Find Academic Supervisors

• Power Systems Engineering
• Renewable Energy Integration
• Machine Learning Applications in Energy
• +3 more

Alessandro Bosisio is a Researcher at the Department of Industrial and Information Engineering, University of Pavia. His research focuses on power distribution systems, renewable energy integration, smart grid technologies, and machine learning applications in energy systems. He has conducted extensive studies on voltage control, fault prediction in distribution grids, and optimization algorithms for grid reliability and resilience. Key research areas include: Impact of extreme weather on power systems Renewables-to-hydrogen systems Machine learning for load forecasting and grid automation GIS-based planning for distribution networks His work emphasizes real-world case studies in Milan and other Italian regions, addressing challenges such as electromobility integration, grid digitalization, and energy transition strategies. Bosisio has also contributed to patents related to distribution network reconfiguration and smart grid automation systems.

YangQuan ChenView profile

Professor

• Unmanned Aircraft Systems (UAS)
• Mechatronics
• Renewable Energy Systems
• +7 more

YangQuan Chen is a Professor in the Department of Mechanical Engineering at the University of California, Merced. His research focuses on unmanned aircraft systems, mechatronics, renewable energy systems, cyber-physical systems, and applied fractional calculus. He leads multiple labs, including locations at SE2-273 and 4225 N. Hospital Rd., Atwater, CA. His work integrates advanced control theory, robotics, and digital twin frameworks for applications in environmental monitoring, precision agriculture, and energy systems. Education: PhD (1998) from Nanyang Technological University, Singapore; M.S. (1989) and B.S. (1985) from Chinese institutions. His research emphasizes interdisciplinary approaches, combining fractional calculus with machine learning for real-world challenges like pandemic modeling, UAV-based emission tracking, and smart agriculture. Key research areas include: Unmanned Aircraft Systems (UAS) for environmental and agricultural applications Cyber-physical systems and digital twin frameworks Applied fractional calculus in control systems and epidemiology Machine learning for soil carbon sensing and crop monitoring Recent work explores UAV swarms for methane emission quantification, battery health-aware mission planning, and physics-informed neural networks for state estimation. His lab develops innovative solutions for renewable energy systems, such as adaptive controllers for proton exchange membrane electrolyzers and robust consensus strategies for multi-agent systems. Awards and recognition include prominent citations on Google Scholar and ResearchGate. Ongoing projects involve collaboration with industry and government agencies on smart agriculture, environmental sensing, and energy efficiency.

Anthony PapavasiliouView profile

Assistant Professor

• Operations Research
• Electricity Market Design
• Power System Operations
• +4 more

Anthony Papavasiliou is an Assistant Professor at the School of Electrical and Computer Engineering, National Technical University of Athens (NTUA). Previously, he held the ENGIE Chair as an Associate Professor at Université catholique de Louvain (UCLouvain) and received the 2018-2021 Franqui Foundation Research Professorship. His research focuses on operations research, electricity market design, power system operations, and renewable energy integration. He holds a BSc from NTUA and MSc/PhD from UC Berkeley, where he collaborated with Professor Shmuel Oren. Education: BSc: Electrical and Computer Engineering, NTUA MSc/PhD: Industrial Engineering & Operations Research, UC Berkeley Research Interests: His work addresses challenges in energy systems optimization, market design for decarbonized grids, stochastic programming, and the integration of distributed energy resources. Key areas include: Electricity market mechanisms Uncertainty management in power systems Flexibility valuation Grid reliability under renewables Awards & Grants: ERC Starting Grant (2019) Bodossaki Foundation Award (2021) INFORMS Best Publication Awards (2015, 2021) Advising & Grants: He leads the ICEBERG research group and has advised on projects for TSOs, regulators, and utilities. His work includes: EU-funded projects on market design Consulting for energy policy development Lab & Teams: Active in the ICCS laboratory and collaborates with international teams on smart grid innovation, particularly in cross-border balancing and zonal pricing frameworks.

• Control Systems
• Fractional-order Systems
• Robust Control
• +2 more

Aleksei Tepljakov is a Senior Researcher at Tallinn University of Technology's Department of Computer Systems, specializing in fractional-order control systems and intelligent automation. His research develops novel methodologies for robust control design. Research focuses on fractional-order PID controllers, robust stabilization techniques, and data-driven control applications in power systems and industrial processes. Recent work integrates digital twin technology for virtual commissioning of control systems. Publications emphasize computational approaches to stability analysis in time-delay systems and practical implementations in energy systems. Article trends show consistent innovation in fractional calculus applications for industrial control challenges. Leads research in extended reality interfaces for control engineering and digital twin implementation frameworks.

