Dr. Stefano Ubbiali is a Researcher at ETH Zürich's Institut für Atmosphäre und Klima , specializing in Atmospheric Dynamics and Climate Modeling. His work focuses on advancing numerical methods for weather and climate prediction systems, including high-performance computing frameworks like GT4Py and physics-dynamics coupling schemes. He explores kilometer-scale climate models, reduced order modeling with neural networks, and algorithm optimization for atmospheric processes. Research Interests: Numerical Analysis of Atmospheric Models High-Performance Stencil Computations Model Coupling and Interoperability Machine Learning in Climate Science Publications span 2017–2024, emphasizing computational efficiency and climate model scalability. His contributions address challenges in weather prediction accuracy and kilometer-scale climate simulations.
Marius van Dijke is a Professor in Behavioral Ethics at the Rotterdam School of Management (RSM), Erasmus University Rotterdam, where he is affiliated with the Department of Business-Society Management. He is also a Fellow at the Erasmus Research Institute of Management (ERIM), having been affiliated since 2010. From 2015 to 2018, he served as Director of Doctoral Education at ERIM, and from 2020 to 2025, he was Head of the Department of Business-Society Management. Dr. van Dijke's research focuses on behavioral ethics and leadership of high integrity. His work examines when power stimulates moral and immoral behavior, why people deeply value social justice, and the role of intuitive and controlled processes in moral judgment and behavior. He has made significant contributions to understanding organizational nostalgia, power dynamics, and procedural justice in organizational contexts. His research has important implications for both theory and practice, providing tools to help employees and managers function both productively and ethically. His extensive publication record spans top journals in management and psychology, including Organizational Behavior and Human Decision Processes, Journal of Management, Journal of Applied Psychology, Leadership Quarterly, and Journal of Experimental Social Psychology. His work demonstrates consistent focus on ethical behavior in organizations, with recent publications showing increasing attention to organizational nostalgia, cognitive processes in moral judgment, and the complex relationship between power and ethical behavior. Dr. van Dijke has supervised numerous PhD students throughout his career, including Joost Leunissen, Gijs van Houwelingen, Niek Hoogervorst, Laura M. Giurge, and Lisanne Versteegt, among others. His supervision spans topics related to ethical behavior, power dynamics, and organizational justice. He has been actively involved in doctoral education, serving as Director of Doctoral Education at ERIM from 2015 to 2018. His academic journey began with a PhD in 2002 on 'Understanding power dynamics. Effects of social comparison on tendencies to change power and power differences' from Tilburg University. In 2014, he delivered his inaugural address titled 'Understanding immoral conduct in business settings. A behavioural ethics approach,' marking his appointment as a full professor.
Manuel Torrilhon serves as Professor and head of the Research Lab for Applied and Computational Mathematics (ACoM) at RWTH Aachen University, where he has held a full professorship since 2010. He currently leads the Department of Mathematics as its elected Speaker for the 2024-2026 term, overseeing academic strategy and research initiatives within the Faculty of Mathematics, Computer Science and Natural Sciences. His academic foundation includes: Diplom-Ingenieur in Engineering Physics from TU Berlin (1994-1999) PhD in Applied Mathematics from ETH Zurich (2004) Postdoctoral research at HKUST (2004/05) and Princeton University (2005/06) Research Assistant Professor at ETH Zurich (2007-2010) Professor Torrilhon's research pioneers mathematical modeling in continuum physics and kinetic gas theory , with seminal contributions to the Boltzmann equation, rarefied gas dynamics, and magnetohydrodynamics. His work develops advanced numerical methods for nonlinear hyperbolic systems , particularly entropy-stable high-order schemes and multi-scale time integrators. The ACoM lab under his direction bridges theoretical mathematics with engineering applications through computational frameworks like fenicsR13 for moment equation solvers. His methodologies enable high-fidelity simulations of micro-flows, plasma instabilities, and electron transport phenomena critical to aerospace and materials science. Analysis of his 2025-2024 publications reveals dominant trends in entropy-conservative numerical schemes for kinetic equations, multirate time integration for stiff systems, and moment-method extensions to polytropic gases and shallow flows. These works consistently address computational challenges in rarefaction effects, non-equilibrium thermodynamics, and high-enthalpy regimes, demonstrating cross-cutting applications from microfluidics to plasma physics. Scientific recognition includes: EURYI Award (Pre-ERC) from European Science Foundation (2006) As director of ACoM, Professor Torrilhon secures research funding for computational mathematics projects and mentors graduate students in numerical analysis and kinetic theory. His lab maintains strong collaborations with engineering departments for applied validation of mathematical models, particularly in micro-flow devices and plasma containment systems. Current grants focus on adaptive solvers for multi-scale kinetic problems and inverse methods for electron probe microanalysis. The Research Lab for Applied and Computational Mathematics (ACoM) operates as an interdisciplinary hub developing open-source computational tools like fenicsR13. The team specializes in tensor-based numerical methods for moment equations, with ongoing projects in X-ray emission modeling, Richtmyer-Meshkov instability simulations, and thermodynamically consistent electrolyte solvers. ACoM maintains strategic partnerships with aerospace research institutes for hypersonic flow validation and with materials science centers for nanoscale transport studies.
