جست‌وجوی استاد و پژوهشگر

Matthew Hirnمشاهده پروفایل

دانشیار

• computational mathematics
• harmonic analysis
• data science
• +۷ مورد دیگر

Matthew Hirn is a computational mathematician with expertise in harmonic analysis, data science, and machine learning. He was a tenured Associate Professor at Michigan State University (MSU), jointly appointed in the Department of Computational Mathematics, Science & Engineering and the Department of Mathematics. As the scientific leader of the ComplEx Data Analysis Research (CEDAR) Team at MSU, he developed innovative methods for analyzing complex data, including wavelet scattering transforms for atomistic systems and geometric deep learning techniques. He is currently a Senior Quantitative Researcher at Citadel LLC, contributing to algorithmic trading strategies. His research bridges theoretical mathematics and applied data science, with notable contributions to graph neural networks, scattering transforms, and privacy-preserving methods. His academic career included organizing conferences such as the SIAM Mathematics of Data Science mini-symposium on geometric learning and co-authoring influential software like the Kymatio library for wavelet scattering transforms. He advised PhD students Ryan LaRose, Xavier Brumwell, and Jieqian He, who have since pursued impactful roles in academia and industry. His work on 3D wavelet scattering networks for material science and geometric scattering on manifolds has advanced the intersection of mathematics and machine learning. Matthew’s research interests span signal processing, graph representation learning, and applications in bioinformatics and many-particle physics. He has published extensively in top journals and conferences, including the Journal of Chemical Physics and the Journal of Machine Learning Research.

• Numerical Probability
• Stochastic PDEs
• Multiscale Modeling
• +۲ مورد دیگر

Charles-Edouard Bréhier is a Professor at the University of Pau and the Pays de l'Adour, specializing in numerical methods for stochastic systems. His research spans stochastic PDEs, multiscale modeling, metastable processes, and geometric numerical integration. He leads investigations into error analysis and computational efficiency for complex stochastic systems. Bréhier's work develops advanced numerical schemes for high-dimensional problems, including splitting integrators, Galerkin approximations, and structure-preserving algorithms. His recent publications focus on error bounds for SPDE discretizations, turbulence modeling, and stochastic geometric mechanics. Articles consistently demonstrate rigorous mathematical foundations for computational methods, with applications spanning fluid dynamics, plasma physics, and optimization. The research provides tools for simulating complex systems with inherent randomness across multiple temporal and spatial scales.

• Materials Science
• Environmental Engineering
• Nanotechnology
• +۳ مورد دیگر

Konstantinos Beltsios is a Professor at the Department of Materials Science and Engineering, School of Chemical Engineering, National Technical University of Athens (NTUA). His work spans interdisciplinary research in materials science, environmental engineering, and cultural heritage science. He leads projects on advanced polymer membrane technologies, sustainable materials recycling, and analytical studies of historical artifacts. Key research interests include: Development of novel polymer membranes for gas separation and water treatment Recycling of polymeric materials like PVB from laminated glass Analysis of pigments and materials in Byzantine and post-Byzantine art CO2 capture using ionic liquid-based adsorbents Nanostructured carbon materials for catalytic and adsorption applications His recent publications (2022–2025) highlight advancements in membrane fabrication techniques, material characterization via X-ray and optical methods, and interdisciplinary studies bridging engineering and art conservation. He actively participates in institutional governance roles such as secretariat work for international student exchange programs (IKY Program) and academic assembly elections. Beltsios collaborates extensively with international institutions on EU-funded projects involving material innovation and cultural heritage preservation. His lab focuses on translating fundamental material science into practical solutions for environmental challenges and historical artifact conservation.

