Find Academic Supervisors

Yves LucetView profile

Professor

• Computational Mathematics
• Optimization
• Convex Analysis
• +6 more

Yves Lucet is a Professor in the Department of Computer Science, Mathematics, Physics and Statistics at the University of British Columbia Okanagan. He holds a PhD from the University of Toulouse. His research spans computational mathematics, optimization, and convex analysis, with a focus on algorithm development for computer-aided convex analysis and road design optimization. He leads the development of the CCA (Computational Convex Analysis) toolbox under Scilab, emphasizing hybrid symbolic-numerical algorithms. His work includes visualizing set-valued operators using Virtual Reality (VR) and collaborating on large-scale road design projects funded by NSERC, aiming to minimize construction costs while meeting safety and environmental constraints. Lucet advises graduate students and contributes to interdisciplinary teams involving mathematics, statistics, and engineering. His research also explores applications in computational geometry, image processing, and network communication. Education: PhD in Mathematics from the University of Toulouse. Research Interests: Computational Convex Analysis Algorithm Design for Optimization VR Visualization of Mathematical Operators Multi-objective Road Design Optimization Data-Driven Process Monitoring Key Projects: Development of the CCA toolbox, road design optimization funded by NSERC, and reinforcement learning approaches for prescriptive process monitoring. His work bridges mathematics and computer science, with applications in engineering and transportation.

Nazrul IslamView profile

Researcher

• Evolutionary Optimization
• Bayesian Optimization
• Multi and Many-Objective Optimization
• +7 more

Dr. Nazrul Islam is a Researcher in the Department of Computer Science at the University of York , specializing in optimization algorithms and machine learning. His research spans Evolutionary Optimization , Bayesian Optimization , and Uncertainty Quantification , with applications in surrogate models, LLMs, and NLP. Education: Ph.D. in Computer Science (2021), Queen Mary University of London MRes in Computer Science, London South Bank University BEng in Computer Science, London South Bank University Research Focus: Multi and Many-Objective Optimization Robust Optimization and Algorithm Benchmarking Machine Learning techniques (surrogate models, LLMs, NLP)

Charbel FarhatView profile

Professor

• Computational Fluid Dynamics
• Fluid-Structure Interaction
• Structural Mechanics
• +7 more

Charbel Farhat is the Vivian Church Hoff Professor of Aircraft Structures and Professor of Aeronautics and Astronautics at Stanford University's School of Engineering. He chairs the Department of Aeronautics and Astronautics and has led significant initiatives such as the Stanford-King Abdulaziz City for Science and Technology Center. His research focuses on computational methods for multiphysics problems in aerospace engineering, including fluid-structure interaction, digital twinning, and uncertainty quantification. Farhat has authored over 650 publications and holds prestigious awards like the Vannevar Bush Faculty Fellowship and multiple honorary doctorates. Education: Ph.D. in Civil Engineering, University of California, Berkeley (1987) MS in Electrical Engineering and Computer Sciences, UC Berkeley (1986) MS in Structural Engineering, UC Berkeley (1984) MS in Applied Mechanics, Université de Paris VI (1983) Engineering Diploma, Ecole Centrale des Arts et Manufactures (1983) Research Interests: Farhat's work emphasizes advanced computational algorithms for aerospace systems, including autonomous carrier landing dynamics, hypersonic trajectory analysis, and physics-based machine learning. His group develops high-performance software for digital twinning and model reduction techniques to address complex engineering challenges. Recent efforts include supersonic parachute dynamics modeling and probabilistic learning frameworks for uncertainty quantification. Awards & Recognition: Member of National Academy of Engineering (U.S.), Royal Academy of Engineering (UK), and Lebanese Academy of Sciences Fellowships from AIAA, ASME, SIAM, and multiple computational mechanics societies Recipient of the Gordon Bell Prize, John von Neumann Medal, and Gauss-Newton Medal Advising & Grants: Farhat supervises doctoral and master's students in advanced computational methods. His research is funded by NSF, AFOSR, ONR, NASA, and industry partners like Boeing and Lockheed-Martin. He advises on national boards and editorial roles for journals like the International Journal for Numerical Methods in Engineering. Labs & Teams: Leads the Farhat Research Group (FRG) at Stanford, focusing on Simulation-Based Engineering Science. Collaborates internationally on projects like the Army High Performance Computing Research Center and the Stanford-King Abdulaziz Center.

