Find Academic Supervisors

• Mechanical Engineering
• Condition Monitoring
• Railway Systems
• +3 more

Dr. Cristina Castejón Sisamon is a Full Professor in the Department of Mechanical Engineering at Universidad Carlos III de Madrid (UC3M), affiliated with the MAQLAB (Machines Laboratory) and the Pedro Juan de Lastanosa Institute of Technology Development and Innovation. She also serves as the Assistant Vice-Rector for Entrepreneurship and In-House Research Program. Her research focuses on mechanical systems, condition monitoring of railway components, vibration analysis, robotics, and educational technology. Key areas include railway axle fatigue detection, wavelet transform applications, and predictive maintenance strategies. Her work spans advanced methodologies for fault diagnosis in rotating machinery, railway systems, and agricultural equipment. She has contributed to projects funded by entities like the EU, John Deere, and Alstom, addressing challenges in mechanical engineering and maintenance optimization. She leads or co-leads over 20 research projects, emphasizing innovation in mechanical systems and digital twins. Dr. Castejón is also active in educational initiatives, developing interactive tools for engineering education and robotics design. Her research outputs include 15+ recent articles (2022–2024), with a focus on vibration-based diagnostics, topology optimization, and historical mechanical treatises analysis. She holds patents related to skin biopsy devices and railway axle monitoring. MAQLAB serves as her primary research lab, fostering interdisciplinary collaboration in mechanical engineering and technology development.

Ed BrambleyView profile

Academic

• Aeroacoustics
• Computational Fluid Dynamics
• Wave Propagation
• +2 more

Dr. Ed Brambley is a Reader at the University of Warwick specializing in aeroacoustics, computational methods, and industrial mathematics. His research combines theoretical fluid dynamics with applied industrial challenges, particularly in acoustic liner optimization for aerospace and mathematical modeling of metal forming processes. Recent work advances boundary layer acoustics, wave propagation algorithms, and rolling process simulations. Collaborations span aerospace engineering and manufacturing sectors.

Colm ConnaughtonView profile

Professor

• Non-equilibrium statistical mechanics
• Fluid dynamics
• Turbulence
• +6 more

Colm Connaughton is a Professor and Director of the Centre for Complexity Science at the University of Warwick's Mathematics Institute. His research focuses on non-equilibrium statistical mechanics, fluid dynamics, turbulence, nonlinear waves, and complexity science. He has contributed to interdisciplinary studies ranging from wave turbulence theory to applications in traffic modeling and machine learning. Connaughton co-organized the Warwick EPSRC Mathematics Symposium and has led workshops on topics like complex networks and nonlinear science. His work bridges theoretical physics and applied mathematics, with recent projects addressing reinforcement learning in traffic control systems and the social complexity of fictional narratives. Connaughton's research often involves collaborations across disciplines, including contributions to understanding epidemic spreading dynamics and the statistical mechanics of cluster aggregation. Notable contributions include the study of Rossby wave turbulence models, the dynamics of energy condensation in two-dimensional turbulence, and the application of kinetic theory to aggregation processes. His work frequently emphasizes the interplay between mathematical rigor and real-world applications, such as optimizing traffic flow prediction and analyzing network-based disease spread. Connaughton maintains active engagement in academic service, including editorial roles and organizing international conferences. His research has been published in high-impact journals across physics, mathematics, and engineering disciplines.

