Find Academic Supervisors

• Machine Learning
• Data-Driven Modeling
• Numerical Solutions of Partial Differential Equations
• +5 more

Kailiang Wu is an Associate Professor at the Department of Mathematics, Southern University of Science and Technology (SUSTech), and holds concurrent roles at the Shenzhen International Center for Mathematics and National Center for Applied Mathematics Shenzhen. His research bridges Machine Learning and Computational Fluid Dynamics , focusing on High-Order Numerical Methods for Hyperbolic Conservation Laws and Relativistic Astrophysics . Education: Ph.D. in Mathematics (Peking University, 2016), B.Sc. in Mathematics and Statistics (Huazhong University of Science and Technology, 2011) His work develops Structure-Preserving Schemes for multidimensional PDEs, including Oscillation-Eliminating Discontinuous Galerkin (OEDG) and Geometric Quasilinearization (GQL) frameworks. These methods ensure positivity , divergence-free , and bound-preservation in simulations of relativistic flows and MHD systems. Recent publications emphasize Deep Learning applications in operator learning (e.g., DUE framework) and Data-Driven Modeling of unknown PDEs. His group has produced 20+ peer-reviewed articles in top journals (Math. Comp., SIAM J. Numer. Anal., JCP) since 2014. Honors: SUSTech President's Research Award (2025) World's Top 2% Scientist (2024) NSFC Major Program (2023, 0.7M CNY) Shenzhen Distinguished Young Scholar (2023, 4M CNY) National Excellent Young Scholar Program (2020, 2M CNY) Zhong Jiaqing Mathematics Award (2019) He advises 10+ graduate students and postdocs, with alumni securing academic positions at Sun Yat-sen University and HKUST. His lab collaborates on relativistic hydrodynamics , traffic models , and uncertainty quantification , supported by competitive funding.

Zohreh DavoudiView profile

Associate Professor

• Quantum Chromodynamics
• Quantum Computing
• Lattice Gauge Theory
• +5 more

Zohreh Davoudi is an Associate Professor in the Department of Physics at the University of Maryland, College Park. She holds additional roles as a Fellow of the Joint Center for Quantum Information and Computer Science (QuICS) and Associate Director for Education at the NSF Institute for Robust Quantum Simulation. Her research focuses on simulating strongly interacting systems using lattice quantum chromodynamics (LQCD), quantum simulation, and quantum computing. She earned her B.Sc. and M.Sc. from Sharif University of Technology in Iran, followed by a Ph.D. in Theoretical Physics from the University of Washington (2014), and served as a postdoctoral researcher at MIT's Center for Theoretical Physics before joining UMD in 2017. Her research interests include developing computational frameworks to study nuclear and particle physics phenomena, such as neutrino interactions, dark matter scattering, and neutron star dynamics. She has pioneered efforts to leverage quantum computing to address the 'sign problem' in fermionic systems and simulate real-time dynamics of early universe matter. Notable awards include the 2025 Presidential Early Career Award, 2024 Simons Emmy Noether Fellowship, and 2019 Alfred P. Sloan Fellowship. Her educational contributions include leading training programs in quantum information science and fostering collaborations across institutes like RIKEN (2017–2021) and the NSF Quantum Simulation Institute. She supervises a dynamic research group focused on lattice gauge theory, quantum algorithms, and interdisciplinary applications such as neutrinoless double-beta decay calculations.

• Multiphase Flow
• Turbulence
• Experimental Fluid Dynamics
• +3 more

Dr. Yinghe Qi is a Professor in the Department of Experimental Fluid Dynamics at ETH Zürich, Switzerland. His research focuses on multiphase flows, turbulence, and free-surface dynamics, with applications in aerospace, marine engineering, and computational fluid dynamics. He has contributed extensively to understanding bubble dynamics, flow instabilities, and turbulence modulation through experimental and phenomenological studies. Research Interests: Dr. Qi’s work addresses complex phenomena in multiphase flow instabilities free-surface turbulence deformable bubble dynamics supersonic jet interactions vortex-induced fragmentation machine learning in fluid dynamics Recent Publications: His recent studies (2023–2025) explore multiscale bubble deformation, free-surface turbulence structure, and supersonic jet-plume interactions. Key themes include turbulent fragmentation, vortex-bubble coupling, and novel computational methodologies. Laboratory Affiliations: He collaborates with the Coletti Group, Jenny Group, and Supponen Group at ETH Zürich, advancing experimental and computational techniques in fluid dynamics.

