strong trend toward integrating artificial intelligence with traditional power engineering, particularly in applying deep reinforcement learning to power system control, state estimation with noisy data, and optimization of power electronic converters. His work shows increasing focus on carbon emissions optimization, thermal management in power electronics, and the application of advanced neural network architectures to energy systems. MPCE 2023 Best Paper Award for research on reinforcement learning applications in electric vehicles MPCE 2022 Best Paper Award for distribution network optimization MPCE 2021 Best Paper Award for reinforcement learning in energy systems WATAB Best Paper Award 2005 for offshore wind farm research IEEE Fellow recognition for contributions to power electronics and renewable energy systems Professor Chen has supervised 27 PhD students throughout his career, demonstrating his commitment to academic mentorship. His current research is supported by significant grants including the Erasmus+ funded S3SF project (Smart Energy Solutions for a Sustainable Future) and multiple projects focused on machine learning-based stability analysis for multi-energy systems. His collaborative work spans numerous international partnerships, with recent projects involving researchers from China, Europe, and other global institutions. Professor Chen leads a research team focused on intelligent energy systems, working on advanced control strategies for microgrids, power quality analysis, and the integration of renewable energy sources into existing power infrastructure. His team is particularly known for innovative approaches to wind power integration and DC microgrid technologies.
