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.
Aníbal T. de Almeida is a Full Professor at the University of Coimbra, Portugal, and Director of the Institute for Systems and Robotics (UC), an interdisciplinary research institute with over 150 researchers. His work spans automation, robotics, and energy-efficient technologies, particularly focusing on advanced electric motors and drives. PhD in Electrical Engineering from Imperial College, University of London Over 50 funded national and international projects in industrial automation and energy efficiency General Chair of IEEE EPQU 2011 and IEEE IROS 2012 (the largest robotics conference globally) His research interests include: Smart microgrid architecture and resiliency Life cycle assessment of electric motor technologies Decarbonization strategies for urban and industrial sectors Recyclable and repairable electronic components Wildfire risk mitigation through robotic systems Sustainable energy access in developing countries Recent publications demonstrate expertise in voltage unbalance analysis, multi-criteria optimization for decarbonization, and 3R (resilient/reparable/recyclable) battery design. Awards include the 2015 IEEE CEMRA Award for educational materials on energy harvesting in mobile robots, IEEE IROS Fellow since 2020, and IEEE Robotics and Automation Society Distinguished Lecturer since 2018. As an international consultant, he advises the European Commission, US Department of Energy, World Bank, UNDP, and CLASP, while serving on the Board of Directors of CLASP (Washington, USA) since 2014. His work bridges cutting-edge robotics with sustainable energy systems, influencing global policy and technological standards.
Ning Wang is a Postdoctoral Researcher at Aalborg University's Faculty of Engineering and Science, working within the Applied Power Electronic Systems department at AAU Energy. Wang serves as Principal Investigator for the PhD project 'Advanced optimized and control strategy of DCDC converter in Renewable DC microgrid' (2021-2024) under the supervision of Chen Z. Wang's research focuses on advanced control strategies for power electronic systems, particularly in DC microgrid applications. Key research areas include parameter design methods for DAB converters, DC-bias current suppression strategies, and operation schemes for retired batteries in secondary usage scenarios. The research employs artificial intelligence techniques including ANN and PSO optimization to address challenges in power converter design and control. Analysis of Wang's recent publications reveals a strong emphasis on practical applications of power electronics in renewable energy systems, with particular attention to DC microgrids, electric vehicles, and power semiconductor device monitoring. The work demonstrates integration of machine learning approaches with traditional power electronics design to solve complex engineering problems in renewable energy integration. As Principal Investigator on a significant PhD project related to DC-DC converters in renewable DC microgrids, Wang directs research activities in this specialized domain, collaborating with multiple researchers including Wang Y., Jiang Y., and Chen Z. The project addresses optimization strategies and control methodologies for power electronic systems in renewable energy applications.
Yifan Zhou is an Assistant Professor in the Department of Electrical and Computer Engineering at Stony Brook University. He holds a PhD (2019) and B.E. (2014) in Electrical Engineering from Tsinghua University. His research focuses on AI-driven smart grids, quantum computing, and formal analysis of power systems, emphasizing resilience and renewable energy integration. He teaches courses on electromechanical energy converters, electric machinery, and AI-driven smart grids. He advises PhD students in power system dynamics and quantum computing applications. Education: Ph.D. in Electrical Engineering, Tsinghua University, 2019 B.E. in Electrical Engineering, Tsinghua University, 2014 Research Interests: Integrates machine learning and quantum computing for power system stability, microgrid operations, and verifiable smart grids. Highlights include quantum ML for stability assessment and neural dynamic equivalence modeling. Awards: 2023 NAI Young Academic Inventor’s Award (for quantum computing in power systems) 2021 Outstanding Reviewer for IEEE Transactions on Power Systems 2014 Tsinghua University Outstanding Thesis Award Grants & Mentoring: Supervises PhD students (e.g., Xuguo Fu, Sijia Yu) and leads projects on quantum-enabled power analytics and learning-based verification. Advises on federal grants for smart grid resilience. Labs/Teams: Leads research in quantum power flow analysis and AI-driven control systems within the Electrical and Computer Engineering department.
Prof. Dr. Marco Jung is a Professor at the Bonn-Rhein-Sieg University of Applied Sciences , affiliated with the School of Engineering and Communication and the Institute for Technology, Resource Conservation and Energy Efficiency (TREE) . He leads the Power Electronics and Power Systems Laboratory (PEPS-Lab) and serves as Director of the International Center for Sustainable Development , while also heading the Power Converters and Electric Drives Department at Fraunhofer IEE. Research Interests: His work focuses on Bidirectional battery chargers (AC, inductive, DC) SiC/GaN semiconductors for power electronics Grid-forming power converters for hydrogen electrolysis and microgrids Power Hardware-in-the-Loop testing Smart grid integration of renewable energy Projects: He manages key initiatives like F-HIL RELOADED (grid-forming converters), GREATER (Rwanda energy transition), HyLeiT (electrolysis converters), and Curriculum 4.0.nrw (digital teaching). His IEEE leadership as German Chapter Chair (IAS/PELS/IES) underscores his industry influence.