• Computational Mechanics
• Machine Learning
• Finite Volume Method
• +7 more

Philip Cardiff is a Professor in Computational Mechanics at the School of Mechanical and Materials Engineering, University College Dublin. He holds a BE (2008) and PhD (2012) in Mechanical Engineering from UCD. His research focuses on computational mechanics, machine learning, and their integration, with expertise in finite volume methods, fluid-solid interaction, and biomechanics. He leads the Bekaert University Technology Centre and contributes to editorial roles in the Journal of Open Source Software and OpenFOAM Journal . Cardiff has secured grants from ERC, I-Form, and the UCD Energy Institute, addressing challenges in offshore energy, advanced manufacturing, and cardiac xenotransplantation. Education: BE in Mechanical Engineering, University College Dublin (2008) PhD in Development of the Finite Volume Method for Hip Joint Analysis, University College Dublin (2012) Professional Diploma in University Teaching & Learning, University College Dublin Research Interests: Computational mechanics, finite volume methods, and machine learning integration Fluid-solid interaction, biomechanics, and materials science Applications in additive manufacturing, energy systems, and biomedical engineering Grants & Awards: ERC Consolidator Grant (2020–2025) Funded Investigator in I-Form and UCD Energy Institute Principal Investigator in UCD Centre for Biomedical Engineering Teaching & Leadership: Programme Director for MEngSc in Materials Science and Engineering (2018–2023) Coordinates modules in computational mechanics and advanced materials processing Advocates constructivist teaching approaches with active learning strategies Labs & Collaborations: UCD Centre for Mechanics Bekaert University Technology Centre MaREI and I-Form Research Centres

• Computational Fluid Dynamics
• Discontinuous Galerkin Methods
• Finite Volume Methods
• +6 more

Michael Dumbser is a Full Professor at the University of Trento's Department of Civil, Environmental and Mechanical Engineering. His research focuses on computational fluid dynamics, numerical methods for hyperbolic conservation laws, and high-performance computing. He specializes in developing structure-preserving numerical schemes such as discontinuous Galerkin and finite volume methods for continuum mechanics, relativistic fluid dynamics, and multiphase flows. Teaching responsibilities include courses like Calcolo numerico e programmazione , High-Performance Computing for Multi-Functional Metamaterials , and Metodi numerici per l'ambiente . His work emphasizes thermodynamically compatible formulations and adaptive numerical methods for complex physical systems. Recent research trends involve hyperbolic reformulations of classical models (e.g., Navier-Stokes-Korteweg, Einstein equations), staggered semi-implicit schemes for incompressible flows, and GPU-accelerated algorithms. His publications span topics from geophysical fluid dynamics to relativistic astrophysics, with a focus on maintaining physical conservation principles in numerical implementations. No scientific awards are explicitly listed in the provided information. His advising record is not detailed here, though his courses suggest involvement in student mentorship. Research collaborations include projects on metamaterials and computational geophysics. Current initiatives include developing unified models for earthquake rupture dynamics, non-Newtonian fluid simulations, and adaptive mesh refinement techniques. His lab work involves high-performance computing frameworks like ExaHyPE for large-scale wave propagation studies.

• Experimental Mechanics
• Mechanical Engineering
• Optical Interferometry
• +3 more

Luigi Bruno is an Associate Professor of Machine Design at the Department of Mechanical, Energy and Management Engineering (DIMEG), University of Calabria. He has held this position since 2014, following 12 years as an Assistant Professor at the same institution and Visiting Professorships at IIT Gandhinagar (2012), University of Alabama at Birmingham (2013-2017), and Free University of Bozen-Bolzano (2021). 1999 : Master's in Mechanical Engineering, University of Calabria (110/110 cum laude) 2003 : PhD in Mechanical Engineering, University of Pisa His research interests span: Experimental Mechanics : Pioneering speckle interferometry for micro-displacement measurement and residual stress analysis. Materials Science : Elastic characterization of anisotropic materials, biomedical applications of soft substrates, and 3D-printed composites. Biomedical Engineering : Mechanical behavior of biological tissues, ocular biomechanics, and dental implant material testing. Recent research trends focus on: Integrating artificial muscles into rehabilitation devices Advancing full-field optical measurement via microCT/DVC Optimizing 3D printed polymer adhesion for industrial components Exploring neuronal biomechanics on soft surfaces Scientific contributions include: CS2007A00010 patent for dual-focus speckle interferometers Deputy Editor of Optics and Lasers in Engineering (2019-present) Guest Editor for special issues on optical methods in experimental mechanics and nanobiotechnology Academic leadership extends to coordinating Mechanical Engineering committees (2021-present), serving on editorial boards, and organizing international conferences like AIAS National Conference (2018). He has secured multiple MIUR research grants and industry collaborations with Alfagomma, 3DNA, and Ferrovie della Calabria. His laboratory, Mechanics of Materials and Structures , supports both research and teaching activities with advanced optical measurement systems and computational tools for mechanical design.

