Find Academic Supervisors

Mithat Can ÖzinView profile

Researcher

• Mechanical Engineering
• Force/Moment Sensors
• Structural Optimization
• +2 more

Mithat Can Özin serves as a Researcher in the Department of Mechanical Engineering at Istanbul Technical University (ITU), specializing in precision sensor design and biomechanical systems. His work bridges theoretical modeling with experimental validation across multiple engineering domains. Research Focus: Dr. Özin's primary investigations center on multi-axis force/moment sensors , particularly cross-beam configurations requiring advanced optimization techniques. His expertise spans Structural optimization of compliant mechanisms Friction dynamics in surgical robotics Stress topology analysis for sensor calibration Genetic algorithm applications in sensor design His recent publications demonstrate consistent advancement in Gaussian process modeling and Pareto optimality approaches for force sensor development. Awards & Recognition: While specific awards aren't documented, his work shows significant impact with 18 Scopus h-index 20 Mendeley readers across publications 2 patents referencing his research Multiple publications in IEEE Sensors Journal and Mechanics Based Design Collaborative Networks: Özin maintains active research partnerships within ITU's mechanical engineering faculty, particularly with Professors Sümer and Koç. His work intersects with robotics, biomechanics, and microscale material science, evidenced by publications spanning surgical tools to PDMS-glass interfaces. Current projects focus on next-generation force sensors with applications in minimally invasive surgery and industrial robotics.

• Variational Inequalities
• Optimal Control
• Numerical Analysis
• +2 more

Dr. Constantin Christof is a researcher at the University of Duisburg-Essen, Germany, leading the AG Optimal Control of Partial Differential Equations research group. His academic activities include teaching Mathematical Imaging (lectures/exercises), Practical Course in Numerical Mathematics (case studies), and Bachelor Seminar Mathematics for the Summer Semester 2025. His research spans Variational Inequalities , Optimal Control , Numerical Analysis , PDE-Constrained Optimization , and Nonsmooth Optimization . Key contributions focus on theoretical foundations of obstacle problems, directional differentiability, and stability analysis for variational inequalities. Recent work extends to machine learning applications like physics-guided neural networks for gas source localization and neural network optimization landscapes. Christof's publication trend (2021-2025) reveals deep specialization in nonsmooth optimization for PDE-constrained problems, with 15+ high-impact journal articles in SIAM, ESAIM, and IEEE venues. His work bridges theoretical analysis (e.g., Lipschitz stability, strong stationarity) and computational methods (semismooth Newton techniques), addressing challenges in rate-independent systems and non-Lipschitzian nonlinearities. Scientific Awards: No awards documented in available records. Advising and Grants: Current information does not specify student supervision or grant funding details. His research group structure suggests active mentorship of junior researchers through collaborative publications. Labs and Teams: Heads the AG Optimal Control of Partial Differential Equations research group, driving interdisciplinary projects connecting mathematical optimization with environmental monitoring and machine learning applications.

Dehan ChenView profile

Associate Professor

• Inverse Problems in PDEs
• Regularization Theory
• Evolution Equations
• +7 more

Dehan Chen is an Associate Professor at the School of Mathematics & Statistics, Central China Normal University, and currently a Researcher at the University of Duisburg-Essen. His research lies at the intersection of applied analysis and inverse problems, with a strong focus on partial differential equations and regularization theory. His research interests include: Inverse Problems in PDEs Regularization Theory Evolution Equations Ill-Posed Problems Mathematical Modeling Nonlinear Dynamics The analysis of his recent publications reveals a consistent and deep engagement with variational source conditions, Tikhonov regularization in Banach spaces, and inverse problems in both elliptic and parabolic systems. His work often bridges theoretical analysis with applications in physics and biology, such as electromagnetic modeling and population dynamics. A strong emphasis is placed on convergence rates and stability in ill-posed settings. His notable scientific recognition includes the prestigious Alexander von Humboldt Fellowship. This award highlights his international research impact and collaborative work in Germany. While no formal students are listed, his frequent collaborations with prominent mathematicians such as Jun Zou, Bernd Hofmann, and Irwin Yousept suggest active participation in research teams and potential advisory roles. He has not received mention of external grants, but his sustained publication record in top-tier journals indicates strong research productivity. He is affiliated with research groups focusing on optimal control and inverse problems at the University of Duisburg-Essen, particularly within the AG Optimal Control of Partial Differential Equations, contributing to a vibrant mathematical research environment.

