Find Academic Supervisors

• Fluid Dynamics
• Turbulence
• Heat Transfer
• +6 more

Andrea Ianiro is a Full Professor in the Aerospace Engineering Department at Universidad Carlos III de Madrid (UC3M), where he leads research in fluid dynamics, turbulence, and heat transfer. His work bridges experimental techniques and machine learning applications for flow analysis and control. He serves as Associate Editor of the International Journal of Heat and Mass Transfer (2025-2028) and directs the EFM Lab (Experimental Fluid Mechanics Laboratory) at UC3M. Professor Ianiro's research focuses on turbulence characterization, boundary layer flows, and the application of machine learning to fluid mechanics problems. His work spans experimental techniques including Particle Image Velocimetry (PIV), infrared thermography, and advanced data processing methods. Recent research emphasizes data-driven approaches for flow field reconstruction, turbulence control, and heat transfer optimization in wall-bounded flows. His projects often combine theoretical, experimental, and computational approaches to address complex fluid mechanics challenges. The analysis of his recent publications reveals a strong trend toward integrating machine learning with traditional fluid mechanics. His work increasingly focuses on using deep learning techniques (particularly CNNs and GANs) for flow field prediction from limited measurements, developing meshless computational methods for flow analysis, and applying optimization techniques (including genetic algorithms) to heat transfer enhancement. His research maintains a strong experimental foundation while embracing data-driven approaches to tackle turbulence modeling challenges. Associate Editor of the International Journal of Heat and Mass Transfer (2025-2028) Professor Ianiro leads multiple significant research projects including SPANDRELS (SParse AND paRsimonious Event-based fLow Sensing, 2025-2030), HumanIC (Human-Centric Indoor Climate for Healthcare Facilities, 2024-2027), and EXCALIBUR (Extraction of machine learning strategies for turbulent flow control, 2023-2026). His work has attracted funding from the European Commission, Spanish National Research Agency, and industry partners including Airbus. He has supervised numerous theses on topics including AI-based sensing of turbulent flows, convective heat transfer control, and turbulent boundary layers. At UC3M, Professor Ianiro directs the Experimental Fluid Mechanics Laboratory (EFM Lab), which focuses on advanced measurement techniques for fluid flow and heat transfer characterization. The lab specializes in PIV/PTV techniques, infrared thermography, and the development of novel experimental approaches for turbulence research. Current research directions include machine learning applications for flow field reconstruction, plasma-based flow control, and heat transfer optimization in complex flow configurations.

Ang Kok KengView profile

Associate Professor

• Structural Stability and Vibration
• Smart Adaptive Structures
• Computational Mechanics
• +5 more

K.K. Ang is an Associate Professor in the Department of Civil and Environmental Engineering at the National University of Singapore (NUS) Faculty of Engineering. He has been affiliated with NUS since 1987 and currently serves as Director of the Centre for IT and Applications (CITA Engineering) within the Faculty of Engineering. Qualifications: BEng (1st Class) from University of Singapore (1977) MEng from National University of Singapore (1980) PhD from University of New South Wales (1987) His research focuses on structural stability and vibration , smart adaptive structures , very large floating structures , computational mechanics , and meshless methods . Recent publications analyze carbon nanotube composites, high-speed rail dynamics, and offshore structural interactions. His awards include the NUS Teaching Excellence Award , Engineering Educator Award , and the IES/IStructE Best Structural Paper Award . He has taught courses in Computing, Structural Mechanics, Steel Design, and Finite Element Methods. Professor Ang is a Singapore-registered Professional Engineer (1991) and serves on the Singapore Structural Steel Society council. His work bridges structural engineering, IT applications, and advanced computational modeling.

Fernando CamelliView profile

Associate Professor

• Computational Fluid Dynamics (CFD)
• Urban Environmental Modeling
• Contamination Dispersion Modeling
• +3 more

Fernando Camelli is an Associate Professor in the Physics & Astronomy Department at George Mason University, holding dual roles as Instructional Faculty and Faculty. His research focuses on computational fluid dynamics (CFD), urban environmental modeling, and high-performance computing. He specializes in simulating complex fluid flows in urban environments, subway systems, and industrial applications, with particular emphasis on turbulence modeling, fluid-structure interaction, and GPU-accelerated algorithms. Key research areas include: CFD for urban airflow and contamination dispersion Meshless and immersed boundary methods Integration of geographic information systems (GIS) with CFD Large-scale simulations using parallel computing His work addresses practical challenges such as subway ventilation optimization, emergency contaminant dispersion prediction, and urban infrastructure design. Recent studies emphasize scalability improvements for fluid-structure interaction simulations and GPU-based code modernization.

