Find Academic Supervisors

Majid SalimibeniView profile

Research Fellow

• High Performance Computing
• Parallel Computing
• Message Passing in Large-scale HPC systems
• +6 more

Majid Salimibeni is a PostDoc Researcher and Research Fellow at Vienna University of Technology (TU Wien) , affiliated with the Parallel Computing research unit. He previously held a Postdoc position at the Department of Computer Science at the University of Salerno, Italy (2024), and has been a visiting researcher at TU Wien (2023). Currently based in Vienna, Austria, his work focuses on High Performance Computing (HPC) and its interdisciplinary applications. Education: Ph.D. in Computer Science, University of Salerno, Italy (2020–2024) M.Sc. in Computer Engineering (Software), Shiraz University, Iran (2017–2020) B.Sc. in Computer Engineering (Software), University of Birjand, Iran (2012–2016) Salimibeni's research explores High Performance and Parallel Computing , with a focus on optimizing GPU communication via NCCL , improving energy efficiency in heterogeneous computing environments , and enhancing MPI collective algorithms for large-scale systems. His work bridges HPC infrastructure with emerging applications in Distributed Deep Learning and AI . Recent publications highlight his expertise in GPU frequency scaling , NCCL profiling , and process arrival pattern analysis to improve distributed system performance. These works appear at top conferences like IPDPS , Cluster Computing , and ASHPC . Scientific Awards: Best Paper Award, Bench 2022 Salimibeni contributes to academic committees as a program committee member for IEEE ICPP 2025, ISC 2025, and BigHPC 2024, and serves on artifact evaluation committees for ASPLOS and CF conferences.

• Hybrid Variational/DL methods under topological constraints
• Hybrid Variational/DL methods under geometric constraints
• PDE Analysis and numerical simulation
• +5 more

Carole Le Guyader is an Associate Professor in the Mathematical and Software Engineering Department at the National Institute of Applied Sciences of Rouen (INSA Rouen), France. She has been actively contributing to the fields of image processing, mathematical analysis, and computational methods since joining INSA Rouen in 2009, after previously serving as an Assistant Professor at INSA Rennes from 2005 to 2009. Her academic leadership extends to various administrative roles including Head of Mathematics teaching at the STPI INSA Rouen department since 2019 and Director of the Mathematical Engineering Department since 2013. Dr. Le Guyader earned her Habilitation to Supervise Research on November 16, 2012, at Rouen University with the thesis titled "Contribution to the mathematical analysis of images: segmentation, registration and decomposition." Her habilitation jury included distinguished mathematicians such as Grégoire ALLAIRE from Ecole Polytechnique and Gabriel PEYRE from University Paris-Dauphine. Her primary research interests focus on the development and application of variational methods and deep learning techniques for image processing, with particular emphasis on incorporating topological and geometric constraints. She investigates mathematical models for image segmentation, registration, and decomposition, applying these techniques to biomedical imaging, geophysics, and other scientific domains. Her work bridges theoretical mathematics with practical applications in medical diagnostics and industrial settings. Le Guyader's publication record demonstrates a consistent focus on advancing image processing methodologies, particularly in the integration of traditional variational approaches with modern deep learning techniques. Her recent work shows increasing emphasis on medical applications, especially in radiotherapy planning and MRI reconstruction, where geometric and topological constraints play critical roles in ensuring clinically meaningful results. Dr. Le Guyader has successfully supervised multiple PhD students throughout her career, demonstrating her commitment to mentoring the next generation of researchers. Her collaborative approach is evident in her extensive network of academic and industrial partnerships across Europe and the United States. She has secured and managed numerous research projects including the LMI project "Mathematical Imaging and Digital Analysis" at CRIHAN since 2013, and the wind field modeling project with LABEX AMIES since 2014. Her leadership extends to coordinating INSA teams at the GENCI/CRIANN Hackathon since 2017 and participating in regional and European research initiatives.

