Find Academic Supervisors

Muhammad WaseemView profile

Assistant Professor

• Sustainable Energy Systems
• Data Analytics
• Business Analytics
• +5 more

Dr. Muhammad Waseem is an Assistant Professor in Business Analytics at the School of Business, Maynooth University. He holds a PhD in Electrical Engineering from Zhejiang University (China), an MSc and BSc from University of Engineering and Technology, Taxila (Pakistan). His research focuses on sustainable energy systems, data analytics, and AI/ML integration into business models. He has held roles including Marie Skłodowska-Curie Research Fellow (2024–2025) and Postdoctoral Fellow at Hong Kong Polytechnic University (2023–2024). His work addresses smart grid optimization, renewable energy integration, and energy storage systems. Education: PhD in Electrical Engineering, Zhejiang University (2018) MSc in Electrical Engineering, UET Taxila (2014) BSc in Electrical Engineering, UET Taxila (2008) Research Interests: Sustainable Energy & Business Analytics Power & Energy Systems Optimization Smart Grids & Demand Management Distributed Energy Integration AI/ML in Energy & Business Awards: Marie Skłodowska-Curie Research Fellow (2024) Academic Research Excellence Award, Zhejiang University (2020–2021) Top 2% Global Scientists in Energy (2024) Grants & Collaborations: SYMECO project under Lero (SFI Research Centre) RTH-ITF Program, Hong Kong (2023) Labs/Groups: Innovation Value Institute (IVI), IRESI Research Group at Maynooth University.

Caroline ChanelView profile

Professor

• Autonomous Systems
• Decision-Making Algorithms
• Robotics
• +3 more

Caroline Chanel is a Professor at ISAE-SUPAERO (DCAS), holding the CASAC Chair since 2022. She has extensive experience in research and teaching, with roles including Associate Professor (2019-2024), Research Engineer (2013-2019), and HDR accreditation (2023). Her research focuses on embedded decision-making for autonomous systems, including proactive architectures, multi-robot cooperation, and human-machine interaction. She leads and participates in high-profile projects like SAMURAI (AID), CHIP-GT (ANR-DFG), and HAICO (CASAC Chair). She has supervised numerous PhD students and post-doctoral researchers, collaborating with institutions like ONERA, INRAe, and international partners. Education: PhD in Control and Embedded Systems (University of Toulouse 2013), Master's in Automatic Systems (Université Paul Sabatier 2008-2009), and Engineer Diploma from PUCRS (2002-2008). Awards include the Amelia Earhart Fellowship (2011) and ISAE-SUPAERO Foundation PhD Fellowship (2013). Research interests span decision-making algorithms, multi-agent systems, and neuroergonomics. Key projects address UAV navigation, blockchain integration for autonomous systems, and physiological computing for human-robot interaction. Teaching responsibilities include modules on autonomous systems, control systems, and neuroergonomics at ISAE-SUPAERO's engineering and master's programs. She co-leads the DECISIO doctoral team (ISAE-ONERA) and is involved in executive roles for conferences like EDSYS'.

Frits de NijsView profile

Research Fellow

• Artificial Intelligence
• Renewable Energy Systems
• Reinforcement Learning
• +3 more

Frits de Nijs is a Research Fellow in the Department of Data Science & AI at Monash University. His work focuses on advancing artificial intelligence techniques for energy systems optimization, multi-agent decision-making, and sustainable infrastructure. He has contributed to projects such as the Human-in-the-loop Microgrid Project and RACE for 2030 CRC, addressing challenges in renewable energy integration and smart grid management. Key research areas include reinforcement learning applications for energy curtailment, HVAC control systems, and resource-constrained multi-agent planning. His projects often bridge AI methodologies with real-world energy challenges, aiming to enhance grid reliability, carbon efficiency, and fair resource allocation. Notable collaborations involve researchers in machine learning, sustainable energy, and building automation. He has actively participated in 6 major research projects since 2020, including initiatives on AI-ready buildings and probabilistic multi-agent learning environments. His publications span conferences like AAAI, NeurIPS, and IEEE Power & Energy Society events, emphasizing peer-reviewed contributions to energy-AI intersections.