Li HuaView profile

Professor

• Multiphysics Modeling
• Soft Matter
• Machine Learning
• +5 more

Professor Li Hua is a faculty member at the School of Mechanical & Aerospace Engineering, Nanyang Technological University (NTU), Singapore. He holds a Ph.D. in Mechanical Engineering from the National University of Singapore (1999) and has been recognized as a Fellow of the American Society of Mechanical Engineers (ASME) since 2019. His academic journey includes postdoctoral research at the University of Illinois at Urbana-Champaign (2000-2001), a visiting scientist role at Johns Hopkins University (2005), and research scientist positions at A*STAR’s Institute of High Performance Computing (2001-2006). Research Interests: Professor Li specializes in multiphysics modeling of soft matters (e.g., smart hydrogels in BioMEMS and biological cell microscale dynamics), machine learning-based prediction for 3D printing process-microstructure-property correlation, numerical computational methodologies (meshless and multiscale algorithms), sustainable energy simulations (building efficiency and fuel cells), and structural dynamics of high-speed rotating shells and composites. His work bridges computational mechanics, materials science, and biomedical applications. Publications & Collaborations: With over 200 peer-reviewed journal articles, he has authored/co-authored monographs such as Smart Hydrogel Modelling (Springer) and Rotating Shell Dynamics (Elsevier). His research is funded by agencies like NRF, EDB, SMI, and industry partners including Rolls-Royce, Emerson, and ABB. Scientific Awards: Fellow of ASME (2019) ICCM Investigator Award (2018) SMI Top Project Winner (2015) IBM & IHPC Silver Award (2003) Advising & Grants: He has advised Ph.D. students like Meng Zhang (awarded Best Student Paper at ICMFM XIX, 2018) and secured grants for computational BioMEMS, sustainable buildings, and advanced manufacturing projects. His industrial collaborations span aerospace, maritime, and energy sectors. Labs & Teams: Professor Li leads a research group at NTU, focusing on interdisciplinary projects with institutions like Imperial College, Technische Universität Dresden, and Johns Hopkins University. He contributes to academic governance as an NTU Senator and active member of professional societies.

• Fluid Dynamics
• Biomedical Fluid Mechanics
• Microfluidics
• +3 more

Dario De Marinis is an Assistant Professor at the Department of Mechanics, Mathematics & Management, Politecnico di Bari, Italy. His research focuses on fluid dynamics with applications in biomedical engineering, aerospace, and computational physics. Research Interests Fluid-structure interaction modeling Microfluidics and particle transport Biomedical applications (blood flow, valve mechanics) Aerospace engineering (hypersonic flows, turbulence) Numerical methods (Lattice Boltzmann, immersed boundary) Publications Trend Dario's recent work (2015–2025) spans computational fluid dynamics, with emphasis on multiphase flows, viscoelastic material behavior, and biomedical microfluidic devices. He has contributed to aerospace applications and turbulent thermal flows.