• Control Systems
• Energy Systems
• Artificial Intelligence
• +5 more

Eduard Petlenkov is a Tenured Full Professor at the Department of Computer Systems at Tallinn University of Technology (TalTech), leading the Centre for Intelligent Systems. His research focuses on advanced control systems, energy efficiency, robotics, and artificial intelligence applications. He holds the award of Recognized Lecturer 2023 . Key research areas include: Fractional-order control systems Data-driven optimization for energy systems Robotics and autonomous systems Model predictive control (MPC) Power grid stability and renewable integration Recent work emphasizes: Stabilization techniques for hybrid power systems Radar technologies for unmanned ground vehicles Energy harvesting in metamaterials Real-time control of unknown linear systems Smart grid digital twin frameworks His contributions span 20+ peer-reviewed articles (2022–2025), addressing topics like reinforcement learning in microgrid control and fractional-order PID tuning methods. Active in bridging industry 5.0 education through blended learning approaches.

• Control Systems
• Aerospace Engineering
• Renewable Energy Systems
• +3 more

Seyyedmohsen Azizi is an Assistant Professor in the School of Applied Engineering and Technology at New Jersey Institute of Technology. His research focuses on control systems, fault diagnosis, and renewable energy integration. He has led projects funded by the National Science Foundation and Natural Sciences and Engineering Research Council of Canada. Education: Ph.D. in Engineering (inferred from academic rank) Key research areas include satellite formation flight control, fault detection in power systems, and decentralized control of wind-diesel hybrid systems. His work combines machine learning with traditional control theory for robust system design. Active grants include the Renewable Energy Systems Training (REST) Laboratory Development and Workforce Training project (2019-2023) and multiple fault diagnosis initiatives in satellite constellations since 2007. No scientific awards explicitly listed in profile. Advising focuses on graduate students working in control systems and renewable energy. He collaborates on interdisciplinary projects involving aerospace and electrical engineering.

• Power Systems
• Microgrids
• Power Electronics
• +3 more

Dr. Md. Rasheduzzaman serves as an Assistant Professor in the Department of Engineering Technology at Southeast Missouri State University's School of Engineering and Technology. His office is located in Polytech 117C with contact details including phone (573) 651-2654 and email mrasheduzzaman@semo.edu. Educational background: Ph.D. – Missouri S&T, Rolla, 2015 MS – Purdue University, Hammond, 2010 BS – Chittagong University of Engineering and Technology, Bangladesh, 2006 His research centers on Power Systems , Microgrids , and Power Electronic Converters , with significant contributions to small-signal modeling of inverter-dominated microgrids and advanced control strategies for power converters. He teaches core courses including Electrical Circuits, Electronics, Digital Logic, Control Systems, Power Systems, Electric Drives, and Energy Management. Analysis of his publications reveals consistent focus on renewable energy integration through microgrid stability and power converter optimization, particularly addressing challenges in islanded microgrid operations and single-phase inverter control. Professional recognition: IECON2018 Best Paper Award Dr. Rasheduzzaman advises students in the Engineering Technology - Electrical and Control Systems program, emphasizing problem-solving methodology over rote solutions to foster independent thinking, self-direction, and self-generated ideas among students.

Hossam GaberView profile

Professor

• Resilient Smart Energy Grids
• Advanced Plasma Generation for Fusion Energy
• Nuclear Power Plant Safety Systems
• +3 more

Dr. Hossam Gaber is a Professor in the Department of Energy and Nuclear Engineering at Ontario Tech University, within the Faculty of Engineering and Applied Science. He holds a PhD in Safety Engineering from Okayama University, Japan, and is a Professional Engineer (PEng) and Fellow of the Institution of Engineering and Technology (IET). His research focuses on resilient energy systems, nuclear safety, and advanced plasma applications. Education: PhD (Safety Engineering), Okayama University, Japan MSc (Computer & Automatic Control), Alexandria University, Egypt BSc (Computer & Automatic Control, First-class Honours), Alexandria University, Egypt Dr. Gaber’s research interests span resilient smart energy grids, fusion energy plasma generation, nuclear power plant safety systems, fault diagnosis, risk-based energy conservation, and process systems engineering. He has authored over 30 peer-reviewed publications and holds several awards, including the Ontario Tech Achievement Award (2017-2018) and the IEEE Best Paper Award (2007). He teaches advanced courses such as Advanced Energy Process Control , Hybrid Energy System Design , and Reactor Control . His work emphasizes interdisciplinary solutions for energy sustainability and industrial safety.