Zebang ShenView profile

Lecturer

• Optimization
• Machine Learning
• Data Science
• +3 more

Dr. Zebang Shen serves as a Lecturer in the Department of Computer Science at ETH Zurich, affiliated with the Institute for Machine Learning (Institut für Maschinelles Lernen). His research activities are centered at Andreasstrasse 5, 8092 Zürich, Switzerland, with teaching responsibilities confirmed for the Autumn Semester 2025. His primary research domains include Optimization, Machine Learning, and Data Science, with specialized focus on Federated Learning, Stochastic Optimization, and Reinforcement Learning. Shen develops algorithmic solutions for projection-free optimization, minimax problems, and diffusion model applications, emphasizing theoretical guarantees alongside practical implementations in distributed learning environments. Analysis of his 2021-2025 publications reveals consistent innovation in optimization frameworks for machine learning, particularly in federated settings where privacy-utility tradeoffs and straggler resilience are addressed. His work bridges mathematical rigor (e.g., Poincaré inequalities, McKean-Vlasov equations) with scalable algorithms for real-world data science challenges. No scientific awards were documented in the available sources. While the sources confirm his faculty role and publication record, specific details regarding student advising, grant funding, or laboratory leadership were not provided. His current teaching activities indicate ongoing academic engagement at ETH Zurich. Shen operates within ETH Zurich's Institute for Machine Learning, contributing to the Department of Computer Science's research ecosystem focused on advancing machine learning theory and applications.

Saverio BolognaniView profile

Lecturer

• Control Systems
• Power Systems
• Optimization
• +3 more

Dr. Saverio Bolognani is a Lecturer at the Department of Information Technology and Electrical Engineering at ETH Zürich. His research focuses on feedback optimization, control systems, and game theory, with applications to power grids and multi-agent systems. He is affiliated with the Institut für Automatik and teaches courses like 'Game Theory and Control' in the Autumn Semester 2025. His work integrates optimization, stochastic processes, and distributed algorithms to address challenges in energy systems and resource allocation. Recent contributions include PRIME (a feedback optimization framework) and karma-based economies for dynamic resource management. His experimental studies validate real-time control strategies for distribution grids and compressor stations. Research interests span control theory, power systems, game theory, and distributed optimization. He emphasizes practical implementations, such as online feedback optimization for grid flexibility and voltage regulation. His work often bridges theoretical foundations (e.g., Wasserstein space optimization) with real-world applications in smart grids and multi-modal mobility systems. No scientific awards are explicitly listed. His advising and grants involve experimental validation of control systems and participation in collaborative projects. He is involved in labs/teams focused on power distribution networks, reactive power control, and urban motion planning games.

• Blockchain Technology
• Edge Computing
• Federated Learning
• +4 more

Pubudu N. Pathirana is a Professor at the University of Melbourne's College of Engineering, specializing in Blockchain Technology, Edge Computing, Federated Learning, and Machine Learning for Healthcare . His work bridges theoretical advancements with practical applications in smart cities, cyber-physical systems, and medical diagnostics. Education : PhD in Electrical Engineering, Monash University His research focuses on privacy-preserving machine learning and blockchain integration with edge networks , demonstrated through collaborations with Dinh C. Nguyen, Minh K. Quan, and Bipasha Kashyap. Key contributions include quantum-enhanced models for acoustic classification and federated learning frameworks for IoT environments. Recent publications (2025) highlight advancements in spatio-temporal traffic modeling , adaptive privacy clipping , and secure client aggregation . Earlier work (2021-2024) explored applications in Covid-19 detection , Friedreich Ataxia severity classification , and entropy-based cerebellar ataxia analysis . He mentors students like Ali Reza Sattarzadeh and Kanishka Ranaweera, with grants supporting projects on blockchain-enhanced federated learning and quantum-inspired edge computing . His lab collaborates with institutions such as Monash University and the Florey Institute of Neuroscience.

