جست‌وجوی استاد و پژوهشگر

Sigrun Ortlebمشاهده پروفایل

دانشیار

• Discontinuous Galerkin methods
• Numerical methods for fluid-structure interaction
• Positivity-preserving time integration
• +۴ مورد دیگر

PD Dr. Sigrun Ortleb is an Associate Professor at the Institute of Mathematics, University of Kassel , Germany. Her academic affiliation spans over two decades at the same institution. Current Position: Associate Professor (Privatdozentin) since 2021 Academic Background: Habilitation (2021), PhD (2011), Diplom (2006) from TU Braunschweig Research Focus: Sigrun Ortleb specializes in advanced numerical methods for partial differential equations, with particular emphasis on: Discontinuous Galerkin (DG) methods for conservation/balance equations (Euler, Navier-Stokes, shallow water) Positivity-preserving time integration techniques Summation-by-parts operators for high-order accuracy IMEX and multi-rate time integration Efficient shock filters for DG processes Image processing methods for numerical solution post-processing Publication Trends: Her work primarily addresses computational fluid dynamics challenges through: Development of stable, high-order DG schemes IMEX time integration for stiff systems Geometric conservation laws in FSI Adaptive filtering techniques Positivity preservation in shallow water equations Summation-by-parts operators for conservation Teaching Contributions: Dr. Ortleb has taught advanced mathematics courses for mechanical engineers since 2007, including: Higher Mathematics I & II Numerics of Stiff Problems Fluid-Structure Interaction Numerical Analysis of ODEs/PDEs Mathematical Modeling Discontinuous Galerkin Methods Key Collaborations: She has collaborated with experts in: Computational mechanics (J. Boungard, J. Wackerfuß) Exponential integrators (V. Straub, P. Birken, A. Meister) Geometric conservation laws (S. Bremicker-Trübelhorn)

• Numerical Linear Algebra
• Optimization Methods
• Statistical Methods
• +۳ مورد دیگر

Dr. Mei Yang is an Assistant Professor of Instruction in the Division of Data Science at The University of Texas at Arlington, where she has been serving since September 2024. Previously, she held positions as a Visiting Assistant Professor of Instruction in the Department of Mathematics (2023-2024) and as a Postdoc Researcher at both UT Arlington and Shanghai Jiao Tong University. Her academic journey includes a PhD from UT Arlington focused on Krylov subspace optimization. Her research spans three primary domains: Numerical Linear Algebra : Developing advanced iterative methods for matrix computations Optimization Methods : Creating efficient algorithms for mathematical programming Statistical Methods : Applying mathematical techniques to data analysis Her recent publications demonstrate a consistent focus on computational mathematics, particularly in developing novel Krylov subspace methods for linear systems, saddle point problems, and inverse problems. This work bridges theoretical mathematics with practical applications in engineering and measurement sciences. Dr. Yang has received significant recognition for her contributions: Innovative Teaching Award (College of Science, 2024) UTA CARES GRANT (2025) She teaches extensively in data science and mathematics, covering courses such as Machine Learning, Optimization, Statistical Inference, and Linear Algebra across undergraduate and graduate programs. No information is currently available regarding student advising or laboratory leadership roles.

Cezary Czaplewskiمشاهده پروفایل

استاد

• Coarse-grained molecular modeling
• Protein structure prediction
• Molecular dynamics simulations
• +۳ مورد دیگر

Professor Cezary Czaplewski of the University of Gdańsk is a leading researcher in computational chemistry and coarse-grained molecular modeling. Affiliated with the Faculty of Chemistry and the Department of Theoretical Chemistry , he heads the UNRES server for protein simulations and contributes to projects like Multi-GPU UNRES and MAGENTA . His work bridges physics-based simulations with experimental data integration. Key roles : Professor, Computational Biophysics, University of Gdańsk Projects : EuroHPC PL, Harmonia9, Enerliq, Parylens Research Focus : His research spans coarse-grained modeling , protein structure prediction , and hydrophobic interaction studies , with applications in virology (SARS-CoV-2), antimicrobial peptide design, and large-scale biomolecular simulations. He specializes in GPU-accelerated algorithms and multi-GPU implementations for scalable systems. Teaching : Currently teaches Molecular mechanics & dynamics , Introduction to Python programming , and Parallel programming (Bioinformatics III) . Past courses include Electronic chemical diagnostics and Software in biomacromolecular calculations for PhD students. Scientific Contributions : Developed the UNRES server for polypeptide simulations, extended the UNRES force field to nucleic acids and membrane proteins, and participated in multiple CASP/CAPRI experiments for protein structure prediction. His 2025 publications focus on time-averaged restraints and multi-GPU scalability. Contact : cezary.czaplewski@ug.edu.pl Location : Room B328, Faculty of Chemistry, University of Gdańsk

