جست‌وجوی استاد و پژوهشگر

David Cruz-Uribeمشاهده پروفایل

استاد

• Harmonic Analysis
• Partial Differential Equations
• Weighted Inequalities
• +۳ مورد دیگر

David Cruz-Uribe is a Professor in the Department of Mathematics at the University of Alabama. His research focuses on harmonic analysis, with emphasis on weighted and variable exponent Lebesgue spaces, extrapolation theory, and applications to partial differential equations, particularly degenerate elliptic equations. He has authored/co-authored multiple books, including The Stieltjes Integral (2023) and Variable Lebesgue Spaces: Foundations and Harmonic Analysis (2013). His work bridges abstract harmonic analysis with practical applications in PDEs, contributing to the understanding of maximal operators, singular integrals, and fractional operators in nonhomogeneous spaces. His research interests include the study of classical harmonic analysis operators (maximal functions, Hilbert transform, fractional integrals) on weighted spaces, and their role in solving degenerate elliptic and parabolic equations. He also explores the interplay between weighted norm inequalities and functional spaces like Orlicz and variable exponent spaces. Key contributions include advancements in the theory of Rubio de Francia extrapolation, reverse Hölder inequalities for matrix weights, and the development of modular inequalities in variable Lebesgue spaces. These tools underpin his work on PDEs, where he addresses regularity of solutions and existence under non-standard conditions. He has secured grants such as the RUI: Harmonic Analysis on Weighted Lebesgue Spaces (2014). His publications span over two decades, reflecting sustained innovation in his field. While no specific awards are listed, his extensive publication record and authorship of foundational texts highlight his scholarly impact.

Jeff Borggaardمشاهده پروفایل

استاد

• Numerical Analysis
• Computational Science
• Control Theory
• +۷ مورد دیگر

Jeff Borggaard is a Professor of Mathematics at Virginia Tech, affiliated with the College of Science and the Interdisciplinary Center for Applied Mathematics (ICAM). His research focuses on numerical analysis, computational science, and control theory, with emphasis on optimization and control of systems governed by partial differential equations (PDEs). He specializes in sensitivity analysis, reduced-order modeling, and their applications in fluid dynamics and engineering systems. His work includes developing computational methods for PDE-constrained optimization, control of fluid flows, and uncertainty quantification. Key collaborations involve researchers at institutions like Florida State University and École Polytechnique de Montréal. Borggaard has been funded by agencies including the Air Force Office of Scientific Research (AFOSR) and the National Science Foundation (NSF), supporting projects on model reduction, flow control, and energy-efficient building systems. Research highlights include advancements in proper orthogonal decomposition (POD) for turbulent flows, nonlinear balanced truncation techniques, and applications of reduced-order models in control and optimization. His contributions also extend to thermal energy modeling in buildings and parameter estimation in groundwater flow systems. Borggaard holds positions at both the Department of Mathematics (McBryde Hall) and ICAM (Wright House), and maintains active involvement in professional societies such as the Society for Industrial and Applied Mathematics (SIAM) and the American Mathematical Society (AMS).

• Mathematical Biology
• Numerical Simulation
• Fluid-Structure Interaction
• +۴ مورد دیگر

Prof. Elfriede Friedmann is a Professor at the University of Kassel's Department of Numerics and Mathematical Modelling. Her research focuses on interdisciplinary applications of numerical methods in biomedical engineering, particularly in ophthalmology and systems biology. She specializes in fluid-structure interaction, pharmacokinetics modeling, and developing computational tools for medical diagnostics and treatment optimization. Education: Advanced degrees in mathematics and applied sciences (details not explicitly provided) Key Research Areas: Mathematical modeling of ocular systems, drug delivery mechanisms, and biological signaling pathways Her work emphasizes translating complex biological systems into mathematical frameworks, such as the 'Virtual Eye' project simulating human eye dynamics. Recent studies investigate retinal neurogenesis, intraocular lens stability, and viscoelastic fluid dynamics in ocular environments. Prof. Friedmann collaborates with clinical partners to validate numerical models against experimental data. Notable contributions include: Development of coupled PDE/ODE systems for intercellular signaling Optimization algorithms for medical device performance High-resolution MRI-based anatomical modeling Numerical methods for drug diffusion analysis Her lab integrates computational fluid dynamics, finite element analysis, and systems biology to address unmet clinical needs in ophthalmology. Current projects explore personalized treatment simulations for retinal diseases and glaucoma.