Feifei BaiView profile

Senior Lecturer

• Renewable Energy Integration
• Power Systems
• Smart Grid Technology
• +4 more

Dr. Feifei Bai is a Senior Lecturer at the School of Electrical Engineering and Computer Science, University of Queensland (UQ), and an adjunct Senior Research Fellow at Griffith University. She holds an Advance Queensland Fellowship (2018) and has secured over $4.6M in research grants. Her research focuses on renewable energy integration, power system stability, and smart grid technologies. She collaborates with industry partners like Energy Queensland and Powerlink, and her work has led to commercialized patents and an Australian Engineering Excellence Award (2020). Education: PhD in Engineering from Southwest Jiaotong University. Active editorial roles include Associate Editor for IET Generation, Transmission & Distribution and Assistant Editor for International Journal of Green Energy. Research Interests: Renewable energy systems, synchrophasor applications, power system oscillation control, and cybersecurity. Her work bridges theoretical advancements with practical industry solutions, emphasizing grid resilience and efficient energy management. Publications: Over 75 journal/conference papers (2025 highlights include EV charging optimization, microgrid scheduling, and Bayesian frequency modeling). Her research trends emphasize smart grid innovation, renewable integration challenges, and advanced control strategies for high-renewable grids. Awards: Advance Queensland Fellow (2018), Australian Engineering Excellence Award (2020). Grants & Industry: $4.6M in funded projects. ARENA-backed research on EV charging infrastructure received national recognition. Active in industry partnerships for technology deployment. Labs/Teams: Leads the Power & Energy Systems Research Group at UQ, contributing to initiatives like the Flexible Urban Energy Systems special issue (2021).

Chunnong ZhaoView profile

Associate Professor

• Gravitational Wave Astronomy
• Optomechanics
• Laser Interferometry
• +3 more

Chunnong Zhao is an Associate Professor at the School of Physics, Maths and Computing at The University of Western Australia. His research focuses on experimental gravitational wave physics and precision measurement technologies, particularly addressing high-optical power effects in laser interferometers. He pioneered work on parametric instability mitigation, which was critical for the Advanced LIGO project. His innovations include opto-acoustic parametric amplifiers and optomechanical filters, enabling ultra-sensitive gravitational wave detection. Current projects involve developing optomechanical negative dispersion technology to improve detector sensitivity at high frequencies. Research Interests: Gravitational wave detection technology Optomechanical systems and quantum optomechanics Parametric instability control in interferometers Optical cavity stabilization techniques Laser interferometry applications Binary neutron star merger signal analysis Grants & Projects: Tilt sensors for 6D seismology Australian Partnership in Advanced LIGO+ Quantum Australia Centre Renewable Microgrid Pilot for Gravitational Wave Facilities Led collaborations on Advanced LIGO's parametric instability suppression and developed novel optomechanical devices for next-generation detectors. Current research explores quantum-limited optomechanical sensors and negative dispersion applications in gravitational wave astronomy.

Somayeh AsadiView profile

Professor

• Smart Infrastructure Systems
• Sustainable Built Environment
• Machine Learning Applications
• +7 more

Somayeh Asadi is a Professor in the Department of Civil and Environmental Engineering at the University of Virginia (UVA), following her tenure as an Associate Professor at Pennsylvania State University. She holds M.Sc. and Ph.D. degrees in engineering science from Louisiana State University. Her research focuses on smart infrastructure systems, integrating machine learning and AI to advance sustainable built environments, including net-zero energy buildings, resilient systems, and the food–energy–water nexus. She leads a dynamic research team exploring topics like digital twins, occupant health, and equitable energy access. Education: M.Sc. and Ph.D. in Engineering Science (Louisiana State University), with specialization in construction engineering. Professional roles include past chair of the ASCE Global Computing Committee and advisor to the NECA student chapter for eight years. Research interests emphasize data-driven solutions for urban challenges, including power system resilience, EV integration, and climate-responsive design. Her work bridges academia and industry through projects funded by the NSF and collaborative initiatives like the UVA Engineering AI education grant. She actively promotes equitable infrastructure development and has published extensively on topics ranging from BIM-to-robotics integration to socio-economic vulnerability analysis. Awards: While no specific awards are listed, her contributions to sustainable engineering and AI applications are highlighted through her numerous peer-reviewed publications and grant leadership. Advising & Grants: Secured a $1M NSF grant for inclusive AI education and advises on construction robotics and energy policy. Her labs and teams focus on transforming infrastructure through interdisciplinary approaches, including parametric modeling for robotic construction and mobile energy storage solutions.

