Tarik Namas is a Senior Lecturer in the Department of Electrical and Electronics Engineering at the International University of Sarajevo's Faculty of Engineering and Natural Science. With over 15 years of academic experience, he has held roles including Teaching and Research Assistant (2006-2009), Technical and Industrial Instructor at Jubail Industrial College (2009-2011), and IT Assistant at Prince Sultan Military College. His expertise spans Electrical Engineering , Signal Processing , and Power Systems , with a focus on fault detection, impedance analysis, and augmented reality applications in healthcare.
Dr. Inaolaji Adedoyin is an Assistant Professor in the Department of Electrical Engineering at the University at Buffalo (School of Engineering and Applied Sciences). Her research focuses on optimizing renewable energy integration into power grids, particularly through distributed control, smart inverters, and grid reliability enhancement. She holds a PhD in Electrical Engineering from Florida International University (2023), an MS from the same institution (2019), and a BEng from Covenant University, Nigeria (2014). Education: PhD in Electrical Engineering, Florida International University, 2023 MS in Electrical Engineering, Florida International University, 2019 BEng in Electrical and Electronics Engineering, Covenant University, Nigeria, 2014 Research interests revolve around modeling and optimization of power grids with high renewable energy penetration, including distributed control strategies, smart inverter coordination, and electric vehicle integration. Her work addresses challenges such as voltage regulation, grid stability, and reliability in unbalanced distribution systems. Key methodologies involve consensus-based optimization algorithms, ADMM frameworks, and phase-coupled models. Recent articles highlight advancements in 5G-enabled grid testbeds, cellular network delay impacts, and optimal allocation of battery storage. These publications emphasize practical solutions for grid modernization and renewable integration. Her work often bridges theoretical optimization with real-world grid applications. Grants and advising details are not explicitly mentioned in the provided text.
Geunyeong Byeon is an Assistant Professor in the School of Computing and Augmented Intelligence at Arizona State University (ASU). She previously held postdoctoral positions at Argonne National Laboratory and Los Alamos National Laboratory. Her research focuses on large-scale optimization methodologies for decision-making in energy systems and interdependent infrastructure networks. Byeon teaches courses in operations research, optimization, and data-driven methods at both undergraduate and graduate levels. Education Ph.D. in Industrial & Operations Engineering (2020), University of Michigan, Ann Arbor M.S. in Industrial Engineering (2016), Seoul National University, South Korea B.S. in Industrial Management Engineering (2014), Korea University, South Korea Research Interests Byeon specializes in developing optimization frameworks for complex systems, including energy grids and interdependent infrastructures. Her work emphasizes distributionally robust optimization , Benders decomposition , and federated algorithms , with applications to power systems, resilient distribution networks, and multi-agent decision-making under uncertainty. She bridges theoretical advancements with practical computational implementations. Teaching Highlights Optimization I (2022-2024) Computing for Data-Driven Optimization (2021-2025) Probability and Statistics for Engineering (2021) Awards MICDE Fellowship (University of Michigan, 2018) Rising Stars in Computational & Data Sciences Nominee (2020) Richard & Eleanor Towner Prize for Outstanding Ph.D. Research (Honorable Mention, 2019) Professional Service Byeon has reviewed for prestigious journals including INFORMS Journal on Computing , Management Science , and IEEE Transactions on Power Systems , and conferences like the Power Systems Computation Conference.
Sherif Faried is a Professor in the Department of Electrical and Computer Engineering at the University of Saskatchewan, based in Room 3B13 of the Engineering Building at 57 Campus Drive, Saskatoon, SK S7N 5A9. His research spans critical areas of modern power systems engineering with direct industry applications. His research interests encompass: Power Systems Renewable Energy Integration Smart Grids Distribution System Automation Power System Protection Optimization Analysis of his 15 most recent publications (2022-2025) reveals concentrated expertise in renewable integration challenges, particularly photovoltaic systems. Dominant themes include Conservation Voltage Reduction (CVR) techniques for energy efficiency in high-renewable networks, soft open point applications for dynamic network reconfiguration and service restoration, and multi-energy system optimization (electricity-natural gas). His work demonstrates methodological sophistication through stochastic, two-stage, and multi-scenario optimization approaches applied to real-world grid modernization problems. His publication portfolio includes both original research and authoritative reviews on battery storage ancillary services, cyber-physical power system optimization, and microgrid-based resilience strategies, establishing him as a significant contributor to distribution system evolution.
