İlker Temizer is a Professor and Chair of Mechanical Engineering at Bilkent University, where he leads the Computational Multiscale Mechanics Laboratory (CMML). His research focuses on computational mechanics, multiscale-multiphysics modeling of heterogeneous materials, and interface behaviors. Research interests include computational homogenization techniques for materials and interfaces, thermomechanical contact problems, and isogeometric analysis. His work bridges theoretical mechanics with applied engineering solutions. Recent publications emphasize multiscale modeling in density functional theory, hydrodynamic lubrication texture optimization, and smart material design, demonstrating consistent innovation in computational mechanics methodologies.
Peter D. Minev is a Professor in the Department of Mathematical and Statistical Sciences at the University of Alberta, Canada. His research specializes in computational fluid dynamics, numerical methods for partial differential equations (PDEs), and large-scale simulations for scientific and engineering problems. He develops advanced algorithms for incompressible Navier-Stokes equations, fluid-structure interaction, multiphase flows, and magnetohydrodynamics (MHD). Education: MSc and PhD from Sofia University, Bulgaria. Research Focus: Minev's work spans: High-order time-stepping schemes and splitting methods for complex PDEs Fictitious domain approaches for fluid-structure interaction Parallel algorithms for supercomputing applications Modeling of multiphysics phenomena (e.g., porous media flows, phase-field equations) He has created benchmark results for 3D lid-driven cavity flows and simulations of particle sedimentation/bubble dynamics. Publications: His recent articles (2014–2021) focus on: Efficient splitting schemes for stress/vorticity formulations High-order adaptive time integration Algorithms for spherical/heterogeneous geometries Applications in fuel cells, geophysics, and biomechanics A consistent theme is minimizing computational complexity while maintaining accuracy.
Nicholas J. Zabaras is a Professor in the College of Engineering at the University of Notre Dame and serves as director of the Warwick Centre for Predictive Modelling at the University of Warwick. He holds a Hans Fischer Senior Fellowship at the Technical University of Munich Institute for Advanced Study (TUM-IAS) since 2014. His academic journey began with a diploma in Mechanical Engineering from the National Technical University of Athens (1982), followed by an M.Sc in Material Science and Engineering from the University of Rochester (1983), and a PhD in Theoretical and Applied Mechanics from Cornell University (1987). His research spans computational mathematics, computational statistics, and scientific computing with focus on predictive modeling of complex multiscale and multiphysics materials systems. Key research themes include Bayesian uncertainty quantification, high-dimensional problem modeling, information-theoretic coarse graining, stochastic model reduction, and optimization under uncertainty. His work has significant applications in materials science, particularly in uncertainty propagation from ab initio to continuum simulations and modeling of random microstructures. His recent publications demonstrate strong activity in Bayesian coarse-graining techniques, deep Gaussian processes, and uncertainty quantification for multiscale materials systems. The research shows consistent focus on developing computationally efficient methods for high-dimensional problems with applications across materials science and engineering disciplines. Major Awards and Recognitions: Royal Society Wolfson Research Merit Award (2014) Research Fellow, Isaac Newton School of Mathematical Sciences, University of Cambridge (2011) Michael Tien'72 College of Engineering Teaching Award, Cornell University (2009) Fellow, American Society of Mechanical Engineers (2006) Presidential Young Investigator Award (1991) Zabaras leads the Scientific Computing and Artificial Intelligence (SCAI) Laboratory and the Computational Science and Engineering (CSE) Laboratory at Notre Dame, where his team develops innovative mathematical and statistical approaches addressing unique challenges in predictive modeling. His research integrates computational mathematics, machine learning, and multiscale/multiphysics modeling to address problems in materials physics, geological sciences, and climate modeling.
Dr. Yanzhi Zhang is a Professor of Mathematics at the Department of Mathematics and Statistics, Missouri University of Science and Technology (Missouri S&T), where she has held academic positions since 2010. She earned a Ph.D. in Mathematics from the National University of Singapore and a B.Sc. in Applied Mathematics (with a Computer Science minor) from Chongqing University. Her research spans computational and applied mathematics, focusing on fractional PDEs, nonlocal models, machine learning applications in materials science and business, Bose-Einstein superfluids, and seismic wave inversion. Dr. Zhang has been awarded the Faculty Excellence Award from Missouri S&T and other accolades. Her work is supported by grants from the National Science Foundation, including projects on fractional viscoacoustic wave equations, Bose-Einstein superfluids, and dispersive wave equations. She advises Ph.D. students in interdisciplinary research, such as Yumeng Wang (machine learning for eye-tracking data) and Yixuan Wu (seismic inversion). Her research integrates mathematical rigor with computational innovation, addressing real-world challenges in geophysics, materials science, and business analytics. Collaborations with industry and academia enhance her contributions to data-driven modeling and open-source strategies for digital transformation.