Maurice HenselView profile

Researcher

• Computational Electromagnetism
• Optimal Control
• Numerical Analysis
• +7 more

Dr. Maurice Hensel is a researcher in the Faculty of Mathematics at the University of Duisburg-Essen, Germany, affiliated with the research group on Optimal Control of Partial Differential Equations led by Prof. Dr. Irwin Yousept. He holds a PhD in Mathematics, defended in April 2023, and is actively engaged in research and teaching in numerical analysis and applied mathematics. Research Interests: His work focuses on computational electromagnetism , optimal control of PDEs , numerical analysis , regularization techniques , and non-smooth optimization , with applications in superconductivity and electromagnetic shielding. His research bridges theoretical analysis and computational methods for variational inequalities in Maxwell's equations. Publication Trends: His recent publications (2022–2023) center on Maxwell variational and quasi-variational inequalities, exploring well-posedness, numerical schemes, and control problems in models such as the Bean-Kim superconductivity model and ferromagnetic shielding. These works appear in top-tier applied math journals like SIAM and Interfaces and Free Boundaries, demonstrating a strong focus on rigorous analysis and computational solutions. Teaching: He has taught and assisted in courses including Introduction to Numerical Methods , Advanced Numerical Methods , and Optimal Control of PDEs , indicating an active role in academic instruction. Advising and Grants: There is no mention of graduate students or grant funding in the provided text. Labs and Teams: He is a member of the research group Optimal Control of Partial Differential Equations at the University of Duisburg-Essen, collaborating closely with Prof. Irwin Yousept and other researchers.

Malte WincklerView profile

Research Fellow

• Numerical Analysis
• Partial Differential Equations
• Maxwell's Equations
• +6 more

Dr. Malte Winckler is a research-active academic in the Faculty of Mathematics at the University of Duisburg-Essen, Germany. His work centers on the numerical analysis of partial differential equations with applications in superconductivity and electromagnetic field modeling. His research focuses on numerical analysis of PDEs , particularly Maxwell's equations , variational inequalities , and shape optimization . He applies advanced mathematical techniques to model physical phenomena in type-II superconductors , including Bean's critical-state model and H(curl)-elliptic systems. His methodological expertise lies in finite element methods, adaptive approximation schemes, and optimal control. The recent publications show a consistent focus on developing and analyzing numerical schemes for non-smooth and nonlinear PDEs arising in superconductivity. Key themes include adaptive edge elements , fully discrete approximations , and shape optimization under physical constraints . These works appear in top-tier applied mathematics journals such as SIAM Journal on Numerical Analysis and Computational Optimization and Applications . Dr. Winckler is involved in the DFG SPP 1962 Priority Programme as a contributor to project 22 on non-smooth distributed parameter systems. He teaches courses such as Introduction to Numerical Methods and collaborates closely with Prof. Dr. Irwin Yousept. He has no listed scientific awards in the provided text. He maintains active research profiles on Google Scholar , ResearchGate , ORCID , and GitLab , indicating engagement with the broader scientific community. He advises no students listed in the material and is not indicated as part-time, retired, or former staff.

Noemi PetraView profile

Associate Professor

• Large-scale inverse problems
• PDE-constrained optimization
• Uncertainty quantification
• +4 more

Noemi Petra is an Associate Professor in the Department of Applied Mathematics at the University of California, Merced , with research focusing on large-scale inverse problems , PDE-constrained optimization , uncertainty quantification , and optimal experimental design . Her work bridges mathematical theory with practical applications in geophysics and energy systems. Current Role: Associate Professor at UC Merced Research Interests: Large-scale inverse problems, PDE-constrained optimization, uncertainty quantification Her research has produced significant contributions in Bayesian inference for ice sheet modeling, power system estimation , and scalable optimization algorithms . She has developed open-source software frameworks like hIPPYlib-MUQ for data-model integration. Key research trends include: Bayesian Methods for geophysical inverse problems Optimization Techniques for power systems and ice dynamics Scalable Algorithms for high-dimensional uncertainty quantification Software Development for large-scale inverse problems Scientific awards: NSF CAREER award (2017) for large-scale Bayesian inverse problems She actively collaborates on ice sheet dynamics and power grid modeling , with software tools adopted in academic and industrial applications.