Magdalena Schreter-FleischhackerView profile

Researcher

• Computational Mechanics
• Additive Manufacturing
• Multi-phase Flow Modeling
• +3 more

Dr. Magdalena Schreter-Fleischhacker works at the Technical University of Munich within the Professorship of Simulation for Additive Manufacturing . Her research focuses on physics-based computational modeling of coupled liquid-powder-gas dynamics in metal additive manufacturing, including melt pool dynamics and powder-gas interactions . She specializes in multi-phase flow modeling using cut-element and diffuse interface methods with continuous/discontinuous Galerkin schemes . She also develops constitutive models for quasi-brittle materials like 3D printed concrete and rock, incorporating anisotropy , gradient-enhanced damage mechanics , and micropolar continua . Her computational work leverages matrix-free algorithms and parallel computing , with significant contributions to the deal.II finite element library . Research Interests Physics-based computational modeling of coupled liquid-powder-gas dynamics in additive manufacturing Multi-phase flow simulation using sharp/diffuse interface methods Advanced constitutive modeling for quasi-brittle materials (rock, soils, 3D printed concrete) High-performance computing and matrix-free algorithms Notable Contributions Development of consistent diffuse-interface models for melt-vapor dynamics Improvements to continuum surface flux models in additive manufacturing Formulation of gradient-enhanced damage-plasticity models for geological materials Principal contributor to the deal.II library (version 9.6) Supervised Student Projects Johannes Resch (2024): DG-based thermo-hydrodynamic melt pool simulations Julian Brotz (2024): DEM-FEM coupling for fluid-powder interaction Andreas Ritthaler (2024): Matrix-free cutDG formulation for complex flows Tinh Vo (2023): Laser modeling for melt pool simulations Scientific Awards ERC Starting Grant recipient

• Thermal Engineering
• Renewable Energy
• Nanotechnology
• +3 more

Omid Mahian is a professor at Ningbo University, Ningbo, China, with significant contributions to thermal engineering, renewable energy, and nanotechnology. His research focuses on optimizing heat transfer mechanisms in systems like supercritical CO 2 cycles, printed circuit heat exchangers, and photovoltaic thermal modules. He has over 14,597 documents cited, with an h-index of 81 and 287 publications on Scopus. Key research areas: Thermal Load, Surface Roughness, Microchannel Flow, Exergy Destruction, Renewable Energy, Forced Convection. Recent work explores advanced cooling techniques (e.g., wicked heat pipes, grooved copper foam) and nanofluid applications for atmospheric water harvesting and CO 2 absorption. His studies address energy efficiency in off-grid systems, including predictive dispatch strategies for hybrid renewable energy, supersonic separation for carbon capture, and thermal management in electric vehicle motors. Omid Mahian has authored 180 articles on ScienceDirect, with a focus on improving energy systems through innovative designs and materials. His collaborations span numerous disciplines, emphasizing sustainability and technological feasibility.

• Turbulence Modeling
• Computational Fluid Dynamics (CFD)
• Large Eddy Simulation (LES)
• +7 more

Lars Davidson is a Professor in the Department of Fluid Dynamics at Chalmers University of Technology. His research focuses on numerical simulations of fluid flow and heat transfer, with an emphasis on turbulence modeling for Large Eddy Simulation (LES) and hybrid LES/RANS methods. He has developed computational codes CALC-BFC and CALC-LES based on finite-volume techniques, and recently integrated machine learning to enhance wall functions and turbulence models. Key projects include Hybrid LES/RANS for wall-bounded flows Machine learning applications in fluid dynamics Aeroacoustic noise reduction in automotive and aerospace systems Wind turbine load analysis in forested regions . His publications span 302 articles in journals and conferences, with recent work on Neural networks for turbulence closure Plasma actuators for drag reduction Lattice Boltzmann wall-modeled LES . Collaborations include teams at Volvo, Siemens, and international research groups.