Dr. Sergio Felicelli is a Professor and Chair in the Department of Mechanical Engineering at the University of Akron's College of Engineering and Polymer Science. He holds a Ph.D. in Mechanical Engineering from the University of Arizona (1991) and B.S./M.S. in Nuclear Engineering from Instituto Balseiro (1985). His expertise spans Solidification Modeling, Transport Phenomena, and Computational Mechanics. Dr. Felicelli has authored pioneering works on computer modeling of freckle segregation during solidification and has directed projects on casting, additive manufacturing, and parallel simulations of microstructures. His research focuses on advancing computational methods for material processing, including dendritic solidification under microgravity conditions, multiphase flow dynamics, and large-scale parallel simulations. He has secured $15M in grants through 23 funded projects and is an ASME Fellow. His work integrates experimental validation with numerical modeling, particularly using lattice Boltzmann and phase field methods. Dr. Felicelli has led international collaborations on casting defects and serves in academic leadership roles. Education: Ph.D., Mechanical Engineering, University of Arizona, 1991 B.S./M.S., Nuclear Engineering, Instituto Balseiro, 1985 Dr. Felicelli's research trends emphasize computational modeling of materials under extreme conditions, with recent articles addressing microgravity solidification effects, multiphase interactions, and high-performance computing for microstructure prediction. His work bridges fundamental science and industrial applications in additive manufacturing and energy storage systems. Awards: ASME Fellow (prestigious honor in mechanical engineering) Grants & Funding: Over $15M secured through 23 grants, focusing on solidification science and advanced manufacturing technologies. He advises on courses such as Heat Transfer and Numerical Methods, and his lab explores cutting-edge techniques in computational materials science. Collaborations include NASA and international institutions, with a focus on solving complex industrial and aerospace material challenges.
Prof. Dr. Johannes Kraus is a faculty member at the University of Duisburg-Essen , affiliated with the Faculty of Mathematics . His research focuses on advanced numerical methods for partial differential equations and their applications across multiple disciplines. University: University of Duisburg-Essen Faculty: Faculty of Mathematics Contact: Thea-Leymann-Str. 9, 45127 Essen, Germany Dr. Kraus specializes in numerical solution of partial differential equations , discretization techniques (including finite element and isogeometric analysis), numerical linear algebra , and subspace correction methods like domain decomposition and multigrid. His work extends to high-performance computing and machine learning applications in medicine, engineering, and sciences. Recent publications highlight collaborations on nonlinear poroelasticity , space-time finite element methods , and preconditioning techniques for complex systems. Articles span domains such as biomechanics , biomolecular electrostatics , and multiscale modeling , demonstrating interdisciplinary impact. Teaching responsibilities include Numerical Mathematics II (Summer 2025) and Numerical Mathematics I , with seminars on multiphysics finite element software . He leads the Numerical Mathematics group and has secured third-party funding from DFG and FWF grants.
