جست‌وجوی استاد و پژوهشگر

Tao Gaoمشاهده پروفایل

مدرس

• Nonlinear Waves
• Fluid Mechanics
• Numerical Mathematics
• +۳ مورد دیگر

Dr Tao Gao is a Lecturer in Mathematical Sciences at the University of Essex, affiliated with the School of Mathematics, Statistics and Actuarial Science. Prior to this role, he held positions including Research Fellow at the University of Greenwich, Research Associate at the University of Bath, and LMS Research Fellow at Imperial College London. His expertise spans free boundary problems, nonlinear waves, numerical and applied mathematics, uncertainty quantification, and optimisation. Dr Gao holds a PhD in Mathematics from University College London (2016), an MSc in Mathematical Modelling from UCL (2011), an MSc in Applied Mathematics from Imperial College London (2010), and a bachelor's degree in Mathematics from Université Paris VI (2009). His research focuses on fluid mechanics, particularly free-surface waves, fluid-structure interactions, and electrohydrodynamics. Recent work explores stochastic systems, reinsurance optimisation, and nonlinear wave dynamics under electric field influences. His publications demonstrate contributions to numerical methods for hydroelastic waves, bifurcation mechanisms in solitary waves, and flow structures in complex fluid environments. Dr Gao's academic journey includes postdoctoral research at Imperial College London, the University of Bath, and the University of Greenwich, alongside teaching roles at Essex International College. His research interests emphasize both theoretical advancements and practical applications in mathematical sciences.

Stella Hadjiantoniمشاهده پروفایل

مدرس

• Statistical Computing
• Numerical Linear Algebra
• Large-Scale Models
• +۶ مورد دیگر

Dr. Stella Hadjiantoni is a Lecturer in Data Science and Statistics at the University of Essex's Department of Mathematical Sciences, part of the School of Mathematics, Statistics and Actuarial Science (SMSAS). She holds a BSc in Mathematics and Statistics and an MSc in Finance from the University of Cyprus, and a PhD from Queen Mary, University of London. Her research focuses on numerical algorithms for large-scale statistical problems, machine learning methods for regression and dimensionality reduction, and the estimation of multivariate models for functional and time-series data. Education BSc in Mathematics and Statistics, University of Cyprus MSc in Finance, University of Cyprus PhD, Queen Mary, University of London Research Interests Dr. Hadjiantoni’s work bridges numerical linear algebra and statistical computing. She develops computational methods to handle high-dimensional datasets and large-scale models, with applications in machine learning, econometrics, and functional data analysis. Her research emphasizes scalable algorithms for regression, regularization, and time-series modeling. Grants and Funding Grants and funding information is listed but not detailed in the provided text. Professional Appointments Lecturer in Data Science and Statistics, University of Essex (2019–present) Lecturer in Statistics, University of Kent (2016–2019) Guest Teacher in Statistics, London School of Economics (2015–2019) Research Associate, University of Oviedo, Spain (2016) Labs and Teams No specific lab or team affiliations are explicitly mentioned in the text.

John P. Harnadمشاهده پروفایل

استاد

• Mathematical Physics
• Integrable Systems
• Random Matrices
• +۵ مورد دیگر

John P. Harnad serves as a Full Professor in the Department of Mathematics and Statistics at Concordia University, contributing to advanced mathematical research and academic instruction. He earned his Ph.D. from the University of Oxford, United Kingdom, in 1972, establishing a decades-long scholarly foundation. His research is deeply rooted in Mathematical Physics , with concentrated expertise in Integrable Systems (exact solutions to nonlinear differential equations), Random Matrices (statistical properties of matrix ensembles), Gauge Theory (geometric frameworks for fundamental forces), and Relativity . He further advances knowledge in Group Theory (algebraic symmetry structures), Differential Equations (modeling dynamic systems), and Differential Geometry (curved space analysis), demonstrating interdisciplinary rigor across theoretical mathematics and physics. No scientific awards were documented in the provided biography. Details regarding student mentorship, grant funding, or collaborative projects remain unspecified in the available information. Descriptions of research laboratories, team structures, or future initiatives were not included in the source material.

