Find Academic Supervisors

Dr. Jose Paolo Talusan is a Research Scientist at the Department of Computer Science and Computer Engineering , Vanderbilt University, specializing in smart transportation systems , distributed computing , and cyber-physical systems . He is affiliated with ScopeLab , a research group focused on smart cyber-physical systems. Education: PhD from Nara Institute of Science and Technology, Japan (2020) Research Interests: His work addresses challenges in urban mobility through middleware architectures, optimization algorithms, and machine learning. Key areas include incident detection in transportation systems, privacy-preserving route planning, and vehicle-to-building charging optimization. Publication Trends: Recent publications focus on real-time transit optimization (2024-2025), leveraging reinforcement learning for heterogeneous agents in vehicle-to-building systems, and privacy-aware route planning in smart cities. His work integrates IoT , edge computing , and graph neural networks to tackle imbalanced data and sparsity issues in transit analytics. Labs & Teams: Actively contributes to ScopeLab at Vanderbilt University, collaborating on interdisciplinary projects with researchers in computer science, electrical engineering, and urban planning.

• Smart Transportation Systems
• Cyber-Physical Systems
• Middleware
• +4 more

Gurunath Gurrala serves as an Associate Professor in the Department of Electrical Engineering at the Indian Institute of Science (IISc), Bangalore. His research focuses on power systems dynamics, high-performance computing applications, and renewable energy integration. He maintains active collaborations with international institutions including Oak Ridge National Lab and Texas A&M University. His research interests center on Power Systems Analysis and Control , with specialization in High Performance Computing Applications, Nonlinear and Intelligent Control, Weak Grid Integration of Renewables, Microgrid Protection, and Smart Grid Stability. His work bridges theoretical control systems with practical power grid challenges, particularly for renewable-rich grids. His recent publications demonstrate a strong interdisciplinary trend, spanning power systems (35%), control theory (25%), renewable integration (20%), and emerging applications in biomedical engineering and environmental systems (20%). Key recurring themes include grid stability under high renewable penetration, advanced protection schemes for microgrids, and computational methods for power system analysis. IEEE Power and Energy Society (PES) Outstanding Engineer Award 2018 Young Engineer Award 2015, Indian National Academy Engineers Best Conference Paper, IEEE PES General Meeting 2015 Best Ph.D Thesis Award (Prof.D.J.Badkas Medal) 2010 Elevated to Senior Member IEEE (2016) Professor Gurrala has secured competitive research funding including the Young Scientist Grant from DST (2015) and International Travel Support from SERB (2017). He actively mentors students through PhD and Master's programs while teaching advanced courses including Power System Dynamics and Control (E4 231), Computer Control of Power Systems (E4 233), and Selected Topics in Integrated Power Systems (E4 237). His research group collaborates with power utilities and international research labs on grid modernization challenges.

• Power Systems Analysis and Control
• High Performance Computing Applications
• Nonlinear and Intelligent Control
• +4 more

Nikolaos Paterakis is an Assistant Professor of Power System Optimization and Electricity Markets with the Electrical Energy Systems research group at the Department of Electrical Engineering, Eindhoven University of Technology (TU/e). He is the founder and principal investigator of the Electricity Markets & Power System Optimization Laboratory (EMPSOLab) established in 2019, and a member of the Cyber-Physical Systems Center Eindhoven (CPSe). His research focuses on applying optimization and machine learning techniques to power system and electricity market problems, particularly regarding renewable energy integration and smart grid technologies. Dr. Paterakis received his Dipl.Eng. from Aristotle University of Thessaloniki in 2013, followed by a PhD in Industrial Engineering and Management (cum laude) from the University of Beira Interior in 2015. After serving as a post-doctoral fellow at TU/e from 2015-2017 and working as a consultant for the Energy Market Regulatory Authority of Turkey, he was appointed Assistant Professor at TU/e in April 2017. His research spans power system optimization, electricity market design, renewable energy integration, and the application of machine learning techniques to grid management problems. Recent work emphasizes distributed energy resource integration, local electricity markets, congestion management in low-voltage grids, and real-time grid control using advanced optimization techniques. His publications demonstrate a clear trajectory toward increasingly sophisticated methods for managing grid constraints while enabling market participation of distributed energy resources. Dr. Paterakis has received several prestigious awards including IEEE SEGE'15, SEST 2019, and SEST 2020 Best Paper Awards, and recognition as a Best Reviewer for IEEE Transactions on Smart Grid (2015, 2017) and IEEE Transactions on Sustainable Energy (2016). He serves as Associate Editor for multiple journals including IET Renewable Power Generation, IEEE Systems Journal, IEEE Transactions on Intelligent Transportation Systems, and Elsevier's e-Prime. He leads multiple research projects including MEGAMIND (NWO-funded), P2P-TALES (NWO-funded), and the Electricity Markets Game series (TU/e BOOST!-program). His educational contributions include teaching courses on power system analysis and optimization, electricity markets modeling, and developing innovative educational tools for power systems education. In 2021, he was elevated to Senior Member of the IEEE Power & Energy Society.

