Find Academic Supervisors

Eike MuellerView profile

Associate Professor

• Numerical Analysis
• Parallel Computing
• Partial Differential Equations
• +2 more

Dr. Eike Mueller is a Reader (Associate Professor) in the Department of Mathematical Sciences at the University of Bath. His research focuses on developing fast numerical algorithms for solving physical problems across scales, from atmospheric models to subatomic particles. As part of the Numerical Analysis group, he specializes in parallel computing implementations using frameworks like DUNE and Firedrake/PyOP2. His work is characterized by close collaboration with the Met Office on weather and climate forecasting models. Research interests center on numerical techniques for complex systems, emphasizing parallel computing architectures including GPU clusters. Key areas include massively parallel solvers for PDEs, multilevel Monte Carlo methods for atmospheric dispersion, and performance-portable frameworks for physics simulations. Recent work explores machine learning integration with traditional numerical methods for enhanced computational efficiency. Publications demonstrate consistent focus on accelerating scientific computation through novel algorithms. Recent articles emphasize the application of multigrid methods to climate modeling, neural networks in numerical integration, and Bayesian inference techniques. A significant portion of work targets performance optimization on modern hardware architectures including GPU acceleration. Research leadership includes principal investigator roles for major EPSRC projects: 'MGHyPE: An ExaHyPE version with a multigrid solver' and 'IAA – Accelerating climate- and weather-forecasts with faster multigrid solvers'. Collaboration with the Met Office has resulted in operational improvements to weather prediction models through bespoke solver development.

Konstantinos DaniilidisView profile

Professor

• Computer Vision
• Robotics
• 3D Perception
• +7 more

Konstantinos Daniilidis is the Ruth Yalom Stone Professor at the Department of Computer and Information Science, University of Pennsylvania, within the School of Engineering and Applied Science. He holds an adjunct position at Archimedes, Athena Research Center, Greece. His research focuses on computer vision, robotics, and geometric methods in AI, with a strong emphasis on 3D perception, event-based vision, and neural networks for motion analysis. Education : - PhD in Computer Science, University of Karlsruhe (1992), advised by Hans-Hellmut Nagel. - Diploma (Master's equivalent) in Electrical Engineering, National Technical University of Athens (1986). Research Interests : Daniilidis explores equivariant neural networks for structure from motion and robotics, event-based vision systems for low-latency perception, and 3D human motion capture using monocular and multi-sensor approaches. His work integrates geometric priors with deep learning to address challenges in SLAM, object pose estimation, and autonomous navigation. He also investigates robotic systems for tasks like object catching and fluid dynamics modeling, applying uncertainty-aware methods for robust decision-making. Articles Trends : Recent publications highlight advancements in event camera processing (e.g., EV-TTC, EqNIO), equivariant architectures for shape and motion analysis (SE(3)-Equivariant Networks), and multi-agent robotics with distributed perception systems. Collaborations with institutions such as ETH Zurich and Boston University reinforce his contributions to scalable and geometrically grounded AI solutions. Advising & Grants : He advises over 30 PhD students and postdocs, many now faculty or industry leaders. His grants include NSF awards (NCS-FO 2124355, CPS 2038873), ARL DCIST, and ONR funding, focusing on robotics, vision, and AI. His work often bridges theoretical insights (e.g., matrix permanent for localization) with practical robotic systems like the Tiercel drone. Labs & Teams : Affiliated with the GRASP Laboratory at Penn, a premier robotics & vision research center. Collaborates widely, including with the Robotics Lab at ETH Zurich and researchers at the University of Coimbra. His group develops open-source tools like EV-FlowNet and EventGAN for event-based vision.