• Artificial Intelligence
• Smart Grids
• Machine Learning
• +5 more

Carlos Ramos is a researcher affiliated with the University of Porto's Faculty of Engineering and the Polytechnic Institute of Porto's GECAD Research Group. He holds a PhD in Electrical Engineering and Computers from the University of Porto (1993). His work focuses on AI applications in energy systems, ambient intelligence, and agent-based systems. Ramos has collaborated extensively with researchers like Zita A. Vale and Sabah Mohammed on projects involving smart grids, manufacturing optimization, and cyber-physical systems. Research Interests : His primary areas include artificial intelligence, smart grids, machine learning, agent-based systems, and ambient intelligence. Recent work emphasizes explainable AI models in building energy management and genetic algorithms for industrial scheduling. Publications : Ramos has over 138 publications spanning journals like Engineering Applications of Artificial Intelligence and IEEE Transactions on Intelligent Systems . Key themes include AI-driven energy optimization, IoT applications in smart cities, and multi-agent systems for market simulation. His work often bridges theoretical AI with practical industrial and healthcare applications. Labs/Teams : Active in the GECAD Research Group, focusing on intelligent engineering solutions. Collaborates with international partners on projects like the Mascem electricity market simulator and the ISEM agent-based marketplace.

James McLellanView profile

Professor

• Mathematical Modelling
• Process Control
• Chemical Engineering Design
• +6 more

James McLellan is a Professor in the Department of Chemical Engineering at Queen's University, affiliated with Smith Engineering. He holds cross-appointments in the Department of Mathematics and Statistics and the School of Drama and Music within the Faculty of Arts and Science. His research focuses on dynamic modeling, control, and simulation of chemical processes, emphasizing nonlinear systems and stochastic models. McLellan received his B.Sc. in Mathematics and Engineering (Process Control Option) from Queen's University (1981), M.A.Sc. in Chemical Engineering from the University of Waterloo (1983), and Ph.D. in Chemical Engineering from Queen's University (1990). He has been Department Head since 2006 and Academic Director of the Dunin-Deshpande Queen's Innovation Centre. His research interests span stochastic modeling, molecular design, computational models, and chemical engineering design. Key areas include extremum-seeking control for distributed systems, bioscrubber modeling for toxic gas treatment, and optimization of bioreactors. He has contributed to advancements in process control for industrial applications like polyethylene reactors and bioenergy systems. McLellan's work integrates mathematical rigor with engineering practice, addressing challenges in environmental biotechnology, energy systems, and nonlinear control. His publications emphasize system dynamics, parameter estimation, and controller design for complex processes. He currently teaches ENIN 400: Innovation Design Sprint and has advised on grants related to wastewater treatment, bioreactor optimization, and sensor development. His collaborative projects include cross-disciplinary initiatives in innovation and entrepreneurship through Queen's Innovation Centre.

Maria Domenica Di BenedettoView profile

Professor

• Nonlinear control
• Hybrid systems
• Networked embedded control systems
• +4 more

Maria Domenica Di Benedetto is a full Professor of Control Theory at the University of L'Aquila, Italy, leading the Department of Electrical and Information Engineering. She holds a Dr. Ing. (summa cum laude) from Sapienza University of Rome (1976), a Docteur-Ingenieur (1981), and a Doctorat d'Etat Sciences (1987) from Université de Paris-Sud. Her academic journey includes roles as Assistant Professor at Sapienza (1983–1987), Associate Professor at Istituto Universitario Navale (1987–1990) and Sapienza (1990–1993), and Adjunct Professor at UC Berkeley (1995–2002). She has held visiting roles at MIT, University of Michigan, and others. Her research focuses on nonlinear control, hybrid systems, and networked embedded systems, with applications in automotive and air traffic control. She pioneered the DEWS Center of Excellence, addressing embedded controllers and wireless systems, and co-founded WEST Aquila S.r.L. for industrial collaboration. Key roles include IEEE Fellow (2002), editorships for major journals (e.g., IEEE Transactions on Automatic Control, International Journal of Robust and Nonlinear Control), and leadership in global research initiatives like HYCON and iFly projects. Notable contributions include fault-tolerant control of wireless networks, symbolic control for nonlinear systems, and safety-critical analysis of air traffic systems. Her work bridges theory and practice, addressing real-world challenges in control engineering and cybersecurity. Over 150 peer-reviewed publications and 10+ patents reflect her prolific output. Awards: IEEE Fellow (2002), multiple editorial roles, and leadership in international research consortia. Labs/Teams: Director of DEWS Center, Scientific Committee member of CETEMPS, and collaborator with industry partners like Volvo Technology and BlueCrest Capital.

