High-Contrast Interface Problems Professors and Supervisors

Shuchin Aeronمشاهده پروفایل

دانشیار

Tufts UniversityUnited States

Shuchin Aeron is an Associate Professor in the Department of Electrical and Computer Engineering at Tufts School of Engineering, with joint appointments in the Departments of Computer Science and Mathematics. He holds a Ph.D. from Boston University (2009) and completed postdoctoral research at Schlumberger Doll Research, focusing on borehole acoustic signal processing. His research spans statistical signal processing, machine learning, compressed sensing, and information theory, with applications in geophysics, bioengineering, and imaging. Aeron has authored over 175 publications and holds patents in acoustic signal processing. He received the NSF CAREER Award (2016) and is a Senior Member of the IEEE. Educations: Ph.D., Electrical Engineering, Boston University, 2009 M.S., Electrical Engineering, Boston University, 2004 B.Tech., Indian Institute of Technology, 2002 Research Interests: Statistical signal processing (SSP), inverse problems, compressed sensing, information theory, convex optimization Machine learning applications in geophysical signal processing, imaging, and bioengineering His work emphasizes optimal sampling and recovery of multidimensional signals, with contributions to compressed sensing architectures and generative models for particle physics experiments. He leads NSF-funded projects on data science and domain generalization, and collaborates with industry partners like Schlumberger and Mitsubishi Electric Research Labs. Awards: NSF CAREER Award (2016) Mitsubishi Electric Research Lab Research Gift (2015) Grants and Funding: NSF HDR TRIPODS (2019–2023) AFOSR: Enabling Trusted Human-Like Artificial Teammates (2018–2023) NSF: Optimal Sampling and Recovery for Multilinear Signals (2013–2016) Aeron teaches advanced courses in probabilistic systems analysis, information theory, and machine learning. He directs the Tufts Data Science undergraduate and graduate programs, and serves on editorial boards of journals including Frontiers in Signal Processing and IEEE Transactions on Geoscience and Remote Sensing .

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Florian Frankمشاهده پروفایل

مدرس ارشد

University of Erlangen–NurembergGermany

PD Dr. Florian Frank is Privatdozent (senior lecturer with full teaching licence) for Applied Mathematics at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and heads the Bavarian research project „Parallel mesh loading and partitioning for large-scale simulation“ . His expertise spans high-performance computing, phase-field and discontinuous Galerkin methods, digital-rock physics, and reactive transport in porous media. Education & career 2022 – Venia legendi (private lecturer), Mathematics, FAU 2019 – Dr. habil., Mathematics, FAU 2013 – Dr. rer. nat., Applied Mathematics, FAU 2008 – Graduate Mathematician, University of Frankfurt 2021-2022 (acting) W2 Professor Scientific Computing, FAU 2018-2021 (acting) W2 Professor Mathematical Modelling, FAU 2017-2018 Senior Postdoc, CAAM, Rice University, USA 2014-2017 Postdoc, CAAM, Rice University, USA Research interests Frank focuses on the development and analysis of numerical schemes for partial differential equations that govern multiphase, multicomponent and reactive processes in porous or biological media. Key themes include discontinuous Galerkin and finite-volume methods , physics-preserving discretizations , high-performance computing , and digital-rock-based pore-scale simulations . He couples phase-field approaches with (Navier–)Stokes, Cahn–Hilliard, Nernst–Planck and density-gradient equations to quantify flow, transport, colloid dynamics and interfacial phenomena. Recent publications reveal a clear trend toward data-driven modelling : convolutional neural networks are trained with direct numerical simulation data to predict permeability and diffusion coefficients from 3-D micro-CT images, while advanced preconditioners and regularization techniques accelerate multiphase thermodynamic computations. Awards & recognition 2020 – Emmy-Noether-Prize der Naturwissenschaftlichen Fakultät, FAU 2017 – Promotion to Senior Postdoctoral Research Associate , George R. Brown School of Engineering, Rice University Projects, tools & supervision Frank currently leads a Bavarian state-funded project on parallel mesh handling for large-scale simulations. Together with collaborators he maintains the open-source MATLAB/GNU Octave toolbox FESTUNG for discontinuous Galerkin methods. Since 2018 he has (co-)supervised ten BSc and MSc theses on topics ranging from Stokes preconditioning to enriched Galerkin shallow-water solvers, regularly serves as reviewer for more than a dozen international journals, and is guest editor of special issues in Computational Geosciences and Oil & Gas Science and Technology .