Burak KantarciView profile

Professor

• Internet of Things (IoT)
• AI-based Security
• Vehicular Networks
• +6 more

Dr. Burak Kantarci is a Full Professor at the School of Electrical Engineering and Computer Science, Faculty of Engineering, University of Ottawa. He holds a University Research Chair in AI-Enabled Secure Networking for Smart Critical Infrastructures and a courtesy faculty appointment at Clarkson University. His research focuses on IoT, AI-based security, vehicular networks, and edge computing. Education and Career: Previously an Assistant Professor at Clarkson University (before joining University of Ottawa) and a Research Fellow at University of Ottawa (2009–2014). He is a Professional Engineer (P.Eng) in Ontario. Research Interests: Includes continuous authentication, communication models for big data, crowdsensing, and vehicular sensing. He leads the NEXTCON-SCVI Labs and has published over 200 peer-reviewed works. Articles Trends: Recent work emphasizes AI-driven security solutions, autonomous systems safety, and 5G/6G networking challenges. Topics include adversarial detection, UAV optimization, and edge computing efficiency. Awards: Distinguished Lecturer (IEEE Communications & Systems Societies), ACM Distinguished Speaker, Senior Member (ACM/IEEE). Grants & Advising: Principal Investigator on NSERC/CFI/NSF-funded projects. Advises on grants focused on smart infrastructure and secure networking. Labs/Teams: Founder of NEXTCON-SCVI Labs, focusing on communications, computing networks, and smart vehicles innovation.

Dario BausoView profile

Professor

• Optimization
• Optimal and Distributed Control
• Game Theory
• +4 more

Dario Bauso is a Full Professor and Chair of Operations Research for Engineering Systems at the University of Groningen's Faculty of Science and Engineering. He concurrently holds a Professorship at the Dipartimento di Ingegneria, University of Palermo, Italy, since 2005. His research focuses on Optimization, Optimal and Distributed Control, and Game Theory. He has held visiting roles at institutions including Keio University (Japan), MIT, and Imperial College London. Education: Laurea (Aeronautical Engineering, University of Palermo, 2000) and PhD (Automatic Control and System Theory, University of Palermo, 2004). Past positions include Reader at the University of Sheffield (2015–2018) and Research Fellow at the University of Trento (2012–2014). Research interests emphasize applications to energy systems, financial networks, and multi-agent systems. His work has been published in top journals like Dynamic Games and Applications and Automatica . Awards include multiple Italian National Qualifications to Full and Associate Professor roles. Teaching includes courses on Game Theory, Control Systems, and Operations Research at both undergraduate and graduate levels. Current PhD student: W. Baar ('Diffusion models on networks'). Grants include leadership in projects like the ADEPT initiative (2017–2018) and the Italian-funded 'Smart PORt Terminals' (2016–2019).

Marcello RestelliView profile

Professor

• Reinforcement Learning
• Machine Learning
• Artificial Intelligence
• +3 more

Marcello Restelli is a Professor at the Department of Electronics, Information, and Bioengineering (DEIB) of Politecnico di Milano, affiliated with the School of Industrial and Information Engineering. His research focuses on Reinforcement Learning (RL), Machine Learning, Robotics, and Control Systems. He explores challenges in real-world RL applications, multi-agent systems, and optimization under uncertainty. Key contributions include developing scalable algorithms for sparse-reward environments, POMDPs, and safe robotics. He leads projects like the EU-funded I3LUNG initiative, integrating AI for personalized lung cancer care. His work bridges theory and practice, addressing topics such as exploration strategies, regret minimization, and causal feature selection. He has contributed to frameworks like Gym4ReaL for benchmarking real-world RL systems and Storehouse for warehouse optimization. Recent efforts emphasize energy systems, financial decision-making, and explainability in extreme event prediction. His publications highlight advancements in inverse RL, online learning, and transfer learning. Collaborations span academia and industry, reflecting his impact on both theoretical and applied AI domains.

Foad TaghizadehView profile

Lecturer

• Electric Vehicle Technology
• Power Electronics
• Renewable Energy Integration
• +3 more

Dr Foad Taghizadeh is a Lecturer in electrical engineering at Macquarie University, Australia, where he also held prior research fellowships. He holds honorary visiting scholar positions at the University of Technology Sydney and Griffith University. His research focuses on electric vehicle (EV) charging systems, grid integration of renewables, and microgrid technologies. He earned his PhD in Electrical Engineering from Macquarie University in 2018. Education: PhD in Electrical Engineering, Macquarie University (2018) Research interests include EV charging infrastructure, grid impact analysis, renewable energy integration, and energy storage systems. His work addresses challenges in high-speed EV charging, microgrid stability, and converter control for distributed energy resources. Awards and Honors: Fellow of Higher Education Academy (FHEA), UK (2020) Highly Commended Finalist, Vice-Chancellor's Learning & Teaching Early Career Award (2022) New South Wales Fresh Science Award (2019) FSE Excellence in Early Career Teaching Award (2022) Current Projects: Wireless power transfer for heavy EVs and marine vessels (2024–2027) Development of economically sustainable EV charging facilities Modular microgrid for NSW sustainable development Labs/Teams: Active in Macquarie University's energy systems research group and collaborates with industry partners on EV charging infrastructure projects.