Philippe PoncetView profile

Professor

• Numerical Analysis
• High Performance Computing
• Fluid Dynamics
• +5 more

Philippe Poncet is a Professor at the Université de Pau et des Pays de l'Adour (UPPA) and Vice-President for Digital Environment. He leads the Numerical Methods and Complex Fluids team at the Laboratory of Mathematics and their Applications (LMAP). His expertise spans particle/vortex methods, high-performance computing, and applications in geoscience, microfluidics, and biomathematics, with a focus on reactive flows and porous media. Poncet's research integrates numerical analysis with practical challenges in environmental and biomedical engineering. Key interests include Lagrangian methods for fluid dynamics, optimization algorithms, pore-scale modeling, and cystic fibrosis biomechanics. His work bridges theoretical mathematics and industrial-scale simulations. Recent publications demonstrate advances in pore-scale dissolution modeling, Bayesian neural networks for inverse problems, and viscous flow simulations. His articles consistently address multi-scale challenges in geosciences and biomedicine using innovative computational frameworks. He has led significant projects including the ANR-funded MucoReaDy (mucus dynamics in respiratory diseases) and MicroMineral (CO₂ mineral storage). These collaborations involve multidisciplinary teams addressing environmental and health-related fluid dynamics.

Frederick GentView profile

Assistant Professor

• Magnetohydrodynamics
• Dynamo Theory
• Solar Magnetic Activity
• +3 more

Frederick Gent is an Assistant Professor at the Nordic Institute for Theoretical Physics (Nordita) and a Research Fellow at the Department of Computer Science, affiliated with the Korpi-Lagg Maarit and Vehtari Aki professorships. He holds a PhD in Natural Sciences from Newcastle University (2012) and has been a Visiting Researcher there since 2018. His research focuses on magnetohydrodynamics, dynamo theory, and interstellar medium dynamics, with applications to solar magnetic activity and supernova-driven turbulence. Gent has been awarded the 2018 Grand Challenge Award for his work on galactic dynamo interactions. He supervises postgraduate students, including A. Capobianco and P. Tengnér, and collaborates internationally, notably through the Pencil Code project. His work bridges computational astrophysics and theoretical physics, addressing challenges in high-performance computing and numerical simulation. Education: Doctoral degree in Natural Sciences (Newcastle University, 2012). Research interests include magnetic field dynamics in galaxies, supernova-driven turbulence, and the interplay between magnetic fields and interstellar medium structures. His computational methods leverage advanced numerical models to simulate astrophysical phenomena, such as magnetic buoyancy instabilities and dynamo transitions. Recent studies examine the destruction of supernova dust in magnetized environments and the transition from small- to large-scale dynamos in multiphase media. Scientific awards include the 2018 Grand Challenge Award for interdisciplinary dynamo research. He actively participates in conferences and workshops, notably organizing the Pencil Code User Meeting (2024) and presenting invited talks on galactic dynamo models. Gent’s advising roles include co-supervising doctoral students alongside Prof. Shukurov and mentoring visiting researchers at Nordita and Newcastle University. Labs/Teams: Pencil Code Collaboration (software developer), Korpi-Lagg Maarit Research Group (visiting fellow), and Nordic Institute for Theoretical Physics (primary affiliation).

• High Performance Computing
• Parallel Computation
• Novel Architectures
• +3 more

Dr. Steven Wright is a Senior Lecturer in Computer Science at the University of York, specializing in High Performance Computing (HPC), Parallel Computation, and Novel Architectures. He joined the Department of Computer Science in 2018 and is a member of the Real-Time and Distributed Systems research group. His research focuses on optimizing supercomputers and scientific applications for performance, energy efficiency, and programmability. He collaborates with national laboratories, universities, and industry partners such as the UK Atomic Energy Authority and Intel. Prior to York, he was a Research Fellow at the University of Warwick, working on projects involving fusion physics and Rolls-Royce collaborations. Dr. Wright holds a Ph.D. (2014) and M.Eng. from the University of Warwick, where his doctoral thesis addressed I/O optimization in parallel applications. His teaching responsibilities include leading the High-Performance Parallel and Distributed Systems (HIPC) module and serving as Programme Lead for on-campus Undergraduate CS Programmes. He has also contributed to the Software Testing (SOTE) module and departmental roles such as Chair of the Board of Examiners. Research interests span HPC, energy-efficient computing, and application optimization. Recent work emphasizes performance portability, fusion simulations, and parallel I/O systems. His projects, such as the P3 Explorer database and EMPIRE-PIC framework, highlight contributions to HPC software and simulation tools. Collaborations with institutions like Sandia National Laboratories and industry partners like NVIDIA reflect his interdisciplinary approach to advancing computational science.