• Applied Mathematics
• Theoretical Mechanics
• Computational Mechanics
• +7 more

Dr. Luis Espath is an Assistant Professor of Applied Mathematics at the University of Nottingham, affiliated with the School of Mathematical Sciences. His research spans theoretical and computational mechanics, uncertainty quantification, and machine learning. He has held postdoctoral and research scientist roles at KAUST and RWTH Aachen, respectively, and authored the Springer Nature book Mechanics & Geometry of Enriched Continua . Education : Engineering Diploma (2007) at PUCRS, Brazil; Master’s and Ph.D. (2013) at UFRGS, Brazil; Habilitation in Mathematics (2021) at RWTH Aachen, Germany. Dr. Espath’s research focuses on the application of phase-field and continuum mechanics to model complex systems, with an emphasis on thermodynamic consistency and computational efficiency. He integrates stochastic optimization and machine learning into fluid dynamics and structural modeling. His recent publications highlight expertise in phase-field gradient theory, fluid-structure interaction, and Bayesian experimental design. The 2021 articles reflect his work on Navier-Stokes equations, reactive Cahn-Hilliard systems, and divergence-free fluid flow approximations.

Eric HallView profile

Lecturer

• Uncertainty Quantification
• Stochastic Simulation
• Predictive Modelling
• +5 more

Dr. Eric Hall is a Baxter Fellow and Lecturer in Applied Mathematics at the University of Dundee's School of Science and Engineering. He holds a PhD in Mathematics from the University of Edinburgh (2013) and a B.A. in Mathematics from the University of Pennsylvania. Prior to joining Dundee in 2020, he held postdoctoral positions at KTH Royal Institute of Technology, University of Massachusetts Amherst, and RWTH Aachen University. His research focuses on the mathematical foundations of data science, specializing in uncertainty quantification , stochastic simulation , and predictive modeling for complex systems. Current work develops domain-aware surrogate models and sensitivity analysis techniques for scientific machine learning, with applications spanning materials science, finance, geophysics, and solar physics. Publications demonstrate a strong focus on multi-scale systems and scientific machine learning , with recent work expanding into astrophysical applications. Research consistently integrates mathematical rigor with practical applications across physics and engineering domains. Awards and Honors Dundee Difference Awards 2025 - Innovation of the Year Fellow of the Institute of Mathematics and its Applications (2022) Science and Engineering Staff Awards - Innovation in Teaching (2022) Dr. Hall actively supervises PhD students in uncertainty quantification and scientific machine learning, and serves as second supervisor for doctoral projects on chaotic differential equations. He has secured research grants including STFC PhD funding for Solar Physics applications and Heilbronn Focused Research Group funding. He maintains memberships in the Edinburgh Mathematical Society (Trustee), Institute of Mathematics and its Applications, Society for Industrial and Applied Mathematics, and American Mathematical Society. Dr. Hall leads research in uncertainty quantification within the Mathematics division and collaborates internationally on multi-scale modeling projects.

Markus RenoldnerView profile

Research Fellow

• Numerical Analysis
• Partial Differential Equations
• Finite Element Methods
• +3 more

Markus Renoldner is a Doctoral Assistant at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Basic Sciences, Institute of Mathematics, specifically within the Chair of Numerical Modelling and Simulation (MNS) and the Picasso Group. His office is located at MA C2 622 in Building MA, Station 8, 1015 Lausanne. His educational background includes Doctoral studies in Mathematics at EPFL (2023-present), MSc in Computational Science and Engineering at TU Wien (2021-2023), Master's Thesis in Applied Mathematics at ETH Zürich (2022-2023), and an Exchange semester in Applied and Computational Mathematics at KTH Stockholm (2022). Renoldner specializes in numerical analysis of partial differential equations (PDEs) , with current research focusing on Finite Element approximation of solutions to PDEs in fusion plasma physics. His work spans PDE theory and control systems engineering, as evidenced by his GitHub repositories containing numerical implementations for Navier-Stokes equations and shallow water models. His publications demonstrate expertise in developing mass, energy, and helicity conserving discretization methods for fluid dynamics problems. His professional experience includes simulation engineering at Team Tumbleweed (2021-2024), a visiting associate position at Boston Consulting Group (2021), systems engineering at RUAG Space (2020), and data analysis at KPMG (2017). He has also held leadership roles including CEO for one month at Adecco Group (2020) and Vorstand/Consultant at icons - consulting by students (2017-2020).