• Human-centered building design
• Urban sustainability and resilience
• Energy efficiency and sufficiency
• +5 more

Elie Azar , a Professor at Carleton University's Department of Civil and Environmental Engineering , specializes in human-centered building design and urban sustainability. His work bridges occupant behavior, energy efficiency, and computational modeling. B.Eng., Polytechnique Montréal MSc. and Ph.D., University of Wisconsin-Madison Current teaching includes ACSE 3201 Introduction to Building Performance Simulation and BLDG 5103 Research Methods in Building Engineering . Previous courses span energy economics, green building design, and advanced optimization techniques. Research explores: Agent-based modeling of urban energy systems Machine learning applications in building simulation Energy equity across Canadian households Climate resilience in Gulf region construction Smart city transitions beyond technological determinism Occupant behavior integration in policy frameworks Recent publications examine the WELL Building Standard, energy storage for renewable communities, and urban form impacts on energy performance. His methodological focus combines: Surrogate modeling with physics-based simulations Cross-national behavioral datasets Techno-economic analysis of green building systems Multi-objective optimization under uncertainty Key collaborations involve Gulf region climate change initiatives and Canadian building energy codes reviews.

Homam Nikpey SomehsaraeiView profile

Associate Professor

• Energy Systems
• Micro Gas Turbines
• Carbon Capture and Storage
• +12 more

Homam Nikpey Somehsaraei is an Associate Professor in the Department of Energy and Petroleum Engineering at the University of Stavanger, Faculty of Science and Technology. His research focuses on sustainable energy systems, with an emphasis on micro gas turbines, carbon capture, renewable integration, and artificial intelligence applications in energy modeling and optimization. He is actively involved in advancing decarbonization technologies and improving energy efficiency in industrial and residential sectors. His research interests span a wide array of topics including micro gas turbines , CO 2 heat pumps , hydrogen energy , phase change materials , and AI-driven energy system modeling . He applies artificial neural networks and machine learning techniques to optimize performance, enable condition monitoring, and enhance data reliability in distributed energy systems. His work often integrates experimental validation with theoretical modeling to ensure practical applicability. The recent publications highlight a strong trend towards decarbonization , renewable integration , and smart energy systems . Key themes include solar-assisted heat pumps, hydrogen applications in steel production, and AI-based optimization of micro gas turbines. These works reflect a multidisciplinary approach combining mechanical engineering, energy systems, and computational intelligence to address global sustainability challenges. Scientific Awards: No scientific awards mentioned in the provided text. Advising and Grants: While no formal list of students or grants is provided, Dr. Nikpey Somehsaraei has supervised or co-supervised numerous research projects, particularly in the areas of micro gas turbines and AI applications in energy. His collaborations with industry and academic partners suggest active involvement in funded research initiatives, though specific grant details are not listed. Labs and Teams: He is associated with advanced research in energy systems at the University of Stavanger, likely contributing to or leading work within experimental and computational labs focused on micro gas turbines, carbon capture, and sustainable heating technologies. His frequent collaboration with researchers such as Mohsen Assadi and Ward De Paepe indicates participation in a dynamic, interdisciplinary research team.

Jeffrey ChanView profile

Professor

• Machine Learning
• Recommender Systems
• Responsible AI
• +6 more

Jeffrey Chan is a Professor and Associate Dean of Data Science & Artificial Intelligence at RMIT University's School of Computing Technologies. His research focuses on machine learning, recommender systems, responsible AI, and interdisciplinary applications in sustainability, healthcare, and transportation. He has held roles such as Assistant Associate Dean and led projects on fairness-aware systems, energy-efficient recommendations, and AI-driven circular economies. His work bridges theory and practice, collaborating with industry and non-profits. He teaches courses in algorithms, network analytics, and machine learning, and actively supervises PhD and Honours students. Notable contributions include grants from the Australian Research Council and a Best Paper Award at ACM International Conference on Web Science 2011. His research also addresses ethical challenges in AI, such as fairness and transparency, and explores mobility solutions like e-scooter dynamics and parking optimization. Research Interests: Machine Learning, Recommender Systems, Data-Driven Optimization, Social Network Analysis, Responsible AI, Cloud Security, and Sustainability. Awards: Best Paper Award at ACM International Conference on Web Science 2011 Grants: Australian Research Council Discovery Project (2017–2019), Early Career Researcher Grant (2015), and EU FP7 ROBUST project (2011–2013). Labs/Teams: Leads initiatives in AI ethics, mobility systems, and interdisciplinary data science collaborations.