Christian BischofView profile

Professor

• High Performance Computing
• Automatic Differentiation
• Parallel Algorithms
• +1 more

Prof. Dr. Christian Bischof is Professor of Computer Science at TU Darmstadt, leading research in high-performance computing and automatic differentiation. His work includes adaptive computing systems and parallel algorithms.

• Seismology
• Earthquake Source Processes
• Tectonic Processes
• +3 more

Jochen Braunmiller is a Research Assistant Professor in the School of Geosciences at the University of South Florida, within the College of Arts and Sciences. His research focuses on seismology, earthquake source processes, tectonic dynamics, and Earth structure. He holds a Ph.D. from Oregon State University (1992–1998), an M.S. from the same institution (1988–1991), and a Vordiplom from the University of Karlsruhe (1985–1988). His work spans diverse tectonic settings, including oceanic transform faults, volcanic systems, and continental intraplate regions. Key areas of investigation include moment tensor analysis, seismic swarm mechanisms, and post-seismic deformation. Braunmiller has contributed to projects like the Gorkha Earthquake Nepal Aftershock Study (NAMASTE) and the Montserrat Volcano Observatory data preservation initiative. Research highlights include studies on the 2020 Nevada M 6.5 earthquake, magma intrusions in Iceland’s Reykjanes Peninsula, and crustal structure in Iran’s Central Alborz. He also emphasizes education, developing computational tools for seismologists and integrating fieldwork into student training. His publications often bridge seismology with geodesy, geophysics, and applied geology. Braunmiller collaborates internationally, with contributions to the Middle East Moment Tensor Database and the Blanco Transform Fault OBS Experiment. Despite no explicitly listed scientific awards, his extensive peer-reviewed output reflects sustained recognition in the field. His advising record remains unspecified, though his involvement in student-led field studies (e.g., Idaho’s Lost River Valley) suggests mentorship in field methodologies. Grants or lab affiliations are not detailed in the provided texts.

Raffaele De AmicisView profile

Associate Professor

• Virtual Reality
• Augmented Reality
• Human-Computer Interaction
• +7 more

Raffaele De Amicis is an Associate Professor at the School of Electrical Engineering and Computer Science , Oregon State University. With a PhD in Industrial Engineering from the University of Bologna (2001) and a professional engineering license (1997), he bridges technical expertise with immersive technology applications. His career spans roles at Fraunhofer Institute (1999-2002), founding directorship of GraphiTech in Trento, Italy, and consulting professorship at the University of Trento (2003-16). Degrees: Ph.D., Design and Methods of Industrial Engineering, University of Bologna M.S., Mechanical Engineering, University of Calabria Professional Engineer License, Italian Charter Research focuses on Virtual Reality , Augmented Reality , and Human-Computer Interaction , particularly for industrial training, geospatial visualization, and educational applications. His work on digital twins, immersive engineering instruction, and XR-based disassembly systems demonstrates interdisciplinary impact. Scientific contributions include over 120 publications, three books, and 15 edited texts. Key projects involve VR safety training for confined spaces, AI-augmented AR guidance, and forest digital twin frameworks. Articles span 2012-2025, emphasizing 3D visualization , multimodal interfaces , and smart manufacturing . Scientific Leadership: Vice-President, GraphicsMedia.net (2010-present) Project Coordinator for EC/NATO/industry grants (€20M total) Founder, GraphiTech Research Center His expertise in multinational research coordination and technology transfer has shaped collaborations across Europe, with applications in forestry, urban planning, and hazardous environment training.