Nathanael Skrepekمشاهده پروفایل

استادیار

• Partial Differential Equations
• Port-Hamiltonian Systems
• Control Theory
• +۷ مورد دیگر

Dr. Nathanael Skrepek is an Assistant Professor in the Department of Applied Mathematics at the University of Twente, Faculty of Electrical Engineering, Mathematics and Computer Science. Previously, he was a Postdoctoral Researcher at TU Bergakademie Freiberg and completed his PhD at Bergische Universität Wuppertal with a prestigious Marie-Curie Fellowship in the Horizon 2020 project ConFlex. His educational background includes: B.Sc. in Mathematics, TU Wien (2011–2015) M.Sc. in Mathematics, TU Wien (2015–2018) Ph.D. in Mathematics, Bergische Universität Wuppertal (2018–2021) Dr. Skrepek’s research centers on mathematical analysis, particularly in port-Hamiltonian systems, partial differential equations, control theory, functional analysis, and operator theory . His work bridges abstract functional-analytic frameworks with physical modeling, especially in wave and electromagnetic systems. He investigates well-posedness, stability, boundary control, and homogenization in multidimensional domains. The recent publications reflect a strong focus on operator-theoretic methods in PDEs , including boundary triples, Sobolev space characterizations, and stabilization of Maxwell’s and wave equations. His work often involves geometric and energy-based modeling, with applications in control and mathematical physics. His scientific recognition includes: Marie-Curie Fellowship in the Horizon 2020 project ConFlex He advises no students listed publicly and has not disclosed grant details beyond his fellowship. He teaches core mathematics courses such as Analysis, Vector Calculus, Optimal Control, and Probability Theory at the University of Twente. His collaborative publications indicate active engagement in the mathematical research community.

• Speech Signal Processing
• Machine Learning
• Noise Reduction
• +۳ مورد دیگر

Ziye Yang is a researcher active in Speech Signal Processing and Machine Learning applications. His work focuses on advanced techniques like weighted prediction error , deep learning models , and noise reduction in audio systems. He has collaborated extensively with Jie Chen and Cédric Richard on topics including speech dereverberation and nonlinear residual echo suppression . His research spans multiple subfields including distributed speech processing , attention-based architectures , and deconvolution regularization . Recent publications (2024-2025) emphasize hybrid methods combining traditional signal processing with modern neural networks, particularly for reverberation modeling and echo suppression. Key trends in his work include Integration of data-driven priors in signal enhancement Development of plug-and-play frameworks for audio processing Application of attention mechanisms in dual-stream networks

• Applied Machine Learning
• Computer Vision
• Signal Processing
• +۷ مورد دیگر

Overview Manfredo Atzori serves as a Scientific Assistant (Adjoint-e scientifique HES A) at HES-SO Valais-Wallis's Haute Ecole de Gestion. His work focuses on advancing machine learning applications in biomedical engineering, digital pathology, and neuroimaging. He leads development of open-source tools like BIDSAlign for EEG standardization and SelfEEG for self-supervised learning in biomedical signals. Research Interests Atzori's research bridges AI and healthcare, emphasizing: Medical Imaging : Histopathology WSI analysis, artifact correction, and cross-modal fusion Prosthetics Control : sEMG-based gesture recognition and adaptive neural interfaces Deep Learning Methodology : Transfer learning optimization, self-supervised pretraining, and reproducibility frameworks Key Contributions 2024 highlights include: Developed RegWSI - winning ACROBAT 2023 challenge for WSI registration Pioneered multimodal WSI-report integration to halve annotation needs Published benchmark studies on Gleason grading deep learning methods Collaborations Active in international projects with institutions like DeeperHistReg group and OpenNeuro. Tools developed are open-source (GitHub: @manfredoatzori) to advance reproducible research practices.

