جست‌وجوی استاد و پژوهشگر

• Artificial Intelligence
• 3D Scene Reconstruction
• Simultaneous Localization and Mapping (SLAM)
• +۳ مورد دیگر

Yiming Zhou is a researcher at Saarland University of Applied Sciences (htw saar) in Saarbrücken, Germany. Their work spans multiple engineering domains with emphasis on artificial intelligence integration, 3D scene reconstruction, and advanced imaging techniques. Contact: yiming.zhou@htwsaar.de Location: Goebenstraße 40, 66117 Saarbrücken Research Focus Zhou's research explores cutting-edge technologies in: AI applications for non-destructive evaluation Dynamic SLAM systems for robotics Next-generation 3D reconstruction methods Multimodal deception detection frameworks Novel Gaussian splatting techniques Semantic encoding for spatial data Recent Publications Trends Their scholarly output reveals a trajectory toward Real-time spatial mapping solutions Hybrid neural-implicit representations CAD-integrated building documentation AI-enhanced image translation pipelines Signal processing innovations Cross-modal data fusion Laboratory Affiliation Zhou contributes to the faculty laboratories at htw saar, focusing on engineering research through practical implementations and algorithm development.

• Parallel Programming
• GPU Programming
• Image Processing
• +۵ مورد دیگر

Sándor Szénási is a Professor at the Department of Informatics within the Faculty of Economics and Informatics at J. Selye University, where he serves as the person responsible for the Applied Informatics study program. With over two decades of academic experience, he has established himself as a leading researcher in parallel programming, GPU programming, and image processing, with recent expansion into machine learning applications. Eötvös Loránd University, Faculty of Science and Informatics (2001-2004): Information technology teacher Budapest Polytechnic, John von Neumann Faculty of Information Technology (1997-2001): B. Engineer in Information Technology Óbuda University (2010-2013): PhD in Applied Informatics Habilitation at Óbuda University (2019): Information Science and Technology Professor inauguration at Óbuda University (2022) Szénási's research spans computational methods with practical applications across multiple domains. His early work focused on parallel and GPU programming for image segmentation and heat transfer simulation. More recently, he has integrated machine learning techniques with traditional computational approaches, particularly in metaheuristic optimization, speech processing, and inverse problem solving. His interdisciplinary research bridges computer science with transportation safety, manufacturing, and medical applications. His recent publications reveal a clear evolution toward hybrid computational approaches that combine machine learning with traditional algorithms. There is a strong emphasis on optimization techniques, particularly metaheuristics enhanced with machine learning components. His work spans diverse application areas including speech emotion recognition, autonomous vehicle control, additive manufacturing, and heat transfer simulation, while maintaining a core focus on computational efficiency and parallel processing. Szénási has been actively involved in multiple EFOP-funded research projects including 'Improvement of higher education institutes for better teaching quality and accessibility,' 'Dynamics and control of autonomous vehicles,' and 'Solving the Inverse Heat Conduction Problem with Machine Learning.' His collaborative work with researchers like Gábor Kertész, Zoltán Vámossy, and Imre Felde demonstrates his commitment to interdisciplinary research.

Eduard Campillo-Funolletمشاهده پروفایل

مدرس

• Bayesian Statistics
• Inverse Problems
• Statistical Ecology
• +۳ مورد دیگر

Dr. Eduard Campillo-Funollet is a Lecturer in Statistics at the School of Mathematical Sciences , Lancaster University. His research focuses on inverse problems and inference in biology, ecology, epidemiology, and industrial applications, using Bayesian methods for parameter estimation in differential equations. Research Interests include: Bayesian inference for dynamical systems Inverse problems in biological and industrial contexts Statistical ecology and epidemiological modeling Computational methods for ordinary/partial differential equations Publication Trends show expertise in epidemiological modeling (e.g., COVID-19 transmission dynamics ), computational biology (e.g., DNA replication studies ), and industrial applications (e.g., gas quality prediction ), with a consistent emphasis on robust Bayesian methodologies. Projects include collaborations on Nigeria-UK epidemiological research , Finland-Lancaster synthetic gas analysis , and Diabetic Foot Ulcer measurement accuracy , highlighting interdisciplinary applications of statistical methods. Contact: e.campillo-funollet@lancaster.ac.uk

Jim E. Morelمشاهده پروفایل

استاد

• Deterministic Particle Transport
• Hybrid Deterministic/Monte Carlo Methods
• Radiation-Hydrodynamics and Multiphysics Methods
• +۲ مورد دیگر