• Differential-algebraic Equations
• Systems and Control Theory
• Data-driven Modelling
• +۳ مورد دیگر

Jan Heiland is a Lecturer at TU Ilmenau, focusing on systems and control theory, with affiliations to the Max Planck Institute for Dynamics of Complex Technical Systems (MPI Magdeburg). His work emphasizes differential-algebraic equations, data-driven modeling, and flow control. He holds a PhD in Applied Mathematics from TU Berlin. Education: PhD in Applied Mathematics (TU Berlin). Research Interests: Control theory, numerical methods for PDEs, model reduction, and applications in fluid dynamics. Current courses include Numerical Solutions of PDEs and Scientific Computing. He leads projects on nonlinear controller design (LPV Approximations), scientific computing in MaRDI, and fluid dynamics modeling. Grants & Projects: Projects include LPV Approximations for Nonlinear Controller Design (2023), MaRDI (2023), and MRI Fingerprinting with Philips (2021). His research also involves uncertainty quantification and robust stabilization techniques. Talks/Publications: Recent talks address H-infinity robust control and computational approaches for large-scale systems. Key publications focus on autoencoder-based reduced-order modeling and low-rank solutions for Riccati equations.

Brian Spencerمشاهده پروفایل

استاد

• Continuum mechanics
• Materials modeling
• Bifurcation and stability
• +۱۰ مورد دیگر

Brian Spencer is a Professor in the Department of Mathematics at the University at Buffalo. His research focuses on the applied mathematics of materials science, combining physics-based models with analytical and numerical methods to study growth processes, instabilities, and microstructure formation in materials. He holds a PhD from Northwestern University. His research interests span: Crystal growth mechanisms and anisotropy effects Elastic stress analysis in solid films Morphological instabilities in epitaxial growth Nanowire and quantum dot shape transitions Computational methods for free boundary problems Recent work explores corner wetting phenomena in vapor-liquid-solid nanowire growth and elastic stress influences on equilibrium corner angles. His 2024 paper on hexagonal substrate drop shapes highlights advancements in understanding faceting dynamics. Current teaching includes MTH 306 (Differential Equations), MTH 412/512 (Statistical Inference), and MTH 446/546 (Stochastic Processes). Office hours are Mon 2:30-4:30 and Tue/Thu around class times. His research lab focuses on interdisciplinary materials modeling, with ongoing interests in strain-engineered materials and numerical methods for solid-state phenomena.

Anuj Karpatneمشاهده پروفایل

دانشیار

• Knowledge-guided Machine Learning (KGML)
• Spatio-temporal Data Mining
• Environmental Science Data Analysis
• +۵ مورد دیگر

Anuj Karpatne is an Associate Professor in the Department of Computer Science at Virginia Tech, leading the Knowledge-guided Machine Learning (KGML) Lab. He joined VT in 2018 and earned tenure in 2023. His research focuses on integrating scientific knowledge into machine learning, pioneering the KGML paradigm to create explainable and generalizable AI solutions for interdisciplinary scientific challenges. He holds a PhD from the University of Minnesota (2017) and an M.Tech. from IIT Delhi (2011). Research Interests: Knowledge-guided ML frameworks (KG-Loss, KG-Neural Architectures, Hybrid-Science-ML) Applications in physics (fluid dynamics, quantum mechanics), environmental sciences (aquatic systems, remote sensing), and biology (organismal biology, virology) Interdisciplinary collaborations across 15+ institutions for NSF-funded projects Recent Contributions: Developed Fish-Vista dataset for biological trait analysis Co-organized KDD 2024 workshop on Generative Models for Sustainable Development Secured $18M NSF PIPP II grant as co-PI for pandemic prediction systems Grants & Leadership: Lead PI on NSF CAREER Grant ($586K) for unifying ML with scientific knowledge Co-PI on NSF Imageomics Institute ($15M) integrating biology with AI Site PI for macrosystems biology grant ($1.1M) modeling lake water quality Labs & Teams: KGML Lab at VT collaborates with 30+ scientists across disciplines Leadership roles in NSF PIPP Institute and Imageomics Center

Olalekan Babaniyiمشاهده پروفایل

استادیار

• Inverse Problems
• Biomechanical Imaging
• Computational Mechanics
• +۳ مورد دیگر

