Find Academic Supervisors

• Numerical Analysis
• Applied Mathematics
• Magnetic Refrigeration
• +3 more

Bahodir Siddikov serves as Professor in the Department of Mathematics within Ferris State University's College of Arts, Sciences and Education. His academic foundation includes dual doctoral qualifications: a PhD in Mathematics from the University of Wisconsin and a PhD in Applied Mathematics from Kiev University, complemented by BS and MS degrees in Applied Mathematics from Kiev University. His educational credentials are detailed as follows: PhD in Mathematics, University of Wisconsin PhD in Applied Mathematics, Kiev University MS in Applied Mathematics, Kiev University BS in Applied Mathematics, Kiev University Siddikov's research centers on numerical methods for partial differential equations with engineering applications, particularly in magnetic refrigeration systems and hydroacoustic signal processing. His work bridges theoretical mathematics with thermal physics and fluid dynamics, evidenced by publications spanning computational simulations of regenerators, heat capacity modeling in magnetic materials, and inverse problem solutions for wave propagation. Analysis of his publication timeline reveals evolving research trajectories: early work (1989) addressed hydroacoustic signal inversion, while mid-career research (2002-2005) focused on magnetic regenerator simulations. His most recent publications (2010-2012) demonstrate advanced numerical techniques for singular perturbation problems and material property modeling, alongside pedagogical contributions to calculus education through computational software development. He maintains active scholarly engagement through presentations at major international forums including SIAM's 50th Anniversary Conference, International Conference on Mathematics in Istanbul, and IEOM conferences across Asia and Hawaii, covering topics from Mathematica-based numerical analysis instruction to magnetic refrigeration advancements.

Griffith BoyceView profile

Professor

• Computational mathematics
• Applied mathematics
• Cardiovascular physiology
• +4 more

Griffith Boyce is a Professor of Mathematics and Biomedical Engineering at the University of North Carolina at Chapel Hill (UNC), with an adjunct appointment in Applied Physical Sciences. He serves as Associate Chair for Research in the Department of Mathematics and leads the Cardiovascular Modeling and Simulation Laboratory. His affiliations include the Carolina Center for Interdisciplinary Applied Mathematics, Computational Medicine Program, and McAllister Heart Institute at UNC School of Medicine. Dr. Griffith earned his BA and BS in Mathematics from Rice University (2000), followed by a PhD in Mathematics from New York University (2005). He completed postdoctoral training at NYU (2005-2008) before joining NYU as faculty (2008-2014), then UNC in 2014. His research focuses on developing computational tools to predict cardiovascular physiology and pathology through fluid-structure interaction (FSI) modeling. Key applications include cardiac hemodynamics, bioprosthetic heart valves, ventricular assist devices, and thrombosis in atrial fibrillation. His group advances the immersed boundary (IB) method and maintains the IBAMR software infrastructure for adaptive mesh refinement in FSI problems. Recent publications highlight innovations in divergence-free numerical methods, viscoelastic flow modeling, and patient-specific cardiovascular simulations. His team's work spans from fundamental FSI algorithms to clinical translation in medical device design. Dr. Griffith collaborates with clinicians, engineers, and computational scientists across academia, industry, and regulatory agencies. His lab includes PhD alumni like Marshall Davey, Charles Puelz, Aaron Barrett, and Cole Gruninger, alongside current researchers. The Cardiovascular Modeling and Simulation Laboratory develops open-source software and interdisciplinary frameworks for predictive biomedical simulations. Their research integrates mathematical modeling, high-performance computing, and experimental validation to address cardiovascular disease mechanisms and therapy optimization.

• Magnetic Resonance Imaging
• Deep Learning
• Compressed Sensing
• +3 more

Mehmet Akcakaya is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Minnesota. His research focuses on machine learning for medical imaging, especially MRI reconstruction using deep learning and compressed sensing. He leads NIH-funded projects and is accepting PhD students. His research interests include physics-informed deep learning , compressed sensing , and MRI reconstruction . He develops algorithms for high-resolution, accelerated MRI with applications in cardiac and brain imaging. Key challenges addressed are reference-free reconstruction and robustness in inverse problems. Recent work shows a trend toward self-supervised and unsupervised deep learning for MRI, with innovations in cycle-consistent learning and diffusion models for inverse problems. There is increasing emphasis on few-shot and zero-shot adaptation to handle limited training data. Dr. Akcakaya leads the NIH-funded project Robust and Efficient Learning of High-Resolution Brain MRI Reconstruction and collaborates on the Center for Mesoscale Connectomics . He is accepting PhD students and mentors research in medical imaging and machine learning. His work is conducted at the University of Minnesota's Center for Magnetic Resonance Research, leveraging interdisciplinary collaborations in biomedical engineering and neuroscience.