Jim E. Morel is a Professor and the Nancy & Ron Stinson Chair in Nuclear Engineering at Texas A&M University. He serves as Director of the Center for Large-Scale Scientific Simulations (CLASS) and holds a joint faculty appointment at Los Alamos National Laboratory. Educational Background: Ph.D., Nuclear Engineering, University of New Mexico (1979) M.S., Nuclear Engineering, Louisiana State University (1974) B.S., Mathematics, Louisiana State University (1972) His research focuses on Discretization and Multilevel Solution Techniques for Deterministic Particle Transport , Hybrid Deterministic/Monte Carlo Methods , and Radiation-Hydrodynamics . He has developed advanced methods for neutron transport, radiation diffusion, and multiphysics simulations. Recent publications explore topics in deterministic transport discretization, Monte Carlo integration, and radiation-hydrodynamics, with applications to reactor physics, radiative shocks, and inverse radiation problems. His work emphasizes numerical accuracy, parallel computing, and physics-based reduced-order modeling. Scientific Awards: Nancy & Ron Stinson Professorship in Nuclear Engineering For contact, email morel@tamu.edu or call 979-845-6072 at his office (AIEN M306).

Cassiano Endres de Oliveiraمشاهده پروفایل

استاد

• Applied Computational Radiation Transport
• High Performance Computing
• Nuclear Reactor Physics
• +۳ مورد دیگر

Cassiano Endres de Oliveira is a Professor in the Department of Nuclear Engineering at the University of New Mexico (UNM). He has held academic positions at institutions including Georgia Institute of Technology and Imperial College London. His research focuses on computational radiation transport, multiphysics coupling, uncertainty quantification, and high-performance computing. Education : Ph.D. in Nuclear Engineering (Queen Mary College, University of London, 1987); M.Sc. in Nuclear Engineering (Federal University of Rio de Janeiro, 1980); B.Sc. in Physics (Catholic University of Rio de Janeiro, 1976). Research Interests : His work centers on developing numerical technologies for simulating complex physical phenomena, particularly in nuclear radiation transport and thermofluid dynamics. He emphasizes multiphysics coupling, optimization, and inverse problems to create high-fidelity tools for environmental and engineering challenges. Publication Trends : His recent publications highlight applications of finite element methods, spherical harmonics, and adaptive algorithms to radiation transport, nuclear reactor design, and ocean modeling. Key areas include computational validation benchmarks, photon migration in biological media, and anisotropic geophysical inversion.

Eric M. Hernandezمشاهده پروفایل

دانشیار

• Structural Engineering
• Structural Dynamics
• Earthquake Engineering
• +۲ مورد دیگر

Eric M. Hernandez is an Associate Professor of Structural Engineering at the University of Vermont , where he directs the Structural Monitoring, Diagnostics, and Prognostics Lab. His research focuses on extracting actionable insights from wave and vibration data in structures, with applications in structural health monitoring, earthquake engineering, and biomechanical systems. His work spans seismic risk assessment, damage detection, and model-data fusion, supported by grants from the National Science Foundation (including a CAREER award), NASA, and the Vermont Agency of Transportation. He has taught courses such as Structural Analysis , Earthquake Engineering , and Reliability of Engineering Systems over 20+ years at institutions including Instituto Tecnologico de Santo Domingo and Northeastern University. His recent publications highlight advancements in nonlinear model-data fusion for seismic monitoring Bayesian updating in structural diagnostics sensor optimization for state estimation His accolades include the NSF CAREER Award (2015) and the Gregory N. Sweeny Green and Gold Professor (2018).

Hodjat Pendarمشاهده پروفایل

مدرس

• Animal Locomotion
• Biomechanics
• Robotics
• +۳ مورد دیگر

Hodjat Pendar is an Instructor in the Department of Mechanical Engineering at Virginia Tech, part of the College of Engineering. His research focuses on biomechanics, animal locomotion, robotics, and inverse problems, with particular emphasis on insect physiology and fluid dynamics. He holds a Ph.D. in Engineering Mechanics from Virginia Tech (2015), and M.Sc. and B.Sc. degrees in Mechanical Engineering from Sharif University of Technology (2004 and 2002, respectively). His research interests span biomechanical systems analysis, including the study of animal locomotion mechanics, fluid-structure interactions in robotics, and physiological responses to environmental factors like gravity. His work integrates computational tools for data inversion and uncertainty estimation, particularly in respirometry systems, and explores functional compartmentalization in insect circulatory systems. Pendar’s publications highlight trends in biomimetic robotics design, optimization of caudal fin dynamics for swimming efficiency, and the mechanical adaptations of insects to gravitational and hemolymph pressure challenges. His studies bridge mechanical engineering principles with biological systems, offering insights into both natural and engineered systems. No scientific awards are explicitly listed for Dr. Pendar. His advising and grant activities are not detailed in the provided information, and no affiliated labs or teams are mentioned.