Olalekan Babaniyi is an Assistant Professor in the School of Mathematics and Statistics at the Rochester Institute of Technology (RIT), within the College of Science. His research focuses on inverse problems with applications in biomechanical imaging, computational mechanics, and PDE-constrained optimization. He develops mathematical techniques to improve medical imaging and environmental modeling, including real-time imaging of soft tissue mechanical properties and ice flow prediction. He teaches courses such as Probability and Statistics, Boundary Value Problems, and Partial Differential Equations. His work has been highlighted in media for innovative applications in noninvasive disease diagnosis and climate science. Babaniyi runs the Inverse Problems Seminar at RIT (InvPrS) and has published extensively in journals like The Cryosphere, Journal of Nonlinear and Variational Analysis, and Physics in Medicine & Biology. His research emphasizes uncertainty quantification, image denoising in biomedical contexts, and solving inverse problems in viscoelasticity and geophysics. While no personal awards are listed, his contributions have supported student achievements, such as Quinn Kolt’s Goldwater Scholarship in 2021.

Nathan Cahillمشاهده پروفایل

دانشیار

• Machine Learning
• Computer Vision
• Medical Imaging Analysis
• +۳ مورد دیگر

Nathan Cahill is an Associate Professor in the School of Mathematical Sciences and Associate Dean for Industrial Partnerships in the College of Science at Rochester Institute of Technology (RIT). He holds a DPhil in Engineering Science from the University of Oxford and is the Director of RIT's PhD Program in Mathematical Modeling. His research focuses on computer vision, machine learning, medical imaging analysis, and mathematical modeling. He directs the Image Computing and Analysis Laboratory (ICAL), which develops mathematical models for imaging analysis and computer vision tasks like registration, segmentation, and classification. Dr. Cahill has extensive industrial experience, having worked at Eastman Kodak and Carestream Health, earning 26 US patents in computer vision and medical imaging. He teaches advanced courses in numerical analysis, mathematical modeling, and imaging science. His work bridges academia and industry, with collaborations spanning biomedical engineering, neuroscience, and astrophysics. He advises numerous graduate and undergraduate students, many of whom have pursued PhDs or industry roles in tech and healthcare. His research outputs include influential papers in medical image registration, graph theory applications in social networks, and hyperspectral imaging. He actively contributes to open-source tools for image processing, including MATLAB implementations of segmentation and clustering algorithms. Cahill also holds an Erdős number of 3 and has been recognized for his teaching, offering unique incentives to engage students in applied mathematics and computer science. Key affiliations include the Center for Imaging Science and the PhD Program in Computing and Information Sciences. His current projects involve machine learning applications in healthcare analytics, pulsar signal analysis, and improving wastewater surveillance during pandemics. He maintains an active lab environment fostering interdisciplinary research with collaborators at institutions like the University of Rochester Medical Center and the Mind Research Network.

Omar Lakkisمشاهده پروفایل

عضو هیئت علمی

• Scientific Computing
• Numerical Analysis of Partial Differential Equations
• Finite Element Methods
• +۳ مورد دیگر

Dr. Omar Lakkis is a Reader in Mathematics at the University of Sussex, United Kingdom, within the School of Mathematical and Physical Sciences. He holds a PhD in Applied Mathematics and Scientific Computing from the University of Maryland (2002) and a Laurea in Matematica (MSc equivalent) from the University of Trieste (1996). His academic journey includes roles such as Associate Professor at the Free University of Bozen-Bolzano (2016–2017), Senior Lecturer at Sussex (2011–2018), and Lecturer (2004–2011). He has also held a Marie Curie Fellowship at the University of Crete (2003–2004) and a postdoctoral position at the University of Milan (2002–2004). His research focuses on scientific computing and numerical analysis of partial differential equations (PDEs), particularly finite element methods for nonlinear and evolutionary PDEs, adaptive error control, geometric motions, and stochastic PDEs. His work emphasizes a posteriori error estimation, mesh adaptation, and applications to fluid dynamics, materials science, and environmental modeling. Notable contributions include models for floodplain inundation, lithium battery crystal growth, and Turing patterns in reaction-diffusion systems. Dr. Lakkis has secured grants from the Engineering and Physical Sciences Research Council (EPSRC), including projects on stochastic shallow water equations and machine learning integration. He has supervised numerous research initiatives, including a PhD studentship on numerical analysis of PDEs. His work is disseminated through high-impact journals like SIAM Journal on Numerical Analysis, Numerische Mathematik, and IMA Journal of Numerical Analysis. He actively participates in academic service, including committee roles and editorial work. His research portfolio reflects a commitment to advancing computational methods for complex physical systems and their real-world applications.