Andrea Lodiمشاهده پروفایل

استاد

• Mixed-Integer Programming
• Data Science
• Operations Research
• +۴ مورد دیگر

Andrea Lodi serves as the Andrew H. and Ann R. Tisch Professor at the Jacobs Technion-Cornell Institute at Cornell Tech and the Technion, and holds the Canada Excellence Research Chair in “Data Science for Real-time Decision Making” at Polytechnique Montréal. He earned his Ph.D. in system engineering from the University of Bologna in 2000 and was an IBM Goldstine Fellow (2005–2006). Previously, he was a full professor at the University of Bologna (2007–2015). His research focuses on mixed-integer programming, nonlinear optimization, and data science applications in decision-making systems. Notable contributions include advancements in cutting-plane algorithms, machine learning integration in optimization, and fair dynamic resource allocation. Awards include the IBM and Google Faculty Awards, and leadership roles in EU projects and IVADO (Montréal Institute for Data Valorization). Education: Ph.D. in System Engineering, University of Bologna (2000) Consulting: IBM CPLEX R&D team since 2006 Grants: Canadian Federal Government’s Apogée Programme (2024), EU projects His work bridges theoretical optimization and practical applications, including healthcare scheduling, transportation logistics, and algorithmic fairness. Recent projects include developing reinforcement learning frameworks for bike-sharing rebalancing and fairness-aware dynamic decision systems.

• Physics
• Computational Science
• Parallel Computing
• +۲ مورد دیگر

Dr. David W. Chappell is a Professor of Physics at the University of La Verne, affiliated with the Natural Science Division within the College of Arts and Sciences. He holds a Ph.D. in Astronomy from the University of Texas at Austin. His academic focus aligns with his department's areas of study, though specific research interests are not explicitly detailed in the provided texts. He is currently active in his role, with no indication of part-time status or retirement. No academic awards or grants are mentioned in the available data. His contact information includes an email address: dchappell@laverne.edu, and he is based at Founders Hall 9 on the La Verne campus.

• Computer Science
• Parallel Computing
• Data Preservation
• +۳ مورد دیگر

Dr. Hongnai Zhang is an Assistant Professor of Marketing at the University of La Verne's College of Business, within the Marketing and Law department. His research focuses on computational methods, parallel computing, data preservation, machine learning, and power systems. He has contributed to advancements in Hessian-based simulation techniques, sensor network efficiency, and deep learning models. His work spans interdisciplinary fields, including collaborations on chemical dynamics simulations and energy-efficient data dissemination mechanisms. Key research interests include optimizing algorithms for high-performance computing, improving data storage in low-duty-cycle sensor networks, and developing stable recurrent neural networks for power load forecasting. His publications reflect expertise in computational science, parallel processing, and applied mathematics, with notable contributions to journals like the Journal of Chemical Physics and IEEE conferences.

• Numerical Analysis
• Finite Element Methods
• Fluid Mechanics
• +۶ مورد دیگر

Gabriel Barrenechea is a Reader in the Department of Mathematics and Statistics at the University of Strathclyde, Faculty of Science. His academic work centers on the development and analysis of finite element methods with strong theoretical foundations, particularly applied to fluid mechanics and partial differential equations. His research interests lie at the intersection of numerical analysis and computational mathematics, with a focus on ensuring stability, accuracy, and physical consistency in simulations. Key areas include discrete maximum principles, bound-preserving schemes, stabilization techniques for convection-dominated flows, and multiscale methods. His work often addresses challenges in non-Newtonian fluids and high-Weissenberg number problems. The recent publications reflect a sustained focus on finite element discretizations that preserve physical bounds and mathematical monotonicity. These works span elliptic, parabolic, and fluid flow problems, employing hybrid, nodal, and implicit-explicit formulations. The trend shows increasing sophistication in handling nonlinearities, variable coefficients, and complex physical constraints through mathematically rigorous approaches. Best paper award Sociedad Espanola de Matematica Aplicada (SEMA) 2018 Gabriel Barrenechea has been actively involved in research leadership and student training. He has served as Principal Investigator on multiple EPSRC-funded and institutional projects, including doctoral training partnerships. He supervises postgraduate research and contributes to academic development frameworks. His professional activities include organizing major conferences such as the Biennial Conference on Numerical Analysis and the Scottish PDE Colloquium. He is a key organizer of academic events including the Impact Case Studies in Maths & Stats workshop and the Early Career Network in Mathematics and Statistics. His collaborations span the UK and international institutions, reflected in co-authored works and joint projects.

