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• Computer Vision
• Machine Learning
• Signal Processing
• +6 more

Qian Huang is a Professor in the Department of Computer Science at Sun Yat-sen University's School of Computer Science and Engineering. With over 350 publications spanning from 1992 to 2025, Dr. Huang has established themselves as a leading researcher in multiple interdisciplinary fields at the intersection of computer science, engineering, and applied mathematics. Dr. Huang's research spans several critical domains in modern computing. Their primary interests include computer vision with applications in medical image analysis, machine learning with emphasis on transformer architectures and federated learning, signal processing for video compression, and wireless communications for IoT applications. Recent work demonstrates significant contributions to nuclei segmentation in cervical cell images, advanced video compression techniques using spatiotemporal modeling, and predictive maintenance systems for industrial equipment that incorporate uncertainty quantification. An analysis of Dr. Huang's 15 most recent publications reveals a strong trend toward interdisciplinary research that bridges theoretical computer science with practical applications. Their work consistently addresses real-world challenges in healthcare diagnostics, industrial automation, and communication systems. The publications demonstrate expertise in developing novel deep learning architectures while maintaining theoretical rigor in mathematical foundations. Multiple publications in IEEE Transactions journals across various domains Regular contributions to top-tier conferences including ICASSP, ICIP, NeurIPS, and CVPR Collaborations with researchers from leading institutions globally Dr. Huang's research program appears well-funded through collaborations with industrial partners and Chinese national research grants, though specific grant information isn't detailed in the publication record. Their work on federated learning frameworks and medical image analysis suggests strong connections with healthcare technology companies and medical research institutions. The extensive publication record across multiple domains indicates leadership of a substantial research group with expertise spanning computer vision, machine learning, and signal processing.

Markus HeglandView profile

Professor

• Computational Mathematics
• High-Dimensional Problems
• Ill-Posed Problems
• +4 more

Markus Hegland is a Professor and Head of the Centre for Mathematics and its Applications (CMA) at the Australian National University (ANU). He holds a PhD from ETH Zurich (1988) and has been affiliated with ANU since 1992, focusing on High-Performance Computing (HPC) and numerical analysis. As a Hans Fischer Senior Fellow at TUM-IAS, his research emphasizes high-dimensional problems, ill-posed systems, and data mining applications. His work bridges computational mathematics with practical domains like systems biology and spectral enhancement. Research interests include sparse grid techniques, regularization methods, and algorithm development for HPC. Notable contributions include the OPTICOM method for stable sparse grid solutions and convergence theory for variable Hilbert scales regularization. He has led projects on fault-tolerant HPC algorithms and collaborated with Fujitsu on HPC applications. Publications span numerical analysis, bioinformatics, and computational physics. His work on the chemical master equation and gyrokinetics showcases interdisciplinary impact. Currently, he explores resilient grid-based solvers and machine learning integration with HPC frameworks. No awards are explicitly listed, but his senior fellowship underscores recognition in his field. Grants and collaborations include ARC-funded research in bioinformatics and HPC resilience. His work on digital twins and algorithm optimization reflects broader interests in advanced computational modeling. He is actively involved in teaching and supervising in computational mathematics and data science at ANU.

George BirosView profile

Professor

• Scientific Computing
• Parallel Algorithms
• Fast Numerical Algorithms
• +5 more

George Biros is a Professor at The University of Texas at Austin, holding the W.A. “Tex” Moncrief Jr. Chair in Simulation-Based Engineering Sciences at the Cockrell School of Engineering’s Institute for Computational Engineering and Sciences (ICES). He leads the Parallel Algorithms for Data Analysis and Simulation (PADAS) group. His research focuses on high-performance computing, fast numerical algorithms, and applications in fluid dynamics, biomedical engineering, and inverse problems. Education: BSc, Aristotle University of Thessaloniki (1995) MSc and PhD, Carnegie Mellon University (1996–2000) Research Interests: Biros develops scalable algorithms for scientific computing, including parallel methods for integral and differential equations, complex fluids dynamics, and medical image analysis. His work bridges computational mathematics, engineering, and biomedical applications, with a focus on solving large-scale problems in heterogeneous computing environments. Awards: IEEE/ACM SC10 Gordon Bell Prize (2010) J. Tinsley Oden Faculty Fellowship (2006–2008) Early Career Young Investigator Award, U.S. Department of Energy (2005) Grants & Labs: Leads the PADAS group, which specializes in high-performance algorithms for data and simulation. His work has been supported by the DOE, NSF, and industry partnerships. Collaborates with institutions like TUM through fellowships. Labs/Teams: Director of the PADAS group at UT Austin, focusing on scalable numerical methods and their applications in science and engineering.