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Mario Ohlbergerمشاهده پروفایل

استاد

University of MünsterGermany

Prof. Dr. Mario Ohlberger is a faculty member in the Department of Mathematics and Computer Science at the University of Münster, Germany. As a leading researcher in numerical analysis and scientific computing, he contributes to the Cluster of Excellence 2044 'Mathematics Münster: Dynamics – Geometry – Structure' and the Mathematical Research Data Initiative (MaRDI). His work integrates machine learning with classical numerical methods. University: University of Münster Research Focus: Numerical analysis for PDEs, model reduction, machine learning, multiscale methods Key Projects: EXC 2044 (subprojects C2 and C4), MaRDI His recent publications emphasize adaptive reduced basis methods, multi-fidelity learning, and surrogate modeling for parameterized PDEs, with applications in oil recovery and reactive transport. He has supervised numerous theses and taught courses on numerical analysis, scientific computing, and model reduction. Scientific awards include the Eliteförderprogramm für Postdoktoranden (2002), Ferdinand-von-Lindemann-Preis (2001), and a 1994 teaching award. Current teaching includes lectures on differential equations and seminars on advanced numerical methods.

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Kent-Andre Mardalمشاهده پروفایل

استاد

University of OsloNorway

Kent-Andre Mardal is a Professor at the Department of Mathematics , University of Oslo . He specializes in computational mechanics with a strong focus on biomechanical applications in medicine , particularly in modeling brain clearance mechanisms during sleep. His work integrates multi-physics modeling , fluid-structure interaction , and poroelastic couplings to advance understanding of the glymphatic system and cerebrospinal fluid dynamics. Education: PhD (2002) – Simula Research Laboratory Research Interests: Mardal's research spans a wide range of disciplines including: Computational Mechanics – developing robust numerical algorithms for complex physical systems Biomechanical Applications – particularly in neuroscience and medical imaging Brain Clearance During Sleep – modeling the glymphatic system and CSF flow dynamics Multi-Physics Modeling – integrating fluid dynamics, elasticity, and neural networks Finite Element Methods – for accurate and efficient simulations Neural Networks in Scientific Computing – exploring physics-informed neural networks Research Trends from Publications: Mardal's recent publications (2022–2025) demonstrate a clear focus on brain fluid dynamics , particularly the glymphatic system , CSF circulation , and neurodegenerative disease modeling . He employs advanced numerical techniques such as isogeometric analysis , physics-informed neural networks , and parameter-robust preconditioning to solve complex multi-physics problems. His work bridges medical imaging (MRI) with computational modeling to provide insights into brain clearance mechanisms and their impairment in diseases like Alzheimer's. Scientific Awards: No specific awards are mentioned in the provided text. Grants and Projects: Currently, Mardal is the Principal Investigator (PI) of three active research projects: Alzheimer's Physics – exploring the role of fluid dynamics in neurodegeneration Scientific Machine Learning – advancing numerical methods with AI Computational Hydrology – modeling subsurface fluid flow Affiliations and Teams: Mardal was previously a group leader at the Centre of Excellence “Biomedical Computing” at the Simula Research Laboratory. He has authored over 100 papers and several books, and his research homepage is available at https://kent-and.github.io/ .

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Marcelo Reggioمشاهده پروفایل

استاد

Polytechnic School of MontrealCanada

Marcelo Reggio is a Full Professor in the Department of Mechanical Engineering at Polytechnique Montréal, where he has established a significant research career spanning fluid dynamics and computational methods. He is an active member of the Fluid Dynamics Laboratory (LADYF), focusing on cutting-edge simulations of complex fluid phenomena. His educational background includes: Baccalaureate from Chile Master of Applied Science (M.Sc.A.) from Polytechnique Montréal Ph.D. from Polytechnique Montréal Research Focus: Professor Reggio's work centers on Computational Fluid Dynamics (CFD) with specialization in the Lattice Boltzmann Method (LBM). His research addresses fundamental and applied challenges in: Turbomachinery design optimization Wind turbine aerodynamics and icing effects Rarefied gas flows through porous media Multicomponent and multiphase flow modeling River current dynamics This work bridges theoretical fluid mechanics with industrial applications in energy and manufacturing. Publication Trends: Analysis of his 15 most recent articles (2016-2024) reveals dominant themes: advanced LBM techniques for non-equilibrium gas flows, validation of open-source CFD codes, multiphase interactions, and stochastic modeling of porous materials. His methodological innovations consistently target improved accuracy in complex physical scenarios. Academic Supervision: Professor Reggio maintains an active research group, having supervised: 12 doctoral students 20 master's students Student theses frequently explore LBM applications, turbomachinery, and numerical method development. Laboratory Leadership: At the Fluid Dynamics Laboratory (LADYF), he contributes to developing computational frameworks for fluid simulation, emphasizing GPU acceleration and industrial problem-solving.

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