Ramón Rodríguez Pecharromán is an Associate Professor in the Department of Electronics, Automation, and Communications at the Higher Technical School of Engineering of Comillas Pontifical University. He has been affiliated with the university since 1992 and is also a researcher at the Technological Research Institute (IIT). He served as the Director of his department from 2012 to 2021. Research Interests: His primary research areas include Control Systems, Electrification of Railway Systems, and Thermoelectricity. His work focuses on enhancing energy efficiency in DC-electrified mass transit systems through advanced modeling, optimization algorithms, and energy storage integration. He investigates regenerative braking, demand charge reduction, and smart traffic simulation to improve railway electrical infrastructure. Publication Trends: His recent publications emphasize energy storage systems in railways, optimization using nature-inspired algorithms (e.g., coral reefs, swarm intelligence), and simulation-based assessment of reversible substations. There is a strong focus on sustainable urban transit, economic impact analysis, and high-impact applications in real-world metro systems. Scientific Awards: K. F. Alcock RIA Memorial Prize, Institution of Mechanical Engineers, August 2013 Distinción Honorífica a la mejor Tesis Doctoral, Universidad Pontificia Comillas, January 2021 Premio Cátedra de Industria Conectada al mejor Proyecto Fin de Carrera, April 2023 Advising and Grants: He has supervised PhD students, including D. Roch Dupré and A.J. López López. His research has been funded by major institutions such as ADIF, Metro de Madrid, the Ministry of Science and Innovation, and the European Regional Development Fund. Projects include energy management in rail systems, smart microgrids, and regenerative energy utilization for electric vehicle charging. Labs and Teams: He is a key member of the railway systems research group at IIT, contributing to applied projects in railway electrification and control. His team collaborates with national and international transportation authorities and industry partners to implement energy-efficient solutions.
Deepthi Vaidhynathan is a Researcher IV in the Computational Science Center at the National Renewable Energy Laboratory (NREL), where she works in the Complex System Simulation and Optimization Group. Her research focuses on energy system integration, grid modeling and simulation, and high performance computing for scientific applications in power systems and building energy management. Her educational background includes: Bachelor of Electronics and Communication Engineering from Anna University Master of Electrical and Computer Engineering from the University of Colorado Boulder Deepthi's research spans energy systems integration, grid modeling, high performance computing, computer architecture, and control theory. She applies quantum computing principles and microprocessor optimization to develop advanced simulation tools for modern power grids with high renewable energy penetration. Her work bridges theoretical algorithms with practical energy system challenges. Analysis of her 2024-2025 publications reveals concentrated expertise in hybrid simulation techniques for inverter-dominated power grids and AI-enhanced control frameworks. She specializes in partitioning algorithms for large-scale electromagnetic transient simulations and neural-network-based predictive control for building energy systems, addressing critical needs in grid stability and renewable integration. Deepthi is an active contributor to NREL's Complex System Simulation and Optimization Group, which develops high-fidelity tools for modeling next-generation energy infrastructure. The group's work supports U.S. Department of Energy initiatives for grid modernization and renewable energy deployment. No scientific awards are documented in available sources. Public records indicate no formal student advising roles or disclosed grant funding details.
Professor Ali Arefi serves as a Professor in the School of Engineering and Energy at Murdoch University, Western Australia, with prior experience as a Lecturer and Research Fellow at Queensland University of Technology (2012-2015). His educational qualifications include a B.Sc. (Honours) in Electrical Engineering (1999), M.Sc. in Electrical Engineering (2001), and Ph.D. in Electrical Engineering (2011). Research focuses on transformative energy solutions: Sustainable and Zero Emission Energy Systems Reliability of 100% Renewable-Based Grids Advanced Energy State Estimation Techniques Smart Grid and Microgrid Integration Energy Efficiency Optimization Secured over $11 million in competitive research funding ($4m government, $7m industry) through ARC Discovery Projects, ARENA, and partnerships with Horizon Power, Western Power, and Energy Queensland. Authored 180+ publications including 34 Q1 journal papers, 78 conference proceedings, and 58 industry technical reports. IEEE Senior Member active in standards development with six years of industry consultancy experience, including 18 distributed generation interconnection projects and energy audits for 35+ utilities.
