Nada Sissouno is a Professor of Mathematics and Didactics of Mathematics at the Faculty of Electrical Engineering, Media and Informatics at Amberg-Weiden University of Applied Sciences since November 2023. She also serves as Vice Dean and Co-head of the Competence Center Grundlagen (CCG). Additionally, she maintains a position as a guest researcher at the Research Group: Applied and Numerical Analysis and Optimization and Data Analysis at the Technical University of Munich (TUM). Her educational background includes a Doctorate in Mathematics (Dr. rer. nat.) from TU Darmstadt (2007-2011) and a Diplom in Mathematics with a minor in psychology from TU Darmstadt (2000-2007). She has completed further education as a Diversity Manager in 2021 and holds certificates in teaching in higher education from the Bavarian Universities (2014-2016). Professor Sissouno's research focuses on mathematical methods in signal and image processing, data science, dynamical systems, numerical simulation, and approximation theory. Her work particularly emphasizes spline functions on domains, wavelets and frames, and evidence-based development of teaching methodologies. Her recent publications demonstrate strong expertise in mathematical imaging, phase retrieval problems, and approximation theory, with applications spanning ptychographic imaging, variational inpainting methods, and structural sparsity in multiple measurements. Her collaborative research bridges theoretical mathematics with practical applications in signal processing and image analysis, with a particular focus on developing robust numerical algorithms for complex data analysis problems. She has published in prestigious journals including Advances in Computational Mathematics, Journal of Fourier Analysis and Applications, IEEE Transactions on Signal Processing, and Inverse Problems. Referentin für Talentmanagement & Diversity at TUM (2022-2023) Deputy spokesperson of Research Associates' Council of the TUM (2019-2023) Gender equality officer of Department of Mathematics (2019-2022) Professor Sissouno teaches mathematics courses for engineering and computer science students, with a focus on making mathematical concepts accessible and relevant to practical applications. She has been involved in teacher training and the evidence-based development of teaching methodologies, demonstrating her commitment to both research excellence and educational innovation.
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برای مسیر پژوهشیتان، استاد راهنما، استاد دانشگاه و پژوهشگر مناسب را پیدا کنید و با آنها ارتباط بگیرید.
نمایش ۶۱-۷۵ از ۲۲۸ استاد راهنما
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Enrico Bertolazzi is an Associate Professor in Numerical Analysis at the University of Trento's Department of Industrial Engineering. He specializes in numerical methods for optimal control, finite element methods, sparse matrix computations, and symbolic computation. His work integrates advanced programming skills in C/C++, MATLAB, and other languages to develop computational tools for engineering and mathematical challenges. Bertolazzi's research addresses complex systems such as vehicle dynamics, trajectory optimization, and real-time simulations, with applications in automotive engineering, renewable energy, and robotics. Research Interests: Numerical Optimal Control and Trajectory Planning Finite Element/Finite Volume Methods for PDEs Sparse Matrix Algorithms Clothoid Curve Modeling Symbolic-Numeric Hybrid Techniques Vehicle Dynamics and Autonomous Systems Publications: Recent work focuses on real-time tire models, DAE index reduction, and autonomous vehicle path planning. His contributions span algorithm development, computational toolboxes (e.g., TrussMe-Fem), and interdisciplinary applications combining mathematics with engineering. Grants & Advising: Advises on optimal control, robotics, and simulation projects. Active in developing educational software like the virtual racing car championship platform. Labs/Teams: Involved in research groups focused on intelligent vehicles, computational mechanics, and numerical methods for industrial applications.