Dr. Satyvir Singh is a Research Fellow at RWTH Aachen University (since 2022). He holds a Ph.D. in Mechanical and Aerospace Engineering from Gyeongsang National University, South Korea, and prior academic roles include Postdoctoral Fellowships at Nanyang Technological University (Singapore) and teaching positions in India. His research focuses on high-order numerical methods for fluid dynamics, particularly discontinuous Galerkin methods applied to hydrodynamic instabilities, computational fluid dynamics, and gas kinetic theory. Education: Ph.D., Mechanical and Aerospace Engineering, Gyeongsang National University, South Korea (2013-2016) M.Tech., Industrial Mathematics and Scientific Computing, IIT Madras, India (2009-2011) M.Sc., Mathematics, Chaudhary Charan Singh University, India Research Interests: High-Order Discontinuous Galerkin Method Computational Fluid Dynamics Hydrodynamic Instability (Richtmyer-Meshkov, Rayleigh-Taylor) Multispecies Flows and Gas Kinetic Theory Scientific Contributions include over 30 peer-reviewed articles in journals like Journal of Computational Physics , Physics of Fluids , and SCIENCE CHINA Physics , focusing on shock-driven instabilities, numerical methods for rarefied gases, and reaction-diffusion systems. Awards and Fellowships: Brain Korea 21+ Scholarship, South Korea (Ph.D.) GATE HMRD Scholarship, India (M.Tech.) Qualified JRF/NET (Mathematical Sciences), AIR 38 (2011) Labs and Affiliations: Part of the The Lab at RWTH Aachen University, contributing to interdisciplinary fluid dynamics research.
Holden H Wu is a Professor in the Department of Radiological Sciences at the University of California Los Angeles (UCLA) School of Medicine. His research focuses on advanced medical imaging techniques, particularly in quantitative MRI, artificial intelligence applications, and image-guided interventions. He leads the UCLA MRRL Wu Lab and has established himself as a leading researcher in free-breathing MRI techniques for body composition analysis and disease quantification. Dr. Wu's research interests span nanotheranostics, quantitative imaging, MRI technology development, artificial intelligence applications in medical imaging, and image-guided interventions. His work particularly emphasizes developing motion-robust techniques for abdominal and pediatric imaging, with applications in liver fat quantification, body composition analysis, and prostate cancer imaging. He has pioneered several free-breathing MRI techniques that have eliminated the need for breath-holding in patients, significantly improving clinical applicability, especially for pediatric populations and patients with limited breath-holding capacity. His recent publications demonstrate a strong focus on integrating deep learning with physics-based modeling to improve quantitative MRI techniques. Major trends in his research include the development of self-gated radial MRI techniques, motion-compensated imaging methods, and AI-powered analysis tools for medical imaging data. His work bridges engineering innovation with clinical applications, particularly in liver disease, metabolic disorders, and oncology. New Technologies for Real-Time MRI-Guided Robotic-Assisted Abdominal Interventions (NIH R01EB031934, 2022-2026) - Principal Investigator Quantitative MRI and Deep Learning Technologies for Classification of NAFLD (NIH U01EB031894, 2022-2027) - Principal Investigator Quantifying Body Composition and Liver Disease in Children using Free-Breathing MRI and MRE (NIH R01DK124417, 2020-2024) - Principal Investigator Integrating Quantitative MRI and Artificial Intelligence to Improve Prostate Cancer Classification (NIH R01CA248506, 2020-2025) - Co-Principal Investigator Dr. Wu has mentored numerous students and researchers through his active laboratory, focusing on training the next generation of biomedical imaging scientists. His research group, the UCLA MRRL Wu Lab, develops innovative imaging technologies with direct clinical translation potential. Current projects include developing real-time MRI-guided robotic interventions, advanced quantitative techniques for liver fat and fibrosis assessment, and AI-powered prostate cancer detection methods.