Dwight BarkleyView profile

Professor

• Applied Mathematics
• Computational Fluid Dynamics
• Nonlinear Dynamics
• +8 more

Professor Dwight Barkley is a leading academic in applied and computational mathematics at the University of Warwick's Mathematics Institute. He holds the rank of Professor of Mathematics and serves as Deputy Head of Department and Director of Research. His work focuses on nonlinear phenomena, fluid dynamics, and turbulence, with significant contributions to understanding transition to turbulence in shear flows and pattern formation in excitable media. Education: Ph.D. in Physics, University of Texas at Austin (1988) M.A. in Physics, University of Texas at Austin (1984) B.S. in Physics, Catholic University of America (1980) Research Interests: Professor Barkley's research spans applied mathematics and fluid dynamics, including nonlinear systems, hydrodynamic stability, reaction-diffusion equations, and numerical methods. He investigates turbulence transition in shear flows, such as pipe and channel flows, and develops models for pattern formation and spatio-temporal chaos. His work combines high-performance computation with theoretical analysis to address complex fluid phenomena. Awards & Honors: SIAM J.D. Crawford Prize (2005) Fellow of the European Mechanics Society (2024) Fellow of SIAM (2016) and APS (2008) Royal Society-Leverhulme Trust Senior Research Fellowship (2009–2010) University of Warwick Teaching Prize (2014) Advising & Grants: Professor Barkley has advised numerous PhD students and postdocs, including Emanuele Zuccoli, Jacob Langham, and David Moxey. His research is supported by grants from the Royal Society, EPSRC, and other institutions. He has contributed to projects on turbulence modeling, nonlinear dynamics, and fluid-structure interactions. Professional Activities: Barkley has held editorial roles for journals such as SIAM Journal on Applied Dynamical Systems and JFM Perspectives. He has organized conferences, including the Warwick Symposium on Turbulence, and delivered keynote lectures globally.

Qingwei MaView profile

Professor

• Hydrodynamics
• Fluid-Structure Interaction
• Offshore Engineering
• +12 more

Professor Qingwei Ma is a full Professor of Hydrodynamics at City, University of London, where he also serves as the Director of the Research Centre for Fluid-Structure Interaction. He holds a PhD in Ocean Engineering & Naval Architecture from University College London and has built a distinguished career in fluid dynamics and offshore engineering. His academic journey includes positions at Ocean University of Qingdao, University College London, Robert Gordon University, and City, University of London, where he has been since 2002. PhD, Ocean Engineering & Naval Architecture, University College London, 1998 MEng, Naval Architecture & Ocean Engineering, Harbin Engineering University, 1984 BEng, Naval Architecture & Ocean Engineering, Harbin Engineering University, 1982 Professor Ma’s research focuses on hydrodynamics of marine structures , nonlinear wave-structure interaction , and advanced numerical methods such as QALE-FEM, MLPG_R, and SPH. His recent work emphasizes multi-scale simulations for offshore wind turbines, wave energy converters, and floating platforms. He has developed and applied hybrid numerical models to simulate extreme wave events, vortex-induced vibrations, and fluid-structure interactions under complex environmental conditions. The 15 most recent publications reflect a strong trend toward hybrid computational methods , machine learning integration (e.g., GNN, CNN), and multi-physics coupling in offshore and renewable energy systems. His work spans journal articles, conference proceedings, and books, with a consistent focus on improving accuracy and efficiency in simulating violent wave interactions. His scientific recognitions include: CH Kim Award, ISOPE (2016) Vice-chancellor’s Award for Excellence in Learning and Teaching (2015) Chang Jiang Scholarship, China Professor Ma has supervised numerous PhD and master’s students, led major research projects, and contributed to editorial boards of journals such as Ocean Systems Engineering (Editor-in-Chief), Computer Modeling in Engineering & Sciences , and Journal of Ocean Engineering and Marine Energy . He is actively involved in international collaborations, particularly with Harbin Engineering University, where he holds a Visiting Chair Professorship. He leads the development of innovative simulation tools for offshore renewable energy systems, with applications in floating wind turbines, wave energy devices, and hybrid platforms. His lab focuses on high-fidelity numerical modelling using particle and finite element methods, with strong ties to experimental validation and industrial applications.