• Power System Optimization
• Electricity Markets
• Renewable Energy Integration
• +5 more

Suleiman Sharkh is Professor of Electrical Machines and Drives at the University of Southampton within the Faculty of Engineering and Physical Sciences. His primary affiliation is with the Department of Electrical and Electronic Engineering, where he leads research in critical energy technologies. His work spans multiple interdisciplinary groups including Mechatronics Southampton, the Southampton Marine and Maritime Institute, Ocean Energy research, and Maritime Decarbonisation initiatives. Professor Sharkh's research focuses on electric machines, power electronics, and microgrids , with specialized expertise in battery management systems, energy harvesting, and electromagnetic field effects on aquatic life. His current projects investigate novel power electronic converters for grid-battery interfaces, hybrid dual-chemistry battery characterization, multi-degree-of-freedom actuators for vibration control, and electromagnetic guidance systems for fish migration. His work bridges theoretical innovation with commercial applications in marine propulsion, renewable energy integration, and electric vehicle infrastructure. Analysis of his recent publications reveals strong trends in electrification of marine systems (rim-driven thrusters, tidal turbines), advanced battery-grid interfaces (electrochemical impedance spectroscopy, hybrid chemistries), and bioelectromagnetic applications (fish behavior studies). His work consistently addresses real-world challenges in energy security, system reliability, and environmental sustainability through rigorous experimental validation. Scientific Awards The Engineer Energy Innovation and Technology Award (2008) for rim-driven marine thrusters Royal Academy of Engineering ExxonMobil Teaching Excellence Award (2013) Professor Sharkh actively supervises five PhD students while leading major research projects funded by EPSRC, Innovate UK, and industry partners. His current grants include the FEVER project on electric vehicle networks, Shark S research exchanges with China/India, and investigations into electromagnetic effects on eel migration. His laboratory work focuses on high-speed electrical machines, battery characterization rigs, and electromagnetic field exposure systems for biological studies. Future work emphasizes maritime decarbonization through integrated power electronics and machine design for zero-emission vessels.

• Electric Machines
• Power Electronics
• Microgrids
• +4 more

Kevin Hughes is a Senior Lecturer in the Energy Engineering Group at the Department of Mechanical Engineering, School of Mechanical, Aerospace and Civil Engineering, University of Sheffield. He holds a PhD and first degree in Chemistry from the University of Leicester (1987) and focuses on fuel combustion, fuel cells, and process modelling in carbon capture and storage (CCS) systems. His research combines experimental and theoretical approaches, including planar laser diagnostics, quantum chemistry, and CFD simulations. Education: PhD and BSc in Chemistry from University of Leicester. Research Interests: Fuel combustion, pollutant chemistry, PEM fuel cells, CCS process modelling, catalyst development, and combustion in supercritical CO2. Grant Projects: FP7-ENERGY-2010-2 (RELCOM), Gas-FACTS (EPSRC), EP/J020788/1, EP/M001482/1 (Selective EGR), TEABPP (Energy Technology Institute). Scientific Contributions Publications: Over 50 papers on fuel combustion mechanisms, fuel cell optimization, CCS systems, and alternative fuels. Collaborations: Regular work with M. Pourkashanian, D.B. Ingham, S. Michailos, and M.S. Ismail. Technical Expertise Chemical Kinetics Validation Quantum Chemistry Applications Gas Diffusion Layer Analysis Surrogate Fuel Development Supercritical Combustion