Domenico AmmendoleaView profile

Researcher

• Structural Engineering
• Fracture Mechanics
• Composite Materials
• +7 more

Domenico Ammendolea is a Research Grant Holder (Assegnista di Ricerca) in Structural Science at the Department of Civil Engineering, University of Calabria. He holds a Master’s degree in Civil Engineering (Structures) and a PhD in Environmental, Construction, and Energy Science and Engineering, both from the same institution, having graduated with top honors. He is actively involved in research and teaching, contributing to graduate courses such as Bridge Analysis and Design, Structural Modeling of Buildings, and Seismic Dynamics of Structures, while also serving as a mentor. His research focuses on advanced numerical modeling of damage and fracture in composite and quasi-brittle materials under static and dynamic loading. Key interests include moving mesh techniques, phase-field modeling, cohesive zone methods, and finite element analysis for simulating crack propagation in heterogeneous, anisotropic, and functionally graded materials. He applies these methods to cutting-edge materials like nano-filled ultra-high-performance concrete and CNT-reinforced composites. The recent publications highlight a strong trend in computational fracture mechanics, particularly in dynamic and multiscale crack propagation, utilizing adaptive and moving mesh strategies combined with integral methods for enhanced accuracy. His work bridges theoretical mechanics with practical structural applications. TC3 Young Scientist Award – Best contribution by a young researcher at the 8th International Conference on Crack Paths He has been actively teaching in master’s programs and mentoring students. Though no formal grants are listed, his research grant position and publications indicate active funding support. He collaborated remotely as a Visiting PhD Student at the University of Dundee (UK) in 2022, reflecting international engagement. His research is conducted within the Department of Civil Engineering at the University of Calabria, collaborating closely with researchers including Fabrizio Greco, Lorenzo Leonetti, Paolo Lonetti, and Arturo Pascuzzo. The work is integrated into broader research activities on structural integrity and advanced materials modeling.

Tonatiuh Sanchez-VizuetView profile

Assistant Professor

• Numerical Analysis
• Scientific Computing
• Computational Physics
• +6 more

Tonatiuh Sánchez-Vizuet is an Assistant Professor in the Department of Mathematics at the University of Arizona , where he develops numerical methods for magnetically confined plasma simulations and computational physics problems. Education B.Sc. in Physics, National Autonomous University of Mexico (UNAM) M.Sc. in Mathematics, National Autonomous University of Mexico (UNAM) Ph.D. in Applied Mathematics, University of Delaware Research Interests center on numerical analysis, scientific computing, and computational physics, with specific focus areas including: Hybridizable Discontinuous Galerkin (HDG) methods Boundary Element Method (BEM) for time-domain wave propagation Coupling integral and partial differential equations Uncertainty quantification in plasma physics Transient electromagnetic scattering analysis Thermo-piezoelectric wave interactions Scientific Contributions include 15+ peer-reviewed publications on HDG discretizations, boundary integral formulations, and surrogate modeling. His work spans applications in: Magnetic confinement fusion reactors Wave-structure interaction Transient thermoelasticity Dielectric scattering problems Grants NSF-DMS-2137305 (2021-2023): $250,000 as single PI for HDG methods in plasma confinement Awards Featured by Lathisms.org as prominent Hispanic mathematician (2018) Teaching includes graduate-level real analysis and mathematical statistics courses, with class notes freely available for student use.

Franck CappelloView profile

Academic

• High-performance parallel and distributed computing
• Resilience and fault tolerance at extreme scale
• Lossy compression
• +3 more