• Reactive Control Strategies
• Supervision and Prognosis
• Large Scale Systems
• +11 more

Eric Duviella is a Professor at IMT Nord Europe, specifically within the Department of Automatic Control and Computer Sciences. He has been a permanent faculty member since 2007 and was promoted to full Professor in 2015. His academic affiliations include strong ties with the University of Lille, Universitat Politècnica de Catalunya, and the University of Seville through collaborative research and joint Ph.D. supervision. Education: Diplôme d'Ingénieur, Ecole Nationale d'Ingénieurs de Tarbes (ENIT), 2001 M.S., Institut National Polytechnique de Toulouse (INPT), 2001 Ph.D. in Industrial Systems, Institut National Polytechnique de Toulouse, 2005 HDR (Habilitation à Diriger des Recherches), University of Lille 1, 2014 His research interests center on Reactive Control Strategies , Supervision and Prognosis , and the Modeling of Large-Scale and Environmental Systems , particularly Hydrographical and Water Systems . His work aims at predictive maintenance, adaptive management under climate change, and improving navigation and water quality through advanced control techniques. The 15 most recent publications reflect a strong trend toward Model Predictive Control (MPC) , distributed and decentralized control architectures , leak detection in water networks , and resilience of inland waterways under climate stress . Keywords across these works include control engineering, water systems, fault diagnosis, and data-driven modeling, indicating a multidisciplinary approach combining automation, environmental science, and computational intelligence. Scientific Awards: Best paper award at CODIT 2017 Eric Duviella has actively supervised numerous Ph.D. and Master’s students from institutions including IMT Lille Douai, Universitat Politècnica de Catalunya, and the University of Seville. He has led significant research grants such as GEPET-Eau, CHEEF2, and CASTR-Eau, focusing on sustainable water management, hydropower optimization, and robotic monitoring. His work often involves partnerships with agencies like VNF, EDF, and local water authorities. He is involved in key research labs and teams including the Laboratoire d'Automatique, Génie Informatique et Signal (LAGIS), and collaborates internationally through mobility programs with UPC and the University of Seville. His leadership in organizing special sessions at major conferences (e.g., IFAC, HIC) underscores his role in shaping discourse in water system control and supervision.

• Aerodynamics
• Turbulent Flow Analysis
• Particle Image Velocimetry (PIV)
• +9 more

Stefano Discetti is a Full Professor in the Department of Aerospace Engineering at Universidad Carlos III de Madrid, leading the Aerospace Engineering Research Group. His work bridges experimental fluid dynamics, machine learning, and flow control. Key research themes include turbulence characterization, heat transfer enhancement, and advanced measurement techniques like PIV/PTV. He has developed data-driven methods for flow estimation, sensor placement optimization, and physics-informed neural networks. Research Trends from his publications show a focus on: Machine learning for turbulent flow reconstruction (GANs, CNNs, KNN) Non-intrusive sensing from wall measurements Manifold learning and reduced-order modeling Heat transfer control via plasma actuators and passive structures Time-resolved diagnostics using hybrid experimental/numerical approaches Projects include principal roles in EU and national grants like NEXTFLOW (2021-2026) and EXCALIBUR (2023-2026), with industry collaborations at Airbus and TU Delft.