Fatemeh AzhariView profile

Lecturer

• Structural Engineering
• Computational Materials Engineering
• Finite Element Analysis
• +7 more

Dr. Fatemeh Azhari is a Lecturer in Structural Engineering at the Faculty of Engineering, Monash University, specializing in multi-scale computational tools for advanced materials and structures. She holds a Ph.D. from Monash University (2018), an M.Sc. and B.Sc. from Isfahan University of Technology (2012, 2010). Her research focuses on composite materials, additive manufacturing, and structural mechanics, with applications in sustainable construction and defense projects. Education: Ph.D. Structural Engineering, Monash University (2018) M.Sc. Structural Engineering, Isfahan University of Technology (2012) B.Sc. Civil Engineering, Isfahan University of Technology (2010) Research Interests: Multi-scale modeling, finite element analysis, composite materials (CFRP/GFRP), titanium alloys, fire dynamics, and structural stability. She leads projects funded by DST Group and collaborates with institutions like UNSW and the University of Melbourne. Research Trends: Her work emphasizes integrating computational models with experimental data to predict material behavior under extreme conditions, with recent studies focusing on pseudo-ductile composites, fire dynamics, and additive manufacturing. Awards: 2023 Advancing Women’s Success Grant 2023 ECA Seed Program Best Research Paper Award (2018, 2021) Supervision & Grants: Supervises multiple PhD candidates and co-leads projects on titanium alloys and dental implant mechanics. Active in teaching structural mechanics and materials courses at Monash. Teams/Labs: Collaborates on ICME projects and leads multi-institutional teams focusing on advanced materials and structural systems.

• Computational Mathematics
• Numerical Analysis
• Finite Element Methods
• +3 more

Prof. Joachim Schöberl is a faculty member at TU Wien's Faculty of Mathematics and Geoinformation, leading the Scientific Computing and Modelling research group. His academic career includes roles as a university professor (Univ.Prof.) with engineering and technical doctorates (Dipl.-Ing., Dr.techn.). Research focuses on advanced numerical methods, including finite element methods, computational fluid dynamics, and partial differential equations. He has pioneered high-order schemes for fluid-structure interaction, shell mechanics, and electromagnetic simulations. Notable contributions include the NGSolve finite element library and innovative approaches to curvature approximation in discrete geometry. Recent work emphasizes nonlinear elasticity modeling, fractional diffusion problems, and shape optimization for biomembranes. His team collaborates on projects like metascreen upscaling, micromorphic continuum models, and eddy current simulations in laminated materials. Prof. Schöberl advises PhD students researching mixed finite element methods, fractional operators, and computational mechanics. His lab develops open-source tools for high-performance scientific computing.

• Additive Manufacturing
• Biomass Compaction
• Material Science
• +7 more

Prof. Ing. Juraj Beniak, PhD is a leading academic at the Institute of Production Engineering and Production Quality (Faculty of Mechanical Engineering, Slovak University of Technology in Bratislava, STU). He serves as a Professor CSc., PhD and holds external collaborator roles at the Institute of Computer Engineering and Applied Informatics (FIIT) and the Institute of Manufacturing Technologies (MTF). His research bridges mechanical engineering and sustainable production. Office: U.V.I.P. SjF, Office 537 Contact: +421 2 57296 537 | +421 905 593 953 (mobile) Beniak's research focuses on additive manufacturing , biomass compaction , and smart production technologies . He investigates 3D printing parameter optimization, surface modification techniques, and composite material development from renewable sources. His work addresses both industrial applications and environmental sustainability through advanced manufacturing. Key projects include: OP R&I: Automation in freight railway vehicle production (2019–2023) APVV-18-0527: Additive manufacturing technology development (2019–2022) Recovery Plan: AI-driven waste management systems (2024–2026) As a KEGA grant guarantor and APVV co-investigator , he leads initiatives in CAx education, virtual laboratories, and biofuel production optimization. His contributions span from experimental equipment design to mathematical modeling of compaction processes.