Vinh Nguyenمشاهده پروفایل

استادیار مهمان

• Partial Differential Equations (PDEs)
• Stochastic Partial Differential Equations (SPDEs)
• Mathematical Models of Collective Behavior
• +۳ مورد دیگر

Vinh Nguyen is a Visiting Assistant Professor in the Department of Mathematics at Michigan State University (MSU), working with Professors Keith Promislow and Andrew Christlieb. She holds a Ph.D. in Applied Mathematics from the University of Illinois at Chicago (2024) and a Master of Arts in Mathematics from Indiana University Bloomington (2019). Her research focuses on Partial Differential Equations (PDEs), Stochastic PDEs, and mathematical models of collective behavior, with additional interests in nonlinear Schrödinger equations, linear programming, and interpolation theory. Education highlights include a M.Sc. from the Institute of Mathematics, Hanoi (2012), and a B.A. in Mathematics Education from Thai Nguyen University of Education (2010). Professional experience includes roles as a Researcher at the Institute of Mathematics, Vietnam Academy of Science and Technology (2013–2017). Her work has been recognized through awards such as the Damber Singh Tomer Endowed Scholarship (2024) and the Victor Twersky Memorial Scholarship (2022). Research interests emphasize interdisciplinary applications of PDEs and SPDEs to real-world phenomena like collective behavior and material interface dynamics.

• Machine Learning
• Neural Networks
• Stochastic Processes
• +۳ مورد دیگر

Ella Tamir is a Visiting Professor at the Department of Computer Science , School of Science , University of Helsinki. She concurrently holds a Doctoral Student position. Her research focuses on stochastic processes, neural networks, and sequential learning. Education: Master's degree in Natural Sciences, University of Helsinki (2017) Bachelor's degree in Natural Sciences, University of Helsinki (2016) Research Interests: She explores stochastic differential equations, function-space representations, and diffusion processes. Her work emphasizes applications in neural networks, data generation, and sequential learning. Recent studies involve diffusion generative models for particle smoothing and sparse parameterization techniques. Research Output: Her recent publications address topics like function-space neural networks, diffusion bridges for time series, and scalable inference methods. These contributions highlight advancements in both theoretical and applied machine learning domains. Grants & Advising: No specific grants or advisees are listed in the provided materials. Labs/Teams: No lab affiliations or team collaborations are explicitly mentioned.

Michael Salinsمشاهده پروفایل

دانشیار

• Applied Mathematics
• Probability and Statistics
• Stochastic Analysis
• +۳ مورد دیگر

Michael Salins is an Associate Professor in the Department of Mathematics at Boston University and serves as Director of Graduate Admissions for Statistics. He is affiliated with the Applied Mathematics and Probability and Statistics research groups. His work focuses on stochastic partial differential equations, reaction-diffusion systems, and large deviations principles. His research explores topics such as global solutions to stochastic heat equations, non-explosion criteria for superlinear systems, and rare event simulation techniques. He has contributed to understanding the interplay between multiplicative noise, nonlinear dynamics, and spatial dimensions in stochastic models. Salins' recent publications emphasize the analysis of stochastic reaction-diffusion equations under various conditions, including superlinear terms and non-Lipschitz coefficients. His work often addresses existence, uniqueness, and regularity of solutions in both bounded and unbounded domains. No scientific awards are explicitly listed. His advising and grant activities are not detailed in the provided materials. He maintains an active research program in stochastic analysis and its applications to complex systems.

• Bio-inspired computation
• Neural networks
• Unconventional computing
• +۳ مورد دیگر

Simon O'Keefe is a Professor in the Department of Computer Science at the University of York. He holds a DPhil in Computer Science from the University of York, preceded by MSc degrees in Operational Research (Lancaster) and Information Processing (York), and a BA in Natural Science/Engineering from the University of Cambridge. His research focuses on bio-inspired computation, particularly neural networks and unconventional computing paradigms. He has held roles including Deputy Head of Department (Online) and previously served as Reader (2022-2024), Senior Lecturer, Lecturer, and Research Assistant at York. His work spans computational neuroscience, neuromorphic engineering, and AI applications in crisis response. Notable contributions include studies on reservoir computing using nanomagnetic devices, Arabic temporal understanding, and domain adaptation techniques. His publications explore topics like spike-based computing, cellular automata classification, and dependency-based bilingual embeddings. Prof O’Keefe’s research integrates theoretical computer science with practical applications in robotics, disaster informatics, and chemical computing systems. He leads the Artificial Intelligence research group at York and has contributed to interdisciplinary projects involving biochemistry and materials science.