• Hydraulic Energy
• Hydroelectric Power
• CFD Simulation
• +6 more

Cécile Münch-Alligné is a Professor in Hydraulic Energy at the University of Applied Sciences and Arts Western Switzerland (HES-SO) in Sion, where she serves as the Head of the Hydroelectricity Research Group and the Renewable Energy Program. She leads the Hydro Alps Lab, which conducts applied research in hydropower combining experimental and numerical approaches. Her work focuses on enhancing the flexibility of both small and large hydropower plants, with particular emphasis on adapting these systems to the evolving energy landscape and integration of renewable energy sources. Her educational background includes a BSc in Energy and Environmental Techniques, an MSc in Engineering, and a BSc in Industrial Systems, all from HES-SO Valais-Wallis. Her research spans multiple domains within hydraulic engineering and renewable energy systems, with particular expertise in CFD simulation, numerical methods, and hydraulic machine design. Münch-Alligné's research interests primarily center around improving hydropower flexibility through innovative approaches such as hydraulic short-circuit operating modes, variable speed operation, and energy recovery systems in water networks. She investigates both large-scale pumped storage power plants and micro-hydropower systems for urban water networks, with a strong focus on practical implementation and commercialization of research findings. Her work bridges theoretical modeling with experimental validation to address real-world challenges in the energy transition. Her research has been published extensively in leading journals, covering topics from Pelton turbine dynamics and Francis turbine vortex analysis to micro-turbine implementations in drinking water networks. The publications reveal a clear trend toward enhancing operational flexibility of hydropower systems to better integrate with intermittent renewable energy sources, with increasing emphasis on practical demonstration projects and commercial applications. As Principal Investigator, she has led multiple significant research projects including the SCCER 4 WP 3.2.0 2017-2020 (Supply of Electricity), Hydrolienne pour canaux artificiels Centrale de Lavey, and SOLUTION DE TRANSFERT D'ENERGIE PAR POMPAGE-TURBINAGE A PETITE ECHELLE. These projects, totaling over 2 million CHF in funding from sources including CTI, OFEN, and industrial partners, demonstrate her ability to secure substantial research funding and collaborate effectively with both academic and industry partners. Münch-Alligné leads the Hydro Alps Lab research team, which includes numerous researchers such as Steiner Amandus, Walpen Olivier, Vaccari Aldo, and others. Her collaborative approach extends to partnerships with institutions like Stahleinbau GmbH and The Ark Energy, facilitating the transfer of knowledge from research to industry application. The lab's work spans from fundamental fluid dynamics research to full-scale demonstration projects, creating a comprehensive pipeline from theory to practical implementation.

• Numerical Methods
• Partial Differential Equations
• Computational Mathematics
• +3 more

Francisco Manuel Bernal Martínez is an Associate Professor in the Department of Mathematics at Carlos III University of Madrid. His research focuses on numerical methods, partial differential equations, and computational mathematics, with applications in industrial engineering and materials science. He leads projects such as 'Financiación adicional 5º año (2022)' and collaborates on initiatives like 'Clustering Automático de Comportamientos de Invertebrados en Libertad mediante Imagen 3D.' His work emphasizes domain decomposition algorithms, radial basis functions, and stochastic control problems. He has advised at least one PhD thesis and holds grants from regional and national funding bodies. Key research interests include meshless methods, probabilistic domain decomposition, and uncertainty quantification in energy systems. Recent publications highlight advancements in hybrid algorithms for large-scale PDEs and volatility modeling. Bernal Martínez actively participates in academic networks and has presented at international conferences on computational methods and industrial mathematics.

James CampbellView profile

Professor

• Numerical Modelling
• Impact Engineering
• Crashworthiness
• +4 more

Dr James Campbell is a Reader in Structural Integrity at Brunel University London's Department of Mechanical and Aerospace Engineering within the College of Engineering, Design and Physical Sciences. With a PhD in hypervelocity impact on spacecraft and 20+ years of experience leading multidisciplinary projects, he specializes in non-linear numerical methods (FE and SPH), structural integrity, and impact analysis across aerospace, defense, and automotive sectors. BEng in Aeronautical Engineering, Imperial College London MSc and PhD in Astronautics and Space Engineering, Cranfield University His research focuses on: Transient response of materials/structures (e.g., space debris impact, aircraft crashworthiness) Meshless methods like Smoothed Particle Hydrodynamics (SPH) Constitutive models for isotropic/orthotropic materials Fluid-structure interaction in ditching and extreme wave events Recent work trends include space debris removal tools, composite material development for offshore energy, and advanced SPH algorithms for impact simulations. Awards include the Derek George Astridge Safety in Aerospace Award (2009) and Royal Institute of Naval Architects Medal (2010). He supervises PhD/MSc students and delivers CPD courses for industry (Boeing, Leonardo). Research group: IMM (International Marine and Offshore). Collaborations include Airbus, DLR, and ESA.

Seth DillardView profile

Academic

• Biomedical Engineering
• Mechanical Engineering
• Cardiovascular Biomechanics
• +5 more

