جستجوی استاد راهنمای دانشگاهی

Nikolay Gromovمشاهده پروفایل

استاد

• Theoretical Physics
• Quantum Field Theory
• String Theory
• +۴ مورد دیگر

Professor Nikolay Gromov is a faculty member at King's College London within the Department of Mathematics, part of the Faculty of Natural, Mathematical & Engineering Sciences. He holds the rank of Professor of Theoretical Physics and has been affiliated with King's since 2010, progressing from Lecturer to Reader and then to Professor in 2018. His research focuses on non-perturbative Yang-Mills theory, integrability, and the AdS/CFT correspondence, particularly exploring the quantum spectral curve (QSC) for exact spectrum calculations in supersymmetric gauge theories. He has contributed significantly to understanding string theory, conformal field theories, and integrable systems through collaborations with institutions like CERN, École Normale Supérieure, and DESY. Education: Bachelor's degree from Saint-Petersburg State University (2005) PhD from École Normale Supérieure (Paris) and Saint-Petersburg Nuclear Physics Institute (2007) Research Interests: His work bridges quantum field theory and string theory via integrability, emphasizing exact solutions and duality. Key areas include: Quantum spectral curve applications in gauge/gravity duality Defect conformal field theories and bootstrap methods Strongly γ-deformed SYM theory as integrable systems Non-perturbative methods for confinement and phase transitions Grants & Leadership: He secured major grants (e.g., for QSC tool development) and served as Deputy Head (Research) of the Department of Mathematics. His work has led to computational tools like the Fast QSC Solver. Labs/Teams: Active in collaborative theoretical physics groups at King's and international partners, focusing on integrability and holography.

• Machine Learning
• Neural Networks
• Quantum Computing
• +۳ مورد دیگر

Zheng Zhang is a Professor in the Department of Electrical and Computer Engineering at the University of California, Santa Barbara. His research focuses on neural networks, quantum computing, uncertainty quantification, and optimization, with particular emphasis on tensor networks, low-rank compression methods, and hardware-efficient machine learning systems. He leads efforts in developing memory-efficient training algorithms for large language models (LLMs), tensorized optical networks, and physics-informed neural PDE solvers. Key contributions include FLAT-LLM for LLM compression, FETTA hardware accelerators, and DeepOHeat for thermal simulation in 3D-IC design. His work spans cross-disciplinary areas such as quantum-inspired algorithms, stochastic control, and yield-aware optimization of photonic ICs. He holds a faculty position in the College of Engineering and is affiliated with the ECE department. Research trends in his 2025 publications emphasize scalable training techniques for transformers, zeroth-order optimization methods, and optical computing integration. His work consistently addresses computational efficiency, memory constraints, and hardware acceleration across domains like AI, quantum computing, and electronic design automation. Notable grants and lab affiliations include projects on FPGA-based neural training, quantum circuit simulation, and tensor-compressed PDE solvers. He advises on edge computing, neuromorphic systems, and uncertainty-aware design tools for integrated circuits.

Jason Howellمشاهده پروفایل

استاد آموزشی

• Numerical Analysis
• Computational Mathematics
• Finite Element Methods
• +۵ مورد دیگر

Jason Howell is a Teaching Professor and Associate Department Head in the Department of Mathematical Sciences at Carnegie Mellon University. He holds a Ph.D. in Mathematical Sciences from Clemson University. His research focuses on numerical analysis and computational mathematics, particularly finite element methods for fluid and structural dynamics, fast linear solvers, and applications in natural and social sciences. He actively mentors undergraduate research projects in fluid-structure interaction, numerical linear algebra, and differential equations. His work spans theoretical foundations of finite element methods, including compatibility conditions and mixed formulations, as well as applied projects with chemists and physicists on topics like solubility parameters and nanostructures. Current research activities include computational fluid dynamics, iterative solvers for large systems, and mathematical modeling in biomedical and engineering contexts. Notable contributions include studies on cantilever dynamics, sparse matrix preconditioning, and viscoelastic flow modeling. He collaborates across disciplines, integrating numerical methods with real-world applications in materials science and engineering systems. His teaching and research emphasize bridging mathematical theory with practical scientific challenges.