Aysegül Kahraman serves as a Postdoctoral Researcher (Research Fellow) at the Department of Wind and Energy Systems, Technical University of Denmark (DTU), with active projects running through 2028. Her work directly contributes to UN Sustainable Development Goals in renewable energy and climate action through advanced energy systems research. Her research expertise spans Wind Power integration , Microgrid optimization , and Machine Learning applications for energy systems. She specializes in deploying Reinforcement Learning and Deep Learning techniques to solve critical challenges in load forecasting, home energy management, and multi-energy system operations under uncertainty, with particular focus on transactive control frameworks and EV scheduling. Analysis of her 2022-2024 publications reveals a strong trend toward AI-driven solutions for renewable energy integration , with consistent emphasis on real-world implementation in microgrids and distribution systems. Her work demonstrates increasing sophistication in handling system constraints while optimizing energy flexibility across multiple domains. As academic supervision, she currently serves as supervisor for PhD candidate Sundhu, A. A. in the Real-Time Digital Twin for Active Distribution Networks project, building on her own recently completed PhD research at DTU.
Laura E. Brown is Associate Professor of Computer Science at Michigan Technological University, also serving as Associate Dean for Data Science Initiatives in the College of Computing and Director of the university’s M.S. and B.S. Data Science programs. She is a core member of the Institute of Computing and Cybersystems (ICC), the Ecosystem Science Center (ESC), and the Center for Agile Interconnected Microgrids (AIM). Education Ph.D., Biomedical Informatics — Vanderbilt University M.S., Biomedical Informatics — Vanderbilt University M.S.E., Electrical Engineering & Computer Science — University of Michigan B.S., Engineering — Swarthmore College Research Interests Laura’s scholarship spans artificial intelligence, machine learning, and data science, with theoretical advances tightly coupled to high-impact applications in energy systems (microgrids, power analytics), health and biomedical informatics, and computer science pedagogy. She is passionate about translating algorithmic innovations into real-world deployments that improve sustainability, clinical decision-making, and equitable education. A second major strand of her work centers on educational innovation: developing automated code-critiquing systems, studying their influence on student self-efficacy—especially for women in engineering—and leading campus-wide initiatives such as Carpentries workshops and ICPC programming competitions. Publication Trends Brown’s 2018–2025 publication record reveals sustained productivity in three overlapping domains: (1) AI/ML methodology—hardware prefetching, neural architectures, transfer learning; (2) energy analytics—forecasting coincident peak load, solar irradiance, battery degradation; and (3) computing education—automated feedback, gender studies, curriculum design. Recent emphasis has shifted toward educational technology, with multiple 2023–2025 papers on code critiquers and their psychological impact. Research Funding & Grants NSF Revolution through Evolution: $699 K, PI (2015–2019) DoD Distributed Agent-Based Management of Agile Microgrids: ~$1 M, co-PI (2013–2017) NSF Adaptive Memory Resource Management in a Data Center: $299 K, PI (2014–2017) NSF Microdevice for Rapid Blood Typing without Reagents: $300 K, co-PI (2016–2019) Google Explore CS Research Workshops: $18 K PI / $35 K co-PI (2018–2020) Student Engagement & Leadership Laura co-advises Women in Computing Science (WiCS) , organizes Michigan Tech’s ICPC regional site, and has led Google-sponsored undergraduate research workshops, Summer Youth Programs, and multiple Carpentries training events.
Szymon Barczentewicz, PhD, Eng., serves as a Lecturer at AGH University of Science and Technology within the Department of Power Electronics and Automation of Energy Conversion Systems, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering. His academic position and research activities confirm active faculty status in Poland's leading technical university. Dr. Barczentewicz's research concentrates on Power Electronics , Power Quality , and Smart Grid technologies, with particular expertise in harmonic analysis, islanding detection for distributed generation systems, and voltage fluctuation compensation. His work bridges theoretical development with experimental validation, frequently utilizing phasor measurement units (PMUs), hardware-in-the-loop simulations, and advanced signal processing techniques to address grid stability challenges in renewable-rich environments. Analysis of his 15 most recent publications reveals consistent focus on grid integration challenges: 60% address islanding detection and prevention, 40% cover power quality monitoring (including supraharmonics), and 30% explore data compression for smart grid applications. His 2025-2024 work demonstrates increasing emphasis on AI-enhanced grid management and experimental validation of control systems for renewable integration. Collaboration with industry partner Enea Operator features prominently in his research program, particularly in power quality monitoring and energy balancing projects. As an active member of AGH's Energy Quality Team ( Zespołu Jakości Energii ), he contributes to experimental developmental research and educational initiatives in electrical power delivery systems.