Victor Churchillمشاهده پروفایل
استادیار
Victor Churchill is an Assistant Professor of Mathematics at Trinity College since 2023. He holds a Ph.D. and A.M. from Dartmouth College, an M.S. from New York University's Courant Institute, and a B.A. from Boston College. His research focuses on computational mathematics, scientific machine learning, and image reconstruction, particularly in Bayesian uncertainty quantification for synthetic aperture radar imaging and learning unknown dynamical systems using neural networks. He has held a postdoctoral position at The Ohio State University under Dr. Dongbin Xiu and previously worked at Dartmouth under Dr. Anne Gelb. Research Highlights: His work includes deep learning of PDEs, ensemble prediction for robust neural network training, and chaotic system learning from partial observations. Recent contributions address coarse time-scale observations and uncertainty quantification in SAR imaging. He was awarded the SIAM Science Policy Fellowship (2023-2024) to engage with federal science policy advocacy. Teaching: He teaches computational science courses at both undergraduate and graduate levels, integrating his research into lectures through case studies and data-driven examples. His pedagogical approach emphasizes applied computational mathematics and real-world problem-solving. Affiliations: Previously affiliated with The Ohio State University as a Visiting Assistant Professor of Scientific Computation. Active in computational math communities, including SIAM policy engagement. Personal Interests: An avid runner with marathon personal bests, he also enjoys bonsai cultivation, architectural design, and animal care. His unconventional hobbies include experimenting with hair color transformations.
Chao Chenمشاهده پروفایل
استادیار
Chao Chen is an Assistant Professor in the Department of Mathematics at North Carolina State University (NC State), where he has been since 2023. His research focuses on developing efficient algorithms for matrix computations, with applications ranging from solving partial differential equations to analyzing large high-dimensional datasets. Prior to this, he was a postdoctoral researcher at The University of Texas at Austin’s Oden Institute, working with George Biros and Gunnar Martinsson. He earned his PhD in Computational and Mathematical Engineering (ICME) from Stanford University in 2018, advised by Eric Darve. He also holds an MS from Stanford’s ICME (2014) and a BS in Information and Computational Mathematics from Nankai University (2012). Education: PhD in Computational and Mathematical Engineering, Stanford University, 2018 MS in Computational and Mathematical Engineering, Stanford University, 2014 BS in Information and Computational Mathematics, Nankai University, 2012 Research Interests: Chen’s work emphasizes high-performance computing, numerical linear algebra, and randomized algorithms. He develops methods to reduce computational complexity and enhance performance on modern hardware. His applications span computational physics, data science, and machine learning. Notably, he has contributed to fast multipole methods, hierarchical solvers, and preconditioners for large-scale systems. Teaching: Chen teaches courses such as Linear Analysis (MA 303), Mathematics of Scientific Computing (MA 402), Linear Transformations and Matrix Theory (MA 523), and Theory of Matrices (MA 723). His courses emphasize theoretical foundations and practical applications, often incorporating computational tools like MATLAB and LaTeX. Presentations: He has presented at conferences worldwide, including SIAM Annual Meetings, Supercomputing Conferences, and specialized workshops on numerical linear algebra and fast solvers.
Stefano Discetti is a Full Professor in the Department of Aerospace Engineering at Universidad Carlos III de Madrid, leading the Aerospace Engineering Research Group. His work bridges experimental fluid dynamics, machine learning, and flow control. Key research themes include turbulence characterization, heat transfer enhancement, and advanced measurement techniques like PIV/PTV. He has developed data-driven methods for flow estimation, sensor placement optimization, and physics-informed neural networks. Research Trends from his publications show a focus on: Machine learning for turbulent flow reconstruction (GANs, CNNs, KNN) Non-intrusive sensing from wall measurements Manifold learning and reduced-order modeling Heat transfer control via plasma actuators and passive structures Time-resolved diagnostics using hybrid experimental/numerical approaches Projects include principal roles in EU and national grants like NEXTFLOW (2021-2026) and EXCALIBUR (2023-2026), with industry collaborations at Airbus and TU Delft.
Eliseo García García is a Professor at the University of Alcalá, affiliated with the Department of Automatic Control and Systems Engineering. He holds a Ph.D. from the University of Alcalá, awarded in 2005 for his thesis on computational electromagnetics. His primary research focuses on computational methods for electromagnetic analysis, including the development of efficient algorithms like the Characteristic Basis Function Method (CBFM) and hybrid techniques with the Multilevel Fast Multipole Algorithm (MLFMA). He is a core member of the GEC (Computational Electromagnetic Group), where he explores geophysical applications, antenna design, and high-frequency electromagnetic problems. His research interests bridge theoretical and applied electromagnetics, with particular emphasis on reducing computational costs in solving large-scale EM problems. Notable contributions include advancements in radar cross-section (RCS) computation, radome structure analysis, and geothermal exploration frameworks using hydrogeophysical methods. He has also published extensively on numerical techniques for antenna trajectory simulations and sparse matrix preconditioning. Eliseo’s work spans multiple disciplines, including aerospace engineering, environmental science, and software development, as evidenced by his contributions to Altair Feko 2023 updates. Despite no listed awards, his prolific publication record (over 70 articles from 2013–2024) underscores his active role in advancing computational electromagnetics and its interdisciplinary applications. His advising and grants section remains unreported, but his involvement in collaborative projects is implied through frequent co-authorships and institutional affiliations. He leads the GEC lab, focusing on cutting-edge computational tools and their practical deployment in real-world engineering challenges.