Michael QuellmalzView profile

Researcher

• Applied Mathematics
• Inverse Problems
• Tomography
• +4 more

Dr. Michael Quellmalz is a Researcher in Applied Mathematics at the Technical University of Berlin (TU Berlin), affiliated with Faculty II - Mathematics and Natural Sciences and the Institute of Mathematics. His research focuses on inverse problems, tomography, optimal transport, and Fourier analysis with applications in medical imaging and computational geometry. He actively contributes to the SFB Tomography Across the Scales collaborative research center, advancing reconstruction techniques in diffraction tomography and motion detection. He teaches a variety of courses including Analysis II for Engineering, Harmonic Analysis I, and Numerical Mathematics, emphasizing both theoretical foundations and their practical implementations. His work has been recognized through awards such as the Universitätspreise 2020 and a Digital Fellowship for innovative educational contributions in adaptive feedback systems. Key publications include advancements in sliced optimal transport on spheres, motion detection algorithms, and Fourier-based reconstruction methods. Dr. Quellmalz's research group develops MATLAB toolboxes for applications like FourierODT and NFFT-Sinkhorn, reflecting his commitment to bridging mathematical theory with computational tools. His academic journey includes a PhD from TU Chemnitz (2019) and extensive participation in international conferences, workshops, and collaborative projects across Europe.

Stéphane Berbenni Vincent TaupinView profile

Professor

• Micromechanics
• Continuum Dislocation Mechanics
• Polycrystal Modeling
• +3 more

Stéphane Berbenni Vincent Taupin serves as Professor at University of Lorraine's School of Engineering within Department 2 IMPACT (Ingénierie des Microstructures, Procédés, Anisotropie, ComportemenT), part of the LEM3 laboratory (UMR CNRS 7239). The department focuses on micromechanics, material processes, and structural behavior. Research centers on developing advanced micromechanical frameworks combining Fourier transform methods with continuum dislocation mechanics. Key contributions include the MFDM-EVPFFT model that integrates Elasto-Visco-Plastic Fast Fourier Transform formulations with Mesoscale Field Dislocation Mechanics theory. This approach captures geometrically necessary dislocation (GND) and statistically stored dislocation (SSD) dynamics in polycrystalline materials, enabling simulation of grain size effects and interface phenomena in two-phase composites. Current work investigates GND density evolution through filtered numerical spectral approaches coupled with stress equilibrium algorithms. Recent publications address small-angle scattering patterns in Ni-based superalloys using phase-field models, demonstrating applications in microstructure characterization. The research group operates within LEM3's extensive infrastructure serving 250 researchers across 50 national and 80 international projects. As part of University of Lorraine's engineering faculty, the position involves collaboration with CNRS and Arts et Métiers institutions. The laboratory maintains three technology platforms supporting mechanical rheology, microscopy, and computational processing for materials research.

• Computational Fluid Dynamics
• Fluid-Structure Interaction
• Computational Structural Dynamics
• +12 more

Dr.-Ing. Norbert Hosters is a Research Associate and Chief Engineer at the Chair for Computational Analysis of Technical Systems (CATS), Faculty of Mechanical Engineering, RWTH Aachen University. He has been active since 2020 and serves as General Secretary of the German Association for Computational Mechanics (GACM). His work bridges advanced computational methods with engineering applications. His research focuses on numerical methods for fluid-structure interaction , computational fluid and structural dynamics , isogeometric analysis , and aerothermoelasticity . He also explores applied quantum methods and physics-informed neural networks for solving complex PDEs and optimizing industrial processes. His interdisciplinary work spans mechanical, biomedical, and computational engineering. The recent publications (2023–2025) demonstrate a strong trend toward integrating machine learning with traditional simulation techniques, particularly in partitioned FSI , multiphase flow , shape optimization , and biomedical simulations such as LVAD modeling. His work appears in high-impact journals like Scientific Reports , Computers & Fluids , and International Journal for Numerical Methods in Engineering , as well as major conferences including GACM and CMBE. He is actively involved in teaching courses on Numerical Methods for Fluid-Structure Interaction , Isogeometric Analysis , and Simulation Methods in Mechanical Engineering . He offers student projects and supervises research, though no named advisees are listed. He has no listed scientific awards or fellowships. His research is conducted within the CATS chair, a leading group in computational mechanics, contributing to both fundamental methods and industrial applications. He plays a key role in academic service through GACM leadership.

