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.
Prof. Jens Markus Melenk is a Professor at TU Wien's Faculty of Mathematics and Geoinformation, leading the Computational Mathematics Research Group (E101-02-1). His research focuses on advanced numerical methods for partial differential equations, with a strong emphasis on hp-FEM (hp-Finite Element Method), fractional diffusion equations, and wave propagation problems. He has contributed significantly to the development of robust and efficient algorithms for complex domains and heterogeneous media. Key research interests include the application of hp-FEM to fractional operators (e.g., integral fractional Laplacian), boundary element methods (BEM), and the analysis of wavenumber-explicit convergence for Maxwell's equations and elastic wave equations. His work bridges theoretical analysis and computational implementation, addressing challenges in multiscale problems and singular perturbations. Notable contributions: Exponential convergence of hp-FEM for fractional Laplacian problems, wavenumber-explicit error estimates for wave equations in heterogeneous media. Collaborations: Active involvement with researchers like Markus Faustmann, Christoph Schwab, and Dirk Praetorius. Supervised theses: Includes works on hp-FEM for fractional operators, RBF interpolation, and error estimators for elliptic PDEs. His research group develops and analyzes numerical schemes for challenging PDE scenarios, with applications in electromagnetism, acoustics, and computational mechanics.
Associate Professor Christopher Wensrich is a faculty member in the School of Engineering at the University of Newcastle, Australia, specializing in Mechanical Engineering. He has a strong background in applied mechanics from both computational and experimental perspectives, with significant expertise in granular mechanics, neutron diffraction strain measurement, and Bragg-edge transmission strain tomography. Education: PhD, University of Newcastle Bachelor of Mathematics, University of Newcastle Bachelor of Engineering, University of Newcastle Professor Wensrich's research focuses on several interconnected areas within mechanical engineering and materials science. His primary expertise lies in granular mechanics, spanning from micromechanics and homogenization of granular systems to analytical modeling of granular dynamics (particularly the silo quaking problem) and computational modeling using the Discrete Element Method (DEM). He is also a pioneer in applying neutron diffraction strain scanning techniques to granular systems. In the broader field of applied mechanics, he has made significant contributions to neutron diffraction-based strain measurement, including breakthroughs in Bragg-edge Transmission Strain Tomography, where he demonstrated the world's first practical application outside of simple axisymmetric systems. His publication record demonstrates a consistent focus on developing and applying advanced techniques for strain measurement and reconstruction in granular and composite materials. His recent work has centered on tomographic reconstruction methods using neutron diffraction, with particular emphasis on Bragg-edge techniques for 2D and 3D strain field reconstruction. His research bridges theoretical mathematics, computational methods, and experimental validation, creating a robust framework for non-destructive stress measurement in complex materials. Professional Recognition: President of the Australian Neutron Beam User Group (ANBUG) since December 2022 Member of the ACNS Program Advisory Team at ANSTO (Australian Nuclear Science and Technology Organisation) since March 2019 Visiting Fellow at Clare Hall College, Cambridge University (January-June 2023) Visiting Researcher at Isaac Newton Institute for Mathematical Sciences (January-June 2023) Professor Wensrich has secured substantial research funding, with a total of $5,478,793 across 42 grants. His funding portfolio includes projects from the Australian Research Council (ARC), ANSTO, and international partners like Oakridge National Laboratory and Japan Proton Accelerator Research Complex. He has successfully supervised 11 PhD and Masters students to completion, with research topics spanning granular mechanics, conveyor systems, and neutron strain tomography. His current research involves collaborations with institutions worldwide, focusing on advanced strain measurement techniques and their application to complex material systems.