Zuomin DongView profile

Professor

• Hybrid Electric Propulsion Systems
• Fuel Cell Systems
• Global Optimization Algorithms
• +4 more

Zuomin Dong is a Professor in the Department of Mechanical Engineering at the University of Victoria (UVic), Canada. He holds a Ph.D. in Mechanical Engineering from the University at Buffalo and has been a faculty member since 1989. He served as Chair of the Department of Mechanical Engineering for over a decade and is a Professional Engineer (P.Eng.) and Fellow of the Canadian Society for Mechanical Engineers (CSME). His research focuses on advanced propulsion systems, including hybrid electric vehicles, marine vessels, and fuel cell technologies, alongside optimization algorithms and intelligent systems applied to manufacturing and energy systems. Dr. Dong's work integrates computational design, global optimization methods, and real-time control systems. Key research areas include PEM fuel cell system modeling, 5-axis CNC machining optimization, and smart grid applications. He has authored numerous patents, including innovations in hybrid drive systems and fuel cell stack designs. His collaborations with industry and government agencies address challenges in green transportation and energy efficiency. Dr. Dong teaches undergraduate and graduate courses such as Computer-Aided Design, Hybrid Electric Vehicle Fundamentals, and Engineering Optimization. He has led editorial roles in journals like the International Journal of Concurrent Engineering and contributed to international conferences. His research has been supported by grants from NSERC, Transport Canada, and other funding bodies, emphasizing practical applications of advanced technologies.

• Machine Learning
• Surrogate Modeling
• Multi-Objective Optimization
• +7 more

Ivo Couckuyt is an Assistant Professor at the Faculty of Engineering and Architecture , Ghent University, affiliated with the Department of Information Technology and the Internet Technology and Data Science Lab . He actively contributes to research projects funded by the Research Foundation - Flanders (FWO) and regional grants. Bayesian multi-objective optimization Surrogate modeling for engineering Uncertainty quantification Radar-based healthcare systems His research spans machine learning , computational engineering , and multi-fidelity simulations , with recent work focusing on physics-informed optimization, efficient data transmission for radar systems, and nuclear fuel analysis. Publications emphasize Bayesian frameworks and gradient-enhanced kriging for complex systems. As a PhD supervisor and promotor , he has guided 16 doctoral researchers across diverse topics including microwave filters , metamaterials , and radar healthcare applications . Current projects target additive manufacturing for electric machines and data-efficient generative models , combining uncertainty quantification with multi-physics simulations .

Saeid NahavandiView profile

Professor

• Artificial Intelligence
• Robotics and Haptics
• Autonomous Systems
• +4 more

Roles and Affiliations: Distinguished Professor Saeid Nahavandi is the inaugural Associate Deputy Vice-Chancellor (Research) and Chief of Defence Innovation at Swinburne University of Technology. He leads defence innovation and research strategy, with a focus on autonomous systems, robotics, and AI. Previously, he served as Pro Vice-Chancellor (Defence Technologies) at Deakin University and founded its Institute for Intelligent Systems Research and Innovation. Research Focus: Specializes in robotics, haptics, autonomous systems, AI, and advanced modelling/simulation. His work bridges academia and industry, with collaborations spanning Airbus, Boeing, NASA, and NATO. He has secured over $150M in funding and established three tech startups. Key research areas include motion simulation, teleoperation systems, and defence technologies. Articles Overview: Over 1,300 publications span AI, robotics, and control engineering. Recent work emphasizes autonomous navigation reviews, uncertainty-aware AI, and motion cueing algorithms. His research addresses real-world applications like driver distraction detection and robotic ultrasound. Awards and Recognition: Recipient of the 2022 Clunies Ross Entrepreneur of the Year Award, 2021 Australian Space Awards Researcher of the Year, and multiple engineering excellence accolades. A Fellow of ATSE, IEEE, and other leading institutions. Grants & Industry Impact: Led ARC Training Centres for automated vehicles and energy storage. Notable grants include a $15M ARC Training Centre for Automated Vehicles in Rural/Remote Regions (2024–2029). Collaborates globally on defence, aerospace, and smart transportation projects. Labs & Teams: Heads Swinburne’s Defence Innovation Group and collaborates with Harvard University (as an Associate) and the University of Windsor (adjunct professor). Advises governments and industries on technology strategy and innovation.