Florian De Vuyst is a Full Professor at the Department of Computational Engineering, University of Technology of Compiègne (UTC), where he conducts research at the Biomechanical Bioengineering Laboratory (BMBI UMR 7338). Previously affiliated with the Applied Mathematics Laboratory (LMAC), his work spans Fluid-Structure Interaction (FSI) , Scientific Machine Learning , GPU Computing , and Reduced-Order Modeling (ROM) . His research focuses on multimaterial flows , automotive crash dynamics , and bioengineering applications like microcapsule deformation in Stokes flows. His recent publications highlight machine learning integration with FSI simulations and GPU-accelerated solvers for compressible flows. Collaborations include Renault , Michelin , and ENS Paris-Saclay on projects like tsunami coastal impact modeling (DIGISCOPE EquipEx) and vehicle drag estimation . He has supervised 15+ PhD students, including Azzedine Tiba (non-intrusive ROM) and Vincent Mahy (multimaterial methods). Scientific Awards: Recipient of the Best Applied Paper Award at EGC 2008 Grants & Collaborations: Involved in CNRS Editions' interdisciplinary volume on tsunamis, ERCOFTAC symposia, and industrial partnerships
Dr. Philip Marmet is a Researcher and Lecturer at the Institute of Computational Physics (ICP) within the School of Engineering at Zurich University of Applied Sciences (ZHAW). His work focuses on Multiphysics and Multiscale simulations, characterization and stochastic modeling of microstructures, with particular expertise in solid oxide fuel cell electrode design. His educational background includes a PhD in Physics/Modeling and Simulation from the University of Fribourg (2019-2023), an MSc in Physics/Soft Matter Theory from the same institution (2013-2016), and an MSc in Engineering from Bern University of Applied Sciences (2011-2013). PhD in Physics / Modeling and Simulation, Solid Oxide Fuel Cells, University of Fribourg (2019-2023) MSc in Physics / Soft Matter Theory, University of Fribourg (2013-2016) MSc in Engineering BFH / Industrial Technologies, Bern University of Applied Sciences (2011-2013) BSc in Mechanical Engineering / Mechatronics, Bern University of Applied Sciences (2003-2007) Dr. Marmet's research spans Multiphysics Simulation, Multiscale Modeling, Microstructure Characterization, and Digital Materials Design. His work bridges theoretical modeling with experimental validation to optimize materials for energy applications. He has developed specialized methodologies for virtual microstructure variation and optimization of porous materials, particularly for solid oxide fuel cells and aerosol filters. His publication record shows a clear progression toward increasingly sophisticated multiscale modeling approaches, with recent work focusing on stochastic microstructure modeling using pluri-Gaussian methods. His research demonstrates strong integration of computational techniques (including GeoDict, Comsol Multiphysics, ANSYS, OpenFOAM, and Matlab/Simulink) with experimental validation. Best graduation results of 2013 "Gold", Master of Science in Engineering Dr. Marmet supervises student projects and lectures Analysis 1 and 2 for bachelor courses. His research has received funding from the Swiss Federal Office of Energy (SFOE) and Eurostars program. He has developed practical software tools including the Python app for stochastic microstructure modeling of SOC electrodes and the Characterization-app for standardized microstructure analysis, demonstrating his commitment to translating research into practical engineering solutions. His work is organized around the Digital Materials Design workflow, connecting virtual microstructure generation, automated characterization, and multiphysics simulation to enable data-driven optimization of energy materials without extensive experimental iteration.
Dr. Ushasi Roy is an Assistant Professor in the Department of Mechanical Engineering at the Indian Institute of Technology Kanpur. She specializes in solid mechanics, fracture mechanics, plasticity, finite element analysis, and microstructure-property correlation. Her research focuses on microstructure sensitive deformation and fracture of metals and alloys, high strain rate deformation behavior of metals, ceramics and composites, and multiscale and multiphysics modeling of deformation and fracture. Dr. Roy's research interests span computational mechanics and materials science, with particular emphasis on understanding how microstructural features influence mechanical properties and failure mechanisms. Her work combines advanced computational techniques with fundamental mechanics principles to develop predictive models that can guide material design and engineering applications. She has made significant contributions to understanding fracture mechanics in various materials systems including high entropy alloys, battery materials, and energetic materials. Her publication record shows consistent contributions to high-impact journals in mechanics and materials science, with recent work focusing on lithium anodes, polycrystalline metals, and multiscale computational frameworks. The research demonstrates a progression from fundamental material characterization to sophisticated computational modeling approaches. Dr. Roy has established herself as a promising researcher in computational mechanics and materials science, with her work addressing critical challenges in understanding material behavior under extreme conditions and developing predictive models for engineering applications.