Margaret Murnaneمشاهده پروفایل

استاد

• Ultrafast Laser Science
• X-ray Photonics
• Nonlinear Optics
• +۵ مورد دیگر

Margaret Murnane is a Distinguished Professor of Physics at the University of Colorado, Boulder, and a fellow at JILA. Her research focuses on ultrafast laser science, attosecond physics, and the development of coherent x-ray sources. She leads efforts to apply these technologies to study electron dynamics in molecules and materials, nanoscale heat transport, and magnetic phenomena. Her group pioneers techniques like extreme ultraviolet scatterometry and vector ptychography for advanced nanoscale metrology. Her educational background includes a focus on physics and optics, though specific degree details are not provided. Research highlights include the creation of tabletop attosecond pulse sources and their application in observing ultrafast spin dynamics in materials. Her lab develops mid-infrared lasers and hollow-core fibers for high-harmonic generation. Key research themes include: High-harmonic generation for coherent x-ray production Ultrafast electron dynamics imaging Multimodal EUV reflectometry systems Topological magnetic structure visualization Quantum materials spectroscopy Recent work advances EUV-based nanoscale metrology for semiconductor manufacturing and explores light-matter interactions at attosecond timescales. Her instrumentation innovations include compact extreme ultraviolet reflectometers and advanced ptychography algorithms.

• Experimental Fluid Mechanics
• Computational Fluid Dynamics
• Heat and Mass Transfer
• +۷ مورد دیگر

Dr Mark Puttock-Brown serves as Senior Lecturer in Mechanical Engineering within the School of Engineering and Informatics at the University of Sussex, where he also holds the position of Associate Dean for the Sussex-Surrey Institute of Technology. As a member of the Thermo-Fluid Mechanics Research Centre, his work bridges fundamental turbomachinery research with practical applications in renewable energy and biomedical devices. His educational background includes: BSc in Theoretical Physics (University of Sussex, 2010) MSc in Advanced Mechanical Engineering (University of Sussex, 2011) PhD in Mechanical Engineering focusing on gas turbine secondary air systems (University of Sussex, 2018) PgCert in Higher Education (University of Sussex, 2020) Puttock-Brown's research centers on experimental and numerical analysis of rotating cavity flows, with particular expertise in buoyancy-driven phenomena within gas turbine engines. His work increasingly integrates AI methodologies like physics-informed neural networks for inverse heat transfer problems while expanding applications to sustainable systems and net-zero technologies. Recent publications demonstrate a clear trajectory from fundamental fluid dynamics studies toward applied research with industrial partners like GE Aviation. His publication portfolio reveals consistent focus on rotating cavity aerodynamics with evolving methodological sophistication - from traditional experimental measurements (2016-2018) to hybrid AI-experimental approaches (2023-2025). Key thematic clusters include buoyancy effects in compressor rotors, thermal wake characterization, and metamaterial applications for acoustic management. Award recognition includes: Fellowship of the Higher Education Academy (2021) He actively supervises graduate projects while securing significant research funding, including a £4M BP grant for cryogenic fluid research (2023-2027) and Innovate UK funding for renewable refrigeration systems. His teaching portfolio spans numerical modeling, computational fluid dynamics, and vehicle technology courses at multiple levels. Current research activities center on the Thermo-Fluid Mechanics Research Centre where he leads projects connecting turbomachinery fundamentals with biomedical device innovation and sustainable energy systems through collaborations with industrial partners including Beko PLC and GE Aviation.