Benjamin KedemView profile

Professor

• Time Series Analysis
• Spatial Statistics
• Spectral Analysis
• +5 more

Benjamin Kedem is a Professor in the Department of Mathematics at the University of Maryland, College Park, with affiliations at the Institute for Systems Research (ISR). His academic career spans several decades with significant contributions to time series analysis, spatial statistics, and statistical methodology. Dr. Kedem's research focuses on time series analysis, space-time statistical problems, and combination of information from multiple sources. His work includes significant contributions to higher order crossings (HOC), contraction mapping methods in spectral analysis, Rice formula applications, threshold methods for rainfall estimation, partial likelihood approaches, Bayesian-transformed-Gaussian spatial prediction, and statistical data fusion. His research has practical applications in environmental statistics, meteorology, and public health. His recent publications demonstrate a continued focus on semiparametric methods, statistical data fusion, and computational approaches to time series and spatial analysis. The 15 most recent articles span from 2017 back to 1994, showing both contemporary relevance and foundational contributions to the field of statistics. Dr. Kedem has directed 15 PhD dissertations, with students completing between 1983 and 2007, indicating his long-standing commitment to mentoring the next generation of statisticians. His former students include George Reed, Donald E.K. Martin, Silvia R.C. Lopes, Haralabos Pavlopoulos, James Troendle, Ta-Hsin Li, John Barnett, Konstantinos Fokianos, Victor De Oliveira, Neal Jeffries, Boris Kozintsev, Richard Gagnon, Haiming Guo, Guanhua Lu, and Shihua Wen. He has authored or co-authored several books including 'Regression Models for Time Series Analysis' (2002), 'Time Series Analysis by Higher Order Crossings' (1994), and 'Statistical Data Fusion' (2017). His teaching portfolio includes graduate courses such as STAT 730 (Time Series Analysis), STAT 740 (Linear Models I), and STAT 741 (Linear Models II).

• Partial Differential Equations
• Harmonic Analysis
• Geometric Analysis
• +3 more

Professor Guozhen Lu is a distinguished faculty member at the University of Connecticut, recently elected to the Connecticut Academy of Science and Engineering (CASE) in 2025. His research bridges advanced mathematical analysis with geometric and functional frameworks, establishing him as a leading figure in modern analysis. Lu's research centers on Partial Differential Equations, Harmonic Analysis, and Geometric Analysis, with particular emphasis on functional inequalities including Sobolev, Trudinger-Moser, and Hardy-Rellich inequalities. His work explores sharp constants, stability phenomena, and extremal problems across diverse geometric settings such as Heisenberg groups, hyperbolic spaces, and CR manifolds. He investigates how geometric structures influence analytical properties, often revealing deep connections between analysis and geometry. Analysis of his 2024-2025 publications shows intense focus on stability of fundamental inequalities (Sobolev, Poincaré, Uncertainty Principle), with innovative approaches to multi-parameter operators and geometric constraints. His work consistently targets optimal constants and dimension-dependent phenomena, pushing boundaries in non-Euclidean analysis. His notable scientific recognition includes: Connecticut Academy of Science and Engineering (CASE) Membership (2025) While specific details about student advising and grant funding are not documented in available sources, Lu's editorial contributions (including special issues honoring Robert Fefferman and David Jerison) demonstrate significant service to the mathematical community. No information is available regarding dedicated laboratories or research teams.

Alicia Cantón PireView profile

Professor

• Mathematics
• Applied Mathematics
• Geometry
• +5 more

Alicia Cantón Pire is a Professor at the Department of Mathematics and Computer Science Applied to Civil and Naval Engineering , affiliated with the Higher Technical School of Naval Engineers (ETSIN) at the Polytechnic University of Madrid (UPM) . Her research spans multiple domains within Mathematics, Applied Mathematics, and Computer Science, focusing on geometric modeling, complex analysis, and graph theory. Research Interests : Asymptotic values of meromorphic functions, Gromov hyperbolicity in planar graphs, geometric characteristics of Bézier surfaces, and isoperimetric inequalities. Teaching : Engaged in academic instruction, with access to the Moodle platform for current courses. Professional Affiliations : Member of mathematical societies including the Royal Spanish Mathematical Society (RSME) , Spanish Society of Applied Mathematics (SeMA) , and the American Mathematical Society (AMS) . Her work integrates theoretical mathematics with practical applications in engineering and computer science, reflecting in her collaborative publications and extensive contributions to geometric and analytic problems. Email : alicia.canton@upm.es