Martin ZimmermannView profile

Associate Professor

• Temporal Logics
• Reactive Synthesis
• Automata Theory
• +3 more

Martin Zimmermann is an Associate Professor in the Department of Computer Science at Aalborg University, where he leads the Distributed, Embedded and Intelligent Systems research group. Previously, he held roles as a lecturer at the University of Liverpool and postdoctoral researcher at Saarland University and the University of Warsaw. He earned his PhD from RWTH Aachen University, with a Fulbright-funded period at DePaul University in Chicago. Education: PhD in Computer Science, RWTH Aachen University MSc in Computer Science, RWTH Aachen University (with Fulbright at DePaul University) Research Interests: Zimmermann focuses on temporal logics, reactive synthesis, and automata theory, particularly exploring HyperLTL, model-checking techniques, and formal verification challenges. His work bridges theoretical foundations with practical applications in real-time systems and neural-network control. Key Contributions: Notable publications include advancements in HyperQPTL model-checking, complexity analysis of second-order HyperLTL, and robust temporal logics. His research often addresses decidability, computational complexity, and algorithmic solutions for infinite games and reactive systems. Teaching: Teaches courses like Computability and Complexity, Algorithms and Computability, and Computer Architecture at Aalborg University. Service: Served on PCs of major conferences (e.g., CONCUR, CSL) and organized events like MOVEP 2022. Awards: Received the Best Paper Award at MFCS 2021 for groundbreaking work on pushdown automata expressiveness.

• Distributed Control Systems
• Complex Networks
• Smart Grids
• +3 more

Guanghui Wen is an Adjunct Professor at RMIT University's School of Engineering. His research focuses on distributed control systems, complex networks, smart grid technologies, and optimization algorithms. He specializes in multi-agent coordination, robotics control, and nonlinear systems. His recent publications demonstrate strong emphasis on distributed optimization methods, marine robotics path-following, and Gaussian process-based control for robotic manipulators. Work frequently appears in premier control theory journals including Automatica and IEEE Transactions. Dr. Wen maintains active collaboration networks and contributes to advancing control methodologies for complex engineering systems.

Xinghuo YuView profile

Professor

• Control Systems
• Complex Networks
• Cyber-Physical Systems
• +3 more

Xinghuo Yu is a Distinguished Professor and Vice-Chancellor's Professorial Fellow in the School of Engineering at RMIT University (Australia). He holds leadership roles, including Associate Deputy Vice-Chancellor and Chair of the RMIT Professorial Academy. His academic career spans over three decades, with prior positions at the University of Adelaide and Central Queensland University. Research & Awards: Yu specializes in Control Systems, Complex Networks, Cyber-Physical Systems, Smart Grids, and Artificial Intelligence. He has been recognized with prestigious awards, including the Australasian Artificial Intelligence Distinguished Research Contribution Award (2018) , M A Sargent Medal (2018) , and IEEE Industrial Electronics Society Achievement Award (2013) . He is a Fellow of the Australian Academy of Science, IEEE, and Engineers Australia. Leadership & Engagement: Yu has held executive roles such as President of IEEE Industrial Electronics Society (2018–2019) and Non-Executive Director of Oceania Cyber Security Centre. His research bridges theory and application, with over 1,000 publications and recognition as a Clarivate Highly Cited Researcher in Engineering (2015–2024). Labs & Collaborations: Leads the Intelligent Informatics and Control Research Group , focusing on smart grid optimization, cyber-physical systems, and AI-driven solutions for energy and security challenges. His work addresses real-world issues like renewable energy integration, electric vehicle infrastructure, and resilient power systems.