Tolga Birdalمشاهده پروفایل
استادیار
Tolga Birdal is an Assistant Professor (Lecturer) and UKRI Future Leaders Fellow in the Department of Computing at Imperial College London. As the Principal Investigator (PI) of the CIRCLE group , his research focuses on topological deep learning, geometric machine learning, and 3D computer vision, with theoretical interests in non-Euclidean inference and deep learning principles. Education: PhD and MSc in Computer Vision from Technical University of Munich (2018), BSc in Computer Science from Sabancı University (2008). Projects: PI for UKRI-EPSRC's UNTOLD (Topological Deep Learning), Royal Society's drug discovery initiative, and EPSRC's GNOMON (Generative Models in non-Euclidean Spaces). Leadership: Area Chair for CVPR, ICCV, and 3DV 2025 Publication Chair. His work bridges differential geometry, algebraic topology, and deep neural networks, with applications in quantum computer vision, 3D/4D generative priors, and medical imaging. Key contributions include novel frameworks for rotation forecasting, graph generation, and topological generalization bounds. Scientific Awards: UKRI Future Leaders Fellowship EMVA Young Professional Award
- Computational Mathematics
- Numerical Methods for Partial Differential Equations (PDEs)
- Stochastic Differential Equations
- +۲ مورد دیگر
Professor Thanh Tran is a faculty member at the School of Mathematics & Statistics, University of New South Wales (UNSW Sydney). His research focuses on computational mathematics, particularly numerical methods for deterministic and stochastic partial differential equations (PDEs), with applications in physical sciences, engineering, and magnetic materials. He has secured multiple competitive research grants, including ARC Discovery Projects and international collaborations. Education: PhD from UNSW Sydney Research Grants: ARC Discovery Projects (2012-2026), UNSW Goldstar Awards (2011, 2015), Go8-Germany Joint Research Scheme (2013) Teaching: Courses include Stochastic Differential Equations (MATH3361/5361), Mathematics for Actuarial Studies (MATH1251), and Applied Analysis (MATH3051) His work on numerical methods spans preconditioning techniques, error estimation, adaptivity, and finite element-boundary element coupling. Recent publications emphasize stochastic PDEs and nonlinear evolution equations, with applications in magnetohydrodynamics and chemotaxis models. Scientific awards highlight his contributions to interdisciplinary mathematics, particularly through collaborative projects on magnetic memory materials and random boundary problems. He actively supervises PhD and Honours students in computational mathematics, focusing on Maxwell-Landau-Lifshitz-Gilbert equations and shape calculus.
