Hrvoje Jasak is a Professor of Continuum Physics at the Department of Physics (Cavendish Laboratory), University of Cambridge. He holds a fellowship at Christ’s College. His academic journey includes a BSc in Mechanical Engineering from the University of Zagreb (1992) and a PhD in CFD from Imperial College London (1996). Prior to academia, he held engineering roles at CD-adapco (now Siemens PLM), Nabla Ltd, and Ansys-Fluent Inc., contributing to CFD software development. His research focuses on numerical simulation methods, continuum physics, multiphase flows, naval hydrodynamics, and software development. He co-created OpenFOAM, chairs its Numerics Technical Committee, and leads the Computational Continuum Mechanics (CCM) research group within the Laboratory for Scientific Computing. His work integrates advanced numerical techniques like the partially rotating grid method, finite volume algorithms, and multiphysics coupling frameworks. Jasak is a seasoned developer with 25+ years of C++ expertise, having authored ~1 million lines of code. His group’s projects include the Naval Hydro Pack , fluid-structure interaction solvers, and the Eulerian multi-fluid model for dense sprays. He actively collaborates on international initiatives like the NUMAP-FOAM Summer School and the OpenFOAM community. His teaching spans MPhil programs, PhD supervision, and specialized CFD courses. Current research explores wave-ice interaction, lubricated contact modeling, and open-source software innovation. The CCM group’s work bridges academia and industry, addressing challenges in marine engineering, energy systems, and computational mechanics.
Professor Omar Matar is a Professor of Fluid Mechanics and RAEng/PETRONAS Research Chair in Multiphase Fluid Dynamics at the Department of Chemical Engineering, Imperial College London. He leads the Matar Fluids Group, focusing on interfacial fluid mechanics, multiphase flows, computational fluid dynamics (CFD), and applications in energy, manufacturing, and nanotechnology. His roles include Head of Department of Chemical Engineering, Director of the PETRONAS Centre for Engineering of Multiphase Systems (PETCEMS), and Editor-in-Chief of the Journal of Engineering Mathematics. Education: PhD in Chemical Engineering, Princeton University (1993) MEng Chemical Engineering, Imperial College London (1989) Research Interests: Interfacial fluid mechanics, multiphase flows, CFD, and machine learning 2D materials exfoliation and scale-up, immersive technologies (AR/VR) Applications in energy systems, nanotechnology, and personalized education Awards: Fellow of the Royal Academy of Engineering (2020) Recipient of the Imperial College President’s Medal (2020) EPSRC Programme Grant Principal Investigator (MEMPHIS, PREMIERE) Grants & Projects: MEMPHIS: £5M EPSRC-funded Programme Grant (2012–2017) PREMIERE: EPSRC Programme Grant (2019–present) PETCEMS: PETRONAS-funded Centre for Multiphase Systems Engineering Labs & Collaborations: Leads the Matar Fluids Group, collaborating with institutions like UCL, University of Edinburgh, and industry partners such as BP and First Light Fusion. Active in developing high-performance CFD codes (e.g., BLUE) and machine learning-driven models for multiphase systems.
Fabian Fritz holds an M.Sc. degree and works at the Technical University of Munich (TUM) within the Chair of Aerodynamics and Fluid Mechanics . His research focuses on computational fluid dynamics (CFD) and numerical simulation of multiphase flows, particularly using Smoothed Particle Hydrodynamics (SPH) . He collaborates on projects like PBF-LB/M (additive manufacturing) and contributes to Lagrangian fluid mechanics benchmarking frameworks. Research Trends: His publications emphasize numerical methods (SPH, level-set, finite-volume), multiphase flow modeling , heat transfer , and thermoacoustic stability . Recent work includes hardware-agnostic code optimization and adaptive mesh refinement techniques. Education: Completed a master’s thesis on Diffusive-Interface Modeling of Multiphase Flows with Surface-Tension Effects , supervised by P.D. Dr.-Ing. habil. Stefan Adami.