• Computational Science
• Numerical Analysis
• Domain Decomposition Methods
• +۷ مورد دیگر

Victorita Dolean Maini is a Visiting Professor in the Department of Mathematics and Statistics at the University of Strathclyde, Faculty of Science. She is actively engaged in research and supervision, with a focus on computational science and numerical methods for partial differential equations. Her work bridges applied mathematics, high-performance computing, and interdisciplinary applications in geophysics, biomedical engineering, and pharmaceutical modeling. University: University of Strathclyde School: Faculty of Science Department: Mathematics and Statistics Academic Rank: Visiting Professor Her research interests center on computational science, particularly in developing mathematical models and algorithms for complex physical systems governed by partial differential equations. She specializes in domain decomposition methods, iterative solvers, and high-performance computing, with recent extensions into scientific machine learning and physics-informed neural networks. Her work emphasizes rigorous analysis and validation of numerical results. The most recent publications highlight a strong trend in robust and scalable numerical methods for multiscale and multiphysics problems. Topics include domain decomposition with GenEO coarse spaces, optimized transmission conditions for diffusion, wave propagation in anisotropic media, and computational epidemiology. These works span disciplines such as applied mathematics, computational physics, geophysics, and biomedical modeling, reflecting a highly interdisciplinary approach. There is a clear emphasis on industrial and real-world applications, including seismic imaging, crystallization processes, and hemodynamic simulations. Scientific awards include: Fellow (awarded 7 September 2020) Prix Bull-Joseph Fourier 2015 (awarded 12 April 2016) She has been a co-investigator and principal investigator on multiple research grants, including projects like PharmaCrystNet, Fast solvers for frequency domain wave-scattering, and Blood flow dynamics in pulmonary hypertension. She actively supervises PhD students and collaborates internationally. She has organized key seminars and workshops, particularly in scientific machine learning and physics-informed learning, contributing significantly to academic community building. She leads and participates in research teams focused on computational modeling, numerical linear algebra, and interdisciplinary applications. Her group collaborates with institutions in physics, engineering, and life sciences, leveraging high-performance computing for large-scale simulations. She is also involved in promoting diversity through initiatives like the Women in Data Science and Mathematics Seminar Series.

Robert Weatherupمشاهده پروفایل

استاد

• Electrochemical Energy Storage
• Heterogeneous Catalysis
• Materials Synthesis
• +۳ مورد دیگر

Robert Weatherup is Professor of Energy Materials in the Department of Materials at the University of Oxford, where he leads the Energy Materials Interfaces Group. Based in the Rex Richards building, his group pioneers interface-sensitive characterization techniques to study reactions critical to electrochemical energy storage, catalysis, and materials synthesis, with strong collaborations at Harwell Campus facilities including Diamond Light Source and ISIS Neutron and Muon Source. His research focuses on understanding interfacial reactions in functional materials under operational conditions. Key interests include solid-gas, solid-liquid, and solid-solid interfaces in Li-ion batteries, solid-state batteries, and catalytic systems for sustainable reactions. The group develops operando X-ray and electron spectroscopy methods to probe buried interfaces, aiming to link interfacial structure to material function for designing advanced energy materials through specialized reaction environments and membrane-based techniques. Recent publications demonstrate a concentrated trend toward operando studies of battery interfaces (cathode stabilization, SEI formation) and catalytic CO2 conversion, utilizing advanced spectroscopic techniques to reveal mechanistic insights under realistic electrochemical and thermal conditions across energy storage and sustainable chemistry domains. Scientific Awards: Royal Society of Chemistry Joseph Black Prize Professor Weatherup supervises a research team of six postdoctoral researchers, nine DPhil students, and two MEng students. His group secures substantial research grants for projects on energy storage and catalytic materials, with close partnerships at Harwell Campus facilities enabling cutting-edge in-situ experiments and methodology development for industrial and academic collaborators. The Energy Materials Interfaces Group operates specialized laboratories in Oxford and maintains deep integration with Harwell Campus infrastructure. They design custom reaction cells for operando studies across electrochemical, gas, and liquid environments, with active beamtime allocations at Diamond Light Source and Alba Synchrotron, fostering cross-institutional collaborations to advance characterization capabilities for next-generation sustainable technologies.

