جست‌وجوی استاد و پژوهشگر

Natalia Nikolovaمشاهده پروفایل

استاد

• Microwave and millimeter-wave imaging and detection
• Inverse problems
• Computational electrodynamics
• +۶ مورد دیگر

Natalia K. Nikolova is a Professor and Canada Research Chair (Tier 2) in High-frequency Electromagnetics at the McMaster University Faculty of Engineering, Department of Electrical and Computer Engineering. Author of Introduction to Microwave Imaging (Cambridge University Press, 2017) Develops cognitive radar systems for concealed weapon detection and microwave scanners for breast cancer screening Commercialization with Patriot One Technologies Ltd. Education: Dipl. Eng. (Radioelectronics), Technical University of Varna, Bulgaria (1989) Ph.D. (Electrical Engineering), University of Electro-Communications, Tokyo, Japan (1997) NSERC Postdoctoral Fellowship (1998-1999) Research Focus: Microwave and millimeter-wave imaging systems for biomedical and security applications through computational electromagnetism and inverse scattering analysis. Her Electromagnetic Vision Laboratory (founded 1999) develops high-speed sensor arrays and computer-aided design tools. Scientific Contributions: Elevated to IEEE Fellow (2011) for sensitivity analysis methods Delivered 50+ international lectures as Distinguished Microwave Lecturer (2010-2013) Holder of 7 patents in microwave imaging technology

Juliette Chabassierمشاهده پروفایل

پژوهشگر

• Acoustics
• Mathematical Modeling
• Numerical Analysis
• +۳ مورد دیگر

Juliette Chabassier is a Researcher at the MAGIQUE-3D project team within Inria Bordeaux Sud-Ouest and affiliated with Université de Pau et des Pays de l'Adour . She holds a Ph.D. in Applied Mathematics from École Polytechnique (2012), advised by Patrick Joly and Antoine Chaigne. Roles: Junior Research Scientist (since 2013), Post-Doc at University of Pau (2012-2013) Research Focus: Acoustics of musical instruments, wave propagation modeling, numerical simulation of piano and wind instruments, energy-preserving discretization schemes, and viscothermal effects in ducts. Her work emphasizes physical modeling and computational acoustics , particularly in piano acoustics, wind instrument design, and heritage instrument digitization. She develops tools like OpenWind for simulating instrument responses and collaborates on projects involving piano soundboard analysis and historical instrument reconstruction. Her recent articles explore topics such as: Simulation of brass instruments using finite element models Thermoviscous effects in conical tubes Energy-conserving discretization for nonlinear wave equations Digital twins for heritage instruments like Besson trumpets She advises doctoral students including Elvira Shishenina and Izar Azpiroz , focusing on applied mathematics and acoustics. Her contributions include advanced numerical methods for acoustic-elastic coupling and boundary layer phenomena in exponential media. Current projects involve homogenization techniques for porous materials, real-time instrument simulation tools, and interdisciplinary work on acoustics education through open-source software.

Olivier Chadebecمشاهده پروفایل

استاد پژوهشی

• Computational Electromagnetics
• Electrical Energy Conversion
• Numerical Modeling
• +۶ مورد دیگر

Olivier Chadebec is a CNRS Research Director at G2Elab, the power electrical engineering research department of Université Grenoble Alpes in France. He leads the 'Models, Methods and Methodologies Applied to Electrical Engineering' research team (MAGE group) and the ERT-CMF (Low Magnetic Fields Technological Research Group) at G2Elab. He was involved in creating the International Laboratory 'James Clerk Maxwell' in collaboration with the University of Lyon and Brazilian universities. Chadebec received his engineer and Ph.D. degrees in Electrical Engineering from the Grenoble Institute of Technology in 1997 and 2001. After a post-doctorate with Schneider Electric, he joined CNRS in 2003 as a Research Associate. He received his 'Habilitation à Diriger les Recherches' in 2011 and became a Research Director in 2015. He also spent a year in 2012 as a research associate at the Federal University of Santa Catarina in Brazil. His research focuses on computational electromagnetics applied to electrical energy conversion, developing numerical models, algorithms, and simulation tools for electromagnetic device analysis. His key research areas include finite element methods, integral methods, inverse problems, and low magnetic field metrology. He actively contributes to the development of the MIPSE platform commercialized by Altair Engineering via Flux software. His recent publications (2023-2025) show a strong focus on advanced computational methods for electromagnetic problems, including multiscale modeling, tensor compression techniques, FEM-BEM coupling for magnetoelectric effects, and optimization algorithms for electrical machine design and fuel cell diagnostics. His work demonstrates a consistent progression toward more efficient computational approaches for complex electromagnetic problems. Chadebec has supervised over 30 PhD students since 2006, with thesis topics spanning computational electromagnetics, inverse problems, fuel cell diagnostics, and submarine magnetic signature analysis. His research has significant applications in electrical machine design, fuel cell technology, submarine degaussing, and electromagnetic compatibility. He leads the MAGE research team and the ERT-CMF (Low Magnetic Fields Technological Research Group) at G2Elab, and has been instrumental in developing the MIPSE simulation platform used in industry through collaboration with Altair Engineering.