Professor Alexander Kushpel is affiliated with Çankaya University, Faculty of Arts and Sciences, Department of Mathematics, Turkey, since 2018. He has held academic roles at the University of Leicester (2011-2016), State University of Campinas (1997-2001, 2006-2010), and Ryerson University (2001-2006). PhD in Mathematics, University of Leicester (2015) PhD in Mathematics, Universidade Estadual de Campinas (2009) PhD in Mathematics, Institute of Mathematics of the National Academy of Sciences of Ukraine (1992) His research focuses on Mathematical Analysis , Applied Mathematics , and Geometry , with emphasis on approximation theory, Fourier analysis, and operator entropy. Key article trends include n-widths, Sobolev classes on manifolds, and financial mathematics applications. He has advised Regis Leonardo Braguim Stabile (MSc thesis: "N-width of set of smooth functions on the sphere SD").
Professor Hoang Xuan Phu is a renowned mathematician affiliated with the Institute of Mathematics , Vietnam Academy of Science and Technology , where he has served since 1984 (Researcher), 1992 (Associate Professor), and 1996 (Professor). He is an elected member of multiple prestigious academies: the Heidelberg Academy of Sciences and Humanities (2004), Bavarian Academy of Sciences and Humanities (2010), TWAS - The World Academy of Sciences (2013), and acatech - National Academy of Science and Engineering, Germany (2019). His email contact is hxphu@math.ac.vn and phu@iwr.uni-heidelberg.de . Education : University of Leipzig (Diploma 1979, PhD 1983, Habilitation 1987) Research Areas : Optimization, Optimal Control, Functional Analysis, Numerical Analysis, Rough Analysis Applications : Inventory Problems, Hydroelectric Power Plant Control, Robotics, Open Channel Hydraulics Editorial Roles : Editor-in-Chief of Vietnam Journal of Mathematics (2011-2022), Honorary Editor-in-Chief (2023-present), Associate Editor for multiple journals His recent publications focus on convex hull algorithms, optimal path planning, and function perturbation analysis, reflecting his expertise in mathematical optimization and computational methods. He has organized numerous international conferences on High Performance Scientific Computing in Hanoi (2000-2024) and Optimization & Scientific Computing (2003-2024).
Josep Maria Bergadà Granyó is an Associate Professor in the Department of Fluid Mechanics at the Escola Superior d'Enginyeries Industrial, Aeroespacial i Audiovisual de Terrassa (ESEIAAT), Universitat Politècnica de Catalunya (UPC). He is actively involved in research through the UPC MICROTECH LAB and CATMech – Centre Avançat de Tecnologies Mecàniques. With a Doctorate in Industrial Engineering, he has a strong academic and research profile spanning decades. His research interests focus on fluid dynamics, particularly Active Flow Control , Fluid Power (Hydraulics) , Piston Pumps , Gas Dynamics , and Computational Fluid Dynamics . He applies these areas to enhance aerodynamic performance in wind turbines and industrial systems. His work employs advanced numerical methods such as the lattice Boltzmann method and RANS simulations, with a strong emphasis on optimizing flow behavior in complex geometries and energy systems. The recent publications highlight a consistent trend in aerodynamic efficiency optimization , especially in wind turbines using active flow control and synthetic jets. His research spans both theoretical modeling and practical applications, including dimensional modifications in fluidic oscillators and turbulence boundary condition effects. These efforts contribute significantly to renewable energy and sustainable engineering solutions. Among his recognitions is the Premi Iniciativa Digital Politècnica 2019 . He has led and participated in multiple competitive R&D projects, demonstrating strong grant acquisition and collaborative capabilities. He has supervised doctoral students such as K. Karimzadegan, M. Baghaei, and B. An, indicating an active role in academic mentoring. His collaborations extend across various research groups at UPC, particularly with experts in mechanical, aerospace, and textile engineering. He is a key figure in the Fluid Mechanics Department, contributing to both teaching and cutting-edge research in fluid dynamics and its industrial applications.