• Mathematical Optimization
• Statistical Modeling
• Machine Learning
• +13 more

Vanesa Guerrero Lozano is an Associate Professor in the Department of Statistics at Universidad Carlos III de Madrid, affiliated with the Flores de Lemus Institute and the UC3M-Santander Big Data Institute. Her work bridges mathematical optimization, statistical modeling, and data science, with applications across disciplines including biomedicine, fluid mechanics, and sustainable development. Her research focuses on developing advanced statistical methodologies using mathematical optimization. Key interests include shape-constrained regression, P-splines smoothing, sparse modeling, clustering of categorical data, and interpretable machine learning. She applies these techniques to complex datasets in turbulence modeling, biological age imputation, and pandemic forecasting. The recent publications reveal a strong trend in integrating optimization techniques with statistical learning, particularly in nonparametric and semiparametric models. There is a consistent emphasis on interpretability, robustness, and scalability, especially for high-dimensional and dynamic datasets. Applications span from fluid dynamics to public health, demonstrating interdisciplinary impact. Scientific Awards and Recognition: Ayuda adicional within the Juan de la Cierva Incorporación Program (2020), awarded by the State Research Agency (AEI) Research Leadership and Advising: She has served as principal investigator on multiple competitive research projects funded by national and regional agencies, including the State Research Agency (AEI), the Jacques Hadamard Mathematical Foundation, and the Community of Madrid. Her projects cover topics such as constrained additive models, machine learning for sustainable fishing, turbulence control, and ADHD diagnosis using data science. She has supervised at least one doctoral thesis on constrained smoothing models, indicating active mentorship in methodological statistics and optimization. Laboratories and Research Groups: She is a member of the Energy Analytics research group and conducts her work within the UC3M-Santander Big Data Institute, which supports interdisciplinary data science research. Her affiliation with the Flores de Lemus Institute further underscores her engagement with advanced statistical and computational methodologies.

• Fluid Dynamics
• Dynamical Systems
• Uncertainty Quantification
• +3 more

Associate Professor Sanjeeva Balasuriya is affiliated with the School of Computer and Mathematical Sciences at the University of Adelaide, within the Faculty of Sciences, Engineering and Technology. His research focuses on fluid dynamics, dynamical systems, uncertainty quantification, and mathematical biology. He has contributed to studies on Lagrangian coherent structures, stochastic sensitivity in flows, and biofilm expansion modeling. His work bridges applied mathematics with real-world phenomena, including environmental modeling and turbulence analysis. Recent publications explore topics like stochastic differential equations, optimal vector field reconstruction, and flow control strategies. He is eligible to supervise PhD and Master’s students in these areas. His research emphasizes interdisciplinary applications of mathematical techniques to complex fluid systems and nonlinear dynamics.

• Structural Engineering
• Composite Structures
• Steel Structures
• +3 more

Dr. Yifan Zhou serves as a Lecturer in Structural Engineering within the Department of Civil, Environmental and Mining Engineering at The University of Western Australia's School of Engineering. Her academic appointment integrates teaching responsibilities with active research in advanced structural systems. Education PhD in Structural Engineering, University of Sydney (dissertation focused on stainless steel–concrete composite structures) Her research program centers on the performance and design of steel/composite structural systems, with growing emphasis on interdisciplinary applications. Key research thrusts include: (1) Mechanical behavior of stainless steel composite elements under extreme loads; (2) Integration of machine learning for structural health monitoring; (3) Sensor technology applications in predictive maintenance; (4) Development of data-informed design methodologies. This work directly contributes to UN Sustainable Development Goals related to sustainable infrastructure. Recent publications (2021-2025) demonstrate consistent focus on stainless steel composite structures, evolving from fundamental beam/column mechanics toward machine learning applications in marine environments. The research trajectory shows increasing interdisciplinary collaboration while maintaining core structural engineering rigor. Dr. Zhou actively supervises HDR candidates as indicated by her acceptance of PhD students through UWA's research portal. Her teaching portfolio includes coordinating CIVL3404/4404 Structural Steel and CIVL5552 Civil Structural Design Project, featuring industry-integrated pedagogies and authentic assessments. Professional engagement includes membership on Standards Australia Committee panel for AS/NZS 2327 revision, translating research into national design standards. This positions her work at the critical interface between academic research and engineering practice.