Wiyao Azoti is a Lecturer at the National Institute of Applied Sciences of Toulouse (INSA Toulouse), where he is a member of the Composite Materials and Structures group (MSC). His work focuses on the mechanics of composite materials and structures, with particular expertise in multi-scale modeling and micromechanics. Dr. Azoti's educational background includes: PhD in Materials Science from University of Lorraine, France (2012) MSc in Mechanical Engineering from University of Lorraine, France (2009) Engineering degree in Mechanical Engineering from ENSI, Togo (2008) His research interests center on the mechanics of materials, with emphasis on linear, nonlinear, and computational aspects; rate-independent and rate-dependent plasticity; micromechanics and mean-fields homogenization techniques; multi-scale modeling of composite materials; damage and fracture behaviors of composite materials; and multiphysics coupling of thermomechanical fields. His work bridges fundamental mechanics with practical applications in automotive, aerospace, and biomedical fields. Dr. Azoti has published extensively in the field of composite materials, with over 50 scientific contributions. His recent work shows a strong trend toward the application of multi-scale modeling techniques to graphene-reinforced composites, biocomposites, and advanced materials for automotive and aerospace applications. He has made significant contributions to understanding the electromechanical behavior of polymer composites, thermomechanical properties of natural fiber composites, and the crashworthiness of hierarchical composite structures. Professional memberships include: African Society of Eco-Materials (ECOMAT-AFRICA) American Society of Mechanical Engineers (ASME) European Mechanics Society (EUROMECH) Dr. Azoti teaches several courses at INSA Toulouse, including Design of Mechanical Systems, Materials Science and Heat Treatment, Automation of Mechanical Systems, Machine Elements and Eco-design, Eco-design and Innovation, and Composite Materials' Projects. His teaching reflects his research expertise, emphasizing sustainable materials and advanced composite technologies.
Sebastian Wohner is a researcher at the Chair of Computer Graphics and Visualization (Prof. Westermann) at the Technical University of Munich. His work focuses on advanced visualization techniques, machine learning applications in graphics, and GPU-accelerated algorithms for 3D design and simulation. He actively contributes to projects such as the NVIDIA CUDA Research Center and ERC-funded initiatives like SaferVis and realFlow, emphasizing real-time liquids and safer visualization systems. His research interests span 3D Gaussian splatting, topology optimization, neural fields for statistical dependencies, and spatio-temporal flow visualization. He has pioneered methods for compressing meteorological ensembles and accelerating novel view synthesis in consumer devices. Wohner also explores GPU-based linear algebra optimizations and efficient rendering techniques for ribbons and twisted lines. In teaching, he leads courses on game physics, visual data analytics, deep learning in computer graphics, and topology optimization. Notable contributions include the development of the Particle Engine and Bunny Demo applications, as well as advancements in differentiable rendering and robotic perception systems. His work bridges theoretical foundations with practical implementations in both academia and industry.
Alejandro Aguirre Ruz is a researcher affiliated with the Universitat Politècnica de Catalunya (UPC), specifically within the Departament d'Enginyeria Civil i Ambiental at the Escola Tècnica Superior d'Enginyeria de Camins, Canals i Ports de Barcelona (ETSECCPB) . His work focuses on advanced computational mechanics, particularly in finite element methods for structural analysis and fluid-structure interaction problems. He is part of the research groups ANiComp - Anàlisi Numèrica i Computació Científica and (MC)² - Mecànica de Medis Continus i Computacional . His research emphasizes stabilized finite element formulations for thin structures, including solid-shell elements, Reissner-Mindlin plates, and Timoshenko beams. Key contributions include variational multiscale methods and stress-displacement analyses in both infinitesimal and finite strain regimes. He has published in high-impact journals like Applied Mathematical Modelling and Finite Elements in Analysis and Design , with recent work extending to 2025 on fluid-structure interaction applications. Aguirre Ruz defended his doctoral thesis in 2023 titled Numerical approximation of thin structures using stabilized mixed formulations... , supervised by Professors Ramon Codina and Joan Baiges. He actively collaborates with researchers such as Inocencio Castañar and Zorrilla Martínez, contributing to projects like the Anàlisi Numèrica i Computació Científica initiative funded under the Supòrt a la Recerca program. His technical expertise bridges theoretical developments in numerical methods with practical engineering applications, addressing challenges in structural dynamics and multiphysics systems.
Michele Terzano is a University Assistant at the Institute of Biomechanics , Graz University of Technology, Austria. Previously, he held postdoctoral positions at Graz (2021-2023) and the University of Parma (2020-2021), with academic visitor roles at Imperial College London (2019-2021). Ph.D. in Civil Engineering (2020), University of Parma M.Sc. in Civil Engineering (2016), University of Parma B.Sc. in Civil Engineering (2016), University of Parma His research spans biomechanics of soft tissues and synthetic polymers , focusing on damage/fracture mechanisms , computational modeling , and robotics in neurosurgery . Key applications include cardiovascular interventions, brain tissue simulation, and tissue engineering hydrogels. The 15 most recent publications reflect trends in multiscale biomechanical modeling (e.g., atherosclerotic arteries, abdominal aortic aneurysms), soft tissue characterization (e.g., SHG microscopy for skin mechanics), and biomaterials development (e.g., poro-viscoelastic hydrogels for brain tissue mimicry). These works integrate experimental data with computational frameworks to address clinical challenges. Scientific awards include a 2020 postdoctoral fellowship at the University of Parma and collaboration on EU-funded Horizon 2020 projects. He teaches Biomedical Engineering courses in Graz while maintaining active research in computational fracture mechanics and bioinspired materials.