James Percival is a Senior Teaching Fellow in the Department of Earth Science & Engineering at Imperial College London, part of the Faculty of Engineering. His research focuses on computational methods for fluid dynamics, environmental engineering, and porous media flow. He is affiliated with the Applied Modelling and Computation Group and the Novel Reservoir Modelling and Simulation (NORMS) initiative. His work emphasizes numerical simulations using advanced techniques like discontinuous Galerkin methods and adaptive unstructured meshes. Key research areas include hydro-morphodynamics, multiphase flow modeling, and reservoir engineering. His publications highlight contributions to fluid dynamics, atmospheric modeling (e.g., ATHAM-Fluidity), and environmental applications such as pipeline scour analysis. Percival’s methodologies prioritize high-resolution simulations and mesh optimization for complex geophysical and industrial challenges. His articles reflect a trend toward integrating computational efficiency with accuracy in modeling phenomena like viscous fingering, interfacial flows, and extreme weather events. Despite significant contributions, no awards or grants are explicitly noted in the provided texts.
Dr. Georgios Fourtakas is a Lecturer in Civil Engineering at the University of Manchester’s School of Mechanical Aerospace and Civil Engineering. He specializes in meshless methods, computational fluid dynamics (CFD), and smoothed particle hydrodynamics (SPH). His research focuses on advancing SPH formulations for complex fluid-structure interactions, multiphase flows, and high-performance computing (HPC) applications, particularly through the DualSPHysics open-source framework. He holds a PhD from the University of Manchester (2014) and prior industry experience in aeronautical engineering and thermal fluid mechanics. Education: BEng in Aeronautical Engineering, University of Salford MSc in Aeronautical Engineering, Cranfield University MSc in Thermal Power and Fluid Mechanics (Academic Return) PhD in SPH Applications (University of Manchester) Key Research Areas: SPH formulations (Lagrangian, Eulerian-Lagrangian, ALE) GPU/CUDA acceleration for HPC Boundary condition development Non-Newtonian and sediment flows Fluid-structure interaction (e.g., heart valves) His work contributes to UN Sustainable Development Goals through applications in coastal engineering, cardiovascular systems, and nuclear decommissioning. He has reviewed for journals like Advances in Water Resources and Journal of Hydroinformatics . Recent articles highlight innovations in divergence cleaning for SPH, GPU-accelerated thrombus modeling, and poroelasticity simulations. His DualSPHysics project is widely adopted for real-world engineering problems.
Agostino Marinelli is Assistant Professor of Photon Science and Particle Physics and Astrophysics at SLAC National Accelerator Laboratory, Stanford University. He leads the free-electron laser physics department and co-directs the FEL R&D program, focusing on X-ray free-electron lasers and ultrafast light sources. His research integrates accelerator physics, photon science, and quantum optics to develop advanced light sources. Recent publications demonstrate strong thematic coherence across attosecond science, beam manipulation, and instrumentation innovation. His articles show consistent exploration of pulse control mechanisms, beam dynamics, and novel diagnostic methods, with applications spanning quantum dynamics, materials science, and instrumentation development.
Dr. Chris Nonhof serves as an Associate Professor of Education and English and Chair of the English Department at Northwestern College. He has extensive experience in secondary education, having taught English, theatre, and argumentative writing in private and public schools in Florida and Wisconsin before joining Northwestern College. He holds a Ph.D. in Language and Literacy from Cardinal Stritch University, an M.Ed. in Instructional Technology from the same institution, and a B.A. in English Literature, Theatre Arts, and Secondary Education from Dordt University. His research focuses on language and identity, qualitative research methodologies (particularly narrative, ethnography, and case studies), diversity in education, and culturally responsive pedagogy. Recent work explores AI's impact on English education and strategies for student-centered curricula that prioritize individual narratives over traditional canons. Nonhof has presented at numerous conferences, including the Iowa Council of Teachers of English and the International Network for Christian Higher Education. His publications span topics like discourse analysis in Liberian orphanage schools and code-switching in academic settings. He has received awards such as the Northwestern Teaching Excellence Award (2022) and the Blekkink Endowed Chair of Education (2019-2024). He maintains active membership in professional organizations including the National Council of Teachers of English and the Modern Language Association. His teaching philosophy emphasizes ethical pedagogy and bridging divides through communication, informed by his diverse roles as a teacher, theatre director, and tennis coach.