• Bayesian Optimization
• Reinforcement Learning
• Artificial Intelligence Ethics
• +12 more

Eduardo César Garrido Merchán is an Assistant Professor in the Intelligent Systems area at the Technological Research Institute of the Comillas Pontifical University. He is affiliated with both the Faculty of Economics and Business Administration, where he teaches Machine Learning, Statistics, and Quantitative Models, and with the Higher Technical School of Engineering - ICAI. His research bridges artificial intelligence, ethics, and societal impact. Education: PhD cum laude in Computer Science and Telecommunications Engineering, Autonomous University of Madrid Master's in Artificial Intelligence, Polytechnic University of Madrid (José Cuena Prize) Bachelor's in Computer Engineering (Honors), Pontifical University of Comillas His research interests lie at the intersection of Bayesian Optimization , Deep Reinforcement Learning , and AI Ethics . He investigates methods to make AI systems more inclusive, fair, and socially responsible, particularly in applications related to ESG finance, social inclusion, and human-AI interaction. He critically examines philosophical aspects of AI, such as machine consciousness and the limits of computational models of mind. His recent publications demonstrate a strong trend in applying advanced machine learning techniques—especially Bayesian optimization and deep reinforcement learning—to complex, real-world problems in finance and ethics. He integrates explainability and multi-objective optimization to address environmental, social, and governance challenges, while also publishing critical perspectives on the philosophical foundations of AI. Scientific Awards: José Cuena Prize for Master’s in Artificial Intelligence He supervises researchers interested in AI, ethics, and society, guiding them toward developing AI agents and policies that promote justice and equality. Though no grants are explicitly mentioned, his active publication record in high-impact journals suggests ongoing research support. He has participated in thesis tribunals and delivered invited lectures, including at the CIUTI Conference 2025 on AI in language mediation. He is actively involved in training and dissemination, having taught courses on Gaussian Processes and Bayesian Optimization and contributed opinion pieces on AI and humanism. His work reflects a commitment to both technical rigor and ethical responsibility in AI development.

• Computational Fluid Dynamics
• Turbulence Modeling
• Machine Learning
• +5 more

Olga Doronina serves as Researcher III in Data Science within the Computational Science group at the National Renewable Energy Laboratory (NREL), applying expertise in turbulence modeling, computational fluid dynamics, machine learning, and statistical methods to advance renewable energy research. Her academic foundation includes: PhD in Mechanical Engineering from the University of Colorado Master of Science in Mechanical Engineering from the University of Colorado Master of Science in Applied Mathematics and Physics from Moscow Institute of Physics and Technology Bachelor of Science in Applied Mathematics and Physics from Moscow Institute of Physics and Technology Her research centers on developing computational tools for energy applications, with current projects focused on surrogate modeling of non-ideal reacting flows in oxy-combustors and wind turbine blade representation. She integrates Bayesian analysis and machine learning with fluid dynamics to solve complex engineering challenges in aerodynamics and combustion systems. Analysis of her 2024-2025 publications reveals strong emphasis on multi-fidelity modeling techniques, uncertainty quantification in scale-up problems, and neural-network-enhanced control frameworks for building energy systems. Her work consistently bridges computational mathematics with practical renewable energy applications, particularly in aerodynamic shape optimization and combustion modeling. Dr. Doronina actively collaborates within NREL's Computational Science division on interdisciplinary projects involving wind energy systems, combustion technologies, and building efficiency solutions, contributing to the laboratory's mission through advanced computational methodologies.

• Simulation-Based Optimization
• Industry 4.0
• Production Engineering
• +4 more

Amos H. C. Ng is a Professor at the School of Engineering Science, University of Skövde, specializing in simulation-based optimization and Industry 4.0 technologies. His research bridges production engineering with human-robot collaboration, ergonomics evaluation, and cloud-based cyber-physical systems for manufacturing efficiency. Key Affiliations: University of Skövde (School of Engineering Science), Uppsala University (Industrial Engineering and Management) Research Themes: Multi-objective optimization, Digital Twin frameworks, Human-centric production systems, Reconfigurable manufacturing, Throughput bottleneck analysis Projects: ACCURATE 4.0 (Knowledge Foundation), VF-KDO (Virtual Factories with Knowledge-Driven Optimization), EWASS (Wire Harness Assembly Optimization) His recent publications demonstrate expertise in applying evolutionary algorithms, machine learning models, and digital human modeling tools to solve complex manufacturing problems ranging from crankshaft machining to wood supply chain robustness. Current work integrates motion capture technology with DHM tools for objective ergonomic assessments in assembly stations. Amos collaborates extensively with industrial partners like Volvo Penta and academic institutions, utilizing simulation-based approaches to enhance decision-making in production systems. His methodological focus includes non-dominated sorting genetic algorithms, surrogate modeling, and parallel computing architectures for optimization tasks.