Professor Jang Yoon is a faculty member in the Department of Computer Engineering at Sejong University, South Korea. He currently holds the position of Daeyang Distinguished Professor and leads the Data Visualization Lab. His academic journey includes postdoctoral research at the Swiss National Supercomputing Center (2007-2009), ETH Zurich (2009-2011), and Purdue University (2011-2012). His educational background includes a Bachelor's degree from Seoul National University in Electrical Engineering (2000), and Master's and Doctoral degrees from Purdue University in Electrical and Computer Engineering (2002 and 2007). His academic progression at Sejong University shows his appointment as Assistant Professor (2012-2016), Associate Professor (2016-2022), and Professor (2022-present). Professor Jang's research spans multiple domains within data science and visualization, with primary focus on data visualization, visual analytics, and their applications in various domains. His work bridges theoretical computer science with practical applications in traffic analysis, healthcare, and smart city infrastructure. He has developed innovative techniques for spatiotemporal data visualization, volume rendering, and causal analysis in complex datasets. His recent publications (2023-2025) demonstrate a strong focus on integrating deep learning with visualization techniques, particularly in traffic analysis, volume rendering, and large language model interpretability. His work shows a clear trajectory toward combining causal inference with visual analytics, applying these methods to urban traffic systems, structural health monitoring, and public relations analysis. Professor Jang has served in numerous leadership roles in major visualization conferences including IEEE VIS, IEEE PacificVis (as General Chair in 2023), EuroVis, and HCI Korea conferences. His service contributions include program committee memberships and chair positions across multiple prestigious conferences in the visualization field. His laboratory work focuses on practical applications of visualization techniques with numerous patents registered in Korea. His research has resulted in multiple practical systems for traffic analysis, VR sickness detection, data quality improvement, and eye-tracking applications. The lab maintains strong industry connections through applied research projects addressing real-world problems.
Benjamin Uekermann is a Jun.-Prof. (Assistant Professor) at the University of Stuttgart's Institute for Parallel and Distributed Systems (IPVS), part of the Faculty of Computer Science, Electrical Engineering, and Information Technology. His work focuses on sustainable simulation software ecosystems , particularly advancing the preCICE coupling library for multi-physics and multi-scale simulations. He leads research in partitioned simulation coupling , reproducible software practices, and high-performance computing tools. His research interests include Parallel computing and distributed systems Numerical methods for coupled PDE-based simulations Scientific software sustainability and open-source frameworks Data-driven adaptive algorithms Validation and testing of simulation ecosystems Recent work emphasizes reproducibility via NixOS integration, user-friendly tools like MetaConfigurator and ASTE, and multi-X coupling across scales and physics domains. His preCICE library enables seamless integration of solvers like OpenFOAM and FEniCS for fluid-structure interaction, CFD/CSD, and thermohydraulics. Key contributions include scalable radial-basis interpolation methods, quasi-Newton acceleration schemes, and geometric multi-scale coupling frameworks. He actively develops educational materials on open-source scientific computing and advocates for sustainable research software practices.
Professor Mahdi Mahfouf holds the Chair in Intelligent Systems at the University of Sheffield's School of Electrical and Electronic Engineering . He obtained his MPhil (1988) and PhD (1991) in Control Systems from the same institution. After postdoctoral research (1992-1996) on Leverhulme-funded projects in Model-Predictive Control and Fuzzy Logic, he progressed through academic ranks at Sheffield to Full Professor (2005). Recipient of the IEE Hartree Premium Award (1992) and MEDIPEX Innovation Award (for ICU Decision Support Systems) Over 370 publications, including 130+ journal papers Head of the Intelligent Systems Research Laboratory Research Themes His work spans fundamental research in Fuzzy Logic (modelling, control), Neural-Fuzzy Systems, Self-Organising Control, and Evolutionary Optimization, alongside applied domains in pharmaceutical manufacturing, aerospace systems, biomedical engineering (ICU monitoring), and intelligent transportation. Recent publications focus on hybrid AI for pharmaceutical processes , type-2 fuzzy control systems , and machine learning in manufacturing metrology . Lab initiatives include multistage process monitoring and human-machine interaction systems for stress management.