Natalie BeamsView profile

Academic

• High-Performance Computing
• Scientific Computing
• Numerical Methods for PDEs
• +3 more

Natalie Beams is a Research Assistant Professor at the Innovative Computing Laboratory (ICL) within the University of Tennessee's Department of Electrical Engineering and Computer Science (EECS). She holds a PhD in Theoretical and Applied Mechanics from the University of Illinois at Urbana-Champaign (UIUC), an MS from UIUC, and a BS in Mechanical Engineering (summa cum laude) from the University of Oklahoma. Her research focuses on numerical methods for PDEs, high-performance computing (HPC), and GPU-accelerated algorithms, with contributions to projects like the Exascale Computing Project's CEED and CLOVER initiatives. Key research areas include finite element methods , integral equation solvers , and mixed-precision algebraic multigrid techniques . She has developed software tools such as the libCEED library for high-order discretizations and contributed to the Ginkgo and MAGMA libraries. Her work emphasizes exascale computing, GPU optimization, and parallel algorithm design. Education: PhD in Theoretical & Applied Mechanics, UIUC (2017) MS in Theoretical & Applied Mechanics, UIUC (2014) BS in Mechanical Engineering, University of Oklahoma (2010) Awards & Honors: Best Workshops Paper Award, PPAM Conference (2022) 2011/2012 Computational Science & Engineering Fellow 2010 College of Engineering Carver Fellow List of Teachers Ranked as Excellent by Students (UIUC, 2014) Grants & Projects: Active contributor to the Exascale Computing Project (ECP), leading efforts in CEED (libCEED library) and CLOVER (MFEM-Ginkgo interoperability). Collaborates with Rice University and other institutions on HPC and numerical algorithms. Labs & Teams: Core member of the Innovative Computing Laboratory (ICL) at UTK, specializing in exascale software and GPU-accelerated computing.

Kevin HolstView profile

Research Professor

• Computational Fluid Dynamics
• Turbulence Modeling
• Finite-Element Method
• +3 more

Kevin Holst is a Research Professor at the University of Tennessee, Knoxville, specializing in Computational Fluid Dynamics (CFD) and high-performance computing. He is a core developer of the Kestrel CFD solver, part of the DoD-funded CREATE-AV program. His work focuses on turbulence modeling, finite-element methods, and GPU-accelerated simulations for aerospace applications. Previously, he worked as a data analyst at Arnold Engineering Development Complex on propulsion systems testing. Holst holds a PhD in Aerospace Engineering from the University of Tennessee Space Institute (2019), an MS from the same institution (2012), and a BS from the University of Tennessee Knoxville (2007). His research emphasizes high-order discretization techniques, hybrid RANS/LES methods, and time-accurate simulations of unsteady flows. Key projects include contributions to the High-Fidelity CFD Verification Workshop and development of performance-portable mesh data models for multi-physics simulations. Holst’s work aligns with the Department of Defense’s HPC Modernization Program, addressing critical needs in aerospace engineering and computational tools. He has no listed scientific awards but has authored numerous peer-reviewed articles on CFD methodology, solver development, and turbulence modeling. His advising and grants focus on advancing Kestrel’s capabilities within the CREATE-AV ecosystem. Holst is affiliated with the Computational Research and Engineering Acquisition Tools & Environments (CREATE) program and collaborates with national labs and industry partners.