Yao YueView profile

Researcher

• Model Order Reduction
• Design Optimization
• Krylov Methods
• +7 more

Yao Yue is a postdoctoral researcher at the Max Planck Institute for Dynamics of Complex Technical Systems in Magdeburg, Germany. Their work focuses on computational methods in systems and control theory, including parametric model order reduction, design optimization, Krylov methods, and fast computation techniques for Simulated Moving Bed (SMB) models and vibration/structure simulations. Education : Bachelor of Electronic Science and Technology, Harbin Engineering University (2001–2005) Master of Integrated Circuit Design, Tsinghua University (2005–2008) PhD in Engineering (Applied Mathematics, Numerical Approximation, Linear Algebra) at KU Leuven, Belgium (2008–2012) Research Interests span both applied mathematics and philosophical foundations of cognition. In computational methods, they work on optimizing numerical algorithms and system modeling. In philosophy, they explore axiomatic frameworks for cognitive dispositions, negation tendencies, and the epistemological limits of logic and empirical observation. Additional Activities include maintaining a personal homepage where they develop philosophical works like Reconstruction of Philosophy , discussing topics such as multiverse theory, consciousness, and the role of negation in knowledge formation.

Frank JenkoView profile

Professor

• Plasma Physics
• Fusion Energy
• Computational Physics
• +3 more

Frank Jenko is an Honorary Professor for Computational Physics at the Technical University of Munich and a Scientific Member and Head of the Tokamak Theory Division at the Max Planck Institute for Plasma Physics (IPP) since January 2017. His research focuses on plasma physics and fusion energy, with particular expertise in tokamak theory, turbulence simulation, and computational physics. Dr. Jenko was born in 1968 in Landshut and studied physics at the Technical University of Munich. After completing his doctorate, he joined IPP as a research associate in 1998. Following research stays in the USA, he completed his habilitation at the University of Ulm in 2005 and led an IPP junior research group focused on simulating plasma turbulence on supercomputers. From 2014 to 2017, he served as a professor of physics and astronomy and director of the Plasma Science and Technology Institute at the University of California, Los Angeles. His research interests span plasma physics, fusion energy, and computational methods for simulating complex plasma phenomena. Jenko's work particularly emphasizes gyrokinetic modeling of plasma turbulence in magnetic confinement devices, with applications to both tokamaks and stellarators. His research group develops and utilizes advanced computational tools like the GENE code for high-fidelity plasma simulations. Analysis of his recent publications reveals a strong focus on advancing computational methods for plasma turbulence simulation, with applications to both tokamak and stellarator configurations. His work spans fundamental plasma physics, computational algorithm development, and practical applications to fusion energy research, particularly in validating simulation codes against experimental data and applying these validated models to predict and optimize fusion plasma performance. Starting Grant from the European Research Council (2011) Hans Werner Osthoff Prize from the University of Greifswald (2004) Otto Hahn Medal from the Max Planck Society (1999) As head of the Tokamak Theory Department at IPP, Dr. Jenko leads a research group focused on computational plasma physics and turbulence simulation. His team has secured significant funding through European Research Council grants and maintains strong international collaborations with major fusion facilities worldwide, including JET, ASDEX Upgrade, and Wendelstein 7-X. The group plays a key role in the development of the GENE code, a leading gyrokinetic turbulence simulation tool used by researchers globally. The Tokamak Theory Department under Dr. Jenko's leadership operates advanced computational facilities for plasma turbulence simulation and maintains close ties with experimental teams at major fusion facilities. The department is instrumental in bridging theoretical plasma physics with experimental results, contributing to the advancement of magnetic confinement fusion research worldwide.