Michael R.A. Abdelmalikمشاهده پروفایل

استادیار

• Computational Fluid Dynamics
• Plasma Physics
• Numerical Analysis
• +۴ مورد دیگر

Dr. Michael R.A. Abdelmalik is an Assistant Professor in the Department of Mechanical Engineering at Eindhoven University of Technology (TU/e). His research focuses on computational methods for kinetic theory, plasma physics, and biomedical engineering, with an emphasis on developing advanced numerical techniques like finite element methods and machine learning-based operator networks. He holds a Master's degree in Mechanical Engineering from TU/e, where he also completed his thesis on 'Numerical approximation of the Boltzmann equation: moment closure' in 2012. His work integrates interdisciplinary approaches, combining fluid dynamics, radiative transfer, and mathematical modeling for applications in plasma simulation and cancer treatment optimization. Notable collaborations include studies on nanoparticle delivery for glioblastoma and variational mimetic operator networks for PDEs. Abdelmalik teaches 'Advanced Engineering Mathematics' and has contributed to over 19 research outputs since 2012. His recent articles address topics such as entropy stable solvers for plasma models and neural network approaches for parametric PDEs. Research Interests: Boltzmann Equation and Kinetic Theory Finite Element Methods and Moment Closure Techniques Machine Learning for Scientific Computing Biomedical Modeling and Medical Imaging Integration

Tony Harkinمشاهده پروفایل

دانشیار

• Applied and Computational Mathematics
• Fluid Mechanics
• Dynamical Systems
• +۴ مورد دیگر

Anthony A. Harkin is an Associate Professor in the Department of Mathematics and Statistics at the Rochester Institute of Technology (RIT), within the College of Science. His academic roles include teaching and research in applied mathematics, fluid mechanics, and dynamical systems. He holds a B.S. from SUNY Brockport, M.S. from MIT, and Ph.D. from Boston University, with postdoctoral research at Harvard University. His expertise spans computational mathematics, fluid dynamics, and network science, with a focus on mathematical modeling and partial differential equations. Education: B.S. in Math/Physics/Computer Science, SUNY College at Brockport (1993) M.S. in Applied Mathematics, Massachusetts Institute of Technology (1995) Ph.D. in Mathematics, Boston University (2000) Postdoctoral Fellow in Applied Mathematics, Harvard University (2001–2005) Research Interests: Fluid Mechanics: Bubble dynamics, multiphase flow, and nonlinear acoustics Dynamical Systems: Perturbation theory, renormalization group methods, and bifurcation analysis Network Science: Community detection, graph theory, and clustering algorithms Data Science: Hyperspectral image processing and machine learning applications Teaching Contributions: Harkin has taught advanced courses at RIT, Harvard, and Boston University in areas such as mathematical modeling, partial differential equations, dynamical systems, and linear algebra. He received the RIT Provost's Award for Excellence in Teaching (2008) and Harvard's Certificate of Distinction in Teaching (2004). Publications: His research spans fluid dynamics, network theory, and mathematical modeling, with notable contributions on bubble dynamics, graph modularity, and renormalization group methods. Recent work includes studies on Rayleigh collapse of bubbles and community detection in networks. Professional Activities: Co-founder of icitizen.com, a political networking platform. Active in editorial roles, including as editor of the International Journal of Applied Nonlinear Science (2013).

• Numerical Analysis
• Uncertainty Quantification
• Computational Mechanics
• +۵ مورد دیگر

Prof. Raul Fidel Tempone is a renowned expert in Numerical Analysis and Uncertainty Quantification (UQ) at RWTH Aachen University, where he established the Lehrstuhl für Mathematics for Uncertainty Quantification . His research focuses on developing efficient numerical methods for stochastic models and differential equations, driven by applications in computational mechanics, quantitative finance, biological/chemical modeling, and wireless communication. Research emphasizes a posteriori error estimation, adaptive algorithms, Bayesian model calibration/validation, and optimal experimental design. Key contributions include multilevel Monte Carlo (MLMC) methods, hierarchical/sparse approximation, stochastic optimization, and machine learning integration for UQ. Recent work includes advancements in: MLMC for PDEs/SDEs and McKean-Vlasov equations Bayesian experimental design with nuisance parameters Uncertainty quantification in porous media and wireless networks Machine learning-based segmentation and filtering techniques He has advised numerous PhD/Master’s students and collaborates widely, producing over 150+ publications in top journals/conferences. His applied research bridges theoretical developments with real-world challenges in engineering, finance, and data science.