Alain Kassabمشاهده پروفایل

استاد

• Computational Biomechanics
• Boundary Element Methods
• Inverse Problems in Engineering
• +۳ مورد دیگر

Alain Kassab is a Professor and Biomedical Engineering Program Coordinator at the University of Central Florida (UCF), affiliated with the College of Engineering and Computer Science. He holds the UCF Pegasus Professor title and has received multiple accolades including UCF Trustee Chair (2019), Fellow of the American Institute of Medical and Biological Engineering (2019), and multiple Excellence in Graduate Teaching Awards since 1994. Ph.D. in Mechanical Engineering – University of Florida M.S. in Mechanical Engineering – University of Florida B.S. in Engineering Sciences – University of Florida Kassab specializes in computational biomechanics and biomedical engineering, with a focus on multiscale hemodynamics modeling for congenital heart disease, boundary element methods (BEM), meshless methods, and inverse problems in engineering. His work spans cardiovascular simulations, thermal analysis, and applications in energy systems. Recent publications emphasize inverse problem algorithms, BEM applications in non-Fourier heat transfer, GPU-accelerated fluid dynamics, and biomedical modeling of Fontan circulation. Keywords include Computational Biomechanics, Boundary Element Method, and Multiscale Hemodynamics. UCF Trustee Chair (2019) Fellow of the American Institute of Medical and Biological Engineering (2019) UCF Pegasus Professor (2017) ASME Fellow (2000) Multiple Excellence in Graduate Teaching Awards (1994–2017) Kassab leads the Computational Mechanics Lab at UCF, collaborating on multidisciplinary projects in medicine and energy systems. His research integrates numerical methods with experimental validation, particularly in pediatric cardiovascular device design and thermal-fluid systems.

Darko Zibarمشاهده پروفایل

استاد

• Machine Learning in Photonic Systems
• Optical Communication
• Quantum Communication
• +۳ مورد دیگر

Darko Zibar is a Professor at the Department of Electrical and Photonics Engineering, Technical University of Denmark (DTU), and leads the Machine Learning in Photonics Systems (MLiPS) group. He holds a M.Sc. in Telecommunication and a Ph.D. in Optical Communications from DTU (2004, 2007). As a Visiting Professor, he has contributed to research at Politecnico di Torino, Friedrich Alexander University of Erlangen, University of California Santa Barbara, and University of Colorado, Boulder. Research Interests: Machine learning applications in optical communication systems Digital signal processing for classical and quantum photonics Optimization of Raman amplifiers and frequency combs Nonlinear fiber optic transmission modeling Photonic reservoir computing with silicon microring resonators Scientific Contributions: His work includes record-breaking achievements in optical phase noise measurement (approaching quantum limits) and programmable gain Raman amplifier design (S+C+L band). He has received prestigious awards such as the ERC Consolidator Grant (2017), Humboldt Bessel Research Award (2021), and Villum Investigator Award (2023).

Cynthia Lesterمشاهده پروفایل

استادیار مهمان

• Category Theory
• Homotopy Theory
• Algebraic Topology
• +۳ مورد دیگر

Cynthia Lester is a Visiting Assistant Professor of Mathematics at the College of Wooster. She joined the institution in Fall 2023. Her research focuses on advanced mathematical areas including category theory, homotopy theory, algebraic topology, and K-theory, with a particular emphasis on extending determinant functors and exploring Grothendieck topologies. Prior to Wooster, she was a Postdoctoral Scholar at Florida State University, collaborating with Ettore Aldrovandi on K-theory in tensor triangulated categories. She holds a Ph.D. in Mathematics from the University of Oregon (2019), an M.S. from the same institution (2015), and a B.A. from Linfield College (2011). Education: B.A., Mathematics, Linfield College, 2011 M.S., Mathematics, University of Oregon, 2015 Ph.D., Mathematics, University of Oregon, 2019 Research Interests: Cynthia’s work bridges abstract algebra and topology, aiming to generalize mathematical structures. Her current projects include categorifying determinants to inform K-theory and analyzing homotopy invariants within Grothendieck topologies. She also explores foundational questions in category theory, such as equipping categories with topological frameworks. Publications: Her recent work includes a 2023 paper on multi-determinant functors and an upcoming publication on canonical Grothendieck covers. Earlier contributions include studies on tiling problems and financial inverse problems. These publications reflect her interdisciplinary approach, spanning pure and applied mathematics. Awards: No awards explicitly listed in the provided text. Advising & Grants: While no advisees are listed, her postdoctoral role at Florida State University involved collaborative research. Funding details are not specified here. Labs/Teams: Collaborates with Ettore Aldrovandi and others in mathematical research networks, though specific lab affiliations are not mentioned.