Franck Cappello is a distinguished computer scientist currently serving as a Project Manager and Senior Computer Scientist at Argonne National Laboratory and as an Adjunct Research Professor at the University of Illinois at Urbana-Champaign. With over 30 years of research experience, he has made significant contributions to high-performance computing, particularly in the areas of resilience and fault tolerance at extreme scale, lossy compression of scientific data, and AI for science. Dr. Cappello received his Ph.D. from the University of Paris XI in 1994 with highest honors ("très honorable avec les félicitations du jury"). His academic journey includes positions as a Junior Researcher at CNRS (1994-2003), Senior Researcher at INRIA (2003-2013), and Visiting Research Professor at the University of Illinois (2009-2013). His research interests focus on high-performance parallel and distributed computing, resilience and fault tolerance at extreme scale, lossy compression of scientific data, and AI for science. Cappello has pioneered several high-impact software tools including XtremWeb (one of the first Desktop Grid software systems), MPICH-V fault tolerance MPI library, VeloC multilevel checkpointing environment, and SZ lossy compressor for scientific data. His work on the Grid'5000 project has enabled hundreds of researchers to conduct experiments in parallel and distributed computing, resulting in over 2000 research publications and supporting 300+ Ph.D. theses. Dr. Cappello's recent publication record shows a strong integration of AI techniques with traditional HPC approaches, particularly in lossy compression, workflow management, and energy efficiency. His research demonstrates a consistent focus on practical solutions for real-world scientific computing challenges with emphasis on maintaining data fidelity while achieving significant data reduction. The publications reveal growing interest in GPU acceleration, wafer-scale engines, federated learning, and energy trade-offs in compressed I/O systems. IEEE Fellow (2017) 2024 IEEE CS Charles Babbage Award 2024 Europar Achievement Award 2022 ACM HPDC Achievement Award 2021 IEEE Transactions of Computer Award for Editorial Service and Excellence 2018 IEEE TCPP Outstanding Service Award Two R&D 100 awards (2019 and 2021) for innovative software 12 Best papers Finalists/Awards Dr. Cappello has advised 22 Ph.D. students and served on 58 Ph.D. defense juries. He has secured over 60 research grants as main PI or Co-PI, including numerous DOE ECP projects, NSF grants, and European projects. His leadership extends to directing the Joint Laboratory on Extreme-Scale Computing (JLESC), which brings together seven prominent research centers in supercomputing. Currently, he leads the AuroraGPT Evaluation Group, focusing on evaluation methods for Large Language Models as research assistants, and continues to lead resilience and compression research at Argonne's Mathematics and Computer Science Division. Dr. Cappello directs several significant research initiatives including the Joint Laboratory on Extreme-Scale Computing and leads resilience and compression research at Argonne's Mathematics and Computer Science Division. His teams have developed groundbreaking software frameworks like SZ and VeloC that are deployed on exascale systems. Through his leadership of the Grid'5000 project and JLESC, he has fostered international collaboration among researchers working on the frontiers of supercomputing. His current work on error-bounded lossy compression, resilient workflow management, and energy-efficient computing represents the cutting edge of scientific computing research with practical applications across numerous scientific domains.

Antoon van HooftView profile

Lecturer

• Fluid Dynamics
• Atmospheric Physics
• Numerical Modeling
• +3 more

Antoon van Hooft is a Lecturer in the Department of Biomedical Engineering at Eindhoven University of Technology (TU/e). His academic background includes a PhD in Atmospheric Physics from Delft and a focus on fluid dynamics during his Applied Physics studies at TU/e. He teaches courses such as Physics for Biomedical Engineering , Linear Algebra & Multivariable Calculus , and Cardiovascular Fluid Mechanics , emphasizing foundational math and physics in biomedical curricula. His research spans fluid dynamics, atmospheric physics, and numerical modeling, with recent work on plant-tissue warming via wind machines and adaptive computational solvers. Notable contributions include studies on boundary layer dynamics, nocturnal inversion regimes, and polar meteorology. He maintains a personal website detailing flow solver development and open-source projects. Collaborations include work with institutions like Wageningen University (fruit frost protection) and the Royal Netherlands Meteorological Institute (Antarctic boundary layer analysis). No ancillary activities are listed, and he holds no formal awards mentioned in the profile.

Euan SpenceView profile

Professor

• Wave Propagation
• Helmholtz Equation
• Numerical Analysis
• +6 more

Euan Spence is a Professor of Mathematics at the University of Bath, part of the Department of Mathematical Sciences and the EPSRC Centre for Doctoral Training in Statistical Applied Mathematics (SAMBa). His research focuses on the numerical analysis and mathematical analysis of wave propagation problems, particularly involving the Helmholtz equation, Maxwell equations, and boundary integral equations. He holds a PhD from the University of Cambridge (2009) and has held academic positions at Bath since 2009, advancing to Professor in 2020. His work bridges semiclassical analysis and numerical methods, addressing challenges in high-frequency wave scattering and uncertainty quantification. Spence has led or contributed to multiple research projects funded by EPSRC and EU, including the ERC Synergy Grant for phase-space-inspired numerical methods. His awards include the London Mathematical Society Whitehead Prize (2022) and the John Todd Award (2019). He supervises PhD students and postdocs, contributing to areas like domain decomposition methods, finite element analysis, and seismic imaging optimization. His educational background includes a Master’s and PhD from the University of Cambridge, specializing in transform methods for linear PDEs under Thanasis Fokas. He teaches courses on partial differential equations and numerical analysis, emphasizing rigorous mathematical foundations and practical computational skills.