Narayana Aluru is a Professor and Cockrell Family Regents Chair in Engineering at The University of Texas at Austin, holding positions in the Walker Department of Mechanical Engineering and the Oden Institute for Computational Engineering and Sciences. He joined UT Austin in 2021 after a 23-year tenure at the University of Illinois at Urbana-Champaign, where he served as a Professor, Director of Computational Science and Engineering (2012–2017), and held leadership roles in academic and professional organizations. Education: B.E. (Hons), Birla Institute of Technology & Science, Pilani, India (1989) M.S., Rensselaer Polytechnic Institute (1991) Ph.D., Stanford University (1995) Postdoctoral Associate, MIT (1995–1997) Research Interests: Dr. Aluru specializes in computational nanotechnology, focusing on multiscale methods integrating quantum, atomistic, mesoscale, and continuum scales. His work addresses nanofluidics, bionanotechnology, nanomaterials, and soft matter, with applications in water desalination, nanopower generation, DNA sequencing, and energy storage. His group develops advanced simulation techniques to study phenomena such as ion transport, surface interactions, and nanoscale energy conversion. Publications: His recent work highlights advancements in nanofluidics, nanomaterials, and multiscale modeling. Key themes include dielectric behavior in confined fluids, charge inversion in electric double layers, and energy harvesting via MoS₂ nanopores. These studies bridge fundamental physics and engineering applications. Awards: Charles Russ Richards Memorial Award (ASME, 2024) Cockrell Family Regents Chair (UT Austin, 2023) Fellow of AAAS, APS, ASME, and IACM National Science Foundation CAREER Award (1999) Advising & Grants: A dedicated educator, Dr. Aluru has advised numerous graduate students and postdocs. He has led research supported by grants from NSF, DOE, and industry, emphasizing computational methods and nanotechnology. His teaching excellence was recognized repeatedly at UIUC. Labs/Teams: He leads the Multiscale Nanotechnology Group and collaborates with the Oden Institute, advancing interdisciplinary research in computational engineering and nanotechnology.
Professor Wei Ji holds the William H. Hernstadt '57 Faculty Fellow position in Nuclear Engineering at Rensselaer Polytechnic Institute (RPI), where he also directs the Walthousen Reactor Critical Facility. He leads the Nuclear Computing and Multi-Physics (NuCoMP) Research Group, focusing on advanced computational techniques for nuclear energy, medical physics, and space exploration applications. He earned his B.S. and M.S. from Tsinghua University and his Ph.D. from the University of Michigan. His research addresses challenges in reactor design, radiation therapy, Monte Carlo methods, and rad-hard electronics for space missions, supported by over $14M in funding from agencies like DOE, NASA, and NIH. Education: B.S. in Engineering Physics, Tsinghua University (1999) M.S. in Engineering Physics, Tsinghua University (2002) M.S. in Nuclear Engineering, University of Michigan (2004) Ph.D. in Nuclear Engineering, University of Michigan (2007) Research Interests: Professor Ji’s work bridges computational methods with nuclear applications, including: Neutron transport modeling and reduced-order methods Advanced reactor design and safety analysis Radiation therapy dose computation Space-compatible power device reliability Multiphysics coupling for reactor transient analysis His recent studies emphasize cryogenic propulsion systems, molten salt reactor chemistry, and radiation-hardened electronics for extreme environments. Notable Achievements: Over 160 peer-reviewed publications since 2008 DOE/ANS awards for research excellence 2019 NEAGS Teaching Award and 2018 RPI Education Innovation Award Grants & Advising: Total funding: $14M+ from DOE, NASA, NIH, NRC, and industry Focus areas include NEAMS Program contributions, NRC safety assessments, and NASA ESI-funded space technology Labs & Teams: His NuCoMP Group collaborates with national labs (LANL, ANL) and industry partners to develop next-generation simulation tools and hardware for nuclear systems. Current projects include: Metamodel-driven Monte Carlo workflows Equation-free multiscale fluid modeling Radiation effects on SiC/Si devices
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