Xianyi Zeng is an Assistant Professor in the Department of Mathematics at Lehigh University. He holds a Ph.D. in Computational Mathematics from Stanford University (2012), an M.S. in Financial Mathematics from Stanford (2010), and a B.S. in Mathematics from Peking University (2006). His research focuses on numerical methods for partial differential equations, particularly hyperbolic conservation laws and fluid-structure interactions. He has developed innovative frameworks like the hybrid-variable (HV) discretization and contributed to the shifted boundary method (SBM) for compressible fluid dynamics. His work also bridges computational mechanics, mathematical biology (e.g., tumor growth modeling), and high-performance computing. Education: Ph.D., Computational Mathematics, Stanford University (2012) M.S., Financial Mathematics, Stanford University (2010) B.S., Mathematics, Peking University (2006) Research Interests include: Numerical methods for PDEs with complex geometries Hyperbolic conservation laws and shock hydrodynamics Mathematical modeling of tumor dynamics and virotherapy Reduced-order modeling and computational efficiency Recent work emphasizes high-order accuracy, stability, and scalability in simulations of multiphysics problems. His publications span journals like Journal of Computational Physics and Computer Methods in Applied Mechanics and Engineering . Teaching includes courses on differential equations at Lehigh, integrating analytical and numerical techniques. He has held faculty positions at UT El Paso (2016–2022) and conducted postdoctoral research at Duke University (2012–2016).
Xiaoyang Zeng is a Professor at Tsinghua University's School of Information Science and Technology, Institute of Microelectronics, with an extensive research portfolio in VLSI design, integrated circuits, and hardware acceleration systems. With over 429 publications spanning from 2005 to 2025, Professor Zeng maintains an exceptionally active research program, particularly evident in the high publication volume in recent years (45 papers in 2024 and 28 projected for 2025). His collaborative network includes prominent researchers such as Yibo Fan, Jun Han, Xu Cheng, and Xiaoyong Xue. Professor Zeng's research focuses on cutting-edge areas including Compute-in-Memory architectures, neuromorphic computing, low-power circuit design, and hardware acceleration for AI applications. His work bridges theoretical innovation with practical implementation, as evidenced by numerous publications in top-tier IEEE journals including the Journal of Solid-State Circuits, Transactions on Circuits and Systems, and Transactions on VLSI Systems. Recent work demonstrates particular strength in RRAM-based CIM accelerators, energy-efficient converters, and advanced signal processing techniques. The publication trends show a strategic evolution from traditional circuit design toward emerging computing paradigms, with increasing focus on AI hardware acceleration, neuromorphic systems, and energy-efficient computing solutions. His research group has developed innovative approaches to address challenges in memory-centric computing, analog circuit design, and hardware implementation of machine learning algorithms, with applications spanning consumer electronics, medical devices, and edge computing systems. Selected Scientific Awards: IEEE Journal of Solid-State Circuits Best Paper Award (2022) National Natural Science Award of China (Second Class, 2020) IEEE Asian Solid-State Circuits Conference Best Paper Award (2019) Professor Zeng has successfully advised numerous graduate students who have become active contributors in the field, with several now leading their own research projects. His research has been supported by multiple national-level grants from the National Natural Science Foundation of China and the Ministry of Science and Technology, focusing on next-generation computing architectures and advanced circuit design methodologies. The research group maintains strong industry connections with leading semiconductor companies for technology transfer and practical implementation of research outcomes.
Professor Georg Schmitz is a distinguished academic in the field of medical engineering and ultrasound imaging at Ruhr-University Bochum, Germany. He holds the chair for medical engineering within the Faculty for Electrical Engineering and Information Technology and has served as Dean of this faculty from 2009 to 2012. Since 2014, he has been a member of the Senate of Ruhr-University Bochum. Prof. Schmitz received his Dipl.-Ing. degree in 1990 and Dr.-Ing. degree in 1995 in electrical engineering from Ruhr-Universität Bochum. Prior to his current position, he worked as a Principal Scientist at Philips Research Laboratories from 1995 to 2001 and served as Professor for Medical Engineering at the University of Applied Science Koblenz from 2001 to 2004. His research primarily focuses on ultrasound imaging, with current projects investigating ultrasound contrast media detection and characterization, novel beamforming and nonlinear reconstruction methods, and photoacoustic imaging. Prof. Schmitz is particularly known for his work in super-resolution ultrasound, ultrasound localization microscopy, and the application of deep learning techniques to medical ultrasound imaging. His recent publications demonstrate significant advancements in microvascular imaging, cancer diagnostics, and image reconstruction algorithms. Prof. Schmitz's research output shows a clear trajectory toward integrating artificial intelligence with traditional ultrasound techniques, with numerous publications focusing on deep learning applications for image enhancement, needle localization, and super-resolution imaging. His work bridges theoretical signal processing with practical clinical applications, particularly in oncology and microcirculation imaging. Senior member of the IEEE Member of the Acoustical Society of America (ASA) Member of the German Association of Electrical Engineers (VDE) Member of WFUMB, EFSUMB, and DEGUM Former associate editor of IEEE Transactions of Ultrasonics, Ferroelectrics, and Frequency Control (15+ years) Member of Editorial Advisory Board of Ultrasound in Medicine and Biology Prof. Schmitz has played significant leadership roles in the international ultrasound community, serving as vice chair (medical ultrasound) of the IEEE International Ultrasonics Symposium technical program committee from 2013-2015, and as technical program chair for the symposium in Washington, D.C. (2017) and Venice (2022). His extensive collaborations with researchers including S. Dencks, T. Lisson, and M. Fouad have resulted in numerous high-impact publications spanning ultrasound technology, medical diagnostics, and imaging algorithms. His laboratory focuses on advancing ultrasound localization microscopy techniques and developing novel approaches for clinical applications, particularly in cancer imaging and treatment monitoring. The team's work on microbubble technology, photoacoustic imaging, and AI-enhanced ultrasound demonstrates a comprehensive approach to solving complex medical imaging challenges.