• Computational Electromagnetics
• Radar Cross-Section Analysis
• Antenna Design
• +3 more

Lorena Lozano Plata is an Associate Professor in the Department of Computer Science at the Universidad de Alcalá. Her research focuses on computational electromagnetics, radar cross-section (RCS) analysis, antenna design, and high-performance numerical methods. She holds a PhD from Universidad de Alcalá (2006) with a thesis on accelerating ray-tracing techniques for RCS reduction using physical optics. She is part of the GEC (Computational Electromagnetic Group) and has developed software tools like NewFasant and FASANT for electromagnetic analysis. Her work emphasizes efficient algorithms for solving large-scale electromagnetic problems, including multilevel fast multipole algorithm (MLFMA), characteristic basis function method (CBFM), and hybrid techniques combining high-frequency asymptotic methods with rigorous solvers. Applications include antenna placement optimization, urban V2X communication channels, wind turbine interference characterization, and aerospace satellite communication modeling. She has contributed to GPU-accelerated parallelization strategies and radar system simulations in traffic scenarios. Lozano Plata's research also involves radio propagation analysis in complex environments, radar Doppler spectrum analysis, and pre-processing/meshing optimization for electromagnetic software. Her work bridges theoretical developments with practical tools used in industry and academia for electromagnetic design and simulation challenges.

Fue-Sang LienView profile

Professor

• Computational Fluid Dynamics
• Turbulence Modeling
• Wind Engineering
• +10 more

Fue-Sang Lien is a Professor in the Department of Mechanical and Mechatronics Engineering at the University of Waterloo, Canada. He serves as the head of Waterloo Computational Fluid Dynamics Engineering Consulting and is affiliated with IMMERSE – The Research Network for Video Game Immersion. His primary research focuses on computational fluid dynamics (CFD), turbulence modeling, wind engineering, and aerodynamics/aeroacoustics applications. Educations: Doctorate in Mechanical Engineering, University of Manchester Institute of Science and Technology, UK (1992) Master's in Mechanical Engineering, National Cheng Kung University, Taiwan (1984) Bachelor's in Mechanical Engineering, National Cheng Kung University, Taiwan (1982) Research interests include urban flow dispersion modeling, LES techniques, free-surface flows, and CFD integration with serious game technologies for emergency response training. Recent publications emphasize immersed boundary methods, GPU acceleration, and wind energy forecasting. No scientific awards explicitly mentioned in the provided text. Teaching & Advising: Active in graduate student supervision, currently accepting applications. Teaches ME 564 (Aerodynamics), ME 566 (CFD for Engineering Design), and ME 663 (Computational Fluid Dynamics).

Sven GroßView profile

Researcher

• Computational Fluid Dynamics
• Numerical Analysis
• Finite Element Methods
• +3 more

