جستجوی استاد راهنمای دانشگاهی

Nina Nikolaevna Uraltsevaمشاهده پروفایل

استاد

• Nonlinear Partial Differential Equations
• Elliptic PDEs
• Parabolic PDEs
• +۲ مورد دیگر

Nina Nikolaevna Uraltseva is a renowned Professor of Mathematics and head of the Section of Mathematical Physics and Partial Differential Equations at Saint Petersburg State University. She has been a leading figure in the study of nonlinear partial differential equations, particularly focusing on elliptic and parabolic PDEs. Her work has significantly influenced the field, with foundational contributions to regularity theory and free boundary problems. Education: Born in 1935 in Leningrad, she graduated from Leningrad State University in 1956 with a physics degree. She earned her Ph.D. in 1960 and a second doctoral science degree in 1964, both under Olga Ladyzhenskaya. She became a full professor in 1966 and has led her department since 1977. Research Interests: Uraltseva's research centers on nonlinear PDEs, including elliptic and parabolic equations, free boundary problems, and variational inequalities. Her collaborations with Ladyzhenskaya produced seminal works like Linear and Quasilinear Equations of Elliptic Type (1964) and Linear and Quasi-linear Equations of Parabolic Type (1968). These texts remain foundational in PDE theory. Awards and Honors: She has received the Chebyshev Prize (USSR Academy of Sciences), State Prize (USSR), Humboldt Research Award (2006), and an Honorary Doctorship from KTH (2006). In 2005, she delivered the European Mathematical Society's annual lecture. Contributions: Uraltseva has authored over 150 research articles and books, advised numerous doctoral students, and played a pivotal role in advancing the field through editorial and leadership roles. Her work on free boundary problems, including obstacle-type equations and two-phase membrane problems, has been widely recognized.

Dehan Chenمشاهده پروفایل

دانشیار

• Inverse Problems in PDEs
• Regularization Theory
• Evolution Equations
• +۷ مورد دیگر

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 Henselمشاهده پروفایل

پژوهشگر

• Computational Electromagnetism
• Optimal Control
• Numerical Analysis
• +۷ مورد دیگر

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 Wincklerمشاهده پروفایل

پژوهشگر ارشد

• Numerical Analysis
• Partial Differential Equations
• Maxwell's Equations
• +۶ مورد دیگر

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.

Peter Ganglمشاهده پروفایل

استاد پژوهش

• Topology Optimization
• Shape Optimization
• Computational Mathematics
• +۳ مورد دیگر

Dr. Peter Gangl is a Senior Research Scientist at the Johann Radon Institute for Computational and Applied Mathematics (RICAM), Austrian Academy of Sciences, where he leads research in optimization and computational mathematics. He holds a Ph.D. (2017) and M.Sc. (2012) from Johannes Kepler University Linz. His research focuses on topology/shape optimization, topological derivatives, and multiphysics simulation of electric machines. Research Interests: Gangl specializes in developing mathematical methods for PDE-constrained optimization, with emphasis on computational electromagnetics, automated sensitivity analysis, and multi-objective design. His work bridges applied mathematics and industrial applications, particularly in electric motor optimization. Research Projects: Principal Investigator for SFB F90 'CREATOR' (2022–2026) on topology optimization under electro-thermal coupling Led FWF project 'Multiphysical Shape Optimization of Electrical Machines' (2020–2024) Awards & Honors: Richard C. DiPrima Prize (SIAM, 2018) Erwin Wenzl Preis (2018) Anile Prize (ECMI, 2018) Promotio sub auspiciis (Austria's highest academic honor, 2017) Students & Collaboration: Supervises PhD candidates (Nepomuk Krenn, Michael Winkler) and collaborates with institutions including TU Graz, TU Darmstadt, and international partners. Organizes workshops/seminars on optimization and computational mathematics.

Noemi Petraمشاهده پروفایل

دانشیار

• Large-scale inverse problems
• PDE-constrained optimization
• Uncertainty quantification
• +۴ مورد دیگر

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.