Xingfu WuView profile

Research Professor

• High-performance computing
• Performance modeling and analysis
• Energy and power modeling and analysis

Xingfu Wu is a Research Professor of Computer Science affiliated with Argonne National Laboratory (ANL). He holds a Ph.D. in Computer Science from Beijing University of Aeronautics and Astronautics and M.S. and B.S. degrees in Mathematics from Beijing Normal University. His research focuses on high-performance computing, performance modeling and analysis, and energy-efficient computing. Education: Ph.D. in Computer Science, Beijing University of Aeronautics and Astronautics, Beijing, China M.S. in Mathematics, Beijing Normal University, Beijing, China B.S. in Mathematics, Beijing Normal University, Beijing, China Research Interests: Xingfu Wu's research is centered around high-performance computing, performance modeling and analysis, and energy and power modeling. His work emphasizes optimizing parallel systems, developing frameworks for performance prediction (e.g., MuMMI), and enhancing energy efficiency in high-performance computing environments. Scientific Awards: Best Paper Award, 14th IEEE International Conference on Computational Science and Engineering (CSE-2011), 2011 Second Place, Beijing Science and Technology Advancement Awards, 1997 Labs/Teams: Wu contributes to research at Argonne National Laboratory, particularly through initiatives like the MuMMI framework for performance modeling and analysis.

Yunqing XuanView profile

Associate Professor

• Hydrology
• Hydrometeorology
• Flood Risk Management
• +5 more

Dr Yunqing Xuan is an Associate Professor in Civil Engineering at Swansea University's School of Aerospace, Civil, Electrical and Mechanical Engineering. His research focuses on interdisciplinary hydro-meteorological processes, extreme event modeling, flood risk management, and climate change impacts. He leads projects integrating advanced computer modeling, remote sensing, and weather radar technologies to address water sector challenges. Roles & Affiliations: Vice Chair of the Welsh Local Centre, Royal Meteorological Society Review Editor, Weather Journal (Wiley) Board Member, Conference Committee of the Institute of Mathematics and its Applications Research Interests: Dr Xuan specializes in hydrological modeling interfacing with atmospheric and societal systems. Key areas include: - Extreme hydrological events under climate change - GIS/remote sensing applications in flood risk management - Weather radar and numerical weather prediction (NWP) for hydrological forecasting - Compound flooding analysis and early warning systems Grants & Projects: PI - HPC & Welsh Extreme Weather Initiative (£600K, TSB/Welsh Government) PI - GCRF Knowledge Network for Vietnam Flood Resilience (£24K) PI - Urban Flood Early Warning System in China (£69K, Royal Academy of Engineering) Awards: FRMetS, FIMA, FIHE fellowships reflect his contributions to meteorology, mathematics, and environmental engineering.

Hartwig AnztView profile

Professor

• High-Performance Computing
• Sparse Linear Algebra
• Iterative Methods
• +6 more

Prof. Hartwig Anzt is a Professor at TU Munich, leading the Chair of Computational Mathematics within the TUM School of Computation, Information, and Technology. He also holds a professorship at the University of Tennessee and directs the Innovative Computing Lab (ICL). His research focuses on high-performance computing, particularly in sparse linear algebra, iterative methods, Krylov solvers, and preconditioning. He emphasizes sustainable software development and leads the Ginkgo open-source library for scientific computing. Academically, Anzt earned his PhD in 2012 from the Karlsruhe Institute of Technology (KIT) and led a Helmholtz junior research group there. He has extensive collaborations with institutions like Sandia National Laboratories, Argonne National Laboratory, and the University of Tennessee. His software projects include Ginkgo and MAGMA-sparse, both part of the xSDK ecosystem. Recent talks highlight his work on exascale computing, GPU optimization, and software sustainability. He advocates for platform-portable numerical libraries and has contributed to the Exascale Computing Project (ECP). His research addresses challenges in energy efficiency, fault tolerance, and algorithm design for multi/manycore architectures.

• Cloud Computing
• High-Performance Computing (HPC)
• Energy-Efficient Systems
• +4 more