Andreas Karageorghisمشاهده پروفایل

استاد

• Numerical Analysis
• Scientific Calculations
• Boundary Element Methods
• +۳ مورد دیگر

Andreas Karageorghis is a Professor in the Department of Mathematics and Statistics at the University of Cyprus, affiliated with the School of Natural and Applied Sciences. His research focuses on numerical analysis, scientific computations, and boundary element methods, particularly employing the Method of Fundamental Solutions (MFS). He holds a DPhil from Oxford University, preceded by a BA and MA. His work emphasizes solving complex elliptic boundary value problems, inverse scattering, and geometric reconstruction. Notable contributions include advancements in MFS algorithms for biharmonic equations, axisymmetric systems, and polyharmonic problems. He has also explored hybrid meshless methods and RBF neural networks for nonlinear problems. Key applications span fluid mechanics (e.g., Brinkman equations), elasticity, and acoustic scattering. His research addresses challenges like void detection, shape identification, and steady-state heat conduction. Karageorghis has published extensively in top journals like SIAM and IMA Numerical Analysis, with over 40 years of continuous academic contribution. His methodological innovations include matrix decomposition algorithms, efficient Kansa-type MFS formulations, and non-iterative approaches for inverse problems. Ongoing work includes optimal parameter selection for RBF methods and extending MFS to higher-order PDE systems.

Michael Frazierمشاهده پروفایل

استاد

• Harmonic Analysis
• Wavelets
• Partial Differential Equations
• +۳ مورد دیگر

Michael Frazier is a Professor of Mathematics at the University of Tennessee, Knoxville, where he has been since 2006. He previously served as the Department Head of Mathematics from 2006 to 2012 and held faculty positions at Michigan State University from 1990 to 2006. Dr. Frazier received his Ph.D. in analysis from UCLA in 1983 under John Garnett. His research focuses on harmonic analysis, wavelets, partial differential equations, and Schrödinger operators, with notable collaborations on Green’s function estimates and solvability of Schrödinger equations. He has mentored five doctoral students and currently co-advises Monty Taylor with Grozdena Todorova. His educational background includes postdoctoral work at Washington University in St. Louis, where he pioneered wavelet techniques in function space analysis with Björn Jawerth. His research contributions span foundational work in Littlewood-Paley theory, matrix-weighted function spaces, and applications of wavelets to signal processing and differential equations. Dr. Frazier’s publications emphasize the interplay between harmonic analysis and PDEs, with recent work addressing fractional Laplacian operators and Schrödinger equation solvability. His teaching and research materials, such as the Introduction to Wavelets Through Linear Algebra textbook, bridge advanced mathematical theory with pedagogical clarity.

Xinyue Zhaoمشاهده پروفایل

استادیار

• Partial Differential Equations
• Applied Mathematics
• Mathematical Biology
• +۵ مورد دیگر

Xinyue Zhao is an Assistant Professor in the Department of Mathematics at the University of Tennessee, Knoxville. Her research focuses on partial differential equations (PDEs), applied mathematics, and mathematical biology. She specializes in analyzing biological and physical models using theoretical and numerical methods, particularly studying stability, bifurcation, and free boundary systems. Education: Ph.D. in Mathematics from the University of Notre Dame. She has held academic positions at Vanderbilt University and the University of Tennessee, Knoxville, where she currently teaches courses including Mathematical Ecology, Matrix Algebra, and Models in Biology. Research Interests: Her work spans tumor growth modeling with time delays, atherosclerosis dynamics, neural network-based numerical methods for free boundary problems, nonlinear cell-cell adhesion models, and optimal control strategies in tumor treatment. Key contributions include bifurcation analysis for arterial plaque development, symmetry-breaking solutions in tumor models, and novel numerical schemes using neural networks. Editorial and Professional Roles: Guest Editor for the Special Issue on Numerical and Theoretical Analysis for PDE Models in Biology (Mathematical Biosciences and Engineering). She co-organized the 2025 SIAM-SEAS Minisymposium on Dynamical Systems in Biology and the 2023 Shanks Workshop on Mathematical and Theoretical Biology. Teaching: Courses taught include MATH 251 (Matrix Algebra), MATH 581 (Mathematical Ecology), and interdisciplinary courses like Models in Biology (MATH 405/EEB 406). She has also taught at Vanderbilt University and the University of Notre Dame.