• Mathematical Biology
• Mathematical Cell Biology
• Developmental Biology
• +۷ مورد دیگر

Maria-Veronica Ciocanel is an Assistant Professor of Mathematics (Primary) and Biology (Joint) at Duke University's Trinity College of Arts & Sciences, where she has held appointments since 2020. Her research sits at the intersection of mathematical biology, cell biology, and computational modeling, with a particular focus on intracellular transport mechanisms and protein organization in cells. Her research interests include mathematical cell and developmental biology, emergent dynamics in intracellular transport, multiscale modeling of filamentous protein organization, parameter inference for models of biophysics experiments, applied dynamical systems, and applied topological data analysis. She develops mathematical and computational techniques to uncover the basic functions of intracellular proteins in development and injury, with applications to RNA transport in frog oocytes, radial glial cells, and neurons. Her work combines differential equations, stochastic modeling, and topological data analysis to address fundamental questions in cell biology. Her recent publications demonstrate a consistent focus on mathematical approaches to biological problems, particularly in cytoskeleton dynamics, intracellular transport, and symmetry analysis in developmental systems. She also maintains an active research program in social justice applications, particularly analyzing racial disparities in federal sentencing through mathematical and statistical approaches. Lee A. Segel Prize for the Best Paper in the Bulletin of Mathematical Biology (2025) for 'Parameter identifiability in PDE models of fluorescence recovery after photobleaching' Red Sock Award (SIAG/Dynamical Systems) for best poster presentation Ciocanel actively mentors undergraduate students through various research programs including DOmath, PRUV, and Muser. Two of her former students, Anna and Niny, have gone on to become Assistant Professors at University of New Mexico and San Francisco State University, respectively. She has secured significant research funding including grants from the National Science Foundation and Pennsylvania State University for projects on microtubule behavior and membrane-actin cortex adhesion. She is also involved in the RTG: Training Tomorrow's Workforce in Analysis and Applications program as a Co-Principal Investigator. Her laboratory focuses on mathematical modeling of intracellular transport processes, with particular emphasis on microtubule dynamics in neurons and mRNA transport mechanisms. She collaborates extensively with experimental biologists at institutions including Brown University, Penn State University, and Duke University to ensure her mathematical models remain grounded in biological reality.

Catalina Sbert Juanمشاهده پروفایل

مدرس ارشد

• Image Processing
• Partial Differential Equations
• Mathematical Modeling
• +۳ مورد دیگر

Dr. Catalina Sbert Juan serves as a Senior Lecturer in the Department of Mathematics and Computer Science at the University of the Balearic Islands. She has been a continuous member of the department since 1987 and attained her University Lecturer position in 2000. Her educational background includes: B.S. in Mathematics (1987) from Universitat Autònoma de Barcelona Ph.D. in Computer Science (1995) from Universitat de les Illes Balears Her research focuses on mathematical models, particularly variational methods and partial differential equations applied to image enhancement and restoration. She is an active member of the Mathematics, Imaging and Learning (MIA) research group where she serves as Main Researcher, with specialties in artificial intelligence and numerical analysis of PDEs. Dr. Sbert Juan teaches across multiple disciplines including Mathematics, Computer Science, Physics, Chemistry, and Economics. Her current teaching portfolio includes Mathematical Analysis I, Advanced Mathematical Analysis, and specialized courses like Introduction to Mathematical Models in Image Restoration for the Master's Degree in Advanced Physics and Applied Mathematics. She has published in JCR-indexed international journals and participated in various international conferences, with research centered on applying mathematical frameworks to image processing challenges.

• Partial Differential Equations
• Mathematical Analysis
• Integral Equations
• +۳ مورد دیگر

Professor Biao B. Ou is affiliated with the Department of Mathematics and Statistics at the University of Toledo's College of Natural Sciences and Mathematics. His research focuses on advanced mathematical analysis of integral equations, partial differential equations (PDEs), and crystallography-related problems. Research Areas: Mathematical Analysis of Physical Systems Nonlinear PDEs and Symmetry Crystal Growth Modeling Publications: 9 works from 1989-2006 covering integral equations, vector fields, and crystallography Contact: biao.ou@utoledo.edu