Rafael Bailoمشاهده پروفایل

استادیار

• Numerical Analysis
• Partial Differential Equations (PDEs)
• Computational Science
• +۳ مورد دیگر

Rafael Bailo is an Assistant Professor of Mathematics at the Eindhoven University of Technology , affiliated with the Centre for Analysis, Scientific Computing and Applications . His primary research focuses on the numerical analysis of kinetic equations and partial differential equations (PDEs), with additional interests in collective dynamics, self-organization, and agent-based modeling. He teaches courses such as Advanced Calculus I and Continuous Optimization . Department: Mathematics and Computer Science His research explores computational methods for complex systems, including uncertainty quantification in kinetic models, pedestrian dynamics with congestion effects, and consensus-based particle methods. Recent work emphasizes the development of numerical schemes for fractional diffusion, plasma physics simulations, and aggregation-diffusion equations. He collaborates internationally on topics like particle-in-cell methods and software tools for interacting particle systems (e.g., the CBX package). Key trends in his publications include advancing finite-volume schemes for nonlinear PDEs, analyzing structural properties of kinetic equations, and bridging theoretical analysis with computational implementation. His work often addresses challenges in preserving physical properties (e.g., positivity, energy dissipation) in numerical solutions. Bailo advises students on topics related to PDEs and computational methods but specific supervisee names are not listed. His contributions span applied mathematics, computational physics, and interdisciplinary modeling, with applications in plasma dynamics, crowd movement, and materials science.

• Analysis
• Fractional Calculus
• Differential Equations
• +۳ مورد دیگر

Jeff Mortensen is a Teaching Professor and Associate Chair in the Department of Mathematics and Statistics at the University of Nevada, Reno (UNR). He holds a Ph.D. from the University of California, Davis (1993). His roles include serving as Math Center Director (2000-2005), Core Mathematics Director (2007-2008), and Interim Department Chair (2017-2018). His research focuses on Analysis, Fractional Calculus, and Differential Equations, with applications in environmental modeling and nonlinear systems. Education: Ph.D. in Mathematics, University of California, Davis, 1993 Research interests emphasize analytical and numerical solutions to differential equations, fractional calculus frameworks for advection-dispersion processes, and symmetry methods in nonlinear PDEs. His work bridges pure mathematics with applications in fluid dynamics and groundwater flow modeling. Teaching and innovation include developing web-based testing systems with dynamic question generation (2001), reflecting his commitment to educational technology. He has advised no listed graduate students but has extensive undergraduate teaching experience in mathematics. Affiliations include the College of Science at UNR, located at Davidson Mathematics and Science Center, Reno, NV.

Mark Pinskyمشاهده پروفایل

استاد

• Dynamics
• Control and Estimation of Nonlinear Systems
• Stability and Bifurcation Theory
• +۵ مورد دیگر

Mark Pinsky is a Professor in the Department of Mathematics and Statistics at the University of Nevada, Reno (UNR), affiliated with the College of Science. His research focuses on dynamics, control and estimation of nonlinear systems, stability and bifurcation theory, asymptotic methods, nonlinear resonances, observers, and control of PDEs. He teaches courses such as Differential Equations, Calculus, Linear Algebra, Probability and Statistics, Numerical Analysis, and Nonlinear Dynamics at both undergraduate and graduate levels. His research interests emphasize theoretical and applied aspects of nonlinear systems, with a strong focus on stability analysis, control methodologies, and dynamical systems theory. Notable contributions include work on Lyapunov-based stability criteria, exponential stabilization, and trapping regions in nonlinear systems. His publications span over two decades, addressing topics like time-varying systems, delay-independent stability, and switched systems. Dr. Pinsky’s work bridges mathematical rigor and engineering applications, particularly in control theory and dynamical systems. His recent studies (2020–2023) explore temporal scales dichotomy and boundedness in multidimensional nonlinear systems, reflecting a sustained engagement with cutting-edge challenges in systems analysis.