Benedikt WirthView profile

Professor

• Optimisation and calculus of variations
• image processing
• scientific computing
• +9 more

Prof. Dr. Benedikt Wirth is a Professor of Mathematics at the University of Münster, Germany, affiliated with the Institute for Analysis and Numerics within the Department of Mathematics and Computer Science. He is an active researcher and educator specializing in optimization and calculus of variations, with significant contributions to mathematical imaging and shape analysis. His research interests include image processing, scientific computing, numerical analysis, optimization, shape spaces, geodesics in shape space, variational methods, elastic deformation, and optimal transport. Wirth has developed innovative mathematical frameworks for shape analysis, particularly focusing on Riemannian metrics for shape spaces and variational approaches to shape comparison and optimization. His recent publications (2023-2025) demonstrate continued leadership in mathematical optimization, with particular focus on PET reconstruction, dimension reduction techniques, manifold embeddings, and branched transport theory. His work bridges theoretical mathematics with practical applications in medical imaging and computer vision, showing particular strength in connecting geometric analysis with computational methods. CRC 1450 - A05: Targeting immune cell dynamics by longitudinal whole-body imaging and mathematical modelling CRC 1450 - A06: Improving intravital microscopy of inflammatory cell response by active motion compensation EXC 2044 - C1: Evolution and asymptotics EXC 2044 - C2: Multi-scale phenomena and macroscopic structures EXC 2044 - C3: Interacting particle systems and phase transitions EXC 2044 - C4: Geometry-based modelling, approximation, and reduction Prof. Wirth actively supervises numerous bachelor's and master's students, with over 40 theses completed under his guidance since 2015. His teaching portfolio includes courses on inverse problems, numerical methods for partial differential equations, shape spaces, optimization, and optimal transport. He has consistently maintained an active research program while contributing significantly to the education of the next generation of mathematicians.

• Knowledge Representation and Reasoning
• Logic in Computer Science
• Ontologies
• +3 more

Professor Frank Wolter is a Full Professor of Logic and Computation at the University of Liverpool , Department of Computer Science, since 2003. His academic journey includes a Habilitation in Computer Science at Leipzig University (2000), PhD in Mathematics at Freie Universität Berlin (1993), and research associations with Japan Advanced Institute of Science and Technology (1994-1997). Education Diploma in Mathematics and Philosophy, Freie Universität Berlin (1991) PhD in Mathematics, Freie Universität Berlin (1993) Habilitation in Computer Science, University of Leipzig (2000) His research focuses on Knowledge Representation and Reasoning , integrating Modal Logics , Description Logics , and Temporal Logics to develop frameworks for dynamic ontologies, ontology versioning, and semantic web applications. Key projects include: quantMD: Ontology-Based Management of Many-Dimensional Quantitative Data (2019-2022) iTract: Islands of Tractability in Ontology-Based Data Access (2015-2018) Logical difference for ontology versioning (2003-2004) Recent publications (2024-2025) demonstrate expertise in Two-Variable First-Order Logic , Interpolant Existence , Temporal Instance Queries , and Non-Rigid Designators in modalized description logics. His work bridges theoretical computer science with practical AI applications. Scientific Awards : EurAI Fellow (2023) PODS Test of Time Award (2023) Multiple Best Paper Awards at KR, IJCAI, PODS, ISWC, and ACM SIGMOD (2008-2023) Professional Activities include editorial roles at Journal of Applied Logic (2004-present) and Reports on Mathematical Logic (2000-present), organizing chairs at Advances in Modal Logic (2000-2008), and REF Coordinator. He has examined PhD theses at universities including Manchester, ANU, Imperial College, and JAIST.

Marcelo ReggioView profile

Professor

• Fluid Mechanics
• Computational Fluid Dynamics
• Lattice Boltzmann Method
• +3 more

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.