Gerlind Plonka is a Professor of Applied Mathematics at the University of Göttingen, specifically within the Institute for Numerical and Applied Mathematics (NAM). Her research focuses on Numerical Fourier Analysis Wavelet Theory Regularization and Nonlinear Diffusion Methods Fast Algorithms and Numerical Stability Signal and Image Processing Applications Her recent publications emphasize structured subsampling in Fourier domains, Prony-type methods for exponential sum recovery, and deep learning integration in medical imaging. She supervises active PhD candidates including Benjamin Kocurov, Anahita Riahi, Yannick Nicola Riebe, and Janina Schmidt, with a legacy of advising over 50 graduates across diverse topics like Sparse FFT Algorithms Phase Retrieval Constraints Wavelet-Based Image Compression Nonlinear Diffusion Filters High-Dimensional Data Approximation
Johannes M. Arendمشاهده پروفایل
استادیار
- Virtual Acoustics
- Spatial Audio
- Psychoacoustics
- +۳ مورد دیگر
Johannes M. Arend serves as an Assistant Professor in the Department of Information and Communications Engineering at Aalto University, Finland. His research centers on virtual acoustics, spatial audio processing, and psychoacoustics, with significant contributions to audio engineering and hearing science applications. His primary research domains include Virtual Acoustics, Spatial Audio, Psychoacoustics, Hearing Science, Acoustics, and Audio Engineering. Leading the Technical Psychoacoustics research group, he investigates sound perception in virtual environments and develops advanced spatial audio techniques. Key methodologies involve spherical microphone array processing, head-related transfer function (HRTF) interpolation, and voice directivity analysis, with applications spanning virtual reality, hearing diagnostics, and audio reproduction systems. Analysis of his 15 most recent publications (2021-2025) reveals consistent focus on spatial audio processing and perceptual validation. Dominant themes include spatial upsampling techniques for microphone arrays (33% of articles), voice directivity modeling with phoneme-level analysis (27%), and HRTF processing for binaural rendering (20%). His work demonstrates strong interdisciplinary integration between audio signal processing, speech science, and virtual reality applications, particularly in developing perceptually validated tools for hearing assessment and spatial audio reproduction. Dr. Arend actively leads the Technical Psychoacoustics research group, directing projects on virtual acoustic environments, spatial hearing assessment, and audio reproduction technologies. His team develops innovative methods for binaural rendering and conducts perceptual studies to validate technical solutions, with recent work focusing on VR-based hearing diagnostics and spatial audio evaluation frameworks.
- Machine Learning
- Computer Graphics
- Computer Vision
- +۳ مورد دیگر
Alejandro Sztrajman is a Researcher and Research Associate at the University of Cambridge's Department of Computer Science and Technology, affiliated with the Rainbow Group. He holds a PhD in Computer Science from University College London (UCL), supported by a Marie Curie Fellowship, and has conducted research internships at Microsoft and Adobe. His work bridges machine learning and visual computing, focusing on neural fields, generative AI, and physics-based rendering, with applications in material appearance modeling, scene illumination, and computational photography. His research spans topics like neural BRDF representations, point cloud generation via diffusion models, and interpretable time series analysis. Key contributions include LSCD (irregular time series imputation), NeuMaDiff (material synthesis via hyperdiffusion), and FrePolad (point cloud generation). He has also developed methods for HDR image deglaring and color calibration for OLED displays. Awards include the Wiley Top Cited Paper and Marie Curie Fellowship funding. His work emphasizes practical applications, with publications in top-tier venues like ECCV, CVPR, and ICML. Collaborations involve Professors Cengiz Öztireli and Rafał Mantiuk at Cambridge, and Tobias Ritschel at UCL. His research aims to advance generative AI, neural rendering, and physically grounded machine learning techniques.
Gregor Kastner is Professor and Deputy Head of the Institute of Statistics at the University of Klagenfurt. His research focuses on Bayesian statistics, time series analysis, econometrics, and computational methods, with applications in finance, economics, and environmental modeling. He develops statistical software including packages for stochastic volatility modeling in R. Kastner's methodological work centers on Bayesian inference for high-dimensional problems, developing efficient computational algorithms for complex models. His applied research examines volatility dynamics in financial markets, macroeconomic forecasting, and spatial analysis of economic indicators. Recent projects include Bayesian nonparametric clustering for evaluating agricultural subsidies in Europe, sparse vector autoregressions for high-dimensional forecasting, and stochastic volatility models for commodity markets. He maintains active collaborations across economics, finance, and environmental science disciplines.
Pawan Goyalمشاهده پروفایل
پژوهشگر
Dr. Pawan Goyal is a Senior AI Engineer/Researcher at appliedAI Initiative GmbH, focusing on generative AI and physics-informed machine learning. Previously, he led the Physics-Enhanced Machine Learning team at the Max Planck Institute for Dynamics of Complex Technical Systems, where he developed surrogate modeling techniques integrating physics into AI. His research spans dynamical systems, model reduction, and materials science, emphasizing stability and interpretability of models. Education: PhD in Applied Mathematics (Max Planck Institute, 2018), M.Tech and B.Tech in Engineering Design (IIT Madras). His work bridges computational science and AI, addressing challenges in engineering design and materials discovery. Key interests include generative AI for design, physics-informed neural networks, and reduced-order modeling for complex systems. Awards: Dr.-Klaus-Körper Prize (GAMM, 2019), Best Ph.D. Thesis (Otto-von-Guericke University, 2018).