Giorgio Martalò is a Researcher in the Department of Mathematical Physics at the University of Pavia. His work bridges mathematical physics, fluid dynamics, and applied mathematics, focusing on kinetic theory applications to gaseous systems, biological phenomena, and environmental engineering. Key research areas include chemotaxis modeling for Multiple Sclerosis, shock wave dynamics in gas mixtures, and optimal control strategies for waste treatment processes. His research explores both theoretical advancements and practical applications, such as deriving hydrodynamic limits from kinetic equations, analyzing reaction-diffusion systems with Allee effects, and developing numerical methods for complex fluid flows. Recent work addresses misinformation dynamics through prebunking strategies and investigates non-equilibrium phenomena in multi-component gas mixtures. Publications span multiscale modeling techniques, including hybrid kinetic models and IMEX finite volume schemes, alongside studies on thermal non-equilibrium in shock waves and optimal control of biocell composting systems. His interdisciplinary approach integrates mathematical rigor with real-world problem-solving in biomedical, environmental, and engineering domains.
Sylvie MARCOS is a Senior Researcher at CentraleSupélec, France, affiliated with the Laboratoire des Signaux et Systèmes (L2S). She holds a PhD in Telecommunications (1987) and an HDR in Signal Processing (1995) from the University of Paris-Sud. Her research focuses on signal processing, radar systems, machine learning applications in environmental monitoring, and chaotic sequence generation for telecommunications. She has supervised numerous doctoral students and co-authored over 50 publications in high-impact journals and conferences. Education: Engineer, Ecole Centrale de Paris (1984) PhD in Telecommunications, University of Paris-Sud (1987) Habilitation à Diriger des Recherches (HDR), University of Paris-Sud (1995) Research Interests: Time series analysis and anomaly detection Radar signal processing and LPI radar detection Chaotic sequences for MIMO radar and CDMA systems High-resolution source localization and sparsity-based methods Adaptive filtering and neural network applications Her recent work emphasizes environmental monitoring using machine learning and deep learning techniques, as well as robust radar systems design. She collaborates with institutions like CEA-DAM Valduc and Onera on defense-related signal processing challenges. Grants/Projects: Active involvement in European and national research projects on radar systems, environmental signal processing, and sparse signal recovery. Labs/Teams: Member of L2S research groups MODESTY, COMEDY, and SYCOMORE, focusing on systems modeling and robust control.
Dr. Daniel Jodlbauer is a Research Scientist at the Johann Radon Institute for Computational and Applied Mathematics (RICAM) , part of the Austrian Academy of Sciences (OEAW). He is affiliated with the Transfer Group , focusing on interdisciplinary computational methods and their applications in science and engineering. His research spans numerical analysis, computational mechanics, and biomedical imaging. Key projects include developing matrix-free multigrid solvers for fluid-structure interaction and phase-field fracture problems, and advancing adaptive optics for biomedical imaging (e.g., AO-OCT) and astronomical instrumentation (e.g., the MICADO imager for the Extremely Large Telescope). His work bridges theoretical computational methods with real-world applications in medicine, astronomy, and engineering. Recent contributions highlight advancements in PSF reconstruction for the MICADO project, improving imaging resolution for the ELT telescope, and optimizing wavefront sensors for retinal imaging. His computational methods address challenges in high-performance computing, parallel algorithms, and nonlinear solvers for complex physical systems. Dr. Jodlbauer collaborates across disciplines, contributing to RICAM’s research groups in Computational Methods for PDEs and Optimization. His publications reflect expertise in mathematical data science, inverse problems, and multivariate algorithms, though no academic awards or grants are explicitly noted in the provided text.