C.A. Pérez Arancibiaمشاهده پروفایل

دانشیار

• Computational Mathematics
• Wave Phenomena
• Boundary Integral Equations
• +۴ مورد دیگر

C.A. Pérez Arancibia is an Associate Professor in the Mathematics of Computational Science (MACS) group at the University of Twente, Netherlands, within the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS). He is also affiliated with the Applied Mathematics department and the MESA+ Institute. Education: Ph.D. in Applied and Computational Mathematics, California Institute of Technology (Caltech), 2016 Master’s and Bachelor’s in Mathematical Engineering, Pontificia Universidad Católica de Chile (PUC) His research focuses on computational approaches to wave propagation, boundary integral formulations, and high-order solvers for partial differential equations. These areas are central to modern scientific computing and engineering simulations, particularly in acoustics, electromagnetics, and fluid dynamics. The trends in his research, though specific articles are not listed, emphasize mathematical rigor combined with algorithmic efficiency, especially in fast methods for PDEs and integral equations. Scientific Awards and Recognitions: Premio Padre Hurtado CONICYT Scholarship He has advised students and contributed to academic training, notably teaching the graduate course Fast Methods for Partial Differential and Integral Equations (18.336) at MIT. While specific grants are not mentioned, his prior affiliations with Caltech, MIT, and PUC suggest a strong record of research funding and academic mentorship. He is associated with the Mathematics of Computational Science (MACS) group and the MESA+ Institute at the University of Twente, indicating active participation in interdisciplinary research in computational science and engineering.

• Numerical Analysis
• Approximation Theory
• Fractional Calculus
• +۳ مورد دیگر

Amin Faghih is a postdoctoral scholarship holder at KU Leuven's Numerical Analysis and Applied Mathematics (NUMA) research unit within the Arenberg campus. His academic work focuses on advanced computational mathematics with emphasis on numerical methods for differential equations and approximation theory. His research interests span Numerical Analysis , Approximation Theory , and Linear Algebra , particularly investigating Sobolev spaces, orthogonal polynomials, and fractional differential equations. His recent work develops novel algorithms for rational approximation and spectral solvers, bridging theoretical mathematics with practical computational implementations. Analysis of his 2024-2025 publications reveals a strong trend toward efficient numerical methods for Sobolev-related problems, with increasing focus on rational Arnoldi approaches and orthogonal polynomial recurrences. His work demonstrates interdisciplinary connections between computational mathematics, linear algebra, and differential equations. Faghih actively contributes to the academic community through seminar presentations at institutions including UC Louvain, KU Leuven, and international conferences such as the International Linear Algebra Society meetings. His research presentations cover specialized topics in numerical linear algebra and approximation theory. Based at Celestijnenlaan 200a (box 2402) in Leuven, he operates within KU Leuven's NUMA research environment, collaborating with prominent researchers including Van Barel, Vandebril, and Van Buggenhout on cutting-edge numerical algorithms.

• Fast computational hardware
• Massively-parallel computing
• Residue number systems
• +۴ مورد دیگر

Tadeusz Tomczak is a faculty member at the Faculty of Information and Communication Technology , Wrocław University of Science and Technology , within the Department of Computer Engineering . His academic rank is Professor . Research Focus: Fast computational hardware, massively-parallel computing, and residue number systems. Expertise: GPU acceleration, fluid dynamics simulations, sparse geometries, and numerical methods. Recent publications highlight his work on Navier-Stokes solvers , Lattice-Boltzmann methods , and residue number systems , with applications in fluid dynamics, biomedical engineering, and high-performance computing. His research spans hardware acceleration, algorithm optimization, and computational modeling on GPUs.

