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David BindelView profile

Associate Professor

• Applied Numerical Linear Algebra
• Eigenvalue Problems
• Network Analysis
• +5 more

David Bindel is an Associate Professor in the Department of Mathematics at Cornell University, affiliated with the College of Arts and Sciences, College of Engineering, and Cornell Ann S. Bowers College of Computing and Information Science. He earned his Ph.D. in Mathematics from the University of California, Berkeley in 2006. His research focuses on applied numerical linear algebra, eigenvalue problems, and their applications in plasma physics, network analysis, and nonlinear systems. He develops methods for analyzing complex systems, including magnetic confinement in stellarators, stability of MHD systems, and community detection in networks. His work bridges theoretical foundations with practical computational tools, such as formal verification of linear algebra algorithms and scalable Gaussian process models. Bindel’s research explores the interplay between structure and computation, leveraging eigenvalue analysis to address challenges in computer vision, opinion dynamics, and engineering design. He has contributed to advancements in numerical methods for large-scale systems, including iterative solvers, spectral approximation techniques, and stochastic optimization. His interdisciplinary approach spans applied mathematics, computer science, and physics, with applications in fusion energy, machine learning, and network science. Recent work highlights include high-order expansions for magnetic confinement, adaptive filtering for dynamical systems, and Bayesian optimization strategies. His publications emphasize rigorous analysis alongside computational scalability, addressing both theoretical and practical aspects of modern scientific computing. Despite no explicitly listed awards, his contributions reflect significant impact in his fields.

• Medical Microwave Imaging
• Microwave Engineering
• Millimeter-wave Engineering
• +3 more

Professor Amin Abbosh is a faculty member at the School of Electrical Engineering and Computer Science, University of Queensland. His research focuses on Medical Microwave Imaging and Millimeter-wave Engineering, with contributions to advanced imaging systems, antenna design, and communication technologies. He leads projects in electromagnetic medical sensing, including portable brain scanners and wearable diagnostic systems. His work integrates applied electromagnetics with AI-driven algorithms, addressing challenges in stroke detection, liver health monitoring, and deep vein thrombosis diagnosis. With over 16 patents and collaborations across biomedical and engineering domains, his research bridges clinical needs with cutting-edge electromagnetic techniques. Key projects include the development of low-cost healthcare monitoring systems and reconfigurable antennas for satellite communications. Research interests span medical imaging systems, antenna array design, and signal processing for healthcare applications. His team innovates in areas like phased arrays, dielectric property analysis, and non-invasive diagnostics. Recent advancements include synthetic microwave focusing techniques and self-supervised deep learning models for clutter removal in imaging. Publications highlight contributions in IEEE journals and conferences, emphasizing clinical applications and device prototyping. Collaborations with institutions like the University of Queensland’s medical faculty and industry partners ensure practical implementation of his research.

• 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.

Glaucio H. PaulinoView profile

Professor

• Topology Optimization
• Computational Mechanics
• Fracture Mechanics
• +3 more

Glaucio H. Paulino holds the Margareta Engman Augustine Professorship in Civil and Environmental Engineering at Princeton University, where he also serves as a Professor at the Princeton Institute for the Science and Technology of Materials (PRISM). His work bridges computational mechanics, topology optimization, and materials science. Paulino leads a research group focused on advancing structural design methodologies, fracture mechanics, and functionally graded materials. His team has pioneered polygonal finite elements and multiresolution topology optimization techniques, addressing challenges in mesh bias and computational efficiency. He has published over 240 peer-reviewed articles and mentored 19 PhD and 11 MS students. Notable contributions include the PPR cohesive model for fracture analysis and adaptive mesh refinement for dynamic simulations. Paulino's research extends to practical applications such as high-rise building design and sustainable construction materials. Awards include election to the European Academy of Sciences and Arts and ASME’s Melville Medal. Current projects involve functionally graded cement-based materials, extrusion processing, and digital image correlation for material characterization. His lab collaborates with industry partners like Skidmore, Owings & Merrill LLP to translate topology optimization into real-world engineering solutions. Paulino’s interdisciplinary approach integrates computational modeling with experimental validation, fostering innovations in civil infrastructure resilience.