Ricardo CamareroView profile

Associate Professor

• Geometric modeling
• Combustion and reactive flows
• Fluids (numerical simulation of flows)
• +4 more

Ricardo Camarero is an Associate Professor in the Department of Mechanical Engineering at Polytechnique Montréal. With a B.Eng., M.Eng., and Ph.D. from McGill University, he has established himself as a leading researcher in computational fluid dynamics and numerical methods for mechanical engineering applications. His research interests span geometric modeling, combustion and reactive flows, numerical simulation of fluid flows, aerodynamics using numerical methods, and turbomachinery design. Professor Camarero's work primarily focuses on developing and applying advanced computational techniques to solve complex engineering problems, particularly in the areas of fluid-structure interaction, high-voltage circuit breaker design, and turbomachinery flow analysis. With 166 publications spanning several decades, his research output demonstrates consistent contributions to the field of computational fluid dynamics. His recent work (2021-2024) shows continued activity in immersed boundary methods, mesh generation techniques, and applications to electrical engineering problems, indicating an active and productive research program. Professor Camarero has supervised 20 PhD students and 15 Master's students, reflecting his significant contribution to graduate education. His teaching responsibilities include courses in numerical methods and fluid mechanics, directly supporting his research areas. His research has strong practical applications, particularly in the electrical engineering sector where his work on circuit breaker design, arc extinction, and alternative insulating gases provides valuable insights for industry. The interdisciplinary nature of his work bridges mechanical engineering, electrical engineering, and computational mathematics.

Fernando Miguel Granja MartinsView profile

Assistant Professor

• Geographic Information Systems
• Remote Sensing
• Wildfires
• +7 more

Fernando Miguel Granja Martins is an Assistant Professor at the University of Algarve's Higher Institute of Engineering since 2012, with a continuous academic career at the institution since 1996. He is an integrated researcher at the Research Center for Tourism Sustainability and Well-being (CinTurs) and the Research Center for Spatial and Organizational Dynamics (CIEO), and a full member of the MED_Soil Research Group and Center for Studies in Heritage, Landscape and Construction. His educational background includes a PhD in Natural Resources and Environment from the University of Seville (2012), an MSc in Geographic Information Systems from Instituto Superior Técnico of Lisbon University (2005), and a degree in Geographical Engineering from the Faculty of Sciences of Lisbon University (1995). Professor Granja Martins specializes in Geographic Information Systems and Remote Sensing with applications in wildfires, soil science, and environmental studies. His research focuses on spatial modeling of environmental phenomena, particularly in Mediterranean contexts. He has developed significant work on rainfall mapping, soil erosion assessment, post-fire landscape recovery, and water resource management in southern Portugal. His research approach combines field data with advanced geospatial techniques to address practical environmental challenges. His recent publications (2022-2024) demonstrate continued productivity with articles in journals like Urban Science, Hydrological Sciences Journal, and Sustainability, focusing on GIS applications for tourist route optimization, rainfall distribution modeling, and remote sensing for water consumption estimation in urban green spaces. These works reflect his consistent research trajectory in applying geospatial technologies to environmental management problems. Active researcher in the eGroundWater project (2020-2025) Member of the Research Center for Tourism Sustainability and Well-being Former Master's Program Director (2016-2023) Extensive experience in forest fire research and management Professor Granja Martins has supervised numerous Master's students whose work spans environmental monitoring, risk assessment, and geospatial applications in Portugal and internationally. His research has practical applications in environmental management, particularly in Mediterranean regions facing challenges like wildfires and water scarcity. He maintains active collaborations with institutions in Spain, Angola, and Russia, demonstrating an international research network focused on geospatial environmental solutions.

• Control Theory
• Dynamical Systems
• Partial Differential Equations
• +2 more

Professor Alisa DeStefano serves as Associate Professor in the Department of Mathematics and Computer Science at College of the Holy Cross. Her academic career spans over three decades, including prior appointments as Assistant Professor at Holy Cross (1992-1999), Visiting Professor at Boston University (1994-1995), and Lecturer at Dartmouth College (1989-1992). Her educational background includes: Ph.D. in Mathematics, Dartmouth College (1992) A.M. in Mathematics, Dartmouth College (1989) B.S. in Mathematics (summa cum laude), Northeastern University (1987) DeStefano's research centers on Control Theory, specializing in Universal and Discrete Observability of Dynamical Systems. Her work rigorously examines state estimation for wave and heat equations on bounded domains and compact homogeneous spaces, while bridging connections to topological dynamics and abstract harmonic analysis. This interdisciplinary approach integrates techniques from partial differential equations, global analysis, and mathematical physics to solve fundamental problems in system observability. Her publication trajectory (1990-2001) reveals consistent contributions to observability theory, evolving from discrete wave equation analysis to sophisticated constructions of universally observable torus flows and topological dynamics equivalences. Key venues include SIAM journals, IEEE conferences, and Birkhäuser's Progress in Systems and Control Theory series. Notable recognitions include: Summer Faculty Fellowship (awarded 1994, 1997, 1998) Association for Women in Mathematics Travel Grant (1993, 1998) NSF ILI-IG Grant (1993) She maintains active professional engagement through the Mathematical Association of America, American Mathematical Society, and Association for Women in Mathematics. Her teaching portfolio spans Real and Abstract Analysis, Fourier Analysis, differential equations, and chaos theory, reflecting her dual expertise in pure and applied mathematics. While specific current grant details aren't provided in the source material, her historical funding record demonstrates sustained research productivity in mathematical control theory.