Dr. Firdous Ul Nazir is a Research Fellow in the Control and Power Research Group at Imperial College London's Department of Electrical and Electronic Engineering. He works on the 'JUICE – Joint UK-India Clean Energy Centre' project, focusing on voltage regulation challenges in distribution networks with high solar photovoltaic penetration. Education includes: PhD in Electrical Engineering from Imperial College London MTech in Power Systems from Indian Institute of Technology Roorkee BTech in Electrical Engineering from National Institute of Technology Srinagar (Gold Medalist) Research develops stochastic optimization methods for Volt/VAr control in distribution grids integrating renewable energy. His work addresses reactive power optimization, distributed computation frameworks, and uncertainty management in active distribution networks with inverter-based resources. Recent publications show progression from theoretical frameworks to practical solutions for distributed control in multi-voltage networks. Later works incorporate machine learning for state estimation and address international solar integration barriers. Prior experience includes roles as an aviation officer at Indian Oil Corporation and teaching positions at NIT Srinagar. Research contributions include decentralized Volt/VAr algorithms, linearized power flow models, and strategic frameworks for unobservable networks.
Manohar Chamana serves as Assistant Professor and Graduate Advisor in the Renewable Energy Program at Texas Tech University's National Wind Institute, specializing in renewable energy and energy storage integration within electrical grids. Education: Ph.D. in Electrical Engineering, University of North Carolina at Charlotte (2016) M.S. in Electrical Engineering, Texas Tech University (2011) B.E. in Electrical and Electronics Engineering, Andhra University, India (2009) Research Interests: Dr. Chamana's work centers on modeling active distribution networks for grid resiliency and DER integration, with emphasis on Volt-VAR operations, ADMS testbed development, microgrid control strategies, and cyber-physical security. His research bridges theoretical modeling of renewable integration challenges with practical hardware testbed validation, particularly focusing on wind farms and hybrid energy systems. Scientific Awards: National Wind Institute Faculty Innovation Award (2021) TTU Decade Service Recognition EPIC Scholarship (2013-14) IEEE PES T&D Best Paper (2014) Advising and Grants: As Graduate Advisor for the Wind Energy Program, he mentors students while securing over $4 million in external funding. He serves as Co-PI on Department of Energy, Department of Defense, and Texas Workforce Commission grants targeting grid modernization and workforce development. Labs and Teams: Dr. Chamana actively contributes to the Global Laboratory for Energy Asset Management and Manufacturing (GLEAMM), developing cyber-physical testbeds for real-time grid simulation and educational applications in renewable integration.
Sylvie Thiebaux is a Professor at the School of Computing, Australian National University (ANU). Her research focuses on artificial intelligence, particularly in planning algorithms, heuristic search, and their applications to energy systems and optimization. She holds a Dipl.Eng. from INSA Rennes, an MSc from Florida Tech, and a PhD from Université de Rennes/CNRS. Her work integrates machine learning with classical AI techniques to address challenges in smart grids, renewable energy integration, and multi-objective decision-making. Key projects include optimizing distribution networks with high PV penetration, developing robust control strategies for volt/Var systems, and advancing neuro-symbolic AI for planning under uncertainty. She has contributed to over 90 publications and actively supervises research students in computational planning and energy systems. Her research also explores ethical considerations in AI, including normative compliance in planning systems. Collaborative initiatives with industry and academia have led to advancements in energy market frameworks and distributed resource coordination.
Ahmad Attarha is a Research Fellow at the School of Computing , College of Engineering and Computer Science , Australian National University (ANU). He recently submitted his PhD thesis and holds a B.Sc. and M.Sc. in Electrical Engineering from Semnan University, Iran (2012 and 2015). Education: B.Sc. (2012), M.Sc. (2015) in Electrical Engineering Current Role: Research Fellow in Power Systems and Smart Grids His research focuses on power systems operation and optimisation , with specific emphasis on Grid Integration of Distributed Energy Resources (DER) , Distributed Optimisation , and Robust Optimisation techniques for distribution networks. Publications highlight trends in DER aggregation , shaped operating envelopes , and robust optimization for distribution systems. Key areas include voltage regulation , network security constraints , and price-sensitive bidding strategies in energy markets. He is affiliated with the Smart Grid group at ANU and contributes to projects related to grid flexibility , distributed battery storage , and renewable energy integration .