• Fuel Combustion
• Fuel Cells
• Process Modelling in Carbon Capture and Storage (CCS)
• +3 more

Dr. Aryan Pasikhani is a Lecturer in Cybersecurity at the University of Sheffield , affiliated with the Security of Advanced Systems research group . Holding a PhD from the same institution, his research focuses on Intrusion Detection Systems , Reinforcement Learning , and Quantum Computing applications in security. Specializes in securing Embedded Systems and Internet of Things architectures Active in Adversarial Machine Learning and Privacy-Preserving Technologies Peer-reviewer for IEEE Internet of Things Journal and IEEE Transactions on Industrial Informatics His recent work explores 6LoWPAN security through reinforcement learning and Federated Learning frameworks for privacy-preserving data analysis. Awards include Fellow of the Higher Education Academy . Current grants involve real-time ransomware detection (CipherGrit) and automated threat modeling for AI systems. He supervises four PhD students and teaches Security of Control and Embedded Systems .

• Intrusion Detection and Prevention Systems
• Reinforcement Learning
• Machine Learning
• +9 more

Pawan Sharma is an Associate Professor at the Department of Electrical Engineering, Faculty of Engineering Science and Technology, UiT The Arctic University of Norway (Narvik Campus). He is affiliated with the Electromechanical Systems research group and the ARC research center. His research focuses on: Power system dynamics and control Distributed generation integration Microgrid optimization Smart grid technologies Reactive power control Electric vehicle-grid interaction Key research trends observed in his publications include: Hybrid microgrid control frameworks Cyber-physical co-simulation for grid applications Machine learning approaches for power optimization Integration of renewable energy sources Advanced converter control techniques Voltage stability and reactive power management He serves on the editorial boards of: International Transactions on Electrical Energy Systems Designs (MDPI) Electrical Engineering (Springer) Teaching responsibilities: Master's course: Power System Fundamentals (10 credits) Special PhD course: Distributed Energy Resources Integration (10 ECTS) Distributed Generation & Microgrids: Role and Concepts (5 credits) Research projects include: Cooperative Isolated Renewable Energy Systems (rural reliability) Hybrid Renewable Energy Micro Grid development Smart Solar PV Control Technology in Arctic regions arcICE, Arctic Energy, nICE initiatives

• Power System Dynamics and Control
• Distributed Power Generation
• Optimization Methods
• +7 more

Sohail Umer is a University Lecturer in the Department of Electrical Technology at UiT The Arctic University of Norway, specializing in power electronics and energy systems. His work focuses on improving power quality, control techniques, and energy efficiency in standalone and grid-connected systems. Research group: Electromechanical Systems Project: DeHigh His research explores advanced control strategies for power electronic converters, particularly Current Source Converters and Modular Multilevel Converters, with applications in renewable energy integration, microgrids, and smart grid technologies. Recent publications highlight innovations in adaptive modulation techniques, overlap-time mitigation, and hybrid PV-battery systems. Key themes include energy storage optimization, distributed generation, and converter design for standalone applications. Collaborations span institutions like UiT and include co-authors such as Trond Østrem, Bjarte Hoff, and Andrei Blinov. His work addresses challenges in renewable energy systems, including grid stability, harmonic reduction, and multi-port converter efficiency.

• Power Electronics
• Control Systems
• Renewable Energy Systems
• +3 more

Dr. Paola Falugi is a Senior Lecturer in Electro-Mechanical Engineering at the University of East London and holds an honorary visiting researcher position at Imperial College London. Her expertise spans predictive control systems, data-driven modeling, and energy network optimization under uncertainty. Senior Lecturer, Department of Engineering & Construction, School of Architecture, Computing and Engineering, University of East London Honorary Visiting Researcher, Imperial College London Research focuses on: Predictive control strategies for uncertain systems Data-driven modeling for control applications Optimization methods in energy network expansion Energy management under stochastic conditions Control systems for robotics and mechatronics Recent publications highlight her contributions to: Robust co-design frameworks for building energy systems Machine learning integration in transmission expansion planning Automated scenario generation for optimal control Control strategies for residential buildings with energy storage Her work bridges theoretical advancements in control theory with practical applications in energy systems and building automation.

