Find Academic Supervisors

Einar IversenView profile

Professor

• Geophysics
• Seismology
• Applied Geophysics
• +3 more

Einar Iversen is a Professor at the Department of Earth Science, University of Bergen (UiB), affiliated with the Geophysics research group. His work focuses on advanced computational methods in geophysics, including seismic modeling, wave propagation in anisotropic media, and dynamic ray tracing. He has contributed to developments in numerical simulation techniques for multiphysics processes in porous media and higher-order Hamilton-Jacobi perturbation theories. Research interests emphasize applications of mathematical physics to geophysical problems, such as full-waveform inversion, traveltime extrapolation, and geometrical spreading analysis. His studies often involve collaborations with institutions like NTNU, Rice University, and international researchers in seismology and applied mathematics. Key contributions include advancing ray-centred coordinate systems for dynamic ray tracing, improving finite-difference modeling accuracy in discontinuous media, and reconstructing metrics from boundary diffraction data. His work bridges theoretical developments with practical seismic data processing techniques, contributing to reservoir simulation and imaging technologies. Funding includes grants from the Research Council of Norway (e.g., projects 294404, 267769). His research outputs span journals like Geophysical Journal International , Computer Methods in Applied Mechanics and Engineering , and Geophysical Prospecting , reflecting a strong focus on computational and theoretical geophysics.

Mikhail GilmanView profile

Research Professor

• Radar Imaging
• SAR Imaging
• Remote Sensing
• +4 more

Mikhail Gilman is an Associate Research Professor in the Department of Mathematics at North Carolina State University (NCSU), based in SAS Hall 3210. He serves as a core faculty member in NCSU's Radar Imaging research group, focusing on advanced radar technologies and their mathematical foundations. Dr. Gilman earned his PhD from the State Institute of Physical and Technical Problems in Russia in 1998. His academic journey spans interdisciplinary research connecting applied mathematics, electromagnetics, and remote sensing. His research centers on radar imaging, particularly synthetic aperture radar (SAR) systems. Key interests include mitigating phase distortions from turbulent media propagation (especially ionospheric effects), developing novel radar target models, and innovating change detection paradigms in SAR imagery. He integrates analytical methods, computational simulations, and machine learning to solve complex problems in transionospheric SAR imaging, with significant contributions to autofocus algorithms and ionospheric distortion correction. His work bridges theoretical mathematics and practical remote sensing applications. Recent publications reveal a concentrated focus on transionospheric SAR challenges, with 10 of the last 15 papers addressing ionospheric effects, autofocus techniques, and machine learning applications. His 2017 book Transionospheric Synthetic Aperture Imaging (Birkhäuser) established foundational frameworks, receiving positive reviews in SIAM Review and AMS MathSciNet. Dr. Gilman actively leads the Radar Imaging group at NCSU, which develops analytical tools and machine learning approaches for radar technology advancement. The group's work, detailed in their research flyer, emphasizes practical solutions for real-world remote sensing challenges.

Marie-Therese WolframView profile

Professor

• Applied Partial Differential Equations
• Mathematical Modeling in Socio-Economic and Life Sciences
• Inverse Problems
• +4 more

Professor Marie-Therese Wolfram holds a faculty position at the Mathematics Institute of the University of Warwick. Her research focuses on applied partial differential equations, mathematical modeling in socio-economic and life sciences, and inverse problems. She is actively involved in organizing international workshops and serves on editorial boards for journals like ESAIM M2AN and Kinetic and Related Models. Her academic journey includes grants such as the Royal Society International Exchange (2022-2024) and the EPSRC First Grant (2017-2019). Notable awards include the Excellence in Gender Equality Award (2023) and the Whitehead Prize (2023). She co-leads the EDI committee at Warwick and contributes to initiatives like the Young Academy of the Austrian Academy of Sciences. Her research spans opinion dynamics, crowd modeling, and optimal transport theory. Recent work explores consensus-breaking in social networks and applications of mean-field games to knowledge growth. She collaborates internationally on projects such as the 'Multiscale Modelling of Crowded Transport' and the 'Wasserstein Gradient Flows' initiative. Teaching responsibilities include MA265 Methods for Mathematical Modelling and MA4M2 Inverse Problems. Her work bridges theoretical analysis with practical applications, reflecting her dual expertise in pure mathematics and interdisciplinary modeling.