Seth Dillard is an Associate Teaching Professor in the Department of Mechanical Engineering and School of Biomedical Engineering at Colorado State University (CSU). He joined CSU in January 2022, teaching courses such as Introduction to Mechanical Engineering, Finite Element Analysis, Machine Design, and Problem-Based Learning in Biomedical Engineering. He advises Senior Design teams and honors students across both disciplines. In 2023, he introduced new courses including Senior Design and a Berlin-focused Education Abroad program. Education: PhD (2011) and BSE (2004) in Mechanical Engineering from the University of Iowa. He also holds an EMT-P certification (1996) and postdoctoral research experience at the University of Iowa's IIHR from 2011–2014. Research and teaching interests include cardiovascular biomechanics, image-based modeling, energy systems, and applying engineering principles to healthcare. His teaching innovations include courses like Quantitative Systems Physiology and Prosthetics Innovation in Ecuador, blending engineering with global health challenges. Dr. Dillard has received multiple teaching awards, including the College of Engineering Faculty Excellence Award (2020) and four Excellence in Teaching awards from the University of Iowa. He has supervised over 30 students in thesis and design projects and actively participates in academic service roles such as Associate Director of SBME and accreditation committee leadership. His research spans computational fluid dynamics, cardiovascular modeling, and biomedical device design. He has published extensively in journals like Journal of Neurosurgery and Theoretical and Computational Fluid Dynamics, with peer-reviewed conference contributions since 2005.

• Computational Methods
• High Performance Computing
• Cloud Computing
• +3 more

Professor Simon Cox is a Professor of Computational Methods and Director of the Microsoft Institute for High Performance Computing within the Faculty of Engineering and Physical Sciences at the University of Southampton. He holds a doctorate in Electronics and Computer Science, complemented by first-class degrees in Mathematics and Physics. His research focuses on computational tools and platforms, particularly in high-performance computing, cloud computing, and interdisciplinary applications in engineering and science. He has secured over £30 million in research and enterprise funding, published over 250 papers, and is a Microsoft Most Valuable Professional since 2003. Research interests include computational electromagnetics, meshless methods, and data management. His team, the Computational Engineering and Design Group, develops high-performance computing systems and commercial distributed computing solutions. Notable projects include a Raspberry Pi and Lego-based supercomputer and a spin-off company from computational electromagnetics research. He actively supervises PhD students in Engineering and Environmental Science. Key awards include the Microsoft MVP title. His work spans environmental monitoring (e.g., methane sensors, IoT-based peatland tracking), cybersecurity for IoT devices, and biomedical applications like 3D X-ray histology. He leads outreach initiatives, including the 'Supercomputing in Engineering Course' since 2005.

Shiqiang YanView profile

Professor

• Wave/current/wind interaction
• Structure responses in extreme sea conditions
• Renewable energy and low carbon technologies
• +2 more

Professor Shiqiang Yan is an ocean engineering expert at City St George's, University of London , with over 25 years of academic experience. He obtained his PhD in Hydraulics (2007) from City University London, following MSc (2004) and BEng (1999) from Dalian Maritime University. His career includes Postdoctoral Research Fellow (2007-2012) and Lecturer (2012–present) positions at City University. Specializes in Wave-Structure Interaction and Extreme Sea Condition Modeling Developed QALE-FEM and ISPH-GNN hybrid numerical methods Research spans Wave Energy , Offshore Wind , and Marine Pollution Control His 15 most recent publications (2021-2025) demonstrate expertise in Wave-Structure Interaction (40% of articles), Hybrid Modeling (30%), and Renewable Energy Systems (25%). Key subfields include Wave-WEC Coupling , Ice Floe Dynamics , and Multi-Scale Hydrodynamic Modeling . He has advised PhD student Hao Yang (2013–present) on submerged oil spill research. Professional memberships include the International Society of Ocean & Polar Engineering (2013–present).

B. V. Rathish KumarView profile

Professor

• Scientific Computing
• Computational Fluid Dynamics
• Numerical Analysis
• +8 more

B. V. Rathish Kumar is a Professor at the Department of Mathematics and Statistics , Indian Institute of Technology Kanpur, with a PhD from SSSIHL, Prasanthinilayam. His research spans Numerical Analysis , Computational Fluid Dynamics , Finite Element Methods , and Biomedical Image Processing . Education: PhD in Applied Mathematics (SSSIHL, Prasanthinilayam) His research interests include Wavelet Methods for PDEs , Cardiac Electrophysiology Modeling , Convection in Porous Media , and AI/ML for Differential Equations . He has pioneered courses like Finite Element Error Estimation and AI/ML Methods for PDEs . His recent publications focus on convection dynamics , image processing , and singularly perturbed equations , contributing to fields like Biomedical Engineering and Thermal Systems . Scientific Awards: Fellow of Indian Association of Mathematical Modelling and Simulation (2018) Fellow of National Academy of Sciences (2009) Erasmus Mundus Fellowship (2004) University Gold Medal (1987)