• Numerical Linear Algebra
• Randomized Algorithms
• Genomics
• +۵ مورد دیگر

Petros Drineas is the Department Head and Professor of Computer Science at Purdue University's College of Engineering. His research focuses on randomized numerical linear algebra (RandNLA), algorithms for large-scale data analysis, computational genomics, and machine learning. He leads the ENIGMA-TS initiative for global neuroimaging genetics collaboration and has developed tools like TeraPCA for tera-scale genotype analysis. Drineas has held leadership roles in NSF and BSF-funded projects, including studies on Tourette syndrome, Alzheimer's disease, and algorithmic efficiency. His work bridges theoretical computer science with applications in genetics, neuroscience, and optimization. Key contributions include matrix sketching frameworks, randomized algorithms for PCA, and methods for GWAS and polygenic risk score analysis. His grants include funding for RandNLA applications in inverse problems, fast least squares solvers, and collaborative research on genetic variation. He has pioneered methods for handling massive datasets in genetics, such as the MaSk-LMM framework for linear mixed models and Patch2Self2 for self-supervised denoising.

• Mathematical Modeling of Electrochemical Systems
• Battery Technology
• Computational Fluid Dynamics
• +۷ مورد دیگر

Jamie Foster McMaster is an Adjunct Associate Professor in the Department of Mathematics and Statistics at McMaster University. His scholarly activity integrates mathematical modeling, computational physics, and interdisciplinary applications in energy storage, biomedical engineering, and astrophysical phenomena. He has collaborated extensively on studies related to lithium-ion battery optimization, perovskite solar cells, and respiratory disease research. Key Research Areas: Mathematical modeling of electrochemical systems, computational fluid dynamics, astrophysical jets, and biomedical applications. Notable Contributions: Development of fast solvers for battery models, studies on adherence behavior in asthma patients, and experimental-validation frameworks for energy materials. Recent Publications: Focused on lithium-ion battery degradation, espresso brewing dynamics, and perovskite solar cell performance optimization. While no explicit awards or student advisement information is available in the provided data, his extensive publication record across diverse fields highlights his interdisciplinary expertise. His work bridges theoretical mathematics with practical applications in renewable energy and healthcare diagnostics.

Jennifer Pestanaمشاهده پروفایل

مدرس ارشد

• Numerical Linear Algebra
• Scientific Computing
• Krylov Subspace Methods
• +۷ مورد دیگر

Dr Jennifer Pestana is a Senior Lecturer in the Department of Mathematics and Statistics at the University of Strathclyde, Faculty of Science. She has been a faculty member since 2015 and is actively involved in research and academic leadership. She is a principal investigator on multiple grants and serves as an editor for SIAM publications. Education: DPhil, University of Oxford, 2012 Postdoctoral positions: University of Manchester, University of Oxford Her research centers on numerical linear algebra and its applications in scientific computing . She investigates the convergence of Krylov subspace methods , develops preconditioners for specific applications, and explores the use of tropical algebra for matrix pre-scaling. Current projects involve fast solvers for fractional diffusion and analysis in broadband signal processing . Her work bridges theoretical numerical analysis with practical computational challenges. The recent publications reflect a strong focus on iterative methods, matrix analysis, and signal processing. Common themes include Toeplitz matrices , eigenvalue decomposition , preconditioning , and covariance estimation , demonstrating her expertise in both algorithmic development and application-driven numerical methods. Scientific Awards: Best Student Paper Award (2018) Dr Pestana actively supervises students and leads research projects, including EPSRC-SFI and Strathclyde ISP Joint PhD initiatives. She has secured significant funding for work on Krylov methods and efficient solvers. Her professional service includes organizing major conferences such as the Biennial Conference on Numerical Analysis and serving on program committees, including for the International Linear Algebra Society. She is also an invited speaker and visiting researcher, demonstrating national and international recognition. She is involved with research teams focused on numerical analysis and scientific computing, contributing to collaborative efforts in developing novel preconditioned iterative solvers. Her lab and project groups work on both theoretical and applied aspects of linear algebra in PDEs and signal processing.