Dr. Lisa Wang is a tenure-track Assistant Professor in the Department of Civil & Environmental Engineering at Old Dominion University (ODU). She holds a Ph.D. and Postdoctoral Fellowship in Structural Engineering from Colorado State University (CSU), with additional research at NIST’s Center for Risk-Based Community Resilience Planning. She is a licensed California Professional Engineer (PE) and has expertise in multidisciplinary community resilience assessment, mitigation strategies, and policy analysis. Her research focuses on integrating physical, socio-economic, and infrastructural systems to enhance disaster resilience in coastal and hazard-prone communities. Education: Ph.D. and Postdoc (CSU, 2022-2024), M.S. in Structural Engineering (University of Colorado Denver & Jilin University, 2015), B.S. in Civil Engineering (Jilin University, 2012). Professional licenses include CA PE #94251 and CO EIT #007558. Research Interests: Community resilience under multi-hazards (tornadoes, floods, climate change), resilience-based design of structural systems, data fusion across disciplines, and equitable decision-making for disaster resilience. Recent grants include $31k for AI-driven coastal resilience strategies and $10k for minimum building portfolio development. Grants & Awards: Principal Investigator (PI): Trustworthy AI in Coastal Resilience (ICAR, $31k), AI-Driven Building Portfolios (ODU PURS, $10k) Awards: O. H. Ammann Fellowship (ASCE), ICAR Travel Grant, AGU NSF Travel Grant, Jack E. Cermak Fellowship Labs & Teams: Wang Research Group and Structural Engineering Research Laboratory at ODU.
Philipp Schlatter is a Professor in the Department of Mechanics at KTH Royal Institute of Technology. His research focuses on fluid mechanics, turbulence, and computational fluid dynamics (CFD), with expertise in high-performance computing and direct numerical simulations (DNS). He leads projects involving scalable CFD frameworks like Neko and Nek5000, and investigates turbulent boundary layers, flow control, and coherent flow structures. His work includes experimental and numerical studies of wing profiles, rotating systems, and transition dynamics. Schlatter teaches courses on computational fluid dynamics and turbulence, emphasizing both theoretical and practical aspects of fluid mechanics. Key research interests include developing numerical methods for high-fidelity simulations, understanding turbulence mechanisms, and optimizing flow control strategies. His contributions span aerodynamics, heat transfer, and the application of machine learning to fluid dynamics problems. Schlatter collaborates extensively on interdisciplinary projects, leveraging advanced computing resources to address complex fluid flow phenomena. Publications highlight advancements in DNS frameworks, Bayesian optimization for flow control, and analysis of turbulent structures in pipe and boundary layer flows. His research also addresses challenges in measurement techniques and uncertainty quantification in CFD simulations.
Brian Vermeire is an Associate Professor in the Department of Mechanical, Industrial and Aerospace Engineering at Concordia University. His research focuses on computational fluid dynamics, aerodynamics, high-performance computing, turbulence modeling, numerical methods, and optimization. He leads the Computational Aerodynamics Laboratory, emphasizing scale-resolving simulations and high-order numerical techniques. Key interests include large eddy simulation (LES), direct numerical simulation (DNS), and gradient-free optimization. His work often involves developing advanced algorithms for unstructured grids and high-performance computing platforms. Research Interests: High-order numerical methods Implicit/explicit time integration schemes Polynomial adaptation for adaptive meshing Aeroacoustic shape optimization Large eddy simulation (LES) and direct numerical simulation (DNS) Software development for CFD (e.g., PyFR) Recent work trends show strong focus on hybridized flux reconstruction methods, energy-conservative algorithms, and industrial adoption of high-fidelity simulations. Major contributions include scalable implementations for petascale computing and open-source tools like PyFR. His group collaborates on applications such as wind turbine aerodynamics and low-pressure turbine design. Labs/Teams: Computational Aerodynamics Laboratory (website: link )
Stefano Markidis is a Professor of Computer Science at KTH Royal Institute of Technology, affiliated with the School of Electrical Engineering and Computer Science and the Digital Futures Faculty. He holds a Ph.D. from the University of Illinois at Urbana-Champaign and an MS from Politecnico di Torino. His research focuses on high-performance computing systems, including supercomputers and quantum computers, with expertise in plasma simulations, quantum algorithms, and scalable computational frameworks. Markidis leads the development of the Neko framework for high-fidelity computational fluid dynamics and the iPIC3D particle-in-cell code for plasma physics. He teaches courses such as Quantum Computing for Computer Scientists, High-Performance Computing, and Applied GPU Programming. His work addresses exascale computing challenges, including optimizing algorithms for GPUs, quantum systems, and distributed architectures. Key research interests include: Parallel Programming Models and HPC Frameworks Quantum Computing Applications in Scientific Simulations Physics-Informed Machine Learning Exascale System Optimization Turbulence Modeling and Plasma Dynamics His publications span over 100 articles in journals like Journal of Computational Physics and Scientific Reports , focusing on topics such as scalable CFD, quantum neural networks, and plasma simulation techniques. He has advised numerous students in these areas. Markidis collaborates with institutions like Los Alamos National Laboratory and RISE Research Institutes of Sweden through the Digital Futures initiative, aiming to solve societal challenges via digital technologies.