Henrik EbelView profile

Assistant Professor

• Robotics
• Cooperative Robotics
• Model Predictive Control
• +6 more

Henrik Ebel is an Assistant Professor (Tenure Track) in Mechanical Engineering at Lappeenranta-Lahti University of Technology (LUT), specifically within the LUT School of Energy Systems. He joined LUT in 2024 after previously working at the University of Stuttgart's Institute of Engineering and Computational Mechanics from 2016 to 2024. Dr. Ebel's research focuses on the intersection of artificial intelligence, machine learning, and mechanical engineering, with particular emphasis on: Distributed cooperative robotics Model predictive control systems Non-holonomic robot control Data-driven approaches to mechanical engineering problems Automation systems development His research aims to bring cooperative robotics from laboratory settings to real-world applications in Finland, focusing on using multiple simple and cost-efficient robots that collaborate to solve problems rather than relying on single complex machines. He believes automation promotes sustainability through better energy conservation, generation, and storage compared to manual processes. His recent publications demonstrate consistent advancement in cooperative robotics, control systems, and machine learning applications in mechanical engineering. As an educator, Dr. Ebel supervises doctoral students and finds fulfillment in seeing them grow and succeed in their research. He values the human aspect of academia and enjoys teaching as a complement to his research work, noting that honing teaching skills helps him explain research better and pass on important future-oriented knowledge.

• Genetic Programming
• Evolutionary Algorithms
• Combinatorial Optimisation
• +6 more

Dr. Edgar Galván is an Associate Professor in the Department of Computer Science at Maynooth University's Faculty of Science & Engineering. He is a leading expert in Genetic Programming (GP) and Evolutionary Algorithms, with a focus on semantic-based approaches, neutrality, and multi-objective optimization. His work spans applications in combinatorial optimization, gaming (e.g., Carcassonne), and software engineering, including neuroevolution for deep learning architectures. Current Affiliation: Maynooth University Previous Roles: Senior Researcher at University College Dublin, Trinity College Dublin, and INRIA Paris-Saclay Research interests include: Semantic-based Genetic Programming Multi-objective Evolutionary Algorithms Monte Carlo Tree Search Circular Economy Applications Privacy-Preserving Optimization Neuroevolution in Autonomous Systems His recent publications analyze semantic diversity in GP, neural architecture search, and privacy-aware swarm optimization. Key awards include being ranked among the top 1% of GP researchers by University College London (2020), a Marie Curie Fellowship (2014), and a Best Paper Award at ECTA 2015. Current Projects: REBUILD (Circular Economy Buildings, 2024-2027), VISION (Circular Business Models, 2023-2026) Previous Grants: Stochastic Bio-inspired Algorithms (2014-2017, €267k), circAI (2022-2023, €142k) Dr. Galván serves on program committees for IEEE, ACM, and Springer conferences, and as Scientific Adviser for institutions in Ireland, France, and Mexico. His work bridges theoretical GP analysis with real-world applications in energy optimization and AI.

• Graph Mining
• Network Analysis
• Graph Representation Learning
• +3 more

Yifei Jin is a WASP Industrial PhD student at KTH Royal Institute of Technology's School of Electrical Engineering and Computer Science, specifically within the Division of Theoretical Computer Science. They are supervised by Professor Aristides Gionis and Associate Professor Sarunas Girdzijauskas at KTH, and also serve as an Experienced Researcher at Ericsson Research and a Visiting Researcher at Yale University under Rex Ying and Leandros Tassiulas. Yifei's research focuses on graph mining , network analysis , and graph representation learning , particularly applied to crowdsourcing data and wireless communication systems. Their work intersects telecommunications network optimization machine learning for graph-structured data AI-driven wireless resource management edge computing and distributed AI as evidenced by their publications spanning 2017–2025. Their academic contributions include 15 recent papers exploring topics such as neural surrogates for voltage drop estimation, wireless ray-tracing models, scalable distributed AI deployment, and vehicle platooning coordination. These publications demonstrate expertise in network traffic reduction KPI conflict analysis graph convolutional networks hyperbolic embeddings for ontologies real-time network diagnostics .