Andres Galvis Rodriguez is a Senior Lecturer at the School of Electrical and Mechanical Engineering, University of Portsmouth. He specializes in multiscale modelling, lithium-ion battery degradation, and micromechanical analysis of heterogeneous materials. His research involves developing novel mathematical models and computational tools, including the BESLE software for boundary element analysis of heterogeneous materials. He collaborates with Brazilian researchers on advancing thermo-elasticity modules for BESLE. Teaching responsibilities include coordinating modules in Solid Mechanics & Dynamics and Engineering Mathematics. His work focuses on finite element and boundary element methods, with applications to battery systems, composite materials, and bone structures. The BESLE software, co-developed by Dr. Galvis Rodriguez, is a pioneering open-source tool for simulating anisotropic and isotropic materials using parallel computing. Current research emphasizes the mechanical behavior of lithium-ion electrodes via homogenization techniques and fatigue analysis in composite-repaired structures. His contributions bridge computational mechanics and materials science, addressing challenges in energy storage and structural integrity.
Brett Meyers is an Assistant Research Professor at Purdue University's College of Engineering, specializing in biomedical engineering and cardiovascular fluid dynamics. His research focuses on advancing echocardiographic techniques, computational fluid dynamics (CFD), and medical imaging technologies to assess cardiac function in patients with congenital heart defects, sepsis, and other cardiovascular conditions. Key contributions include developing the Doppler Velocity Reconstruction (DoVeR) method and enhancing fetal/neonatal echocardiography for early diagnosis. Education details are not explicitly stated in the provided text. His work spans pediatric cardiology, adult heart failure, and biomaterials, with a strong emphasis on translational research. Meyers has pioneered methods to quantify intracardiac flow patterns using 4D flow MRI and ultrasound-based techniques like EchoPIV (Echocardiographic Particle Image Velocimetry). Research interests include hemodynamic biomarker development for sepsis prognostication, single ventricle heart biomechanics, and fluid mechanics in biological systems (e.g., snake tongue flicking). He has authored over 50 peer-reviewed articles since 2013, with recent work focusing on AI-driven analysis of echocardiograms and novel drug delivery systems using synchrotron imaging. While no specific awards are listed, his innovative methodologies have been applied in clinical settings, such as assessing exercise capacity in repaired Tetralogy of Fallot patients and refining therapeutic regimens for thrombotic disorders. Meyers collaborates with multidisciplinary teams in Purdue's biomedical engineering and mechanical engineering departments, contributing to both academic and industrial partnerships.
Sourav Dutta is a Research Fellow at the Oden Institute for Computational Engineering & Sciences at the University of Texas at Austin, where he works with Clint Dawson in the Computational Hydraulics Group. Prior to joining UT Austin in October 2022, he was an ORISE postdoctoral fellow at the Coastal & Hydraulics Laboratory of the U.S. Army Engineer Research and Development Center (ERDC) from September 2017 to August 2022. Dr. Dutta's research lies at the intersection of physics-based computational methods and data-driven machine learning techniques for environmental flow problems. His work focuses on developing efficient numerical approximations by combining physical principles with modern machine learning algorithms. His primary research areas include: Model Order Reduction Computational Fluid Dynamics Scientific Machine Learning Uncertainty Quantification Applied Mathematics His publication record reveals a strong trajectory in reduced order modeling for environmental hydrodynamics, particularly for advection-dominated problems that challenge traditional numerical methods. His research has evolved from foundational work on hybrid numerical methods for porous media flows to cutting-edge applications of deep learning and neural ODEs in computational hydraulics. Dr. Dutta has presented his research at major venues including SIAM conferences, Computational Methods in Water Resources meetings, and the HydroML Symposium. In September 2022, he co-organized a minisymposium on Machine Learning and Data-Driven Methods for Forward and Inverse Problems at the SIAM Mathematics of Data Science meeting. His educational background includes a PhD in Mathematics from Texas A&M University (2017), supervised by Dr. Prabir Daripa, and an Integrated BSc & MSc in Mathematics & Computing from the Indian Institute of Technology, Kharagpur (2010). At the Oden Institute, Dr. Dutta is currently working on compound flood simulation capabilities within Adaptive Hydraulics (AdH), reduced order modeling for coastal engineering applications, and physics-based operator learning frameworks for environmental flows. He has also developed pyNIROM, an open-source Python package for non-intrusive reduced order modeling of time-dependent problems.