Mohammad Sadegh Golsorkhi is an Associate Professor at the Centre for Industrial Electronics within the Institute of Mechanical and Electrical Engineering at the University of Southern Denmark. His work focuses on power electronics, microgrid control, and converter design. Research Interests: Specializes in power electronics, microgrid control, and harmonic compensation. His research explores hybrid AC/DC microgrids, predictive control systems, and wide bandgap devices. Recent Publications: His 2024–2025 work includes AI-driven microgrid optimization, parasitic modeling in SiC converters, and harmonic compensation strategies. Scientific Awards: World's Top 2% Scientists 2023 (Stanford University) ABB research poster award (2014) International Research Postgraduate Scholarship (2013) Australian Postgraduate Award (2013) Projects: Leads EU-funded initiatives like OptiDCG4H2 and CUHIED, focusing on decentralized control of offshore DC grids, hydrogen production, and ultra-high efficiency drives.
Vassilis Kekatos is an Associate Professor at the Elmore Family School of Electrical and Computer Engineering at Purdue University, part of the Schweitzer Power and Energy Systems group. His research focuses on algorithmic solutions for power systems, leveraging machine learning and quantum computing. He holds a prominent role in the university, teaching courses such as Power Distribution System Analysis and Signals and Systems. Research Interests: His work spans power and energy systems optimization, smart grid technologies, and applications of machine learning and quantum computing. Specific areas include optimal power flow, distribution grid management, and voltage regulation algorithms. Key Contributions: His recent projects include NSF-funded research on optimizing power distribution grids and variational quantum computing for constrained optimization. He has published extensively in top journals and conferences, with a focus on practical solutions for modern grid challenges. Students and Collaborations: Kekatos advises a vibrant graduate student group, including PhD students Thinh Le, Ashutossh Gupta, and Ruoyu Yang. His former students hold roles at companies like C3.AI and Invenia. He collaborates with institutions like NREL and the University of Minnesota, advancing interdisciplinary research in energy systems. Grants and Funding: Notable grants include NSF awards for quantum optimization and data-budget solutions, totaling over $1M. He also leads projects on grid stability and resilience through PNNL and UT Austin collaborations. Labs and Teams: His research group actively participates in Purdue’s Grid of Tomorrow Consortium, organizing workshops on emerging grid technologies. The group emphasizes cross-disciplinary innovation, blending electrical engineering with machine learning and quantum computing.
Qianwen Xu is an Assistant Professor at KTH Royal Institute of Technology and a Digital Futures Faculty member. She specializes in power systems, renewable energy integration, and control systems, with a focus on smart grids, hydrogen energy, and machine learning applications in energy infrastructure. Her work addresses challenges in sustainable energy systems, grid stability, and decentralized control mechanisms. Active research projects include the C3.ai DTI-funded initiative on data-driven control of smart converters using deep reinforcement learning, and the 'Autonomous coordination and control of smart converters' project. She explores topics like electric mobility systems, climate-resilient building designs, and cyber-physical system security. Her research spans technical areas such as reinforcement learning for power converter control, hybrid energy storage systems, and resilience of distribution networks against climate change. She collaborates with Digital Futures, a center focused on advancing digital technologies for societal impact, established jointly with Stockholm University and RISE Research Institutes.
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.