• Smart City Technologies
• Traffic Engineering
• Optimization Algorithms
• +4 more

Álvaro Paricio García is an Assistant Professor at the Department of Automation within the School of Telematics Engineering at Universidad de Alcalá. He is affiliated with the NetIS Research Group (Networks and Intelligent Systems). His research focuses on smart city technologies, traffic engineering, optimization algorithms, and environmental engineering, with a particular emphasis on urban mobility, crowd evacuation systems, and emission reduction strategies. Education: He holds a PhD from Universidad de Alcalá, awarded in 2021 for his thesis Estrategias multi-mapa para el enrutamiento dinámico de tráfico urbano , supervised by Dr. Miguel Ángel López Carmona. Research Interests: His work integrates control systems, machine learning, and simulation-based optimization to address challenges in urban traffic management, crowd dynamics, and sustainable transportation. Key themes include: Design of low-emission zones for urban areas Development of adaptive evacuation systems using MPC (Model Predictive Control) Algorithmic innovations in traffic routing and multi-map strategies Article Trends: Recent publications (2021-2025) highlight his focus on: Wind farm layout optimization using metaheuristics Biometric identification via autoencoder-driven systems Dynamic low-emission zones and their policy implications Adaptive crowd evacuation systems like CellEVAC No scientific awards or grants were explicitly mentioned in the provided texts. He has not supervised any listed students. Labs/Teams: Active member of the NetIS Research Group, which develops networks and intelligent systems for urban and industrial applications.

Mustapha S. FofanaView profile

Associate Professor

• Nonlinear Machine-Tool Chatter
• Manufacturing Systems
• Time Delay Dynamics and Control
• +3 more

Mustapha S. Fofana is an Associate Professor in the Department of Mechanical & Materials Engineering at Worcester Polytechnic Institute (WPI). He holds a BS and MS from the Technical University of Budapest (1986), followed by an MS (1989) and PhD (1993) from the University of Waterloo. His research focuses on nonlinear dynamics in manufacturing processes, including machine-tool chatter, time delay systems, and stochastic modeling. He is affiliated with WPI’s Higgins Labs and can be reached at msfofana@wpi.edu. Education: BS, Technical University of Budapest (1986) MS, Technical University of Budapest (1986) MS, University of Waterloo (1989) PhD, University of Waterloo (1993) Research Interests: Prof. Fofana explores advanced topics in manufacturing dynamics, including nonlinear machine-tool chatter mechanisms, time-delay control strategies for manufacturing systems, and stochastic analysis of machining processes. His work integrates delay differential equations and degenerate Hopf bifurcation theories to improve manufacturing health monitoring and process stability. Publications: His notable work includes analyzing nonlinear effects in orthogonal turning operations (2002) and ongoing research on deterministically and stochastically perturbed systems under review in engineering journals. He also investigates parametric software integration into computer-aided manufacturing (CAM) environments. Awards & Professional Highlights: No specific awards or professional highlights are listed in the provided text. Labs & Teams: Affiliated with Higgins Labs at WPI, though specific lab teams or collaborative projects are not detailed here.

• Population dynamics
• Mathematical biology
• Epidemic modeling
• +3 more

Jordi Ripoll Misse is an Associate Professor at the University of Girona (UdG) in the Department of Computer Science, Applied Mathematics and Statistics. He specializes in population dynamics with applications in ecology and epidemiology, and is affiliated with the research group 2021 SGR 00113 focused on stochastic and deterministic models in biosciences. Education: PhD in Mathematical Sciences (2005) from the University of Barcelona Research: Epidemic models, Basic reproduction number (R₀), Metapopulations, Complex networks Research Trends include discrete-time epidemic modeling, analytical computation of R₀ in periodic environments, and structured population models using partial differential equations and evolutionary models. His work connects mathematical theory with practical applications in biology and telecommunications network analysis. Teaching Expertise spans mathematics subjects like algebra, calculus, differential equations, and numerical methods, with a focus on interdisciplinary applications through the Mathematical Models course for Sciences students. He actively integrates AI methodologies into university teaching through the XID group. Academic Affiliations include research stays at Cornell University (2022) and University of Trento (2006-2007), and teaching roles at UNED and UOC universities in Spain.