Ganesh Balakrishnanمشاهده پروفایل

استاد

• Semiconductor Materials
• Optoelectronics
• Photonics
• +۴ مورد دیگر

Dr. Ganesh Balakrishnan is a Professor in the Department of Electrical and Computer Engineering at the University of New Mexico (UNM), part of the School of Engineering. His research focuses on III-Sb compound semiconductors, molecular beam epitaxy (MBE), and optoelectronic devices. He leads the Center for High Tech Materials and has contributed over 60 peer-reviewed publications, 30 conference presentations, and multiple patents. His work emphasizes low-defect epitaxy for advanced semiconductor integration. Education: PhD in Optical Science and Engineering (UNM, 2006), MS in Electrical Engineering (University of Toledo, 2001), and BS in Electronics and Communications Engineering (University of Madras, 2000). Research interests include mismatched semiconductor epitaxy, narrow-gap devices, and photovoltaic systems. His recent studies address high-brightness photonic crystal surface-emitting lasers (PCSELs), quantum dot-based single-photon emitters, and radiation-resistant materials. He collaborates on metamorphic substrates and semiconductor membranes for electromagnetic interference shielding. Dr. Balakrishnan’s lab, the Center for High Tech Materials, focuses on advanced semiconductor fabrication and device applications. His work bridges materials science, photonics, and optoelectronics, with applications in defense, energy, and space technologies.

Sergio Rojasمشاهده پروفایل

مدرس ارشد

• Numerical Analysis
• Scientific Computing
• Mathematical Modeling
• +۴ مورد دیگر

Sergio Rojas is a Senior Lecturer in the Applied and Computational Mathematics section at Monash University's School of Mathematics. His expertise spans numerical analysis, scientific computing, and mathematical modeling, focusing on residual minimization-based methods for solving complex partial differential equations. He holds a PhD and MSc in Engineering Sciences from Pontificia Universidad Católica de Chile, a Master's in Mathematics from the University of Pavia, and a Bachelor's in Mathematics from Pontificia Universidad Católica de Valparaíso (PUCV). Education: PhD and MSc in Engineering Sciences, Pontificia Universidad Católica de Chile Master's in Mathematics, University of Pavia, Italy Bachelor's in Mathematics, PUCV, Chile Research Interests: Dr. Rojas develops advanced numerical methods including Finite Element, Discontinuous Galerkin, and Physics-Informed Neural Networks. His work emphasizes robust algorithms for complex PDEs, with applications in computational fluid dynamics, geophysics, and solid mechanics. Recent studies explore adaptive stabilization techniques and solver optimization for parallel architectures. Article Trends (2023-2025): Recent publications highlight robust neural network approaches for PDEs, adaptive finite element methods, and computational efficiency in parallel solvers. Key themes include residual minimization, stability analysis, and integration of machine learning in numerical simulations. Supervision: Accepts undergraduate, master’s, and PhD students in numerical analysis and scientific computing. Current PhD projects include variational physics-informed schemes and residual minimization methods. Labs/Teams: Collaborates on interdisciplinary projects involving computational mathematics and engineering applications, though specific lab affiliations are not detailed in the provided text.

Yanzhi Zhangمشاهده پروفایل

استاد

• Fractional Partial Differential Equations (PDEs)
• Scientific Computing
• Numerical Analysis
• +۶ مورد دیگر

Dr. Yanzhi Zhang is a Professor of Mathematics at the Department of Mathematics and Statistics, Missouri University of Science and Technology (Missouri S&T), where she has held academic positions since 2010. She earned a Ph.D. in Mathematics from the National University of Singapore and a B.Sc. in Applied Mathematics (with a Computer Science minor) from Chongqing University. Her research spans computational and applied mathematics, focusing on fractional PDEs, nonlocal models, machine learning applications in materials science and business, Bose-Einstein superfluids, and seismic wave inversion. Dr. Zhang has been awarded the Faculty Excellence Award from Missouri S&T and other accolades. Her work is supported by grants from the National Science Foundation, including projects on fractional viscoacoustic wave equations, Bose-Einstein superfluids, and dispersive wave equations. She advises Ph.D. students in interdisciplinary research, such as Yumeng Wang (machine learning for eye-tracking data) and Yixuan Wu (seismic inversion). Her research integrates mathematical rigor with computational innovation, addressing real-world challenges in geophysics, materials science, and business analytics. Collaborations with industry and academia enhance her contributions to data-driven modeling and open-source strategies for digital transformation.