Dr. Sven Groß is a researcher at the Institute for Geometry and Practical Mathematics at RWTH Aachen University, Germany, with an office in Hauptgebäude 237. His work focuses on advanced numerical methods for fluid dynamics, particularly incompressible two-phase flows, and he is a core developer of the DROPS software package for parallel flow simulations. He maintains active research collaborations across computational mathematics and chemical engineering disciplines. His educational background includes a diploma thesis (2002) and doctoral thesis (2008), both completed at RWTH Aachen University. The doctoral work, titled "Numerical methods for three-dimensional incompressible two-phase flow problems," earned him the prestigious Borchers-Plakette award. Groß's research centers on Computational Fluid Dynamics with emphasis on Finite Element Methods for interface problems. His expertise spans adaptive 3D FE techniques, level set methods, XFEM implementations, and parallelization strategies for two-phase flow systems. Current investigations address preconditioning for unfitted finite element methods, mass transfer in falling films, and high-performance computing approaches for gas absorption processes. His methodologies bridge theoretical numerical analysis with industrial applications in chemical process engineering. Analysis of his recent publications reveals a strong trajectory toward robust preconditioning techniques for interface problems and high-fidelity simulations of reactive multiphase systems. The work increasingly integrates high-throughput computing with traditional numerical methods, demonstrating growing emphasis on computational efficiency for complex industrial-scale problems while maintaining mathematical rigor in error analysis. His scientific recognition includes: Borchers-Plakette (RWTH Aachen, 2009) for doctoral studies Friedrich-Wilhelm-Preis (RWTH Aachen, 2003) for diploma thesis Springorum-Denkmünze (RWTH Aachen, 2003) for diploma Groß has secured significant research funding through Collaborative Research Center SFB 540, leading Project C7 on numerical methods for wavy falling film simulations and contributing to former Project C6 on parameter estimation. He actively mentors students through research collaborations and has supervised numerous diploma/PhD projects, with co-authors including Kirchhart, Ludescher, and Jankuhn appearing consistently in his publication record. As a principal investigator for the DROPS project, he leads a cross-disciplinary team developing parallel adaptive multigrid techniques for incompressible flow simulations. The project maintains strong industry connections with chemical engineering groups focused on falling film reactors and mass transfer optimization, with recent extensions into high-performance computing architectures for industrial-scale simulations.

Eftychios SifakisView profile

Professor

• Computer Graphics
• Physics-Based Modeling and Simulation
• Scientific Computing
• +4 more

Eftychios Sifakis is a Professor of Computer Sciences at the University of Wisconsin-Madison. He joined the department in January 2011 after a postdoctoral appointment at UCLA (2007-2010) and holds a PhD in Computer Science from Stanford University (2007). His research focuses on physics-based modeling, computer graphics, and scientific computing, with applications in biomechanics and virtual surgery. Key projects include surgical simulators, material point methods, and real-time deformable body simulation. Education: PhD in Computer Science, Stanford University, 2007 BS in Computer Science & Mathematics, University of Crete, Greece, 2000-2002 Research Interests: His work bridges computational physics and visual computing, emphasizing algorithms for fluid dynamics, topology optimization, and medical simulation. He develops scalable numerical methods for large-scale simulations on heterogeneous hardware. Teaching: Courses include CS559 (Computer Graphics), CS839 (Physics-Based Modeling), and CS412 (Numerical Methods). He has taught at both UW-Madison and UCLA. Labs/Teams: Leads research on computational biomechanics and fluid-structure interaction within the UW-Madison Computer Sciences Department. Collaborates on projects like the Chrono physics engine and surgical simulation frameworks.

Oliver JungeView profile

Associate Professor

• Numerical Mathematics
• Scientific Computing
• Dynamical Systems
• +4 more

Oliver Junge is an Associate Professor of Numerics of Complex Systems at the TUM School of Computation, Information and Technology, Technische Universität München. His research focuses on developing novel numerical methods for dynamical systems, with applications in molecular dynamics, astrodynamics, systems theory, and image processing. He holds a doctorate from the University of Paderborn (1999) and has conducted research at institutions like Georgia Tech and Caltech. Notable awards include the TopMath Supervisory Award (2020) and the Research Prize from the University of Paderborn (2004). Educational Background: He studied mathematics and computer science at the Universities of Darmstadt, Bordeaux, Hamburg, and Bayreuth before completing his PhD in Paderborn. His academic career includes roles as a junior professor in Paderborn and associate professor at TUM since 2005. Research Interests: Junge specializes in numerical mathematics and scientific computing, emphasizing the analysis and implementation of numerical methods for complex systems. His work bridges theoretical rigor and practical applications, particularly in computational fluid dynamics, optimal control, and topological data analysis. Awards: His accolades include the Dilthey Prize (1998) and recognition for his contributions to computational methods in dynamical systems. Grants & Advising: While specific grants or advisee names are not listed, his publications indicate extensive collaborative research and mentorship in computational mathematics and systems theory. Labs/Teams: Collaborates with interdisciplinary teams at TUM and internationally, focusing on numerical methods and their applications in engineering and natural sciences.