Dr. Kidambi Sreenivas is an Associate Professor in Mechanical Engineering at the University of Tennessee at Chattanooga (UTC), affiliated with the College of Engineering and Computer Science. He holds a PhD in Mechanical Engineering and specializes in computational fluid dynamics (CFD), with a focus on unstructured multi-physics flow solvers and applications in aerospace, environmental systems, and biomedical engineering. His research bridges academia and industry, collaborating with NASA, the U.S. Navy, Department of Energy, and private companies. Dr. Sreenivas' research interests include rotating machinery simulations, pre-conditioners for non-ideal fluids, and real-world applications such as submarine hydrodynamics, wind farm optimization, aerodynamic efficiency of vehicles, and contaminant dispersal modeling. He has pioneered methods for simulating complex geometries and physics, including high-fidelity simulations of hypersonic vehicles, weapons bay cavities, and shock-wave interactions. Recent work emphasizes advanced CFD methodologies for high-speed flows, thermal effects on turbulence, and aerothermal characteristics of hypersonic test articles. His collaborations have led to practical solutions for drag reduction on Class 8 trucks and improved accuracy in wind turbine modeling. Dr. Sreenivas also contributes to educational initiatives, such as developing PIV systems for undergraduate fluid mechanics labs. His advising and grants reflect partnerships with federal agencies and private sectors, focusing on projects like microplastic sampling devices for stormwater management. These projects highlight his interdisciplinary approach to solving real-world engineering challenges through cutting-edge computational methods.
Kyle Hanquist is an Assistant Professor in the Department of Aerospace and Mechanical Engineering at the University of Arizona, where he is also a member of the Graduate Faculty. He directs the Computational Hypersonics and Nonequilibrium Laboratory (CHANL), focusing on advanced simulation techniques for high-speed flows. His academic journey includes a PhD and MSE in Aerospace Engineering from the University of Michigan and a BSE in Mechanical Engineering from the University of Nebraska. PhD, Aerospace Engineering, University of Michigan, Ann Arbor MSE, Aerospace Engineering, University of Michigan, Ann Arbor BSE, Mechanical Engineering, University of Nebraska, Lincoln Dr. Hanquist's research centers on hypersonics, aerothermodynamics, and nonequilibrium flows , with strong emphasis on computational fluid dynamics , low-temperature plasmas , and thermal management systems . His work involves modeling complex physical phenomena such as electron transpiration cooling, plasma-assisted flow control, and high-temperature gas effects in reentry environments. He also investigates molecular gas dynamics and finite-rate chemistry in extreme conditions. His recent publications reveal a strong trend in computational modeling of hypersonic boundary layers , plasma sheaths , and shock-tube validation of thermochemical models . The interdisciplinary nature of his work spans aerospace engineering, plasma physics, and materials response under extreme thermal loads. Much of his research integrates multi-physics simulations to address fluid-thermal-structural interactions critical for next-generation hypersonic vehicles. Dr. Hanquist has received several scientific honors, including: 2020 AIAA Plasmadynamics and Lasers Best Paper Award Editor's Choice, AIP Publishing - Physics of Fluids (Summer I 2020) Featured Article, AIP Publishing - Physics of Fluids (Summer I 2021) Frontiers in Physics – Plasma Physics (Spring 2020) As an advisor and lab director, he mentors graduate students in computational hypersonics and collaborates with institutions like NASA and the University of Michigan. His research is supported by grants from aerospace and defense agencies, though specific funding sources are not listed. He teaches courses in fluid mechanics, numerical methods, and nonequilibrium flows, contributing to both undergraduate and graduate education. He leads the Computational Hypersonics and Nonequilibrium Laboratory (CHANL) , which develops and applies high-fidelity simulation tools for hypersonic applications. The lab focuses on kinetic modeling, plasma interactions, and optimization of thermal protection systems, often using massively parallel CFD codes and multi-fidelity surrogate models.
