High Performance Computing
Research Advisors for High Performance Computing
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Daniel Appelö , bioProfessor Daniel Appelö is a numerical analyst with an interest in computational techniques for solving differential equations fast and accurately. He is excited about applications in acoustics, electromagnetics, fluids, and more recently in quantum computing.
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Jeff Borggaard , bioProfessor Borggaard studies the design and control of fluids. This includes computational fluid dynamics, control theory, optimization, sensitivity analysis, uncertainty quantification, and reduced-order models. In each case, the application of these research areas to partial differential equations that describe fluids are of interest.
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Paul Cazeaux , bioProfessor Cazeaux's research deals with multiscale phenomena in mathematical physics and biology, with recent applications in quantum chemistry and condensed matter physics (2D materials).
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Eric de Sturler , bioProfessor de Sturler's research focuses on numerical analysis for large-scale computational problems with an emphasis on fast solvers for linear and nonlinear systems, inverse problems and parameter estimation, optimization, and design, including iterative solvers and numerical linear algebra, randomization, stochastic methods, model reduction, and high performance computing with applications in computational mechanics, such structural optimization and computational fluid dynamics, tomography and image reconstruction, big data, computational physics, biology, and computer graphics.
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Ionut-Gabriel Farcas , bioProfessor Farcaș's research bridges scientific computing, high-performance computing, and computational physics. His work focuses on scientific machine learning, reduced and surrogate modeling, uncertainty quantification, and sparse grid and multi-fidelity methods. These computational techniques are designed to tackle complex, large-scale numerical simulations, such as those arising in turbulent transport in fusion devices or combustion processes in rocket engines.
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Tao Lin , bioProfessor Tao Lin's main research interest is the numerical analysis on computational methods related with differential equations. He designs new numerical methods and carry out their convergence analysis. His recent research focuses on immersed finite element (IFE) methods that can solve interface problems of partial differential equation with interface independent meshes. He is also working on applying IFE methods to interface inverse problems via the shape optimization methodology.
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Agnieszka Miedlar , bioProfessor Miedlar conducts research in numerical analysis and scientific computing, with a focus on iterative solvers for large-scale linear systems and eigenvalue problems, and adaptive finite element methods (AFEMs).
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Mirjeta Pasha , bioDr. Pasha is an Assistant Professor with research interests on high dimensional (tensor) data analysis, regularization for inverse problems, uncertainty quantification, and high-performance computing. She develops computationally efficient methods and algorithms to solve large-scale problems that arise from an extensive list of applications in data science, medicine, and engineering.
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Johann Rudi , bioProfessor Johann Rudi's research is interdisciplinary and spans large-scale parallel iterative methods for nonlinear and linear systems, development and implementation of algorithms for high-performance computing (HPC) platforms, computational aspects of inverse problems, and quantification of uncertainties in the inferred parameters.
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Peter Wapperom , bioProfessor Wapperom conducts research in computational fluid dynamics of complex fluids. This involves the mathematical modeling and numerical simulation of the flow of polymeric liquids and fluids reinforced with rigid particles.
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Tim Warburton , bioProfessor Warburton holds the John K. Costain Chair in the College of Science at Virginia Tech and is a faculty member of both the Department of Mathematics and the Computational Modeling and Data Analytics program. His research interests include developing new parallel algorithms and methods that are used to solve PDE based physical modes on the largest supercomputers.
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Steffen Werner , bioProfessor Werner conducts research at the intersection of scientific computing and numerical linear algebra with particular focus on scientific machine learning, model order reduction, data-driven modeling, optimization and control of partial differential equations, matrix equations and mathematical software development.
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Pengtao Yue , bioProfessor Yue works on the numerical simulation of flow problems with moving boundaries and complex rheology, including multiphase flow, viscoelastic fluids, dynamic wetting, and phase change phenomena.
Researchers of High Performance Computing
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Bio ItemMåns Andersson , bio
Dr. Andersson is a Postdoctoral Associate whose research focuses on large-scale simulations, numerical algorithms, and high-performance computing. He is mentored by Daniel Appelo and Yingda Cheng.
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Nilton Garcia Hilares , bioDr. Hilares' research interests lie in computational and applied linear algebra.
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Kun Huang , bioDr. Huang is a Postdoctoral Associate specializing in numerical methods for kinetic equations, with a particular emphasis on applications in plasma physics.. He is currently interested in low-rank tensor methods for high-dimensional problems. He is mentored by Daniel Appelo and Yingda Cheng.
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Jiuhua Hu , bioDr. Hu is a Postdoctoral Associate whose research focuses on numerical analysis and scientific computing, particularly the development of fast and efficient numerical methods for differential equations. She is mentored by Daniel Appelo and Yingda Cheng.
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Priyanka Sinha , bioDr. Sinha is a Postdoctoral Associate, working on large-scale inverse problems, multi-distribution learning, and adversarial methods, with recent projects on distributed tomographic reconstruction and fairness-driven federated learning. She is mentored by Eric de Sturler.
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Turker Topcu , bioDr. Topcu works in the field of computational science. His research involves developing algorithms and codes to solve partial and ordinary differential equations to simulate quantum dynamical systems.
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Ping-Hsuan Tsai , bioDr. Tsai is a Postdoctoral Associate focusing on the development of data-driven reduced-order models for turbulent heat transfer applications. His research emphasizes stabilization strategies and error indicators for turbulent flows, with the goal of supporting engineering design and analysis. He is mentored by Traian Iliescu.
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Jason Wilson , bioCollegiate Assistant Professor Wilson teaches Math and CMDA classes. His research interests include large scale linear algebra, high performance computing, and the mathematical foundations of data science.
Recently Retired Faculty
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