Mahadevan Ganeshمشاهده پروفایل

استاد

• Computational Electromagnetics
• High-Performance Computing
• Wave Propagation
• +۴ مورد دیگر

Mahadevan Ganesh is a Professor in the Department of Applied Mathematics and Statistics and the Department of Computer Science at the Colorado School of Mines. He also serves as an Affiliate Professor in Electrical Engineering. His primary research interests include stochastic algorithms, model reduction, multiscale methods, uncertainty quantification, computational electromagnetics, and high-performance computing. His work addresses challenges in wave propagation, scattering, and numerical solutions for partial differential equations on complex domains. He has developed advanced algorithms for electromagnetic and acoustic scattering, including T-matrix methods, finite element approaches, and high-order numerical schemes. His research often integrates parallel computing and uncertainty quantification to address real-world applications such as ground motion forecasting and seismic risk modeling. Dr. Ganesh has published extensively in top journals, focusing on topics like scattering theory, integral equations, and computational methods for heterogeneous media. His recent work includes advancements in neural network-driven inverse scattering problems and efficient algorithms for large-scale particle configurations. He teaches courses such as Parallel Scientific Computing, Mathematical Biology, and Numerical Methods for Partial Differential Equations, reflecting his expertise in computational science and engineering. His contributions span theoretical analysis, algorithm development, and high-performance computing applications, with a strong emphasis on interdisciplinary research. He collaborates on projects involving ground motion modeling, stochastic wave propagation, and data-driven uncertainty quantification frameworks.

Ross Chiquetمشاهده پروفایل

دانشیار

• Mathematical Biology
• Population Dynamics
• Differential Equations
• +۲ مورد دیگر

Dr. Ross Chiquet is Associate Professor of Practice in Applied Mathematics at the University of Louisiana at Lafayette, where he also serves as Assistant Department Head. His research specializes in mathematical biology, developing population models for ecological applications including whale conservation and environmental disaster impact assessment. Chiquet employs differential equations and stability analysis to study biological systems. Recent publications focus on marine mammal conservation, particularly modeling sperm whale population recovery post-environmental disturbances. He mentors undergraduate researchers and advises mathematics majors while teaching courses spanning calculus, differential equations, and mathematical modeling.

Leonel Robertمشاهده پروفایل

دانشیار

• C*-algebras
• Operator Algebras
• Functional Analysis
• +۳ مورد دیگر

Leonel Robert is an Associate Professor in the Department of Mathematics at the University of Louisiana at Lafayette. His research focuses on the structure and classification of C*-algebras, the Cuntz semigroup, Hilbert C*-modules, and K-theory. He has organized conferences such as the Mini-workshop on the Cuntz semigroup (2018) and contributed to significant collaborations in operator algebras. His work integrates model theory, functional analysis, and algebraic topology to advance the understanding of noncommutative structures. Key research areas include C*-algebra classification, trace theory, and the interplay between algebraic and topological properties. His recent articles explore topics such as selfless C*-algebras, unitary groups' distinguishing properties, and nuclear dimension analysis. Dr. Robert has published extensively in top-tier journals like Duke Math. J., Advances in Mathematics, and Journal of Functional Analysis. His research bridges pure mathematics with applications in operator theory and noncommutative geometry. Office hours are held Tuesdays and Thursdays in MDD406.