Wieslaw Krawcewiczمشاهده پروفایل

استاد

• Nonlinear Analysis
• Symmetric Differential Equations
• Bifurcation Problems
• +۴ مورد دیگر

Wieslaw Krawcewicz is a Professor in the Department of Mathematical Sciences at the University of Texas at Dallas (UT Dallas), within the School of Natural Sciences and Mathematics. He previously held tenured positions at the University of Alberta from 1988 to 2018, including roles as Assistant, Associate, and Full Professor. His academic journey includes a Ph.D. from Université de Montréal (2018) and an M.S. from Gdańsk University (2018). His research focuses on Topological Equivariant Methods in Nonlinear Analysis (TEMNA) , with applications to symmetric differential equations in various fields such as mathematical biology, fluid dynamics, and electrical engineering. Key interests include symmetric configurations in neural networks, bifurcation analysis in dynamical systems, and the impact of symmetries on periodic solutions. His work applies the Equivariant Degree theory to study symmetric models in population dynamics, electrical transmission lines, and fluid dynamics. Dr. Krawcewicz has been affiliated with institutions globally, including the University of Munich through multiple Alexander von Humboldt Fellowships (1996–2008). He has organized international workshops and conferences, and his research has been supported by grants from NSERC (Natural Sciences and Engineering Research Council) spanning decades. His advising record includes supervising Ph.D. and M.Sc. students in mathematics, such as Huaxing Xia, Paula Vivi, and Haibo Ruan. He has also contributed to expanding UT Dallas' academic programs, including plans for new M.S. and Ph.D. tracks in actuarial mathematics and biostatistics.

Nan Wuمشاهده پروفایل

استادیار

• Manifold Learning
• Nonparametric Statistics
• Massive Data Analysis
• +۵ مورد دیگر

Nan Wu is an Assistant Professor in the Department of Mathematical Sciences at the University of Texas at Dallas (UTD). He holds a Ph.D. in Mathematics from the University of Toronto (2018) and a B.Sc. in Mathematics and Physics from the same institution (2011). His research focuses on manifold learning, exploring geometric structures of high-dimensional data and their applications in nonparametric statistics and stochastic dynamics. Key areas include nonlinear dimension reduction, data denoising, and algorithm development for manifold-based analysis. Education Ph.D. in Mathematics, University of Toronto, 2018 B.Sc. in Mathematics and Physics, University of Toronto, 2011 Research Interests Wu’s work bridges differential geometry and machine learning, emphasizing manifold learning techniques. He develops mathematical foundations for nonlinear dimension reduction algorithms (e.g., Locally Linear Embedding, Diffusion Maps) and applies these to problems in statistics, such as kernel density estimation and efficient solvers for Langevin dynamics. His geometric methods address challenges in data denoising, boundary detection, and manifold reconstruction from noisy observations. Teaching He has taught courses in probability (UTD), multivariable calculus (Duke University), and introductory calculus (University of Toronto). Publications Overview His publications span topics like spectral convergence of graph Laplacians, Gaussian process modeling on manifolds, and geometric analysis of minimal surfaces. Recent work includes boundary detection algorithms and adaptive Bayesian methods for data with low intrinsic dimensionality.

Adnan Shamaoonمشاهده پروفایل

پژوهشگر

• Lie Symmetry Analysis
• Conservation Laws
• Partial Differential Equations
• +۳ مورد دیگر