• Mathematical Analysis
• Finsler Geometry
• Lorentzian Geometry
• +۴ مورد دیگر

Erasmo Caponio serves as an Associate Professor in the Department of Mechanics, Mathematics and Management at the Polytechnic University of Bari, Italy. His academic specialization lies in Mathematical Analysis (MAT/05), with a research focus bridging differential geometry and theoretical physics. Based at Via Orabona 4, 70125 Bari, he maintains active academic engagement through publications, departmental responsibilities, and direct contact via erasmo.caponio@poliba.it or +39 080 596 3673. Professor Caponio's research program centers on Finsler and Lorentzian geometries, with significant contributions to mathematical relativity. His work investigates causal structures of spacetimes, geodesic behavior in non-Riemannian settings, black hole physics, and the interplay between topology and geometric analysis. He employs advanced techniques from homotopy theory, control systems, calculus of variations, and partial differential equations to address fundamental questions in general relativity and geometric modeling. Key themes include metric transitions between Lorentzian and Riemannian frameworks, wind Finsler structures, and the geometric foundations of physical phenomena like the Sagnac effect. Analysis of his 2021-2025 publications reveals a cohesive trajectory exploring Finsler spacetimes through multiple lenses: causal hierarchies with cone Killing fields, splitting theorems beyond Berwald metrics, Randers-Kropina geodesics via control theory, and mean curvature problems in Minkowski spacetime. His research consistently bridges abstract mathematical structures with physical applications, particularly in gravitational physics and spacetime geometry. This work demonstrates exceptional technical depth while maintaining relevance to theoretical physics challenges. No scientific awards or honors were documented in the available materials. Information regarding academic advising of PhD/Master's students, research grant acquisitions, laboratory leadership, or collaborative team structures was not provided in the source documentation. Such details may require consultation of his full curriculum vitae or institutional records for comprehensive coverage.

• Partial Differential Equations
• Variational Methods
• Nonlinear Analysis
• +۵ مورد دیگر

Giuseppe Devillanova serves as Associate Professor of Mathematical Analysis (MAT/05) in the Department of Mechanics, Mathematics and Management at Polytechnic University of Bari, Italy. His research bridges pure mathematical theory with engineering applications through advanced analysis of nonlinear systems and variational structures. His primary research focuses on nonlinear partial differential equations, variational methods for critical problems, and symmetry analysis in elliptic equations. He investigates wave propagation in elastic media with nonlinear adhesion, geometric constraints in structural mechanics, and fractal properties of irrigation networks. His work extends to foundational topics in set theory and historical analysis of Richard Dedekind's contributions to mathematics. Analysis of his 2019-2023 publications reveals three dominant research trajectories: (1) Min-max methods for parametrized elliptic problems on unbounded domains with symmetry constraints; (2) Mathematical modeling of nonlinear adhesion phenomena in thermo-elastic waves and flexural beams; (3) Geometric analysis of singular structures in variational problems, including irrigation networks and elastic multi-blisters. His interdisciplinary approach consistently connects theoretical mathematics with mechanical engineering and biomechanical applications.

• Numerical Analysis
• High-Performance Computing
• Computational Mathematics
• +۴ مورد دیگر

Maciej Woźniak serves as a university professor at the Institute of Computer Science within the Faculty of Computer Science at AGH University of Science and Technology in Kraków, actively contributing to research and teaching staff while participating in the Computer Science Discipline Council and College of the Faculty. His office is located at D-17, ul. Kawiory 21, III, 4.58, with primary contact via macwozni@agh.edu.pl. His research spans computational mathematics and high-performance computing, specializing in parallel algorithms for numerical simulations including isogeometric analysis, finite element methods, and multi-frontal solvers. Key applications address environmental challenges like hail suppression and urban smog reduction, alongside biomedical modeling of tumor growth and airborne pathogen dispersion. Methodological innovations focus on GPU acceleration, shared-memory architectures, and efficient integration techniques for partial differential equations. Analysis of his 2019-2024 publications reveals consistent advancement in parallel computational frameworks for environmental and biomedical applications, demonstrating interdisciplinary impact through experimental validation of hail cannons, smog reduction technologies, and pathogen dispersion modeling during the COVID-19 pandemic. His work bridges theoretical algorithm development with real-world problem solving across physics, engineering, and life sciences. Scientific Awards: No specific awards were documented in the provided sources. Student advising, research grants, laboratory affiliations, and educational background details beyond his PhD, DSc, and Eng. titles remain unspecified in the available materials, though his extensive publication record suggests active mentorship and collaborative research leadership.