Ioannis Economouمشاهده پروفایل

استاد

• Molecular Thermodynamics
• Carbon Capture and Sequestration
• Fluid Simulation (Molecular Dynamics/Monte Carlo)
• +۴ مورد دیگر

Dr. Ioannis Economou is the Executive Associate Dean and Professor of Chemical Engineering at Texas A&M University at Qatar, leading academic strategy and research initiatives. He holds a PhD from The Johns Hopkins University (1992) and a Diploma in Chemical Engineering from the National Technical University of Athens (1987). His research focuses on molecular modeling, thermodynamics, and equation of state development for energy and environmental applications, leveraging high-performance computing. Key projects include carbon capture, confined fluid behavior, and gas separation via MOFs. Research Interests: Molecular simulations (MD/Monte Carlo) and quantum mechanics Equations of state for complex mixtures (electrolytes, hydrocarbons) Carbon sequestration and environmental protection Oil & gas industry applications (EOR, phase equilibria) Zeolitic frameworks and nanoporous materials Publications highlight contributions to Fluid Phase Equilibria , Ind. Eng. Chem. Res. , and Energy & Fuels , with 235 peer-reviewed articles. His 2021-2023 work addresses confined fluids, electrolyte systems, and CO2 sequestration mechanisms. As an AIChE Fellow since 2021, he contributes to editorial roles at top journals and international committees. Collaborations span academia (e.g., Technical University of Denmark, Sorbonne) and industry (ExxonMobil, Texas A&M Qatar HPC facilities). Leadership includes founding the European Federation of Chemical Engineering’s Thermodynamics Working Party (2007-2014), overseeing graduate programs, and serving on research advisory councils. He has examined over 30 PhD theses globally, reflecting his international impact.

Bruno Carpentieriمشاهده پروفایل

دانشیار

• Numerical Linear Algebra
• High-Performance Computing
• Computational Electromagnetics
• +۵ مورد دیگر

Bruno Carpentieri is an Associate Professor in Applied Mathematics at the Faculty of Computer Science , Free University of Bozen-Bolzano , Italy. His work focuses on Numerical Linear Algebra , High-Performance Computing , and Preconditioning Techniques for large-scale scientific problems. Education : Laurea in Applied Mathematics (1997, University of Bari Aldo Moro); PhD in Computer Science (National Polytechnic Institute Toulouse). Research Interests : Sparse linear systems, Krylov subspace methods, computational electromagnetics, PageRank problems, and fractional calculus applications in nonlinear engineering. His publications (1998–2025) emphasize efficient solvers for multi-shifted systems, parallel computing, and preconditioning strategies. Recent works (2025) explore fractional-order methods and multilayer network centrality . He has collaborated on ITER tokamak simulations (F4E/MIUR grants) and biomedical finite element models . Grants : F4E-2008-OPE-06-06-11 (ITER analysis); MIUR PRIN 2010SPS9B3. Key Contributions : VBARMS preconditioner (2014), hybrid block GMRES variants (2018), and chaos-enhanced fractional solvers (2025).

• Quantum Mechanics
• Electromagnetic Scattering
• Stochastic Processes
• +۳ مورد دیگر

Timothy Field is an Associate Professor at McMaster University in the Department of Electrical and Computer Engineering . His research spans quantum mechanics , stochastic processes , and electromagnetic scattering , with a focus on theoretical models for spin noise , NMR measurements , and wireless communication channels . His scholarly activity includes 32 publications (1998-2024) and collaborations with researchers such as Shahram Shirani and Simon Haykin . Key contributions include dynamical theories for entangled spin-1/2 particles under random magnetic fields and K-distributed noise models in scattering phenomena. Teaching involves graduate and undergraduate courses on electromagnetic scattering , probability theory , and applied electromagnetics . Publications highlight interdisciplinary work at the intersection of physics , engineering , and applied mathematics .