Arthur Filocheمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Data Assimilation
• Numerical Weather Forecasting
• +۳ مورد دیگر

Arthur Filoche is a Research Fellow at the UWA Oceans Institute (School of Earth and Oceans), The University of Western Australia. He holds a PhD in electrical engineering with specialization in applied mathematics and data science, focusing on variational data assimilation and deep learning for numerical weather forecasting. His research bridges physics-based inverse problems and machine learning, particularly in improving ocean wave forecasts in Western Australia under Prof. Jeff Hansen's supervision. He volunteers as a science teacher at Currambine Primary School. Education: French engineering degree + PhD in applied mathematics/data science Research interests include machine learning applications for ocean/atmospheric systems, data assimilation techniques, and developing algorithms for undersampled measurement challenges. Recent work emphasizes sea surface height interpolation from satellite data and spatiotemporal wind forecasting models. His publications span environmental science, computer vision, and meteorology domains. Currently supervising PhD projects applying machine learning to correct spectral ocean wave forecasts. Active in collaborative research across Western Australia and international partnerships. No listed scientific awards but maintains an h-index of 5 with 16 total citations.

Heng Xiaoمشاهده پروفایل

استاد

• Data-Driven Turbulence Modeling
• Uncertainty Quantification
• Computational Fluid Dynamics
• +۵ مورد دیگر

Heng Xiao is a Professor of Data-Driven Fluid Dynamics at the University of Stuttgart, affiliated with the Institute of Aerospace Thermodynamics (ITLR) and the Cluster of Excellence EXC 2075 'Data-Integrated Simulation Science' in the Stuttgart Center for Simulation Science (SC SimTech). He previously served as Associate Professor (2020-2022) and Assistant Professor (2013-2020) at Virginia Tech, USA, and was a Postdoctoral Researcher/Lecturer at ETH Zürich (2009-2012). Ph.D., Civil Engineering, Princeton University, 2009 M.S., Scientific Computing, Royal Institute of Technology (KTH), 2005 B.S., Civil Engineering, Zhejiang University, 2003 His research focuses on integrating data science (machine learning, uncertainty quantification, data assimilation) with traditional physical models to advance predictive capabilities in multi-scale fluid systems. Key areas include Data-Driven Turbulence Modeling , Laminar-Turbulent Transition , Subsurface Flows , and Particle-Laden Flows . His work addresses turbulence modeling through neural operators, Bayesian inference, and physics-informed machine learning, with applications in aerospace, ocean engineering, and geosciences. Recent publications highlight trends in Neural Operators for Nonlocal Models , Ensemble Kalman Methods for Turbulence Inference , and Machine Learning for Permeability Prediction . Collaborative projects, such as the DFG-funded development of coupled turbulence and heat-flux models for film cooling, underscore his focus on real-world impact. Fellowship, Center of Turbulence Research Summer Program, Stanford University (2016) Finalist, Undergraduate Research Advisor Award, Virginia Tech (2014) Advisor to doctoral students including Jian-Xun Wang, Rui Sun, Jin-Long Wu, and Carlos Michelén-Ströfer, he leads the 'Data-Driven Fluid Dynamics' group at Stuttgart. The team collaborates with academia and industry, emphasizing high-performance computing and open-source tools like SediFoam for sediment transport simulations.

• Algebraic Geometry
• Symbolic Computation
• Differential Algebra
• +۵ مورد دیگر

Guenter Landsmann is a Researcher at the Research Institute for Symbolic Computation (RISC) of Johannes Kepler University in Linz, Austria. His primary research focuses on Algebraic Geometry and Symbolic Computation, with significant contributions to Gröbner bases, polynomial functions, and differential algebra. His work bridges theoretical mathematics and computational methods, addressing challenges in algebraic structures, geometric modeling, and topological properties. His research interests include Gröbner Reduction techniques, parametrization of algebraic varieties and canal surfaces, and the application of symbolic computation to problems in differential equations and topology. Notable contributions include studies on polynomial functions over GL(2,R), implicitization of algebraic varieties, and the development of algorithms for dimension computation in filtered free modules. Publications span ISSAC conferences, technical reports, and journals like Mathematical and Computer Modelling of Dynamical Systems. His work demonstrates a sustained focus on advancing computational tools for algebraic geometry and their applications in geometric modeling and abstract algebra. While no scientific awards are explicitly listed, his extensive publication record reflects a dedicated academic career in symbolic computation and related fields. His affiliation with RISC positions him at the forefront of interdisciplinary research combining mathematics and computer science.