Yilu Liu serves as the UT-ORNL Governor's Chair Professor at the University of Tennessee's Tickle College of Engineering, holding a joint appointment with Oak Ridge National Laboratory. She is deputy director of the Center for Ultra-wide-area Resilient Electric Energy Transmission Networks (CURENT), a DOE/NSF engineering research center focused on modernizing America's power grid infrastructure. Liu earned her B.S. in Electrical Engineering from Xi'an Jiaotong University (China), followed by M.S. and Ph.D. degrees from The Ohio State University. Before joining UT in 2009, she was faculty at Virginia Tech where she directed its Center for Power Engineering. Her pioneering research centers on power grid monitoring, stability, and resilience. Liu developed the North American power grid Frequency Monitoring Network (FNET/GridEye) - the first continent-wide system using over 300 Frequency Disturbance Recorders to monitor grid health in real-time. Her work addresses critical challenges in renewable energy integration, grid inertia estimation, electromagnetic pulse protection, and time synchronization for smart grids. Recent research focuses on low-inertia systems, forced oscillations, and HEMP vulnerability assessment. Liu's publications show consistent innovation in power systems monitoring, with recent work spanning 2024-2025 covering time synchronization techniques, pulsar-based timing, inertia estimation, and electromagnetic protection. Her research demonstrates strong focus on practical grid applications while addressing emerging challenges from renewable integration and cyber-physical security threats. R&D 100 Award (2022) for FNET/GridEye R&D 100 Award (2021) for GridDamper technology R&D 100 Award (2018) for Mobile Universal Grid Analyzer R&D 100 Award (2014) for Continuously Variable Series Reactor IEEE Power & Energy Society Wanda Reder Pioneer in Power Award (2020) Member of National Academy of Engineering Fellow of IEEE and National Academy of Inventors NSF Presidential Faculty Fellow (1994) NSF Young Investigator Award (1993) Liu leads CURENT (with 35 industry members) and has secured significant DOE and NSF funding. Her former students nominated her for the IEEE Wanda Reder Award, highlighting her mentorship impact. She has published over 500 articles and one book, with research directly influencing grid operations through technologies adopted by NERC, FERC, and power companies. Liu also established FNET/GridEye as a critical monitoring platform used across North America. As deputy director of CURENT, Liu collaborates with industry partners and researchers across multiple institutions. Her team at UT-ORNL focuses on developing next-generation grid monitoring technologies, with recent projects addressing electromagnetic pulse vulnerabilities and solar grid integration challenges in Saudi Arabia.
Dr. Jiajia Yang is a Senior Lecturer in Renewable Energy and Electrical Engineering at James Cook University (JCU), Australia. He holds a Ph.D. from the University of New South Wales (2018) and has extensive experience in power system modeling, renewable integration, and energy markets. Prior to JCU, he worked as a Senior Research Associate at UNSW and a Senior Power System Engineer in industry projects like the Renewable Energy Zone (REZ) programs. Education: B.E., Northeast Electric Power University, China (2011) M.E., Zhejiang University, China (2014) Ph.D., University of New South Wales, Australia (2018) Research Interests: Data science in smart grids Electricity markets for renewable-rich systems Localized/P2P trading mechanisms Hydrogen economy Recent Research Trends: Dr. Yang’s work focuses on decarbonizing power systems through innovative market designs, renewable integration, and cybersecurity. His 2024 studies emphasize hydrogen credit frameworks and carbon-neutral energy systems, while earlier projects address P2P trading and grid stability under high renewables. Awards: Outstanding Editorial Board Member (2023) Best Research Paper Award (2018) Advising & Grants: He coordinates teaching modules like Power Engineering 1/2 and leads the 2024 International Conference on Energy and Environment Engineering. His research is supported by projects like the North Queensland Transmission Grid Planning initiative (2024–2027). Labs/Teams: Engaged in IEEE PES roles, including IEEE Transactions on Industry Applications editorial work and chairing multiple conferences.
Robert Bass is a Professor in the Department of Electrical & Computer Engineering at Portland State University , where he established and directs the Power Engineering Research Group. His work addresses critical challenges in utility-sector power systems and distributed energy resources. Research Focus: DER aggregation, grid cybersecurity, EV charging impacts, and distribution system optimization Industry Partnerships: Collaborations with Portland General Electric, Bonneville Power Administration, Pacific Northwest National Labs, and others Courses Taught: EE 347/348 Power Systems I/II, EE 4/530 Analytical Methods for Power Systems, ECE 411-413 Senior Project sequence Education: Ph.D. in Electrical Engineering from the University of Virginia.