• Predictive Control Systems
• Data-Driven Modelling
• System Modelling and Optimization
• +4 more

Andrea Vinci is an accomplished researcher with 66 publications and 1,261 citations, specializing in the intersection of quantum computing, edge-cloud architectures, and Internet of Things (IoT) systems. His work demonstrates significant contributions to solving complex computational problems through innovative approaches that bridge theoretical quantum algorithms with practical distributed computing applications. His research interests span quantum computing applications for resource management, multi-density clustering techniques for urban analytics, and platform-independent IoT application development. Vinci has pioneered work in variational quantum algorithms for cloud/edge resource allocation, quantum kernels for IoT data classification, and distributed AI for cognitive building systems. His research demonstrates a consistent focus on addressing NP-hard problems through quantum-classical hybrid approaches. Analysis of Vinci's publication trends reveals a strategic research trajectory moving from foundational work in smart city analytics and crime prediction toward cutting-edge quantum computing applications for IoT and edge-cloud systems. His recent publications (2023-2025) show increasing focus on quantum machine learning techniques specifically tailored for IoT data processing, with significant attention to practical implementation challenges. Vinci maintains an extensive collaborative network, frequently publishing with researchers including Fabrizio Marozzo, C. Mastroianni, J. Settino, and Antonio Guerrieri across multiple high-impact venues including IEEE Transactions, ACM conferences, and specialized journals in quantum computing and distributed systems. His technical contributions include the development of the COGITO platform for cognitive buildings, novel approaches to multi-density crime prediction, and significant advancements in quantum kernel methods for IoT data analysis. Vinci's tutorial publications indicate his role in educating the broader research community about emerging quantum computing applications for distributed systems.

• Quantum Computing
• Edge-Cloud Computing
• Internet of Things (IoT)
• +5 more

Maarten Blommaert is an Assistant Professor at the Department of Mechanical Engineering, Faculty of Engineering Technology at KU Leuven. He leads the Applied Mechanics and Energy conversion (TME) unit at the Geel Campus and heads the Subdivisie EnergyVille TME. His research focuses on numerical optimization of thermal systems, particularly district heating networks, additive manufactured heat exchangers, and plasma-facing components for nuclear fusion reactors. Assistant Professor, KU Leuven Head, Subdivisie EnergyVille TME Member, KIES Institute Member, Leuven.AM Institute Member, EnergyVille Blommaert's research explores three main areas: heat network optimization through automated design tools like PATHOPT, additive manufacturing of high-performance heat exchangers, and thermally resistant wall modules for nuclear fusion reactors. His work combines computational modeling with advanced manufacturing techniques to enhance energy efficiency and reduce carbon emissions. Scientific awards include collaborative research contributions in: Optimizing district heating networks for renewable energy integration Developing next-generation heat exchangers Advancing nuclear fusion reactor technology Blommaert actively supervises research projects in thermal-fluid systems and collaborates with institutions like VITO and EnergyVille. His research team IDEAL (Innovative Design for Energy Applications Lab) specializes in free-shape and topology optimization techniques for energy components and systems.

• Heat Network Optimization
• Additive Manufacturing
• Heat Exchanger Design
• +5 more

Jean-Daniel Penot is a Researcher at CESI's Research and Innovation Department , with expertise in additive manufacturing, materials science, and industrial integration. His work bridges advanced manufacturing technologies with environmental sustainability and educational innovation. Doctorate in Materials Physics (2010) Engineering Degree in Physics (2007) Research Master in Optoelectronics (2007) Penot's research spans Additive Manufacturing and its applications in automotive, nuclear, and construction sectors. He focuses on Laser-Material Interaction , Machine Learning for process optimization, and Sustainable Engineering through life cycle assessments and geopolymer applications. His recent publications emphasize BIM , AM Modular Plants , and Defect Analysis in 3D-printed metals. Penot leads France Additive initiatives and contributes to International Standards as a board member. Penot supervises PhD students including Maryam Houhou and Amal Khabouchi , with a focus on Industrial Security and Energy Transitions . His projects integrate Thermal Comfort , Ultrasonic Inspection , and Quality Assurance in additive manufacturing systems.