Dr. Hanif Livani is an Associate Professor in the Department of Electrical and Biomedical Engineering at the University of Nevada, Reno . His research focuses on machine learning , cyber-physical energy systems , and power system state estimation , with applications in smart grid technologies, renewable energy integration, and signal processing for power systems. Research Interests: Machine learning for fault location, grid resilience, voltage control, and real-time power system analytics. Contact: Phone (775) 784-6103, Email hlivani@unr.edu , Office WPEB 335.
Panagiotis Kotsambopoulos is a Principal Investigator in Smart Grids at EPISEY (National Technical University of Athens) and a Senior Member of IEEE. He holds a PhD in Distributed Power Generation from NTUA (2017), an Electrical Engineering degree (2010), and a Primary Education degree from the National University of Athens (2020). His research focuses on distributed energy resource control, real-time simulation, microgrid dynamics, and cybersecurity education. Education: PhD (Distributed Power Generation, NTUA 2017), Electrical Engineering (NTUA 2010), Primary Education (National University of Athens 2020) He pioneered Europe's first Power Hardware-in-the-Loop (PHIL) laboratory setup (2011) and leads NTUA's Power Systems Laboratory. His 36+ journal publications and 37 conference papers explore smart grid validation, energy community business models, and grid-forming inverter dynamics. He coordinates European research projects including H2020 RE-EMPOWERED and APEX. As IEEE PES Task Force Chair for innovative teaching methods, he developed open-source benchmark models and co-led IEEE Working Group P2004 on PHIL testing standards. He has taught power systems courses at NTUA since 2019 and represents EPISEY-NTUA in DERlab, a global network of distributed energy laboratories.
Joao L. Afonso is a Full Professor at the Department of Industrial Electronics, Engineering School, University of Minho, Portugal. He has been with the University since 1993 and served as Department Director from January 2017 to January 2021. He is also Vice-President of TMOB-HUB (Transportation and Mobility Research Hub) and coordinates the Group of Energy and Power Electronics (GEPE). His educational background includes a B.Sc. and M.Sc. in Electrical Engineering from the Federal University of Rio de Janeiro, Brazil (1986 and 1991), and a Ph.D. in Industrial Electronics from the University of Minho, Portugal (2000). He received his Habilitation degree in Electronics and Computers Engineering from the University of Minho in 2016. Professor Afonso's research focuses on Power Electronics applications across multiple domains including Power Quality, Active Power Filters, Renewable Energy, Electric Vehicles, Energy Efficiency, Energy Storage Systems, Innovative Railway Systems, Smart Grids, Smart Cities and Aerospace. His work bridges theoretical advancements with practical implementations in sustainable energy systems. His publication record shows a clear trend toward interdisciplinary research connecting power electronics with renewable energy integration, electric mobility, and smart grid technologies. Recent work demonstrates increasing focus on hydrogen applications, wireless power transfer for medical devices, and advanced converter topologies for grid integration. His research shows strong continuity in power quality improvement while expanding into emerging areas like green hydrogen and biomedical applications of power electronics. World's Top 2% Scientists (2021, 2022) IEEE Senior Member (2016) Member of Editorial Boards for MDPI Energies, MDPI Electronics, and EAI Endorsed Transactions on Energy Web Professor Afonso has supervised 17 Ph.D. and 74 M.Sc. theses. He has participated in 32 research projects, coordinating 21 of them. His funding portfolio includes significant projects like DAIPESEV, newERA4GRIDs, Quality4Power, and IN2STEMPO, reflecting strong connections with both national and European funding bodies. His research group GEPE maintains active collaboration with industry partners in the energy sector. As coordinator of GEPE (Group of Energy and Power Electronics), he leads a research team focused on power electronics applications for sustainable energy systems. The group is part of the ALGORITMI research center's thematic line 'Smart Cities and People.' His leadership extends to the TMOB-HUB, where he contributes to transportation and mobility research initiatives at the University of Minho.
Renqi Guo serves as an Associate Lecturer and PhD student at Lancaster University's School of Engineering. Affiliated with the TALOS research group, their work focuses on advanced power system technologies with active publication output. Research interests center on power systems engineering with emphasis on microgrid stability and renewable energy integration. Key specializations include synchronous condenser technology, virtual synchronous generators, and power quality enhancement in distributed energy systems where grid stability faces challenges from intermittent renewable sources. Recent publication trends demonstrate concentrated expertise in hybrid generator systems for microgrids, specifically examining synchronous condenser and virtual synchronous generator integration. This work addresses critical power quality issues in modern energy networks transitioning toward renewable dominance. Professional activities include participation in the TALOS research group focusing on electrical engineering solutions for contemporary power challenges.