Ivona Brandić is a University Professor for High Performance Computing Systems at TU Wien's Institute of Software Engineering and Interactive Systems. Born in Gradačac, Bosnia and Herzegovina, she moved to Austria in 1992 as a refugee during the Bosnian War. She earned a master's degree (2002) and doctorate (2007) in business computer science from TU Wien and completed her habilitation in applied computer science there in 2013. Her career includes roles as an assistant professor (University of Vienna, 2002–2007) and postdoctoral researcher (University of Melbourne, 2008). She transitioned to a tenure-track position at TU Wien in 2014 and became a full professor in 2016. Brandić’s research focuses on cloud computing, energy-efficient ultra-scale systems, and hybrid quantum-classical computing. She has been recognized with the MiA Award (2011), the Austrian Science Fund's Start-Preis (2015), and membership in the Austrian Academy of Sciences' Young Academy (2016). Her work emphasizes sustainable computing, edge systems, and optimizing resource management for distributed applications. Education: Bachelor's degree in Business Informatics (University of Vienna/TU Wien) Master's in Business Computer Science (University of Vienna, 2002) PhD in Applied Computer Science (TU Wien, 2007) Habilitation in Practical Computer Science (TU Wien, 2013) Research Interests: Brandić’s work spans cloud computing, energy efficiency in HPC systems, edge computing, and quantum-classical hybrid systems. She explores autonomic resource management, distributed system resilience, and sustainability in ultra-scale infrastructures. Her projects often address real-world applications like drug design, environmental monitoring, and smart energy grids. Publications: Her 2009 paper Cloud Computing and Emerging IT Platforms is a seminal work in the field. Recent publications focus on quantum-edge integration, energy optimization in AI models, and adaptive edge analytics frameworks. These contributions highlight trends toward sustainable, distributed, and hybrid computational paradigms. Awards: 2011: MiA Award for distinguished contributions by international backgrounds 2015: Austrian Science Fund’s Start Prize 2016: Austrian Academy of Sciences Young Academy Membership Advising & Grants: Brandić leads research groups and has secured grants for projects like NESSUS (energy-efficient cloud systems) and CHIST-ERA’s SDCDN (distributed networks). She mentors students in HPC, edge computing, and quantum systems. Advised topics include workload scheduling, fault tolerance, and energy-aware algorithms. Labs & Teams: She directs research on autonomic cloud management, edge intelligence frameworks (e.g., Sea-LEAP, FRESCO), and quantum-classical workflow systems (RIGOLETTO). Her teams collaborate internationally, integrating academia and industry for scalable, sustainable solutions.

Suresh S. MuknahallipatnaView profile

Professor

• Medical Image Segmentation
• Deep Learning
• High Performance Computing
• +7 more

Suresh S. Muknahallipatna is a Professor in the Department of Electrical Engineering and Computer Science at the University of Wyoming, College of Engineering and Physical Sciences. His research spans interdisciplinary domains, integrating deep learning, high-performance computing, and augmented reality technologies. Research Interests: His work focuses on Medical Image Segmentation using Deep Learning , where he applies Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Generative Adversarial Networks (GAN) to segment organs and tumors in MRI, CT, and Ultrasound images. He also investigates Transient Stability and Contingency Analysis of Modern Power Grids using GPGPU-based High Performance Computing (HPC) and machine learning. In climate research, he uses HPC to accelerate models like MPAS and WRF. A significant part of his research involves Augmented Reality (AR) for Medical Applications , using devices like Microsoft HoloLens and Meta2 to visualize medical images as 3D holograms for real-time collaboration and training in procedures such as epidural injections with haptic feedback. Scientific Awards: Samuel D. Hakes Outstanding Graduate Research and Teaching Award, 2009 Microsoft Most Valuable Professional Award (2004, 2005, 2006) IEEE Senior Member (Computer Society), 2005 Tau Beta Pi Engineering Honor Society Phi Kappa Phi Honor Society 1st Rank, Master of Engineering, University of Bangalore, India Advising and Grants: While specific student names and grant details are not listed in the text, his academic experience and ongoing research projects suggest active graduate student mentoring and external funding in areas of HPC, medical imaging, and AR. His long tenure and award record reflect strong contributions to research and teaching. Labs and Teams: Dr. Muknahallipatna leads research efforts involving interdisciplinary teams working on GPU-accelerated computing, deep learning applications in healthcare, and AR-based medical visualization systems, likely operating within specialized labs at the University of Wyoming focused on computational engineering and biomedical technology.