• Additive Manufacturing
• Materials Science
• Metallurgy
• +22 more

Dr. Zixu Liu is a Lecturer in Decision Analytics and Risk at Southampton Business School, University of Southampton. He focuses on applying machine learning and optimization techniques to business analytics problems, particularly in decision-making, smart grids, and industrial systems. PhD in Computer Science, University of Manchester (2013-2017) MSc in Computation and Game Theory, University of Liverpool (2012-2013) BSc in Computer Science and Technology, Jilin University (2007-2011) His research integrates advanced algorithms with cloud/web-based information systems to solve real-world challenges in multicriteria decision-making, electricity market pricing, and computer vision applications. Current projects emphasize industry transferability through API-driven solutions. Recent publications highlight diverse applications including: Smart grid optimization and demand response Hesitant fuzzy linguistic decision models Industry 4.0 collaboration platforms Deep learning benchmarks for object counting Wireless mesh network architecture He actively supervises PhD students and teaches undergraduate courses on spreadsheets, databases, algorithmic thinking, and data visualization.

• Machine Learning
• Information System
• Decision Making
• +3 more

Valentina Cecchi is Associate Professor of Electrical and Computer Engineering and Associate Director of the same department at the University of North Carolina at Charlotte (UNC Charlotte), where she has been a faculty member since 2010. She previously served as Graduate Program Director and Associate Chair of the department from 2019 to 2024. Education background: Ph.D. in Electrical Engineering, Drexel University, Philadelphia, PA Research interests center on electric power systems modeling and analysis, with particular emphasis on optimization of transmission and distribution system planning and operation, grid-enhancing technologies, dynamic line rating of transmission lines, and the integration of renewable and distributed energy resources. Her work spans power system protection, resilience, data-driven analytics, and pedagogical innovation in power engineering education. A consistent thread in her recent publications (2023-2025) is the application of advanced analytics and machine learning to improve real-time monitoring, protection, and restoration of active distribution networks. A complementary focus is the development and evaluation of modern educational methodologies to prepare students for emerging challenges in power and energy systems. Scientific awards: William States Lee College of Engineering Graduate Teaching Excellence Award (2022) Advising & grants narrative: While specific PhD/Master’s students are not listed in the provided text, Dr. Cecchi’s service as Graduate Program Director and her active publication record with student co-authors suggest significant mentoring activity. She has led NSF-supported curriculum updates and educational research efforts, and her work on distribution system resilience, renewable integration, and protection coordination has been funded by multiple agencies and industry partners. Laboratory & teams: Dr. Cecchi is affiliated with the EPIC building (Energy Production and Infrastructure Center) at UNC Charlotte, specifically office 1224, and contributes to the university’s power and energy systems research infrastructure.

• Electric power systems modeling and analysis
• Transmission and distribution system optimization
• Grid-enhancing technologies
• +4 more

Professor Sylvie Thiébaux is a leading AI researcher affiliated with the HMI (Human-Machine Interaction) center, serving as Professor in the Department of Computer Science. She leads major projects in fundamental and applied AI research and acts as co-editor-in-chief of a top-tier journal in artificial intelligence. Her research specializes in automated planning and scheduling, reasoning under uncertainty, and their real-world applications in energy infrastructure and transportation systems, particularly through optimization technologies for smart grids. This work bridges theoretical AI advancements with critical societal domains. Professor Thiébaux has earned significant recognition including: Multiple best paper awards at premier AI conferences National innovation awards for smart grid applications Election as Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) She has secured over $6.5 million in research funding during the last five years for basic and applied R&D. Within HMI, she contributes to core initiatives spanning Automating Governance, Personalisation, Algorithmic Ethics, and Human-AI Interaction, driving the center's mission to shape responsible technology development.

• Artificial Intelligence
• Automated Planning and Scheduling
• Reasoning under Uncertainty
• +3 more