Sylvain LefebvreView profile

Researcher

• Computer Graphics
• Texture Synthesis
• Additive Manufacturing
• +4 more

Sylvain Lefebvre is a permanent researcher at INRIA (Institut National de Recherche en Informatique et en Automatique) in France, where he leads the MFX research team since 2018. Previously, he was part of the ALICE group at INRIA Nancy (2009-2018) and the REVES team in Sophia Antipolis (2006-2009). His career includes a postdoctoral position at Microsoft Research Seattle (2005) following his PhD at INRIA Rhones-Alpes under Fabrice Neyret. His educational background includes a PhD in Computer Graphics from Université Joseph Fourier (Grenoble) in 2005, preceded by a Master in Computer Graphics from INP Grenoble in 2001. His habilitation thesis focused on Runtime Texture Synthesis. Lefebvre's research centers on simplifying content creation for highly detailed patterns, structures, and shapes with applications spanning Computer Graphics to additive manufacturing. He develops fast, controllable by-example synthesis approaches that generate content while enforcing user-specified constraints. His work addresses computational challenges through novel data structures and algorithms optimized for GPUs and FPGAs, including his Silice programming language. The ERC-funded ShapeForge project (2012-2017) advanced shape generation for 3D printing, leading to the IceSL software for digital modeling and fabrication. Analysis of his 15 most recent publications reveals a strong focus on additive manufacturing optimization, with recurring themes in structural integrity, material efficiency, and geometric algorithms. His work bridges computer graphics theory with practical fabrication constraints, particularly in microstructure design, slicing techniques, and mechanical metamaterials. The interdisciplinary nature spans computer science, materials engineering, and robotics. EUROGRAPHICS Young Researcher Award (2010) ERC Starting Grant for ShapeForge project (2012) Lefebvre has advised over 25 PhD students and interns including Marco Freire, Thibault Tricard, and Jimmy Etienne. His ShapeForge project received significant ERC funding, supporting research in computational fabrication. He serves on numerous program committees including SIGGRAPH, Eurographics, and SIGGRAPH Asia, reflecting his leadership in the computer graphics community. As leader of the MFX team since 2018, Lefebvre directs research in computational fabrication, focusing on IceSL software development for 3D printing workflows. The team integrates computer graphics techniques with manufacturing constraints, developing tools that simplify complex object design and fabrication while addressing real-world challenges in material usage and structural integrity.

Liew Kim MeowView profile

Professor

• Composite Materials
• Multiscale Modeling
• Structural Optimization
• +5 more

Prof. Liew Kim Meow is a Chair Professor of Civil Engineering at City University of Hong Kong (CityU) since 2005. He previously served as Head of the Department of Architecture and Civil Engineering (2011–2017) and held tenured professorial positions at Nanyang Technological University (NTU), Singapore. He earned BS from Michigan Tech (1985), MEng (1988), and PhD (1991) from the National University of Singapore. His research focuses on composite materials, multiscale modeling, structural optimization, and computational mechanics. Notable contributions include pioneering work on carbon nanotube-reinforced composites and advanced numerical methods. He has published over 800 papers with 38,000+ citations (H-index 97). Education: BS, Michigan Technological University (1985) MEng, National University of Singapore (1988) PhD, National University of Singapore (1991) His awards include Clarivate Analytics' Highly Cited Researcher (2018–2019), Xiangjiang Scholar (2017), and multiple fellowships from professional institutions. He serves as Editor-in-Chief of International Review of Civil Engineering and holds editorial roles in over a dozen journals. He founded key research centers like the Nanyang Center for Supercomputing and Visualization (NTU) and led initiatives in computational mechanics. His work influences global research in composite materials and structural analysis.

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.

Kangkang YinView profile

Associate Professor

• Computer Animation
• Computer Graphics
• Humanoid Robotics
• +3 more

Kangkang Yin is an Associate Professor in the School of Computing Science at Simon Fraser University (SFU). His research focuses on computer animation, computer graphics, humanoid robotics, machine learning, and multimedia analysis. He teaches courses such as Computer Animation and Scientific Computing, and holds a PhD from the University of British Columbia (2007), MSc from Zhejiang University (2000), and BSc from Zhejiang University (1997). His work bridges robotics and animation through projects like physics-based character controllers, motion diffusion models, and robotic manipulation. Key contributions include the SIMBICON biped locomotion framework and research into emotion-driven dance animation. Recent efforts emphasize reinforcement learning applications in motion synthesis and robust visual navigation for unmanned ground vehicles. Yin's publications span over two decades, addressing challenges in motion control, physics-based simulation, and machine learning applications. His lab contributes to both academic advancements and practical robotics solutions. Current research trends show strong emphasis on combining generative AI with traditional animation techniques, as seen in recent work on auto-regressive motion models (AAMDM) and physics-augmented reinforcement learning (PARC).

• 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.

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.

Colin G. FarquharsonView profile

Professor

• Geophysics
• Electromagnetic Modeling
• Geophysical Inversion
• +7 more

Colin G. Farquharson is a Professor in the Department of Earth Sciences at Memorial University of Newfoundland. His career spans roles including Assistant Professor (2008-2018), Associate Professor (2014-2018), and full Professor since 2018. He holds a Ph.D. in Geophysics from the University of British Columbia (1995) and a B.Sc. (Honours) in Geophysics from the University of Edinburgh (1990). His research focuses on forward modeling and inversion of geophysical electromagnetic data, particularly surface geometry inversion (SGI), meshfree methods for unstructured grids, and joint inversion techniques. Key projects include SGI for hydrothermal vent systems, 3D EM modeling for mineral exploration, and computational methods on unstructured tetrahedral meshes. His work addresses challenges in uranium exploration, subsurface imaging, and geological interface reconstruction. Publications emphasize advancements in electromagnetic theory, inversion algorithms, and applications in mineral and hydrocarbon exploration. Notable collaborations include work with Peter Lelièvre on surface-based inversion and stochastic optimization. Research groups include the CAG Group and FacetModeller team, focusing on integrating geological models with geophysical data. Awards and recognitions are not explicitly listed, but his extensive contributions to geophysical methodology and exploration geophysics are well-documented. He advises numerous graduate students and leads projects funded by NSERC, industry partnerships, and international collaborations.

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.