Roberto Zanino is a Full Professor of Nuclear Engineering at the Department of Energy (DENERG) of the Polytechnic of Turin, Italy. He serves as Advisor to the Rector for relations with European and international university networks and for the UniTe project, Undergraduate Research Opportunities Coordinator, and Project management functions of PoliToArgentina. He is also Scientific Advisor for the Partnership Agreement with NEWCLEO. Dr. Zanino earned his Laurea cum laude in Nuclear Engineering from Politecnico di Torino in 1984 and his Ph.D. in Energetics in 1989. His academic progression includes Assistant Professor (1990-91), Associate Professor (1992-2000), and Professor (2001-present). He previously served as Director of Alta Scuola Politecnica (2007-2010) and Head of the Graduate Program in Energetics (2011-present). His research spans computational fluid dynamics, concentrated solar power, controlled thermonuclear fusion, Generation IV nuclear fission reactors, and plasma physics. His work focuses on thermal-hydraulic analysis of liquid metal systems, superconducting magnet design for fusion applications, and concentrated solar power optimization. His recent publications demonstrate strong expertise in coupling computational tools for nuclear applications, particularly in CFD-system code integration for liquid metal systems and fusion magnet analysis. Dr. Zanino has received recognition as an IEEE Senior Member (2012) and has supervised numerous doctoral students working on topics including thermal-hydraulic analysis of heavy liquid metal systems, superconducting magnet simulation for fusion applications, and concentrated solar power modeling. He has extensive international experience, having worked at Max-Planck-Institut für Plasmaphysik, Massachusetts Institute of Technology, and University of Illinois at Chicago. He is actively involved in major fusion projects including ITER, DTT (Divertor Tokamak Test facility), and EUROfusion. His teaching portfolio includes Computational Heat and Mass Transfer, Nuclear Fusion Reactor Engineering, Solar Thermal Technologies, and Computational Thermal Fluid Dynamics at both master's and doctoral levels.
Dr. Zhihua Xie is a Reader in the School of Engineering at Cardiff University. He holds a PhD in Computational Fluid Dynamics from the University of Leeds, funded by the Marie Curie EST Fellowship. His career includes research roles at Cardiff University and Imperial College London. His research focuses on computational fluid dynamics, multiphase flows, and environmental fluid mechanics, supported by grants from EPSRC, Royal Society, and others. He has been awarded the Alexander von Humboldt Research Fellowship and multiple Baker Medals. Education: BEng in Environmental Engineering (Dalian Maritime University, 2003), Postgraduate study in Hydrodynamics (Dalian Maritime University, 2006), PhD in CFD (University of Leeds, 2010). Research interests span development and application of CFD codes for multiphase flows, turbulence modelling, and numerical methods. He is actively involved in editorial boards and professional societies like IAHR and ISOPE. Key contributions include adaptive moment-of-fluid methods, Cartesian cut-cell techniques, and large-eddy simulations. Awards include the Alexander von Humboldt Fellowship (2023), Baker Medal (2021, 2022), and EPSRC funding for wave energy converter modeling (EP/V040235/1). Grants and projects include ARCHER2 eCSE, Newton Advanced Fellowship, and collaborations on coastal engineering and offshore energy systems. His work addresses challenges in wave-structure interaction, fluid-structure dynamics, and environmental hydraulics.
Rainald Loehner is a Distinguished Professor of Fluid Dynamics at George Mason University's Center for Computational Fluid Dynamics. Since 2003, he has led the Center for Computational Fluid Dynamics at George Mason University. He is currently a Hans Fischer Senior Fellow at the Technical University of Munich's Institute for Advanced Study (TUM-IAS) for 2023, hosted by Professors Kai-Uwe Bletzinger and Roland Wüchner in the 'Adjoint-Based System Identification of Large-Scale Structures' Focus Group. Loehner received his Diplom Ingenieur (Maschinenbau) degree from the Technical University of Braunschweig, and his PhD and a DSc in civil engineering from the University College of Swansea, Wales. After teaching at Swansea for a year, he worked at the Naval Research Laboratory in Washington, DC, followed by a research professorship at George Washington University. He joined George Mason University as an associate professor and was promoted to full professor in 1995 and distinguished professor in 2004. With over 35 years of experience, Professor Loehner's research spans the complete pipeline of numerical solvers and simulation tools. His expertise includes pre-processing, grid generation, numerical methods, field solvers, parallel computing, adaptive mesh refinement, fluid-structure interaction, shape optimization, system identification, and computational crowd dynamics. His current work focuses on developing advanced field solvers for compressible and incompressible flows, acoustics, electromagnetic wave propagation, heat and mass transfer, structural mechanics, and fluid-structure interaction. Key application areas include blast mitigation, ship hydrodynamics, blood flow, contaminant transport, and pedestrian safety. Loehner's recent research output (2020-2024) shows a strong trend toward digital twin technology and adjoint-based methods for structural analysis and optimization. His publications focus on high-fidelity digital twins for detecting structural weaknesses, risk assessment in engineering systems, and optimization of sensor placement. His work bridges computational mechanics with machine learning approaches, particularly in system identification and inverse problems, demonstrating how computational methods can solve complex real-world engineering challenges. 