جست‌وجوی استاد و پژوهشگر

• 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).

• Partial Differential Equations
• Interface Evolution Problems
• Fluid Mechanics
• +۲ مورد دیگر

Sebastian Hensel is a Researcher at the Hausdorff Center for Mathematics (HCM) at the University of Bonn since September 2021, under the mentorship of Prof. Tim Laux. He holds a PhD in Mathematics from the Institute of Science and Technology Austria (IST Austria), where he studied under Prof. Julian Fischer. His research focuses on partial differential equations, particularly interface evolution problems, fluid mechanics, stochastic homogenization, and stochastic PDEs. He has contributed to topics like mean curvature flow, free boundary problems, and weak-strong uniqueness principles. Education includes a PhD (2021, IST Austria), Master of Science in Mathematics (2017, Humboldt-Universität zu Berlin), and Bachelor degrees in Mathematics (2015, Freie Universität Berlin) and Business Administration (2013, Freie Universität Berlin). His work explores geometric PDEs, including multiphase systems, contact angle dynamics, and stochastic influences. Key contributions include stability analyses of evolving interfaces and convergence studies of phase-field models. His articles often bridge analytical rigor and applied mathematics, addressing challenges in nonlinear dynamics and calculus of variations. He has presented research at international conferences such as FBP 2020, SIAM MS20, and Equadiff 2019. His academic activities include seminars at institutions like TU Chemnitz, University of Regensburg, and Max-Planck Institute for Mathematics in the Sciences, Leipzig.

• Macroeconomics
• Monetary Policy
• Fiscal Policy
• +۵ مورد دیگر

Marios Zachariadis is a Professor in the Department of Finance at the University of Cyprus, affiliated with the School of Economics and Management. His work bridges macroeconomics, finance, and international economics. Education: BA in Economics (University of Wisconsin-Madison, 1993), PhD in Economics (Ohio State University, 2000) Academic Career: Assistant Professor (Louisiana State University, 2000–2005), Assistant/Associate Professor (University of Cyprus, 2005–2017), Full Professor (2017–present) His research focuses on monetary and fiscal policy , economic integration , and technological diffusion . Key themes include price convergence, trade costs, and expectations formation under macroeconomic shocks. Recent publications analyze nonlinear international price dynamics , public debt effects , and race-based labor market discrimination . His empirical work spans the Euro Area, Cyprus, and global markets. Honors include Featured Article in Economic Inquiry (2023) and Lead Article in Journal of Money, Credit and Banking (2016).

Michael Jeffrey Wardمشاهده پروفایل

استاد

• Applied Analysis
• Singular Perturbations
• Reaction-Diffusion Theory
• +۴ مورد دیگر

Michael Jeffrey Ward is a Professor of Mathematics at the University of British Columbia, where he is also a member of the Institute for Applied Mathematics. His office is located in the Math Annex 1217 at UBC's Department of Mathematics. Ward has established himself as a leading researcher in applied mathematics, with particular expertise in asymptotic and singular perturbation methods applied to nonlinear boundary-value problems. Ward's research focuses on physical applied mathematics with emphasis on modern applications. His primary research areas include Applied Analysis, Singular Perturbations, Reaction-Diffusion Theory, Mathematical Modeling, Scientific Computation, Collective Behavior, and Quorum Sensing. He has made significant contributions to the analysis of nonlinear boundary-value problems for ordinary and partial differential equations arising in various applied fields such as combustion theory, semiconductor device modeling, biophysical diffusion problems, and pattern formation in biological and chemical systems. Analysis of Ward's recent publications reveals a consistent focus on the stability and dynamics of localized patterns in reaction-diffusion systems, particularly spots and stripes. His work combines asymptotic and singular perturbation techniques with numerical methods to analyze nonlinear boundary-value problems. A notable trend in his recent work is the application of these mathematical techniques to diverse fields including urban crime modeling, plant root hair initiation, intracellular transport, and mean first passage time problems. His research often involves collaborations with other leading mathematicians and scientists across various institutions. CAIMS Research Prize (2011) Julian Cole Lectureship (2022), awarded by SIAM Ward serves as Co-Editor-in-Chief for the European Journal of Applied Mathematics and is a member of the Editorial Board for the Journal of Nonlinear Science. While specific details about his advising and grants aren't provided in the available information, his extensive publication record suggests active mentorship of graduate students and successful grant funding for his research programs. His work has involved collaborations with numerous researchers including Juncheng Wei, Justin Tzou, Theodore Kolokolnikov, and others. Though specific laboratory information isn't detailed in the provided content, Ward's research appears to be conducted within the Institute for Applied Mathematics at UBC, which likely provides a collaborative environment for researchers working on mathematical modeling and analysis across various scientific disciplines.

Gianluigi Rozzaمشاهده پروفایل

استاد

• Numerical Analysis
• Computational Fluid Dynamics
• Scientific Computing
• +۶ مورد دیگر

Gianluigi Rozza is a Full Professor in Numerical Analysis and Scientific Computing at SISSA (International School for Advanced Studies) in Trieste, Italy. He leads the SISSA mathLab research group and serves as Coordinator of the Mathematics Area at SISSA. He holds roles such as SISSA Director's Delegate for Innovation and Knowledge Transfer and member of various committees including the SISSA HPC Committee and SIAM Student Chapter. His research focuses on numerical analysis, computational fluid dynamics, reduced order methods (ROM), and their applications in engineering and biomedical fields. Education: Laurea cum Laude in Aerospace Engineering from Politecnico di Milano (2002), PhD in Applied Mathematics from EPFL (2005). Postdoctoral research at MIT (2006-2008), followed by roles at EPFL and SISSA. He has supervised over 30 PhD and Master’s students, with expertise in reduced order modeling and computational mechanics. Research Interests: Reduced basis methods, parametrized PDEs, fluid-structure interaction, cardiovascular simulations, environmental flows, machine learning integration, and industrial collaborations with companies like Danieli and Fincantieri. He coordinates EU projects such as the ERC Consolidator Grant AROMA-CFD (2016-2021) and the ITN ROMSOC project. Publications: Over 90 scientific papers, two books as editor, and contributions to open-source software like rbMIT and ITHACA-FV. Awards include the ECCOMAS Young Investigator Award (2014), SIAM Fellowship (2025), and the Gili Agonistelli Prize (2025). Grants and Projects: Principal Investigator of ERC AROMA-CFD and ARGOS PoC. Coordinates regional projects like UBE (Under Water Blue Efficiency) and TRIM. Engages in environmental modeling (e.g., SOPHYA, PRELICA) and industrial collaborations under MAR tc FVG. Labs/Teams: SISSA mathLab develops open-source tools for computational science, including ROM libraries and fluid dynamics solvers. Active in organizing workshops, serving on editorial boards (e.g., SIAM Journals), and advisory roles for agencies like ARPA FVG.