Nicolas Garcia Trillosمشاهده پروفایل

دانشیار

• Applied Analysis
• Computational Probability
• Statistics
• +۵ مورد دیگر

Nicolas Garcia Trillos is an Associate Professor in the Department of Statistics at the University of Wisconsin-Madison, with research spanning applied analysis, computational probability, statistics, and machine learning. His work focuses on the intersection of calculus of variations, optimal transport, and partial differential equations in learning systems. His educational background includes: Bachelor's degree in Mathematics from Universidad de Los Andes, Bogotá, Colombia (2010) Ph.D. in Mathematics from Carnegie Mellon University (2015) Prager Assistant Professor (postdoctoral position) at Brown University (2015-2018) Garcia Trillos' research centers on the mathematical foundations of machine learning , developing tools in optimal transport and calculus of variations to analyze graph-based learning and continuum models. Key contributions address adversarial robustness , federated learning , and spectral clustering , providing theoretical guarantees for large-scale problems through PDE frameworks. Recent publications (2022-2025) demonstrate a cohesive focus on geometric approaches to adversarial robustness , using optimal transport to derive classification bounds and study solution existence. He has advanced federated learning via consensus-based bi-level optimization (CB2O) and developed Fermat distances for metric approximation, bridging pure mathematics with statistical learning theory. No scientific awards were mentioned in the provided text. There is no information available regarding student advising, research grants, or laboratory affiliations in the source material.

Dominik Stantejskyمشاهده پروفایل

مدرس

• Calculus of Variations
• Geometric Measure Theory
• Partial Differential Equations
• +۴ مورد دیگر

Dominik Stantejsky is a Lecturer at the Mines Nancy department, affiliated with the Faculty of Science and Technology at the University of Lorraine. He is actively involved in the Partial Differential Equations team at the Institut Élie Cartan de Lorraine (IECL) and collaborates with INRIA teams SIMBA, SPHINX, and PASTA. His research focuses on Calculus of Variations and Geometric Measure Theory Mathematical analysis of Partial Differential Equations in material science Numerical methods for optimization and PDEs Applications in liquid crystals and micromagnetism Dominik's publications highlight work on Landau-de Gennes theory , harmonic maps , and defect structures in nematic liquid crystals. His research explores energy minimization, asymptotic behavior, and geometric constraints under external fields. He is based at the IECL facility on the Campus des Aiguillettes in Vandœuvre-lès-Nancy, France, and can be contacted at dominik.stantejsky@univ-lorraine.fr .

Carola-Bibiane Schönliebمشاهده پروفایل

استاد

• Image Analysis
• Image Processing
• Inverse Problems
• +۳ مورد دیگر

Carola-Bibiane Schönlieb is a Professor of Applied Mathematics and head of the Cambridge Image Analysis (CIA) group at the Department of Applied Mathematics and Theoretical Physics, University of Cambridge. She concurrently serves as co-director of the Cambridge Mathematics of Information in Healthcare (CMIH) Hub, leading interdisciplinary initiatives at the intersection of mathematics, healthcare, and data science. Her research centers on variational methods, partial differential equations, and machine learning for image analysis, processing, and inverse problems. She maintains active collaborations with clinicians, biologists, physicists, chemical engineers, plant scientists, artists, and art conservators, driving innovations in biomedical imaging, image sensing, and digital art restoration. This interdisciplinary approach bridges theoretical mathematics with real-world applications across healthcare and cultural heritage domains. Analysis of her recent publications reveals a dominant focus on deep learning applications for medical imaging challenges, particularly in cardiology, oncology, and neuroimaging. Her work consistently addresses inverse problems in reconstruction and segmentation while emphasizing robustness against artifacts, model efficiency, and integration of physical constraints. A clear trend emerges toward foundation models and transfer learning techniques specifically adapted for medical image analysis with limited annotated data. Prof. Schönlieb leads the Cambridge Image Analysis research group and co-directs the CMIH Hub, which unites mathematicians, computer scientists, and clinicians to translate advanced data science into clinical practice through collaborative healthcare innovation.