Hamid OssarehView profile

Associate Professor

• Systems and Control Theory
• Predictive Control
• Nonlinear Control
• +4 more

Hamid Ossareh is an Associate Professor in the Department of Electrical and Biomedical Engineering at the University of Vermont's College of Engineering and Mathematical Sciences. He serves as Co-director of the CREATE Center and is actively involved in the IEEE Control Systems Society, including roles as Associate Editor for IEEE Transactions on Control System Technology and technical consultant for Beta Technologies in flight controls. PhD in Electrical Engineering (Control Theory), University of Michigan, 2013 MS in Mathematics, University of Michigan, 2012 MSE in Electrical Engineering, University of Michigan, 2010 BASc in Electrical Engineering, University of Toronto, 2008 His research focuses on systems and control theory, particularly predictive control, nonlinear control, and constrained control with applications in automotive, aerospace, and power systems. He leads projects funded by the National Science Foundation (CAREER Award) and Department of Energy, including constraint-aware control algorithms for hybrid energy systems and real-time control theory for uncertain systems. Recent research trends include data-driven control formulations, fault detection in electric aircraft, stochastic linearization techniques, and optimization of satellite swarm navigation. His work bridges theoretical advancements with practical implementations across multiple industries. Excellence in Research Award, UVM CEMS, 2021 Faculty of the Year Award, IEEE Green Mountain Section, 2018, 2020 NSERC Postgraduate Awards (PGS-M and PGS-D), Government of Canada Adel Sedra Gold Medal, University of Toronto, 2008 Ossareh has advised numerous PhD and MS students in control systems and power systems, with past advisees including Dr. Sarnaduti Brahma and Dr. Yudan Liu. His current projects include advanced control of hydrogen fuel cells and real-time constraint-aware algorithms for autonomous systems.

Feliks NüskeView profile

Research Professor

• Data-driven modeling
• Molecular simulation
• Koopman operator theory
• +6 more

Dr. Feliks Nüske is a Max Planck Group Leader at the Max Planck Institute for Dynamics of Complex Technical Systems in Magdeburg, leading the Data-driven Modeling of Complex Physical Systems research group. He also holds a position as Guest Professor at Freie Universität Berlin (2023-2024). His research bridges applied mathematics, data science, and molecular simulation to develop novel algorithms for understanding complex physical systems at the molecular level. Dr. Nüske's educational background includes: Ph.D. in Mathematics from Freie Universität Berlin (2012-2017) Postdoctoral research at Universität Paderborn (2019-2022) and Rice University (2017-2019) His research focuses on developing data-driven methods that combine physical insights with machine learning to extract meaningful information from molecular simulation data. Key areas include Koopman operator theory for analyzing nonlinear dynamical systems, dimensionality reduction techniques, tensor methods for efficient computation, and kinetically consistent coarse-graining approaches. His work enables more efficient modeling of complex molecular processes that would otherwise be computationally prohibitive. Dr. Nüske's publication record shows a consistent trajectory of advancing both the theoretical foundations and practical applications of data-driven modeling in molecular science. His recent work emphasizes error analysis for data-driven models, control of stochastic systems, tensor-based dimensionality reduction, and methods to preserve kinetic properties in coarse-grained models. These contributions address critical challenges in scaling molecular simulations to biologically relevant timescales and system sizes. Dr. Nüske actively collaborates with researchers worldwide and has established partnerships with leading institutions including Freie Universität Berlin, Rice University, and TU Ilmenau. His collaborative network spans multiple disciplines, connecting mathematicians, chemists, and computational scientists. Dr. Nüske advises several Ph.D. students at the Max Planck Institute, including Vahid Nateghi, Lei Guo, Minakshi Verma, and Hauke Sprink. He has organized workshops on Uncertainty Quantification for molecular systems and regularly presents at major international conferences including SIAM MS, MTNS, and IMSI workshops. His research group continues to push the boundaries of what's possible in computational molecular science through innovative mathematical approaches.