Matthias Maischakمشاهده پروفایل

استاد

• Elliptic Boundary Value Problems
• Transmission Problems
• Signorini Problems
• +۱۰ مورد دیگر

Dr Matthias Maischak is a Reader in the Department of Mathematics at Brunel University London within the College of Engineering, Design and Physical Sciences . His research focuses on elliptic boundary value problems , transmission problems , Signorini problems , and variational inequalities , with emphasis on boundary and finite element methods , fast solvers , and adaptive algorithms . He is also a key developer of the MaiProgs software package, which implements advanced numerical techniques for engineering applications. Research Areas : Boundary Element/FEM Methods, Contact Mechanics, Adaptive Algorithms, High-Performance Computing, Retarded Potential Analysis Supervision : Ph.D. and MSc students in computational mathematics, including projects on shape analysis, discontinuous Galerkin methods, and boundary integral equations. Publications span topics like dual-dual formulations , FE-BE coupling , hp-adaptive methods , and error estimation in linear elasticity and acoustic fluid-structure interaction . He has co-authored works on nonlinear interface conditions and time-domain boundary element methods , with applications to microstructure analysis , elastoplastic contact , and singular perturbations .

Minghao Rostamiمشاهده پروفایل

دانشیار

• Applied Mathematics
• Numerical Analysis
• Computational Fluid Dynamics
• +۳ مورد دیگر

Minghao Rostami is an Associate Professor in the Department of Mathematics and Statistics at Binghamton University. His research focuses on interdisciplinary problems at the intersection of applied mathematics, computational fluid dynamics, and biophysics, often leveraging machine learning and numerical methods. His recent work spans topics such as mixed noise removal in signal processing, data-driven fluid trajectory prediction, and multi-scale modeling of actin polymer dynamics. Publications highlight collaborations between mathematical theory and biological applications, particularly in biofluid mechanics. The trends in his publications from 2012 to 2025 emphasize scalable numerical algorithms for dense/sparse matrices, stability analysis in incompressible flows, and hybrid modeling frameworks combining machine learning with physical simulations. Specific subfields include multigrid methods, kernel regularization, and bio-inspired fluid pumping optimization.

• Computational Fluid Dynamics
• Fluid-Structure Interaction
• Computational Structural Dynamics
• +۱۲ مورد دیگر

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

• Numerical Analysis
• Iterative Methods
• Nonlinear Equations
• +۷ مورد دیگر

Vicente Fco Candela Pomares is an Associate Professor in the Department of Mathematics at the Faculty of Mathematics, University of Valencia, Spain. His academic career has been centered on numerical analysis and computational mathematics, with a focus on iterative methods for nonlinear equations and multiresolution techniques. His research interests lie primarily in Numerical Analysis , especially iterative root-finding methods such as Halley, Chebyshev, and Steffensen-type algorithms. He has contributed significantly to the convergence analysis of these methods, particularly in Banach spaces and for ill-conditioned problems. His work extends to multiresolution analysis , wavelets , and image restoration , where he applies fractional regularization and nonlinear approximation frameworks. The trends in his recent publications show a sustained focus on derivative-free iterative methods , convergence theory , and applications in image processing . His work often bridges theoretical numerical analysis with practical computational challenges. He earned his PhD from the University of Valencia in 1988 under the supervision of Dr. Antonio Marquina Vila, with a thesis on a priori error estimators for iterative methods. He has collaborated extensively with researchers including Sergio Amat, Sonia Busquier, and Rosa Peris. Notable co-authors include Pantaleón D. Romero and Francesc Aràndiga. His publications appear in high-quality journals such as Journal of Computational and Applied Mathematics , Applied Mathematics and Computation , and SIAM journals. He is actively affiliated with the University of Valencia, as evidenced by his institutional email and ongoing publications. There is no indication of part-time status, retirement, or awards in the available data.