Antonio Huertaمشاهده پروفایل

استاد

• Numerical Methods
• Mechanics
• Applied Mathematics
• +۴ مورد دیگر

Antonio Huerta is a Professor of Applied Mathematics at the Universitat Politècnica de Catalunya (UPC) and Director of ICREA. He leads the Laboratori de Càlcul Numèric (LaCàN) , focusing on numerical methods for scientific and engineering problems governed by mechanics and interdisciplinary principles. His research spans applied mathematics, computational science, and fluid dynamics. Education : Ph.D. from Northwestern University (1987), preceded by studies at the Escuela Técnica Superior de Ingeniería de Caminos, Canales y Puertos de Barcelona (1983). His work emphasizes numerical methods , particularly hybridizable discontinuous Galerkin (HDG), finite volume methods, and proper generalized decomposition (PGD), applied to problems in incompressible/compressible flow simulation , fluid-structure interaction , and magneto-mechanical coupling . Recent projects include data-driven design for automotive battery optimization ( GREEN ) and MATLAB-based open-source tools. Key collaborations include co-authoring the textbook Finite Element Methods for Flow Problems with Jean Donea and developing error estimation frameworks for finite element solutions. His publications highlight interdisciplinary applications in mechanics, mathematics, and computer science.

Orlando Ayalaمشاهده پروفایل

دانشیار

• Multiphase Flows
• Turbulent Flows
• Particle Transport in Fluid Flows
• +۵ مورد دیگر

Dr. Orlando Ayala is an Associate Professor in the Mechanical Engineering Technology Department at Old Dominion University's Frank Batten College of Engineering and Technology. He holds a Ph.D. (2005) and M.Sc. (2001) in Mechanical Engineering from the University of Delaware, and a B.S. in Mechanical Engineering (Cum Laude, 1995) from Universidad de Oriente, Venezuela. His research focuses on multiphase flows , turbulent particle transport , and high-performance computational methods . Key areas include: fluid-particle interactions in pipeline erosion, lattice Boltzmann modeling for turbulence modulation, cloud droplet collision dynamics, and porous media flow. His work integrates advanced numerical simulations with applications in energy systems and environmental fluid mechanics. Publications emphasize turbulent collision statistics, particle-laden flows, and parallel computing algorithms, with recent studies leveraging DNS and lattice Boltzmann methods to resolve microscale interactions in multiphase systems. Scientific Awards & Honors: Certificate of Excellence in Undergraduate Research (2017) Highly Cited Research in Parallel Computing (2016) Outstanding Contribution in Reviewing, Journal of Natural Gas Science (2015) Research Fellowships: Venezuelan Foundation for Promotion of Researchers (2003, 2006, 2008) Teaching Excellence Nominee, University of Delaware (2005) Grants & Projects: Secured $800,000+ in funding, including NSF-supported studies on turbulent particle collisions ($359,861), naval additive manufacturing ($150,000), and solar energy systems ($158,162). Research collaborations span turbulence modulation, photovoltaic tech, and pipeline erosion.

• Formal Methods
• Hybrid Systems
• Probabilistic Systems
• +۱۰ مورد دیگر

Erika Ábrahám is a University Professor in the Department of Computer Science at RWTH Aachen University, Germany, where she leads the research group on the Theory of Hybrid Systems. Her work is centered on formal methods for cyber-physical systems, with a strong focus on hybrid and probabilistic systems, SMT solving, and symbolic computation. Her research interests include Formal Methods, Hybrid Systems, Probabilistic Systems, Satisfiability Modulo Theories (SMT), Symbolic Computation, Cyber-Physical Systems, Model Checking, Reachability Analysis, Automated Reasoning, Verification of Safety-Critical Systems, Robotics, Energy Optimization, and Artificial Intelligence . She develops theoretical foundations and practical tools for the analysis and verification of complex systems, especially in safety-critical domains like automotive and robotics. Her recent publications demonstrate a consistent focus on advancing SMT solving techniques, particularly in real algebra and cylindrical algebraic decomposition, and on analyzing probabilistic hybrid systems, including reachability and hyperproperties. She frequently contributes to and organizes major conferences and workshops in her field. Scientific Awards: No specific awards were mentioned in the provided text. Erika Ábrahám actively supervises students and researchers, with advisees including Jasper Kurt Ferdinand Nalbach, Valentin Maxim Promies, Stefan Schupp, and others. She has been involved in projects related to railway timetables, energy load control, and AI-based robotics. She also contributes to academic service through editing conference proceedings and promoting gender equality in software engineering. She is a key contributor to the HyPro library for hybrid systems reachability analysis and continues to publish in top venues such as LNCS, Springer, and Elsevier journals. Her recent work spans from core theoretical advances in SMT to applied research in sonar object detection and robotics.