Andrew PomfretView profile

Senior Lecturer

• Intelligent Systems
• Robotics
• Flight Control
• +4 more

Dr. Andrew Pomfret is a Senior Lecturer in the Department of Electronic Engineering at the University of York. His research focuses on intelligent systems and robotics, with expertise in avionics, cognitive networking, and control engineering. He actively supervises PhD students in areas like fault-tolerant systems and multi-agent systems. His work spans robotics, control systems, and interdisciplinary applications in aerospace and environmental science. Education & Affiliations: Electronic Engineering (Lecturer, University of York) Research Interests: Flight control, evolutionary algorithms, structural reconfiguration, image processing, and robotics. His recent work emphasizes robotic swarm intelligence, cooperative transport systems, and eigenstructure assignment in control systems. Publications: His research explores topics like error diversity in robotic swarms, cooperative robotic communication, and helicopter handling qualities. Key areas include swarm task partitioning, multi-agent coordination, and control system optimization. Grants & Projects: Upscaling of greenhouse gas emissions from freshwater wetlands (NERC-funded, 2017–2019) SkyGas: Development of a new technique for determining watershed/airshed gas fluxes (NERC-funded, 2012–2013) Labs/Teams: Collaborates with research groups in robotics and control systems at the University of York, focusing on interdisciplinary projects involving autonomous systems and environmental monitoring.

Rafael FierroView profile

Professor

• Hybrid and Embedded Systems
• Heterogeneous Multivehicle Coordination
• Cooperative and Distributed Control of Multi-agent Systems
• +3 more

Rafael Fierro is a Professor in the Department of Electrical and Computer Engineering at the University of New Mexico (UNM), affiliated with the School of Engineering. He leads the MARHES (Multi-Agent, Robotics, Hybrid and Embedded Systems) Laboratory, focusing on interdisciplinary research in robotics, hybrid systems, and multi-agent coordination. His work is funded by the Army Research Office (ARO) and NSF, with notable awards including the 2004 NSF CAREER Award and a Fulbright Scholarship. Dr. Fierro’s research interests include heterogeneous multivehicle coordination, cooperative control, mobile sensor networks, and aerospace applications. He also develops educational initiatives like “Robotic Games” to engage children in STEM. His editorial roles include serving as Associate Editor for the Journal of Intelligent and Robotics Systems and IEEE Transactions on Automation Science and Engineering. Education : PhD in Electrical Engineering, University of Texas at Arlington, 1997 MSc in Control Engineering, University of Bradford, UK, 1990 BS in Electrical Engineering, National Polytechnic School, Ecuador, 1987 Research Trends : Recent articles emphasize safe control for autonomous systems, multi-UAV coordination, and resilient aerospace applications. Key themes include adaptive control for non-rigid structures, vision-based landing, and counter-UAS strategies using reinforcement learning. Awards & Grants : NSF CAREER Award (2004) Fulbright Scholarship Funding from ARO and NSF for robotics and control projects Labs & Initiatives : MARHES Lab explores hybrid systems, robotics, and educational outreach through Robotic Games. Collaborations include field-testing multi-robot counter-UAS systems and environmental monitoring via UAV swarms.

Baoze WeiView profile

Associate Professor

• Microgrid Control
• Power Electronics
• Renewable Energy Integration
• +5 more

Baoze Wei is an Associate Professor at Aalborg University's Department of Electric Power Systems and Microgrids, part of the Faculty of Engineering and Science. His research focuses on advanced control strategies for power electronics, microgrid stability, and energy management systems. He leads and collaborates on projects such as the Digital Twin-based Reliability Framework for Aviation Systems and Holistic Optimization of Green Fuel-Powered Microgrids. Key contributions include work on distributed energy systems, fault-tolerant architectures, and predictive maintenance for power electronics. His research emphasizes practical applications in renewable integration, smart grids, and industrial electrification. Wei has supervised one PhD student, Q. He, and contributed to projects funded by entities like Horizon JU and Huawei. Notable collaborations include work on hybrid-electric aircraft systems (HECATE) and advanced control algorithms for distributed converters. His research spans technical areas such as voltage source inverters, uninterruptible power systems (UPS), and DC shipboard microgrids. His publications reflect expertise in model predictive control, energy trading strategies, and condition monitoring. Current research trends include data-driven lifetime prediction for power electronics components and optimization of multi-energy systems. He actively participates in international conferences, contributing to both theoretical advancements and real-world system implementations.