Mingtao XiaView profile

Lecturer

• Mathematical Modeling
• Numerical Methods
• Kinetic Theory
• +3 more

Mingtao Xia is a Courant Instructor at the Courant Institute of Mathematical Sciences, New York University (2023–present). He holds a PhD in Applied Mathematics from UCLA (2019–2023) and a B.S. in Information and Computing Science from Peking University’s School of Mathematical Sciences (2015–2019). His research focuses on mathematical modeling, numerical methods, and machine learning applications in kinetic theories, stochastic systems, and unbounded-domain differential equations. He has contributed to interdisciplinary areas including biological systems, environmental science, and epidemic control through computational methods. Education: PhD in Applied Mathematics, UCLA, 2019–2023 B.S. in Information and Computing Science, Peking University, 2015–2019 His work integrates advanced numerical techniques like spectral methods and Wasserstein-distance frameworks with real-world problems such as gene regulation dynamics, solar wind analysis, and ozone pollution impacts. Recent publications emphasize efficient algorithms for high-dimensional systems and uncertainty quantification in stochastic models. While no awards are listed, his research demonstrates strong contributions to mathematical modeling across diverse domains. Advising and grant details are currently unavailable. His affiliation with the Courant Institute positions him at the forefront of computational mathematics and its applications in interdisciplinary research.

Patrizio CampisiView profile

Professor

• Digital Signal Processing
• Image Processing
• Biometrics
• +12 more

Patrizio Campisi is a Full Professor in the Section of Applied Electronics at the Department of Engineering, Roma TRE University, Rome, Italy. He leads cutting-edge research in digital signal and image processing with applications to secure multimedia communications and biometrics. He has held visiting positions at the University of Toronto, Beckman Institute (UIUC), and École Polytechnique de Nantes, and has been a Marie Curie Fellow (2010–2014). His educational background includes a Laurea (summa cum laude) in Electronic Engineering from Sapienza University of Rome and a Ph.D. in Electrical Engineering from Roma TRE University. His research focuses on secure biometric recognition (signature, keystroke, EEG, vein), digital watermarking, blind image deconvolution, HDR imaging, and privacy-preserving technologies. He has contributed significantly to template protection, multimodal biometrics, and forensic image analysis. His work bridges engineering, computer science, and security, with strong applications in mobile authentication, border control, and social media safety. The recent publications reflect a strong trend in biometrics, image forensics, and privacy-enhancing technologies, with a focus on real-world applications in mobile systems, healthcare, and law enforcement. Key themes include secure authentication, de-identification, and computational imaging. IEEE Second International Conference on Biometric Systems 2008 Best Student Paper Award IEEE Biometric Symposium 2007 Best Paper Award IEEE International Conference on Image Processing 2006 Best Student Paper Award Marie Curie Fellow (2010–2014) NATO-CNR Advanced Fellowship (2003) IEEE Senior Member Prof. Campisi has supervised numerous students and leads the BioMedia4n6 lab. He has secured major EU grants including H2020 projects AMBER, COSMOS, and ENCASE. His editorial leadership includes serving as Editor-in-Chief of IEEE Transactions on Information Forensics and Security (2018–2020) and Chair of the IEEE Information Forensics and Security Technical Committee (2017–2018). He is actively involved in research networks such as COST Actions on de-identification and biometrics-forensics integration, and has contributed to EU policy via the BEST Thematic Network on biometrics and fundamental rights. His lab, BioMedia4n6, focuses on biometrics, multimedia forensics, and privacy-aware systems.

• Metamaterials
• Electromagnetic and Microwave Applications
• Terahertz Technology
• +7 more

Cumali Sabah is a faculty member in the Department of Electrical and Electronics Engineering at the Middle East Technical University Northern Cyprus Campus (METU NCC), within the College of Engineering. His research is centered on advanced electromagnetic systems, particularly metamaterials and their applications in microwave and terahertz frequency regimes. Research Interests: His primary fields include Metamaterials, Electromagnetic and Microwave Applications, Terahertz Technology, Wave Propagation & Scattering, Antennas, Multilayer Structures, and Metamaterial-Based Sensing. He has pioneered work on fishnet metamaterials, chiral structures, polarization rotators, and absorbers, with applications in sensing and communication systems. The analysis of his recent publications reveals a strong focus on the design, simulation, and experimental validation of metamaterial structures for terahertz and microwave applications. Key themes include dual-band and multiband operation, polarization independence, low-loss configurations, and novel resonator geometries such as split-ring and omega-shaped elements. His work also extends to solar system applications, particularly in photovoltaic panel optimization through sun tracking and cooling. Scientific Contributions: Pioneering work on free-standing and waveguide-based metamaterials Development of THz polarization rotators using chiral metamaterials Innovative designs of multiband and low-loss metamaterial absorbers Contributions to electromagnetic parameter retrieval methods for bianisotropic slabs Advising and Grants: While specific details about students or funded grants are not provided in the text, his extensive publication record and research leadership suggest active involvement in graduate supervision and research projects. He has collaborated with international researchers from Germany, Turkey, and other institutions, indicating strong academic networks. Labs and Teams: Although specific lab affiliations are not mentioned, his research likely involves experimental and computational work in electromagnetics and metamaterials, possibly within dedicated labs at METU NCC’s Electrical and Electronics Engineering Department.