Per-Olof PerssonView profile

Professor

• Applied Mathematics
• Computational Fluid Dynamics
• Numerical Analysis
• +3 more

Per-Olof Persson is a Professor in the Department of Mathematics at the University of California, Berkeley . He is also a Senior Scientist at the Lawrence Berkeley National Laboratory , where he contributes to applied mathematics and computational physics research. Research Interests: Applied Mathematics Numerical Methods Computational Fluid and Solid Mechanics Discontinuous Galerkin Methods High-Order Discretizations Mesh Generation Recent Publications (2023-2025) demonstrate expertise in geometric multigrid, shock capturing, solution transfer between curved meshes, and optimization techniques. Key trends include advancements in high-order accuracy for fluid dynamics, implicit-explicit time-stepping, and interdisciplinary applications in aerospace and biomedical engineering. Advising: Supervised multiple PhD and Master’s students in topics spanning shock tracking, fluid-structure interaction, and computational solvers. Collaborates with institutions like MIT, KTH Royal Institute of Technology, and Lawrence Berkeley National Lab.

• Earthquake Engineering
• Structural Dynamics
• Wave Propagation
• +3 more

Hasan Faik Kara is an Associate Professor at Trakya University Faculty of Engineering , with a focus on earthquake engineering and wave propagation. His academic career spans roles at multiple institutions and includes significant contributions to seismic analysis of geological structures. Bachelor's, Master's, and PhD in Mechanical Engineering from Istanbul Technical University (2006-2014) Vice Dean of Trakya University Faculty of Architecture (2021-2024) Research Interests center on seismic wave behavior in heterogeneous media, including: SH-wave scattering in tunnels and hillsides Material discontinuities in fault zones Seismic response of sedimentary basins Dynamic stress analysis in functionally graded materials Publications demonstrate expertise in numerical modeling of earthquake-induced vibrations and analytical corrections to wave propagation approximations. His work bridges theoretical seismology and applied structural engineering.

Somdatta GoswamiView profile

Assistant Professor

• Scientific Machine Learning
• Artificial Intelligence for Science
• Computational Mechanics
• +3 more

Somdatta Goswami serves as Assistant Professor in Civil and Systems Engineering and Applied Mathematics and Statistics at Johns Hopkins University, with dual affiliations at the Institute for Data Intensive Engineering and Science (IDIES) and Hopkins Extreme Materials Institute (HEMI). She leads the Centrum IntelliPhysics research group developing AI-driven methodologies for scientific discovery. Her educational trajectory includes: Bachelor's in Civil Engineering from Birla Institute of Technology, Mesra (2011) Master's in Structural Engineering from Indian Institute of Engineering Science and Technology (2013) PhD in Civil Engineering and Structural Mechanics from Bauhaus University-Weimar, Germany (2020) funded by DAAD Dr. Goswami's research pioneers Scientific Machine Learning at the intersection of computational mechanics and AI, focusing on neural operator architectures that accelerate physics-based simulations. Her group develops methods for long-time horizon prediction, multiscale multiphysics modeling, and real-time inference in complex systems through latent space representations and physics-informed learning. Current emphases include cardiac digital twins, structural response under natural hazards, and RNA electrophoresis modeling. Analysis of her 2024-2025 publications reveals dominant trends in latent operator learning, physics-informed neural networks, and hybrid solvers combining traditional numerical methods with deep learning. These innovations enable breakthroughs in computational efficiency across engineering and biological domains, particularly in multiscale modeling and uncertainty-aware simulation. Her scientific recognition includes: National Science Foundation’s National Artificial Intelligence Research Resource (NAIRR) Pilot Johns Hopkins University Discovery Award 2024 Dr. Goswami mentors PhD candidates including Dibakar Roy Sarkar (Creel Family Engineering Fellow), Sharmila, and Maryam. Major research funding comprises: NSF grant for "Cardiac Digital Twins" with Kevrekidis, Trayanova, and Maggioni NSF grant for exascale AI-integrated simulations with UT Austin DOE grant for uncertainty-informed latent operators with Shields, Graham-Brady, and Kevrekidis Johns Hopkins Discovery Award for biological systems modeling The Centrum IntelliPhysics group operates within JHU's Latrobe Hall, collaborating with IDIES and HEMI on interdisciplinary projects spanning computational mechanics, materials science, and biological systems. Their work integrates high-performance computing with novel neural architectures to solve previously intractable scientific problems.