• Computational Mechanics
• Biomechanics
• Mechanical and Material Engineering
• +3 more

Professor YuanTong Gu is the Pro Vice-Chancellor (Research Career Advancement) and Head of the School of Mechanical, Medical and Process Engineering at Queensland University of Technology (QUT). He holds an ARC Future Fellowship and leads the Laboratory for Advanced Modelling and Simulation in Engineering and Science. His research focuses on computational mechanics, biomechanics, and nanotechnology, with over $40M in secured research funding since 2000. He has supervised over 15 PhD students and collaborates with global institutions including Tsinghua University and the University of California. His awards include the International Computational Methods Award (2017) and ICACM Computational Mechanics Award (2017). Prof. Gu's work spans interdisciplinary projects in joint biomechanics, advanced materials, and AI-driven engineering solutions. Education: PhD (National University of Singapore), Graduate Certificate in Education (QUT). Research Groups: Leads a team of 3 academics, 2 ARC Future Fellows, and 15+ PhD students. Key projects include diamond nanothread composites, AI in biomedical devices, and fracture detection frameworks. His group collaborates with industry leaders and global universities to advance computational methods and materials science. Editorial Roles: Associate Editor of Engineering Analysis with Boundary Elements and Applied Mathematical Modelling , among others. Conference leadership includes chair roles in international computational mechanics conferences since 2012.

Fayssal BenkhaldounView profile

Professor

• Finite Volume Methods
• Computational Fluid Dynamics
• Shallow Water Equations
• +3 more

Fayssal Benkhaldoun is a Professor at Université Paris 13, affiliated with the LAGA laboratory (UMR7539). He has held leadership roles including former Head of the MCS team (Modeling and Scientific Computing) at LAGA and former President of the Scientific Council at IUT Villetaneuse. He is also the Project Leader of the International Office at IUT Villetaneuse. His research focuses on numerical methods for partial differential equations, particularly finite volume schemes for hyperbolic and elliptic problems. Key areas include shallow water equations, flow in porous media, mesh adaptation, and combustion front propagation. He has organized major conferences such as the International Symposium on Finite Volumes for Complex Applications (FVCA), initiating its first edition in 1996. Recent work emphasizes advanced numerical techniques like stabilized meshless methods, GPU acceleration, and parallel computing for CFD applications. His contributions span environmental modeling (flood simulation, sediment transport) and industrial applications (phosphate slurry rheology). Students advised include Jan Karel (2014, streamer propagation) and Saida Sari (2013, multilayer shallow water equations). He co-organized conferences since 1996 and has been an invited speaker at numerous institutions globally.

Amirreza KhodadadianView profile

Lecturer

• Uncertainty Quantification
• Numerical Analysis
• Computational Mechanics
• +7 more

Dr. Amirreza Khodadadian is a Lecturer in Mathematics at the School of Computer Science and Mathematics, Keele University, since August 2023. He holds a Ph.D. from the University of Vienna (2017), followed by postdoctoral positions at the Technical University of Vienna and Leibniz University Hannover. His research focuses on uncertainty quantification, numerical methods for stochastic PDEs, finite element methods, computational mechanics, and machine learning applications in nanoelectronics and biological systems. Key research interests include Bayesian inversion, multiscale modeling, reduced-order methods, and the design of nanoscale sensors. He has collaborated with institutions like the University of Oxford and secured an Austrian Science Fund (FWF) grant (476k€) for nanozyme sensor research. Dr. Khodadadian mentors postdoctoral researchers, including Dr. Samaneh Mirsian, and actively publishes in top-tier journals such as Journal of Computational Physics and Computer Methods in Applied Mechanics and Engineering . His work bridges applied mathematics with engineering challenges, emphasizing efficient numerical algorithms for real-world problems like battery degradation, groundwater contamination, and biomedical sensor optimization. Recent projects involve machine learning integration for enhanced predictive modeling. Education: Ph.D. in Mathematics, University of Vienna, Austria (2017) Postdoctoral Fellowships: TU Vienna (2018), Leibniz University Hannover (2018–2022) Grants/Awards: Austrian Science Fund (FWF) Grant: Single Atom Catalysts as Nanozymes in FET Sensors (2023) Advising: Postdoctoral Mentor: Dr. Samaneh Mirsian (Keele University) Dr. Khodadadian’s publications span computational mechanics, stochastic modeling, and interdisciplinary applications, reflecting his expertise in translating mathematical theory into practical engineering solutions.