Junpeng WangView profile

Research Fellow

• lightweight structural design
• structural optimization
• scientific visualization
• +5 more

Dr. Junpeng Wang is a Postdoctoral Researcher (since June 2023) in the Chair of Computer Graphics and Visualization at the Technical University of Munich, working under Prof. Rüdiger Westermann. Previously, he completed his PhD at the same institution from November 2018 to June 2023. His research focuses on lightweight structural design and optimization with geometry-based approaches, as well as scientific visualization of tensor and mesh data. Wang's research interests span lightweight structural design , structural optimization , and scientific visualization . His work integrates computational mechanics with computer graphics to develop novel methods for stress analysis, lattice structure optimization, and 3D data visualization. His research has practical applications in mechanical engineering, additive manufacturing, and scientific data analysis. His publication portfolio shows a strong trend toward integrating machine learning with traditional computational mechanics methods, particularly in the areas of 3D Gaussian Splatting and neural data structures. Wang has developed the 3D-TSV (3D Trajectory-based Stress Visualizer), a significant tool for exploring principal stress directions in 3D solids under load, which has been published in Advances in Engineering Software (2022). Wang serves as a reviewer for prestigious journals including IEEE Transactions on Visualization and Computer Graphics (TVCG), Computer Methods in Applied Mechanics and Engineering (CMAME), Journal of Mechanical Design (JMD), Optics Express (OPTE), Computer-Aided Geometric Design (CAG), and Shape Modeling International Conference (SPM[C]). His collaborative work primarily involves Prof. Rüdiger Westermann at TUM and Jun Wu at other institutions, demonstrating strong interdisciplinary connections between computer graphics and mechanical engineering research groups.

• Computational Fluid Dynamics
• Artificial Intelligence in CFD
• Partial Differential Equations
• +3 more

Boyang Chen is a Research Associate at the Applied Modelling and Computation Group (AMCG) within the Department of Earth Science & Engineering at Imperial College London. He specializes in developing AI-enhanced computational fluid dynamics (AI4CFD) techniques, focusing on high-fidelity urban air flow modeling and indoor/outdoor ventilation analysis. His work leverages parallel computing frameworks like Fluidity and integrates machine learning libraries to advance numerical methods for fluid mechanics. Key Projects: INHALE Project (air pollution modeling) and CO-TRACE Project (Covid-19 transmission risk assessment in educational settings). Technologies: Fluidity CFD code, AI4CFD framework, Graph Neural Networks, Discontinuous Galerkin Methods. Research interests include numerical solutions of partial differential equations, multiphase flow modeling, and environmental fluid dynamics. He contributes to open-source projects like Fluidity and has published on AI-driven CFD, neutron transport modeling, and shallow water equations.

Scott W. CampbellView profile

Professor

• New and Emerging Media
• Mobile Communications
• Social Networking
• +3 more

Scott W. Campbell is the Constance F. and Arnold C. Pohs Professor of Telecommunications at the University of Michigan. He holds affiliations with the Nam Center for Korean Studies and is listed as NCKS Faculty. His research explores the social implications of new media, particularly mobile telephony, focusing on how communication patterns intersect with private/public spheres like social networking and civic engagement. He employs comparative methods to analyze media's role across societies. Education: PhD in Telecommunications from the University of Kansas. Research Interests: New and emerging media, mobile communications, comparative media studies, and their societal impacts. His work bridges technological advancement with urban planning challenges, addressing sustainability and equitable development. Recent publications span urban planning frameworks, mobile app ethics, and complex systems modeling. Notable works include Green Cities, Growing Cities, Just Cities? and The Well-Tempered City . Awards: None explicitly listed. Grants and Advising: No specific grants or advisee names provided in texts. Active in academic leadership roles through the Nam Center for Korean Studies. Labs/Teams: Affiliated with the Nam Center for Korean Studies, focusing on interdisciplinary research.