Adnan Shamaoon is a postgraduate researcher in Mathematics at Northumbria University, specializing in mathematical physics and nonlinear dynamics. His research focuses on Lie symmetry analysis, conservation laws, and exact solutions of partial differential equations, particularly hydrodynamic chains from symmetric matrix ensembles and integrable systems. He holds a BSc from the Education University (Okara Campus) and an MSc in Mathematics from Lahore University of Management Sciences. His research interests include analytical reduction of hydrodynamic chains, symmetry analysis applications, and nonlinear dispersion phenomena. He has contributed to studies on the Jaulent-Miodek equation, reaction-diffusion systems, and Korteweg-de Vries equations. His work intersects with UN Sustainable Development Goals through contributions to mathematical advancements in physics and environmental modeling. Adnan has published 7 peer-reviewed articles since 2021, with recent work emphasizing symmetry reductions in ZK-BBM equations and approximate symmetries in perturbed mechanical systems. His research trends highlight a focus on theoretical frameworks for nonlinear systems and their applications in mathematical physics. Collaborations include international co-authors such as Prof. Agarwal, Dr. Cesarano, and Dr. Jain. No specific grants or awards are mentioned in the provided texts. His work is affiliated with Northumbria University’s Mathematics, Physics, and Electrical Engineering department.

Christian Seisمشاهده پروفایل

استاد

• Applied analysis
• Partial Differential Equations (PDEs)
• Mathematical fluid dynamics
• +۵ مورد دیگر

Prof. Dr. Christian Seis is a Professor of Applied Mathematics at the University of Münster, affiliated with the Institute of Analysis and Numerics. He is an Investigator in Mathematics Münster and specializes in applied analysis, partial differential equations (PDEs), and mathematical fluid dynamics. His research focuses on fluid dynamics phenomena, including vortex dynamics, mixing processes, and free boundary problems, with applications to convection and nonlinear diffusions. Education & Career: PhD from University of Leipzig (2011) Postdoctoral Fellow at University of Toronto (2011–2014) Lecturer (Akademischer Rat) at University of Bonn (2014–2017) Current position since 2017 at University of Münster Research Interests: Nonlinear PDEs, particularly fluid dynamics and transport equations Vortex dynamics, mixing in fluids, and boundary layer analysis Asymptotic behavior of solutions to Navier-Stokes and Euler equations Nonlinear diffusions, including thin film and porous medium equations Stability estimates and numerical schemes for advection-diffusion equations Recent Projects: Mathematical analysis of bubble rings in ideal fluids Optimal stability estimates for advection-diffusion equations Crossover of coarsening rates in demixing viscous liquids Analysis of the vanishing viscosity limit in 2D flows Grants & Collaborations: DFG Cluster of Excellence 'Mathematics Münster' (EXC 2044) Individual DFG grants for projects on transport equations and invariant manifolds Co-organizer of workshops on fluid dynamics and geometric PDEs Labs/Teams: Leads the research group on applied analysis and PDEs at the University of Münster, focusing on rigorous mathematical analysis of fluid dynamics and diffusion processes.

Angela Stevensمشاهده پروفایل

استاد

• Applied Mathematics
• Nonlinear Partial Differential Equations
• Mathematical Biology
• +۳ مورد دیگر

Prof. Dr. Angela Stevens is a Full Professor in Applied Mathematics at the University of Münster, Germany, leading the Applied Analysis group within the Institute for Analysis and Numerics. She is an Investigator in the Cluster of Excellence Mathematics Münster and holds an honorary position at the University of Leipzig. Her research focuses on applied analysis, nonlinear partial differential equations, mathematical modeling in biology, and interacting particle systems. Notable contributions include work on chemotaxis, cell motility, and regeneration processes in organisms like planarians. Education & Awards : Stevens earned her PhD in Mathematics from the University of Heidelberg (1992) and won the SIAM Student Paper Competition in 1992. She has held visiting positions at institutions such as the University of Minnesota, Hokkaido University, and Stanford University. Research Interests : Her work bridges theoretical analysis with biological applications, emphasizing mathematical models for cell behavior, pattern formation, and epidemiology. Key areas include taxis-driven dynamics, free boundary problems, and multi-scale processes in biological systems. Awards & Recognition : In addition to the SIAM award, her leadership in interdisciplinary research has been recognized through visiting professorships and contributions to collaborative networks like Cells in Motion and CeNoS. Grants & Collaborations : She leads research projects in Mathematics Münster and collaborates internationally on topics like PDEs and stochastic systems. Her work on the mathematical modeling of planarian regeneration highlights her innovative approach to biological problems. Labs & Teams : Stevens oversees the Applied Analysis research group, which actively contributes to the Mathematics Münster cluster and hosts events like the Workshop on Cell Dynamics and Mathematical Modeling (2023).