Hans-Peter SeidelView profile

Professor

• Computer Graphics
• Neural Rendering
• High Dynamic Range Imaging
• +7 more

Hans-Peter Seidel is a leading researcher in computer graphics at the Max Planck Institute for Informatics, part of the Max Planck Society. His work focuses on advancing the frontiers of image synthesis, neural rendering, and computational photography, with a strong emphasis on high dynamic range imaging, inverse rendering, and perception-aware graphics techniques. His research interests span a broad spectrum of computer graphics and vision, including neural radiance fields, Monte Carlo denoising, image deblurring, and visual perception modeling. He has made significant contributions to real-time rendering, HDR image generation, and uncertainty-aware AI for scientific applications. His lab collaborates closely with experts in rendering, perception, and machine learning, pushing the boundaries of what is possible in digital image creation and manipulation. The most recent publications reveal a strong trend toward integrating deep learning with traditional graphics pipelines, particularly through differentiable rendering, neural fields, and adversarial training. His work frequently appears in top venues such as SIGGRAPH, ACM Transactions on Graphics, and Computer Graphics Forum, reflecting sustained impact and innovation in the field. Hans-Peter Seidel has not been publicly associated with any formal scientific awards in the provided text. However, his extensive publication record and leadership at a premier research institute underscore his influential role in the academic community. While there is no explicit mention of student advising or grant funding in the provided material, his collaborative publications with junior researchers suggest active mentorship. His work is likely supported by institutional funding from the Max Planck Society, enabling long-term, high-risk research in computer graphics and AI. He is part of a vibrant research group at the Max Planck Institute for Informatics, specializing in computer graphics. The team works on cutting-edge problems in rendering, perception, and machine learning, often bridging the gap between theoretical innovation and practical applications in virtual reality, computational photography, and scientific visualization.

Marco GiomettoView profile

Assistant Professor

• Environmental Engineering
• Wind Engineering
• Smart Cities
• +4 more

Marco Giometto is an Assistant Professor of Civil Engineering and Engineering Mechanics at Columbia University, affiliated with the Columbia School of Engineering and Applied Science. His research focuses on fluid dynamics and turbulence, particularly in environmental flows, atmospheric boundary layers, and wind engineering. He employs supercomputing and physics-data-driven methods to advance understanding of natural systems and urban infrastructure resilience. Education: PhD in Civil Engineering (Braunschweig TU University and University of Florence, 2014); PhD in Mechanical Engineering (École Polytechnique Fédérale de Lausanne, 2016, EDME Award recipient). Experience: Postdoctoral roles at UBC and the Center for Turbulence Research (Stanford/NASA Ames); Amazon Visiting Academic (2020–present). Research Interests: Environmental Engineering, Wind Engineering, Smart Cities, Sustainable Infrastructure, and Computational Mechanics. His work bridges fundamental fluid dynamics with applied challenges in urban sustainability and climate resilience. Awards/Grants: NSF CAREER Award (2024), NSF Physical & Dynamic Meteorology Program grant (2024), Army Research Office DURIP Award (2023). Collaborations include Amazon Prime Air and field campaigns like CoURAGE. Lab & Outreach: Leads the Environmental Flow Physics Laboratory, advancing computational tools and field experiments. Teaches at Columbia and mentors students like Gurpreet Hora (PhD Teaching Fellow).