2020: Ranked #15119 in the Stanford List of Most Influential Scientists of the World; #8 in Aerospace and Aeronautics 2010: Distinguished International Career Award, Argentine Association of Computational Mechanics 2008: Fellow, International Association for Computational Mechanics 2006: Associate Fellow, AIAA 2005: Honorary Professor, University of Wales Swansea 2005: Advisory Professor, Shanghai Jiao Tong University 2004: Distinguished Professor of Fluid Dynamics, George Mason University 1999: Computational Mechanics Achievements Award, Japan Society of Mechanical Engineering 1993: Doctor of Science in Civil Engineering, University College of Swansea 1979-1983: Studienstiftung des Deutschen Volkes (Top 1% of German Students) Professor Loehner has mentored numerous students through his work at George Mason University and has supervised research in computational fluid dynamics, structural mechanics, and related fields. His research has been supported by various grants from government agencies and industry partners, enabling the development of advanced simulation tools applied in aerodynamics, hydrodynamics, shock-structure interaction, and medical applications. His codes and methods have been widely adopted in industry and academia for applications ranging from aircraft and ship design to medical simulations and urban pathogen transmission modeling. Loehner leads the Center for Computational Fluid Dynamics at George Mason University, which focuses on developing cutting-edge computational methods for fluid dynamics and related multiphysics problems. The center works on strategic application areas including blast mitigation, ship hydrodynamics, blood flow simulation, and pedestrian movement modeling. As a TUM-IAS Fellow, he collaborates with the Chair of Computational Modeling and Simulation at TUM on adjoint-based system identification of large-scale structures, bringing together expertise in computational mechanics and digital twin technology to address complex engineering challenges.
Dr. Markus Piro is an Associate Professor in the Department of Engineering Physics at McMaster University, specializing in Nuclear Engineering and Energy Systems. He teaches ENG PHYS 3D04, focusing on fission/fusion energy systems, reactor design, and radiation interactions. His research emphasizes thermodynamic modeling of nuclear fuels, computational fluid dynamics (CFD), and severe accident analysis in reactors like CANDU and molten salt systems. Key projects include phase equilibrium studies of advanced fuels, corrosion mechanisms, and coupling CFD with thermodynamic simulations for reactor safety. He leads the Nuclear Fuels And Materials Group, developing tools like Thermochimica and collaborating on fuel design, cladding interactions, and accident mitigation strategies. Recent work includes investigations into Nd-C/Ce-C TRISO coatings, molten salt reactor chemistry, and FeCrAl cladding behavior under accident conditions. Dr. Piro’s computational expertise spans reactor hydraulics, thermal-hydraulic modeling, and material compatibility studies. He actively contributes to international initiatives like the TAF-ID database and engages in experimental validation of corium behavior. Current activities include accepting graduate students and advancing multiphysics simulation frameworks for next-gen reactors.
Dr. Youngchul Ra is an Associate Professor in the Department of Mechanical and Aerospace Engineering at Michigan Technological University. He holds a PhD from MIT (1999) and degrees from Seoul National University. His expertise includes computational fluid dynamics (CFD), combustion modeling, chemical kinetics, and alternative fuel research. His work focuses on advanced combustion strategies like Gasoline Compression Ignition (GCI), engine CFD code development, and high-performance computing. Education: PhD in Mechanical Engineering, Massachusetts Institute of Technology (1999) Masters and Bachelors in Mechanical Engineering, Seoul National University Research Interests: Developing multi-component fuel models for real-world applications Optimizing six-stroke GCI engines with advanced valve technologies Reducing emissions via combustion control and injection strategies Parallel computing techniques for large-scale engine simulations Recent work emphasizes oxygenated fuels in GCI engines and parametric studies of combustion efficiency. His CFD models are validated against experimental data for accuracy. His research has led to advancements in low-temperature combustion and emission reduction without explicit awards listed. He collaborates on engine design optimization and fuel formulation projects.
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.