• Harmonic Analysis
• Partial Differential Equations
• Functional Analysis
• +۳ مورد دیگر

Eric Sawyer is a Professor in the Department of Mathematics and Statistics at McMaster University. His research focuses on harmonic analysis, partial differential equations, and functional analysis with a particular emphasis on Calderón-Zygmund theory, weighted norm inequalities, and operator theory. He has published extensively in top-tier journals such as Revista Matemática Iberoamericana and the Journal of Functional Analysis. His work addresses advanced topics like hypoellipticity in degenerate equations and the stability of weighted norm inequalities. Dr. Sawyer has taught a variety of courses, including Partial Differential Equations, Complex Analysis, and Analysis I at both undergraduate and graduate levels. His contributions to academic service include roles as instructor and co-author in collaborative research projects. While specific awards are not listed in the text, his prolific publication record highlights significant scholarly impact. His research spans theoretical advancements in harmonic analysis and applications to PDEs, with recent work exploring operator boundedness in weighted spaces and the decomposition of symmetric matrices. No specific labs or teams are mentioned, but his involvement in interdisciplinary collaborations is evident through co-authored publications.

• Big Data Analysis
• Bayesian Statistics
• Genomics Data Analysis
• +۳ مورد دیگر

Madhuchhanda Bhattacharjee is a Professor of Statistics and Data Science with expertise in statistical methodology for large-scale data analysis. Her research focuses on Bayesian integrated modeling of omics data, spatio-temporal processes, and stochastic processes. She holds advanced degrees including a Ph.D. in Statistics from the University of Pune (2000), an M.Phil. in Statistics from the University of Pune (1995), and a Master of Statistics from the Indian Statistical Institute (1993). She is a Fellow of the Royal Statistical Society and an Elected Member of the International Statistical Institute, among other prestigious memberships. Her work addresses critical areas such as microbiome analysis, spatial epidemiology of diseases like cancer, and modeling climate variability using random matrix theory. She has contributed to the development of novel statistical methods for assessing dependence structures in spatio-temporal data and has published extensively on Bayesian approaches to integrate multi-omics datasets. Key research themes include: Inference on large-scale data using random matrices and parallel processing Bayesian modeling of heterogeneous biomedical data Spatio-temporal modeling of public health crises like the 2020 pandemic Her recent work explores statistical methods for predicting preterm birth via maternal blood multi-omics integration and spatial association measures for time series. She co-leads the Statistical Advisory Unit (SAU), providing advanced statistical methodologies for interdisciplinary research projects. Major awards include Fellow of the Royal Statistical Society (2005) and Elected Membership in the International Statistical Institute (2005). She has advised on several grants focusing on omics data integration and disease marker identification. Her contributions span academic leadership roles, including memberships in global statistical societies like the International Indian Statistical Association and the Caucus for Women in Statistics. She has pioneered statistical methodologies applied to environmental and healthcare challenges, emphasizing reproducibility in data-intensive research.

Vadim Gorinمشاهده پروفایل

دانشیار

• Integrable Probability
• Random Matrices
• Asymptotic Representation Theory
• +۳ مورد دیگر

Vadim Gorin is an Associate Professor in the Department of Statistics at the University of California, Berkeley. His research focuses on integrable probability, random matrices, asymptotic representation theory, and their applications to statistical physics. His work bridges probabilistic models with exact solutions and deep connections to algebraic structures. Key research areas include the study of six-vertex models, random matrix distributions, Airy processes, and high-dimensional statistical methods. He explores universality phenomena in particle systems and their links to combinatorics and quantum integrable systems. Gorin has contributed to understanding limit shapes, fluctuations, and asymptotic behaviors in models like random sorting networks and lozenge tilings. He collaborates on developing computational tools (e.g., R packages for cointegration testing) and has published extensively on exact solvability in stochastic systems. His research has implications for statistical physics, data science, and theoretical probability.