Luis Romero Benمشاهده پروفایل
پژوهشگر ارشد
Luis Romero Ben is a research-focused academic affiliated with the Institut de Robòtica i Informàtica Industrial (IRI), a joint research center of the Universitat Politècnica de Catalunya (UPC) and the Spanish National Research Council (CSIC). He is actively involved in advanced research in automatic control applied to water distribution and urban drainage systems, with a focus on real-time monitoring, fault detection, and data-driven modeling. His primary affiliation is with the Department of Automatic Control within the College of Engineering at UPC. His research interests lie at the intersection of control engineering and environmental systems, particularly in leak detection and localization , sensor placement optimization , data-driven state estimation , and smart water network management . He employs advanced methodologies such as Unscented Kalman Filters, dictionary learning, graph-based interpolation, and genetic algorithms to enhance the resilience and efficiency of urban water infrastructure. The trend in his recent publications shows a strong emphasis on hybrid approaches combining physical models with machine learning for fault diagnosis in water networks. His work contributes significantly to both theoretical advancements and practical implementations in the field of water system supervision. Scientific Awards: No awards mentioned in the provided text. Luis Romero Ben is actively involved in major European and CSIC-funded research projects such as RUBIES (Real-time pollution-based control of urban drainage systems) and FUSAGUA (Sensor fusion for real-time leak monitoring in water distribution networks). These projects highlight his role in cutting-edge research with environmental and societal impact. While no formal advisees are listed, he collaborates closely with leading researchers including V. Puig, G. Cembrano, and J. Blesa. Labs and Teams: He is a key contributor at the Institut de Robòtica i Informàtica Industrial (IRI) , where he works within a multidisciplinary team focused on control systems for critical infrastructure. The IRI-UPC/CSIC team is recognized for its expertise in fault detection, supervision, and safety of technical processes, particularly in water and energy systems.
Sam Ottoمشاهده پروفایل
استادیار
Sam Otto is an Assistant Professor in the Sibley School of Mechanical and Aerospace Engineering at Cornell University, joining the faculty in July 2024. His research lies at the intersection of machine learning and continuum mechanics, with a focus on developing rigorous, data-driven methods for modeling, forecasting, and controlling high-dimensional nonlinear systems such as fluid flows. He leads the Otto Lab, which advances scientific machine learning through principled algorithms and theoretical understanding. Research Interests: Dr. Otto’s work centers on scientific machine learning, particularly in developing algorithms that integrate physical knowledge—such as symmetry, scale hierarchy, and smoothness—into data-driven models. His research addresses fundamental questions about what can be learned from limited data and how to ensure reliability in engineering applications. Key areas include model reduction, operator learning, sensor placement, and symmetry enforcement in neural networks. He applies these methods to fluid dynamics and other continuum mechanics problems. Publication Trends: His recent publications (2019–2024) show a strong trajectory in developing theory and algorithms for learning in nonlinear dynamical systems. There is a clear emphasis on incorporating physical structure into machine learning models, with recurring themes of symmetry, dimensionality reduction, and operator-based modeling. The work spans theoretical advances and practical computational methods, often leveraging Koopman theory, autoencoders, and covariance-based reductions. Education: Ph.D. in Mechanical and Aerospace Engineering, Princeton University, 2022 B.S. in Aeronautics and Astronautics, Purdue University, 2016 Scientific Awards: Advising and Grants: While no current students or grants are listed, Dr. Otto is expected to advise graduate students through the Mechanical and Aerospace Engineering graduate program at Cornell. As a new faculty member, he is likely pursuing funding for his research in scientific machine learning and dynamical systems. His lab is actively publishing in top journals and preprint servers, indicating ongoing research support, possibly through postdoctoral fellowships or early-career grants. Labs and Teams: Dr. Otto leads the Otto Lab at Cornell, which focuses on machine learning for high-dimensional dynamics. The lab develops computational tools that combine data and physical principles to model complex systems. It is part of the broader research ecosystem in the Sibley School, collaborating with experts in fluid mechanics, control theory, and applied mathematics.