Prof. Florian Wellmann is a University Professor at RWTH Aachen University's Chair of Numerical Geosciences, Geothermal Energy and Reservoir Geophysics within the Faculty of Georesources and Materials Engineering. His research focuses on integrating machine learning with geoscience applications, particularly in geothermal energy, structural modeling, and uncertainty quantification. He leads the CG³ research group, developing open-source tools like GemPy and GemGIS for 3D geological modeling. Notably, he received a KI-Campus fellowship for advancing AI in geoscience education. His work includes projects such as the Horizon Europe GeoHEAT initiative, exploring geothermal systems and repurposing idle wells for energy. Education details are not explicitly provided, but his academic roles indicate expertise in numerical geosciences and geothermal engineering. Research interests span physics-based machine learning, subsurface characterization, and probabilistic modeling. His fellowship highlights contributions to digital education, blending AI with geological curricula. Recent publications emphasize sensitivity analysis, fault modeling, and geothermal reservoir optimization. Prof. Wellmann's awards include the KI-Campus fellowship (2021). His grants and advising efforts are reflected in collaborative projects like GeoHEAT and software development. Labs/teams include the CG³ group at RWTH Aachen, equipped with advanced geophysical instruments for field and lab analysis.
Professor Nam Mai-Duy is a faculty member at the University of Southern Queensland (USQ), holding the position of Professor in Computational Engineering within the School of Engineering. His research focuses on advanced numerical methods for fluid dynamics, including integrated radial basis functions (IRBF), finite volume schemes, and dissipative particle dynamics (DPD). He has expertise in computational fluid dynamics (CFD), viscoelastic fluids, and multiphase systems. Qualifications include a Master of Engineering from Ho Chi Minh University of Technology and a PhD from USQ. His work emphasizes high-order numerical techniques for solving partial differential equations in complex geometries and non-Newtonian fluid behavior. Research interests span computational engineering, numerical analysis, and engineering simulations. His publications address topics like boundary-fitted grids, embedded-boundary methods, and microstructure modeling in viscoelastic materials. He collaborates with the Institute for Advanced Engineering and Space Sciences. Advising involves doctoral research in computational methods for fluid flows, such as soliton propagation in waveguides and particulate suspensions. No scientific awards are explicitly listed, though his contributions to numerical methods are widely recognized.
Dr. Maarouf Saad is a Lecturer in the Department of Electrical Engineering at École de Technologie Supérieure (ÉTS), Université du Québec. He holds a B.Sc.A. and M.Sc.A. from Polytechnique Montréal and a Ph.D. from McGill University. His research spans robotics, control systems, and sustainable energy applications, with a focus on nonlinear control, UAVs, exoskeleton rehabilitation systems, and smart grid technologies. Education: B.Sc.A., M.Sc.A. (Polytechnique Montréal), Ph.D. (McGill) Research Units: GREPCI (Power Electronics), SYNCHROMEDIA (Telepresence), INIT Robots (Haptic Interfaces) His work integrates adaptive control algorithms with artificial intelligence in robotics, particularly for rehabilitation and aerospace systems. He has pioneered fixed-time sliding mode controllers for quadrotors and developed impedance-based methods for distribution system monitoring. In smart grids, he focuses on voltage stability analysis and distributed generation optimization. Key research trends include nonlinear control of electrohydraulic systems, modeling of flexible manipulators, and cooperative control strategies for multi-agent systems. His publications emphasize practical implementations in real-world scenarios, from autonomous airships to exoskeletons for upper-limb rehabilitation. Dr. Saad supervises numerous graduate students across diverse projects, including UAV control, cable robot rehabilitation systems, and energy management in hybrid microgrids. His collaborations extend to institutions like CAE Inc. and laboratories in telepresence and robotics.