Raphaël PestourieView profile

Assistant Professor

• Scientific Machine Learning
• Inverse Design
• Electromagnetism
• +7 more

Raphaël Pestourie is an Assistant Professor in the School of Computational Science and Engineering at Georgia Tech (2023–present). He holds a PhD in Applied Mathematics (Harvard University, 2020) and an MBA from ESSEC. His research focuses on scientific machine learning and inverse design in electromagnetism, combining AI with physics-based models for engineering optimization. Notable contributions include fast approximate PDE solvers, surrogate models for metasurface design, and end-to-end optimization frameworks. **Education**: PhD in Applied Mathematics, Harvard University (2020) Secondary field in Computational Science & Engineering, Harvard MBA, ESSEC Masters in Nanosciences (Paris Saclay), Engineering (École Centrale Paris), and Statistics (Harvard) **Research Interests**: His work bridges applied mathematics and AI to solve large-scale engineering problems. Key areas include: Inverse design of metasurfaces for optical applications Scientific machine learning for PDE-based optimization Hybrid models combining theory-driven and data-driven approaches End-to-end optimization of electromagnetic systems **Awards & Support**: Funded by MIT-IBM Watson AI Lab, DARPA, Simons Foundation, and others Arthur Sachs Fellow (French Fulbright Commission) Member of Harvard's Graduate School Leadership Institute **Advisees & Collaborations**: Advises PhD/MSc students at Georgia Tech and collaborates with MIT, Yale, and UC Berkeley researchers Current projects include metasurface design for chemical detection and imaging **Teaching**: Created new courses: CSE 8803 (Scientific Machine Learning) and CSE 8801 (Linear Algebra/Probability/Stats)

Martijn AnthonissenView profile

Assistant Professor

• Computational Illumination Optics
• Numerical Mathematics
• Scientific Computing
• +5 more

Martijn Anthonissen is an Assistant Professor in the Department of Mathematics and Computer Science at Eindhoven University of Technology (TU/e). He is a core member of the Computational Illumination Optics group, focusing on numerical methods for optical system design, freeform optics, and inverse problems in illumination engineering. His work combines advanced mathematical models with computational techniques to develop efficient optical systems for applications like LED lighting, automotive headlights, and laser technology. Education: Martijn earned his mathematics degree at TU/e and participated in the Japan Prizewinners Program, working at Hitachi in Tokyo. He completed his PhD on numerical combustion before transitioning to computational optics. He has held visiting positions at institutions worldwide, including Yale University and the Weierstrass Institute. Research: His research emphasizes solving Monge-Ampère equations and generated Jacobian equations to design freeform optical surfaces. Key areas include hyperbolic PDEs, Liouville's equation in geometrical optics, and Lie algebraic methods for aberration analysis. Collaborations with industry partners aim to translate theoretical advances into practical optical solutions. Teaching: He teaches courses in numerical linear algebra, mathematical modeling, and scientific computing, contributing to TU/e's teacher training programs. His educational materials, including YouTube videos on numerical methods, are widely used. Publications: Over 50 peer-reviewed articles since 2013, including high-impact work in Journal of the Optical Society of America , Journal of Computational Physics , and Optics Express . Current projects include neural network approaches for PDEs and optimization of gradient-index lenses. Labs/Teams: Leads the Computational Illumination Optics group, which has active PhD and industrial research programs. The group's interdisciplinary approach bridges mathematics, physics, and engineering.

Nuutti HyvönenView profile

Professor

• Inverse Problems
• Electrical Impedance Tomography
• Numerical Analysis
• +3 more

Nuutti Hyvönen is a Professor and Head of Department at the Department of Mathematics and Systems Analysis, Aalto University, School of Science. His research focuses on inverse problems, electrical impedance tomography, and numerical analysis with applications in biomedical imaging and engineering. He holds a Doctor of Science (Technology) degree and has extensive experience in developing mathematical methods for imaging and reconstruction algorithms. His work emphasizes improving the accuracy and robustness of tomographic techniques, particularly in handling modeling errors and electrode configurations. Key research areas include: Inverse problems for partial differential equations Electrical impedance tomography (EIT) and its biomedical applications Bayesian experimental design and uncertainty quantification Numerical methods for nonlinear imaging problems Recent publications highlight advancements in: Optimal electrode positioning Series reversion methods in Calderón problems Feasibility of EIT for monitoring intracerebral hemorrhages Edge-enhancing reconstruction algorithms He has contributed to over 79 peer-reviewed articles, with notable work on monotonicity-based reconstruction methods and stochastic Galerkin finite element approaches. His research bridges mathematical theory and practical applications in medical imaging, engineering, and computational science.