Arthi Jayaramanمشاهده پروفایل

استاد

• Materials Science
• Polymer Science
• Machine Learning in Materials Characterization
• +۳ مورد دیگر

Arthi Jayaraman is a full professor at the University of Delaware, holding dual appointments in the Departments of Chemical & Biomolecular Engineering and Materials Science and Engineering. She directs an NSF-funded NRT traineeship program on computing and data science for materials innovation. Her research focuses on computational design of soft materials using machine learning, polymer physics, and molecular simulations. Education: Ph.D. in Chemical Engineering from North Carolina State University (2000s), postdoctoral research at University of Illinois-Urbana Champaign in Materials Science and Engineering. Her work integrates AI with experimental techniques to solve challenges in materials characterization and industrial applications. Research interests include ML-driven analysis of materials data, polymer composites design, and bioinspired materials. Key projects involve developing algorithms like CREASE (Computational Reverse-Engineering Analysis for Scattering Experiments) to interpret nanoparticle structures. She has pioneered methods for predicting phase behavior in polymer blends and nanocomposites. Her awards span ACS PMSE Fellowship (2025), APS Fellow (2020), AIChE IMPACT Award (2021), and DOE Early Career Award (2010). Over 100 peer-reviewed publications address topics like polymer solution structures, nanoparticle self-assembly, and collagen-mimetic materials. Leadership includes advising graduate students through the NRT program, collaborating with industry on material design problems, and serving as an editorial board member for ACS journals. Her lab employs multiscale modeling strategies to bridge molecular-scale phenomena with macroscale material properties.

Akhtar Khanمشاهده پروفایل

استاد

• Inverse Problems
• Uncertainty Quantification
• Optimal Control
• +۷ مورد دیگر

Akhtar Khan is a Professor in the School of Mathematics and Statistics at Rochester Institute of Technology (RIT), part of the College of Science. He joined RIT's School of Mathematical Sciences in 2008 and advises students in applied and computational mathematics. His research focuses on inverse problems, uncertainty quantification, optimal control, numerical optimization, and PDE-constrained optimization. He holds an MS from Technical University Kaiserslautern (Germany) and a Ph.D. from Michigan Technological University. Current teaching responsibilities include courses such as Linear Algebra (MATH-241), Boundary Value Problems (MATH-326), Numerical Linear Algebra (MATH-412), and Optimization Theory (MATH-603). His work spans theoretical advancements in variational inequalities and practical applications in imaging and stochastic systems. Recent research highlights include contributions to stochastic inverse problems in partial differential equations and regularization frameworks for tumor identification. He collaborates internationally, exemplified by hosting the Abel Visiting Scholar Nguyen Thi Van Anh in 2023. His publications include monographs like Uncertainty Quantification in Variational Inequalities (2021) and edited volumes on deterministic and stochastic control systems. Awards and recognitions are not explicitly listed, but his extensive publication record and editorial roles (e.g., Deterministic and Stochastic Optimal Control and Inverse Problems ) underscore scholarly impact. He actively mentors students through independent studies and graduate research (MATH-790).

• Scientific Computing
• Numerical Linear Algebra
• Parallel Computing
• +۳ مورد دیگر

Michael T. Heath is a Professor and Fulton Watson Copp Chair Emeritus in the Department of Computer Science at the University of Illinois at Urbana-Champaign (UIUC). He holds a Ph.D. in Computer Science from Stanford University (1978). His research focuses on scientific computing, numerical linear algebra, optimization, and parallel computing with notable contributions to sparse matrix computations and parallel algorithm design. Heath has directed major initiatives such as the Computational Science and Engineering Program (1996–2012) and the Center for Simulation of Advanced Rockets (1997–2010), securing over $52M in research funding. He is an ACM Fellow, SIAM Fellow, and European Academy of Sciences member. Teaching excellence defines his career, highlighted by awards including the IEEE Taylor L. Booth Education Award (2009) and UIUC's Campus Award for Excellence in Graduate Teaching (2002). He developed interactive educational modules in scientific computing and teaches courses like CS 450 (Numerical Analysis) and CS 554 (Parallel Numerical Algorithms). His research spans parallel performance visualization, numerical optimization, and mathematical software, with over 50 years of contributions to computational science. Education: Ph.D. Computer Science, Stanford University (1978) Affiliations: Department of Computer Science, UIUC; former Interim Head (2007–2009) Key Roles: Director, Computational Science and Engineering Program; Director, Center for Simulation of Advanced Rockets His publications emphasize parallel algorithms, numerical methods, and computational frameworks for multiphysics simulations. Heath’s work bridges academic research and practical applications, exemplified by his leadership in high-performance computing and rocket propulsion modeling.