• Computational mathematics
• Geometric integration
• Lie theory
• +۶ مورد دیگر

Hans Munthe-Kaas is a Professor of Mathematics at the University of Bergen, where he is also the Center Manager of the Lie-Størmer Center. He serves as Editor-in-Chief of Foundations of Computational Mathematics and President of the Norwegian Mathematical Society. Additionally, he is a member of the scientific advisory committee for the Trond Mohn Foundation and project leader for the RCN-Fripro project CODYSMA. His research focuses on computational mathematics, particularly geometric integration, Lie theory, differential geometry, algebra and combinatorics. Munthe-Kaas investigates algebras of connections on manifolds, combinatorics for formal solutions of dynamical systems, and structure-preserving discretizations. His work bridges pure and applied mathematics with computer science, with an emphasis on mathematical abstractions in computational science as tools for constructing efficient algorithms. Analysis of numerical Lie group integrators has led him to develop Lie-Butcher theory, which combines classical B-series for integration schemes with Lie series. This research connects to control theory, stochastic differential equations, renormalization, combinatorial Hopf algebra theory, and non-commutative symmetric functions. His publications show a consistent focus on geometric integration methods, with recent work exploring post-Lie algebras, aromatic B-series, and integration on symmetric spaces. Esso prize in 1999 for PhD work Munthe-Kaas has led significant research projects including the Lie-Størmer Center and CODYSMA. His work in computational mathematics has applications in numerical linear algebra, parallel algorithms, and fast elliptic solvers. He has also contributed to multivariate Chebyshev polynomials for spectral element methods based on triangular and simplicial domains. He is active in research groups including Applied and Computational Algebra and has been instrumental in developing coordinate-free formulations for numerical algorithm design through projects like SOPHUS. His DiffMan Matlab toolbox implements these ideas for solving differential equations on manifolds.

• Power System Optimization
• Global Optimization
• Stochastic Optimization
• +۶ مورد دیگر

Mahdi Mehrtash is an Assistant Professor in the Department of Electrical and Biomedical Engineering at the University of Nevada, Reno's College of Engineering. He concurrently serves as an Assistant Research Professor at Johns Hopkins University's Whiting School of Engineering, affiliated with the Ralph O'Connor Sustainable Energy Institute (ROSEI). His academic appointments span multiple institutions with active research roles in power systems and energy optimization. His educational background includes: Ph.D. in Electrical Engineering (Power) from Louisiana State University (2019, GPA 4.0/4.0) M.Sc. in Electrical Engineering (Power) from Shiraz University, Iran (2014, First Rank) B.Sc. in Electrical Engineering (Power) from Shahid Chamran University, Iran (2012, First Rank) Mehrtash's research focuses on advanced optimization techniques for power systems, with particular emphasis on global/stochastic/distributed optimization methods applied to zero-energy buildings, electricity markets, and power system expansion planning. His work addresses critical challenges in transmission/generation/distribution expansion planning, security-constrained unit commitment, and optimal power flow under high renewable penetration scenarios. He actively develops computational frameworks for large-scale power system analysis, including the Global-TEP solver for AC transmission expansion planning. His recent publications reveal a strong trend toward decarbonization-focused research, with increasing emphasis on market design under deep decarbonization, resource adequacy in low-carbon grids, and clean energy supply chain analysis. The publications consistently integrate stochastic optimization methods with practical power system applications, demonstrating expertise in both theoretical frameworks and real-world implementation. His scientific recognition includes: Senior Member of the Institute of Electrical & Electronics Engineers (IEEE) Mehrtash advises prospective graduate students through the University of Nevada, Reno's Graduate School, seeking candidates with optimization and power engineering expertise. His research is funded by major entities including the U.S. Department of Energy, National Science Foundation, and State of Maryland. He maintains active research groups at both UNR and ROSEI, focusing on developing the HOPE (Holistic Optimization Program for Electricity) framework and advancing transmission expansion planning methodologies. His GitHub repositories showcase computational tools for large-scale test systems and market research, reflecting strong software engineering capabilities alongside domain expertise.