Xinfeng GaoView profile

Professor

• Computational Fluid Dynamics (CFD)
• High-Performance Computing (HPC)
• Shock Waves
• +7 more

Xinfeng Gao is a Professor of Mechanical & Aerospace Engineering at the University of Virginia, leading the CFD & Propulsion Laboratory. She specializes in high-performance computing (HPC) algorithms for fluid dynamics, combustion, and plasma systems. Her work integrates numerical methods, parallel computing, and data analytics to address complex engineering challenges. Prior to UVA, she held a professorship at Colorado State University from 2011 to 2023, establishing the CFD and Propulsion Lab there. She earned her PhD in Aerospace Engineering from the University of Toronto in 2008, followed by postdoctoral research at Lawrence Berkeley National Laboratory (LBNL). Her research focuses on three core areas: high-order CFD methods for high-speed flows, parallel adaptive algorithms for spatial and temporal domains, and HPC combined with data analytics for aerospace design optimizations. Applications include reduced-order models for turbulence, propulsion device innovation, and quantum computing for fluid simulations. She collaborates with national labs (LLNL, LBNL), aerospace industries (Boeing), and software companies to translate research into practical solutions. Her recent grants include the NSF Mid-Career Advancement Award (2022–2025) for CFD+DA integration in commercial tools and UVA’s RIG Award (2025–2026) for gas-surface material studies under extreme conditions. She teaches MAE 6720 (Computational Fluid Dynamics) and MAE 3420 (Computational Methods). Key awards include the 2023 University of Virginia Research Achievement Award and the 2022 NSF MCA Award. Her work emphasizes cross-disciplinary innovation, blending computational science with experimental validation through initiatives like the Gas-Surface-Materials RIG project, involving experts from MAE, MSE, Chemistry, and Physics.

Theodore KimView profile

Professor

• Computer Graphics
• Computer Animation
• Solid Mechanics
• +3 more

Theodore Kim is a Professor of Computer Science at Yale University, where he co-leads the Computer Graphics Group with Julie Dorsey and Holly Rushmeier. His research focuses on physics-based simulation, including fluid dynamics, solid mechanics, and fractal growth structures. He holds a PhD from the University of North Carolina at Chapel Hill and has held academic positions at UCSB and the University of Saskatchewan. His work has been applied in over two dozen films, earning him SciTech Oscars in 2012 and 2022. He previously served as a Senior Research Scientist at Pixar, contributing to projects like *Cars 3*, *Coco*, and *Incredibles 2*. Education: Ph.D., Computer Science, University of North Carolina at Chapel Hill (2006) M.S., Computer Science, University of North Carolina at Chapel Hill (2006) B.S., Computer Science, Cornell University (2001) Research Interests: Kim’s work bridges academia and industry, emphasizing practical applications of physics-based simulation. Notable areas include hair and skin simulation for animation, fluid dynamics, and the historical context of computer graphics innovations. His research also addresses racial biases in graphics, such as in hair and skin modeling. Articles Trends: Recent work emphasizes diverse representation (e.g., Black hair simulation), biomechanical accuracy (feather modeling), and historical analysis of technical contributions (e.g., Búi Tướng Phong’s legacy). Earlier publications focus on fluid subspace methods, wavelet turbulence, and efficient simulation techniques. Awards: Academy Award for Scientific and Technical Achievement (2012, 2022) NSF CAREER Award (2013–2018) UCSB Harold J. Plous Award (2015) Best Paper Awards at SCA (2011, 2016, 2018) Grants & Labs: Leads Yale’s Critical Computing Initiative and directs undergraduate studies in CS. His lab collaborates with industry (e.g., Pixar) and emphasizes open-source software. Current projects include fractal design tools and anti-racist graphics research.

Gregory J. WagnerView profile

Associate Professor

• Additive Manufacturing
• Multi-scale Simulations
• Multi-physics Modeling
• +4 more

Gregory J. Wagner is an Associate Professor of Mechanical Engineering and Director of Graduate Studies at Northwestern University's McCormick School of Engineering. His research focuses on developing computational methods for multi-scale and multi-physics problems in additive manufacturing, fluid dynamics, and heat transfer. He leads the Wagner Research Group, which specializes in high-performance computing tools for complex engineering simulations. Education includes a Ph.D., M.S., and B.S. in Mechanical Engineering from Northwestern University and Boston University. His work integrates machine learning with traditional computational methods to model material behavior, microstructure evolution, and process-structure-property relationships in advanced manufacturing. Notable contributions include the GO-MELT framework for thermal simulations and the C-HiDeNN neural network approach for large-scale systems. Research interests span additive manufacturing process modeling, multiphysics coupling, and data-driven approaches for material design. Awards include the Bette and Neison Harris Chair in Teaching Excellence. Publications emphasize thermal modeling, phase change phenomena, and computational fluid dynamics innovations. His lab's work bridges mesoscopic and multiscale modeling, with applications in energy systems, biomedical devices, and environmental engineering. Collaborations focus on experimental validation and industrial-scale simulation challenges.