Ivan GentilView profile

Lecturer

• Analysis
• Probability Theory
• Geometry
• +10 more

Ivan Gentil is a Lecturer and researcher at the Camille Jordan Institute, affiliated with Claude Bernard University Lyon 1 (member of the University of Lyon). His primary research interests lie in the interplay of Analysis, Probability Theory, and Geometry , with a focus on functional inequalities (logarithmic Sobolev, Sobolev, Poincaré), optimal transport, Hamilton-Jacobi equations, and the asymptotic behavior of diffusion processes. Education: PhD in Mathematics (2001), directed by Michel Ledoux at Paul Sabatier University. Habilitation à Diriger des Recherches (2008), advised by Jean Dolbeault at Paris-Dauphine University. Research and Publications: He has authored over 30 publications, including books on logarithmic Sobolev inequalities and Markov diffusion operators. Recent work explores dimensional contractions in Wasserstein distance, mean field Schrödinger problems, and sharp Beckner-type inequalities. His research often bridges entropy-energy duality, geometric curvature, and probabilistic methods. Community Engagement: Gentil co-organized the Climat et Transitions teaching unit (2021) and a manifesto (2023–2025) to reduce mathematicians' air travel, amassing 650+ signatures. He delivered seminars on mathematical research and climate urgency, including talks at Rennes (2024) and Paris Cité (2025). Students and Collaborations: He advised PhD students Luigia Ripani, Simon Zugmeyer, Gauthier Clerc, and Mohamed Benghazi. He participated in thesis juries for over 20 candidates, collaborating with researchers like Cédric Villani and Christian Léonard.

• Numerical Analysis
• Scientific Computing
• Partial Differential Equations
• +3 more

Herbert Egger is a Professor of Numerical Analysis and Scientific Computing at Johannes Kepler University (JKU) Linz and heads the Institute of Numerical Mathematics. He is also a Group Leader at RICAM (Johann Radon Institute for Computational and Applied Mathematics) and Scientific Director at the Austrian Academy of Sciences. His research focuses on advanced numerical methods for partial differential equations, including structure-preserving discretizations, inverse problems, and computational electromagnetics. Current projects include SFB F90-N (CREATOR) for electric machine co-simulation, SPP 2256 for phase-field modeling in additive manufacturing, and TRR 146 on multiscale soft matter systems. Key research areas: mixed/hybrid finite element methods, energy-based modeling, model order reduction, radiative transfer, and inverse problem stabilization. His recent work emphasizes nonlinear magnetostatics, phase separation models, and efficient solvers for time-dependent systems. Egger collaborates internationally with institutions like TU Eindhoven and TU Darmstadt.

• Numerical Methods
• Finite Element Analysis
• Mathematical Modeling
• +5 more

Милена Радославова Рачева serves as an Associate Professor at the Department of Mathematics, Informatics and Natural Sciences within the Economic Faculty of Technical University - Gabrovo. Holding a Doctor of Sciences degree in Mathematical Sciences, she maintains an active research profile with consistent publications through 2024. Her institutional affiliation demonstrates long-term commitment to the university's academic mission. Research Interests: Dr. Racheva specializes in advanced numerical methods with particular focus on finite element analysis, eigenvalue problems, and Fourier transforms. Her work bridges theoretical mathematics with practical engineering applications, especially in computational modeling for biomedical and ecological systems. Recent publications indicate expanding interests in data approximation techniques and educational technology solutions for remote learning environments. Publication Trends: Analysis of her 15 most recent publications (2021-2024) reveals three dominant research streams: 1) Core mathematical research on eigenvalue approximation and finite element methods (60% of output), 2) Applied mathematical modeling in engineering and energy systems (25%), and 3) Socio-economic analyses of entrepreneurship and investment climates (15%). Her collaborative work with A.B. Andreev represents a significant portion of her technical publications, while recent interdisciplinary projects extend into photovoltaic systems and hydroelectric power analysis. Professional Activities: She has led participation in externally funded scientific projects, most notably the 2012 project "Numerical Methods for Coupled Systems and Computer Modeling in Biomedicine and Ecology" where she served as scientific organization partner. Her publication record shows consistent output across international journals (Springer, IEEE) and Bulgarian academic venues, with recent work appearing in Mathematical Modeling journal and UNITECH conference proceedings.