Christina KuttlerView profile

Professor

• Biomathematics
• Deterministic Mathematical Models
• Bacterial Communication
• +3 more

Prof. Christina Kuttler holds the position of Professor in the Department of Mathematics at the Technical University of Munich (TUM), within the TUM School of Computation, Information and Technology. Her research focuses on biomathematics, particularly deterministic mathematical models of bacterial communication and diffusion processes, aiming to understand intracellular regulatory mechanisms. She has been at TUM since 2008, following postdoctoral work at the University of Tübingen and the Helmholtz Center Munich. Her academic background includes a doctorate in mathematics (2000) from the University of Tübingen, with further studies in physics and computer science. Awards include the Erwin Schrödinger Prize (2007) and the Tübingen Doctoral Prize (2000). Research interests span mathematical modeling in biology, with publications on quorum sensing, bacterial biofilms, and cancer dynamics. Notable works include models of immunotherapy effects on lung cancer and stochastic approaches in biological systems. She teaches courses such as Mathematical Modelling and Mathematics for Engineers, and collaborates on projects like modeling cerebral hemorrhage in premature infants. Her work bridges applied mathematics, computational biology, and interdisciplinary research.

Robert DouglasView profile

Lecturer

• Applied Mathematical Analysis
• Optimal Mass Transfer
• Weather Front Formation
• +4 more

Robert Douglas is a Lecturer in the Department of Mathematics at Aberystwyth University since February 2000. His research focuses on applied mathematical analysis, particularly in optimal mass transfer, weather front formation, and relaxation spectrum recovery. He holds a PhD in applied mathematical analysis from Bath University, specializing in nonlinear analysis of vortices in ideal fluid flow. His career includes positions as a Research Fellow at the Universities of Cambridge and Reading, and a Visiting Scientist at the UK Meteorological Office. Key research interests include proving rigorous results using measure theory and convex analysis, with notable contributions to the Cullen-Norbury-Purser conjecture and polar factorization theory. Collaborations include work with H.R. Whittle Gruffudd on relaxation spectra and G.R. Burton on uniqueness conditions for polar factorizations. His work spans 1994–2019 with 9 peer-reviewed articles, addressing topics like semigeostrophic equations, viscoelastic fluid dynamics, and inverse problems. His research contributes to UN Sustainable Development Goals through advancements in mathematical modeling of environmental systems. Dr. Douglas actively participates in the Analysis cluster of the Wales Institute for Mathematical and Computational Sciences. His expertise combines theoretical rigor with applications in geophysical fluid dynamics and materials science.

• Inverse Problems
• Image Processing
• Optimization
• +5 more

Markus Grasmair is a Professor at the Department of Mathematical Sciences at the Norwegian University of Science and Technology (NTNU). His research focuses on inverse problems, mathematical methods in image processing, and optimization. He has contributed to nonsmooth variational methods and Lavrentiev regularization for ill-posed problems. Education: Habilitation in Mathematics (University of Vienna, 2011), PhD in Mathematics (University of Innsbruck, 2006), MSc in Mathematics (University of Innsbruck, 2003). Markus's research spans mathematical imaging, biomedical applications, and PDE-based modeling. He has developed regularization techniques for inverse problems, including Lavrentiev and L1 methods, and applied these to medical imaging and cervical cancer risk stratification. His work integrates sparse optimization, shape analysis, and data-driven approaches. Recent publications highlight interdisciplinary trends, combining inverse problems with medical imaging (X-ray calibration, cervical cancer prediction) and extending variational methods to PDEs, multiscale modeling, and graph-based systems. Key subfields include monotone operators, matrix factorization, and elastic shape analysis. He teaches courses in optimization and has held academic positions at the University of Vienna, Catholic University of Eichstätt-Ingolstadt, and University of Innsbruck. Contact: markus.grasmair@ntnu.no. Current Role: Professor (NTNU) Past Roles: Substitute Professor (Catholic University of Eichstätt-Ingolstadt, 2012-2013), Assistant Professor (University of Vienna, 2009-2012)

• Computer Animation
• Evolutionary Algorithms
• Computational Science
• +3 more

Ari Sarafopoulos is a Senior Lecturer at Bournemouth University, specializing in computer animation and evolutionary computation. His work bridges computational methods with artistic applications, focusing on iterated function systems (IFS) and material simulation. Key research areas include evolutionary algorithms, fractal modeling, and computational design. Recent publications address material point methods for elastoplastic simulation and procedural shape generation. He has presented work at major conferences including ACM SIGGRAPH and IEEE Congress on Evolutionary Computation.