Hans ZwartView profile

Professor

• Control Theory
• Partial Differential Equations
• Hamiltonian Dynamics
• +3 more

Hans Zwart is a Part-time Full Professor at the Department of Mechanical Engineering , Eindhoven University of Technology (TU/e), and holds a Professorship in Physical Systems and Control at the University of Twente. His research focuses on control theory for distributed parameter systems and infinite-dimensional systems, leveraging functional analysis and Hamiltonian dynamics. His work spans partial differential equations (PDEs), Hilbert space methods, and transfer functions, with applications in flexible structures and thermal systems. Collaborations include institutions in Besançon, Lyon, Wuppertal, and Eindhoven, while his authorship of two foundational texts— An Introduction to Infinite-Dimensional Systems Theory and Linear Port-Hamiltonian Systems on Infinite-Dimensional Spaces —cements his influence in systems theory. Key publications highlight advancements in boundary control, vehicular platooning, and Riccati equation solutions, with interdisciplinary applications in mechanical, electrical, and transportation systems. Trends in his research emphasize energy shaping, stability analysis, and numerical approximation techniques.

Michael KarowView profile

Researcher

• Numerical Linear Algebra
• Control Theory
• Matrix Analysis
• +6 more

Michael Karow is a Researcher at the Institute of Mathematics , Technical University of Berlin , affiliated with the Faculty II - Mathematics and Natural Sciences . His research focuses on Numerical Linear Algebra , Control Theory , and Matrix Perturbation , with specific interests in Pseudospectra , Stability Radii , and Structured Eigenvalue Problems . Educational background includes a Ph.D. (2003) from the University of Bremen, titled " Geometry of Spectral Value Sets ," and a Habilitation (2009) at TU Berlin. His work spans collaborations with institutions like IIT Guwahati, EPFL Lausanne, and SUNY Utica. His research involves stabilizing discrete-time linear systems via matrix factorizations, structured matrix pencils with no spillover effects, and backward error analysis for palindromic polynomials. Publications highlight applications in finite element model updating , eigenvalue sensitivity , and geometric matrix theory . Teaching includes courses like Numerical Mathematics 1 for Engineering , Differential Equations and Numerics , and Calculus of Variations . He has led workshops in India, Italy, and the U.S., focusing on numerical methods and control theory.

Giovanni FantuzziView profile

Assistant Professor

• Optimization
• Dynamical Systems
• Fluid Mechanics
• +4 more

Giovanni Fantuzzi serves as a W1 Professor (equivalent to Assistant Professor) in the Department of Mathematics at Friedrich-Alexander University Erlangen-Nuremberg. He leads research within the FAU DCN-AvH Chair for Dynamics, Control, Machine Learning and Numerics under the Alexander von Humboldt Professorship framework, holding office in Room 03.318 with contact details including giovanni.fantuzzi@fau.de and +49 9131 85-67134. His educational background includes a PhD and Master of Engineering in Aeronautics from Imperial College London, supplemented by a research position in Engineering Science at the University of Oxford during his doctoral studies. Key academic milestones are documented through his ORCID, Google Scholar, and LinkedIn profiles. Fantuzzi's research program integrates mathematical analysis with computational optimization to solve nonlinear differential equations, focusing on deriving a priori scaling laws for heat transport and developing provable numerical schemes for PDE-constrained optimization. His methodology bridges convex optimization, polynomial optimization, and dynamical systems theory, with recent applications extending to transformer neural networks and sentiment analysis through hardmax mechanisms. Current teaching includes Data-driven methods for dynamical systems and Polynomial optimization and applications for WS 24/25. Analysis of his 15 most recent publications reveals dominant trends in fluid mechanics (particularly convection and heat transfer), polynomial optimization techniques, and data-driven dynamical systems analysis. His work consistently applies convex optimization frameworks to derive rigorous bounds in physical systems while expanding into machine learning applications like transformer model analysis. Geophysical Fluid Dynamics Fellowship at WHOI (2015) EPSRC Doctoral Prize Fellowship (2018) Imperial College Research Fellowship Fantuzzi's research program is supported by prestigious fellowships including the Imperial College Research Fellowship and EPSRC Doctoral Prize. His academic service includes organizing the FAU MoD Lecture & Workshop on AI for maths and maths for AI (June 2025) and co-hosting the #MLPDES25 Machine Learning and PDEs Workshop. He actively supervises research within the FAU DCN-AvH group, focusing on polynomial optimization applications in dynamical systems and PDEs. As core faculty in the FAU DCN-AvH Chair, Fantuzzi collaborates within a multidisciplinary team specializing in dynamics, control, machine learning, and numerical methods. The group maintains strong international connections through workshops like the Oberwolfach Seminar on Polynomial Optimization for Nonlinear Dynamics and participates in conferences including CIN-PDE and Nečas Seminar on Continuum Mechanics, driving innovation at the intersection of mathematics and computational physics.