Jordi Ignés-Mullolمشاهده پروفایل

استاد

• Soft Condensed Matter
• Active Soft Matter
• Langmuir-Blodgett Films
• +۲ مورد دیگر

Jordi Ignés-Mullol is a Full Professor in the Department of Materials Science and Physical Chemistry at the Universitat de Barcelona and a member of the Institute of Nanoscience and Nanotechnology. His research focuses on experimental studies of soft condensed matter systems, including active protein suspensions, surfactant and colloidal Langmuir-Blodgett films, driven colloidal suspensions, and liquid crystals. He earned his PhD in Physics from the University of Pittsburgh and conducted postdoctoral research at École Normale Supérieure de Lyon and Tulane University. Research interests include: Pattern formation in complex fluids Dynamics of structures in liquid crystals Microrheology of Langmuir monolayers Active soft matter systems Colloidal assembly under external fields His recent publications explore topological defects in active nematics, liquid crystal-enabled colloid manipulation, and dynamic transitions in soft matter systems. Research trends show strong emphasis on non-equilibrium physics, interfacial phenomena, and experimental techniques development.

Shukai Duمشاهده پروفایل

استادیار

• Numerical Analysis
• Scientific Machine Learning
• Inverse Problems
• +۶ مورد دیگر

Shukai Du is an Assistant Professor of Mathematics at Syracuse University , within the College of Arts and Sciences . Previously, he served as a Visiting Assistant Professor at the University of Wisconsin-Madison (2020–2024) and earned his Ph.D. in Applied Mathematics from the University of Delaware (2020). His research focuses on numerical analysis , scientific machine learning , and inverse problems , with expertise in discontinuous Galerkin methods , radiative transfer , and adaptive mesh refinement . He is also affiliated with his research website . Education: Ph.D., Applied Mathematics, University of Delaware (2020) M.S., Computational Mathematics, Wuhan University (2015) B.S., Pure Mathematics, Wuhan University (2012) Research Highlights: Dr. Du’s work emphasizes accelerating numerical methods via machine learning (element learning), developing goal-oriented hp-adaptive mesh refinement for inverse radiative transfer, and creating symplectic Hamiltonian finite element methods for electromagnetics. His methods prioritize accuracy, computational efficiency, and preservation of physical invariants. Recent Contributions: Recent publications address operator learning for finite element acceleration, adaptive-mesh inversion for inverse problems, and energy-conserving discretizations for Hamiltonian systems. His work bridges classical numerical methods and modern data-driven techniques. Awards: Wenbo Li Prize (2020, University of Delaware) Grants: "Breaking the 1D Barrier in Radiative Transfer: Fast, Low-Memory Numerical Methods for Enabling Inverse Problems and Machine Learning Emulators" (NSF, 2023–2026) "Convective Processes in the Tropics Across Scales" (NSF, 2024–2026) Labs/Teams: His research collaborates with computational scientists and applied mathematicians, focusing on interdisciplinary challenges in computational physics and data-driven modeling.

• Convex Optimization
• Nonconvex Optimization
• Monotone Operators
• +۵ مورد دیگر

Radu Ioan Boț is a Professor at the Faculty of Mathematics at the University of Vienna . Since 2020, he has served as Dean of the Faculty of Mathematics and Head of the Institute of Mathematics . He is also the Speaker of the FWF DK Vienna Graduate School on Computational Optimization and a Founding Member of the Research Platform Data Science@Uni Vienna . Education Ph.D. in Mathematics (2003, Chemnitz University of Technology, Summa cum laude ) M.Sc. in Mathematics (1999, Babeş-Bolyai University, Grade: 10 ) Diploma in Mathematics (1998, Babeş-Bolyai University, Grade: 10 ) Research Interests focus on convex and nonconvex optimization , monotone operators , duality theory , and proximal algorithms . His work bridges continuous and discrete time models , emphasizing fast convergence rates and Tikhonov regularization for problems in image processing , machine learning , and inverse problems . Recent Publications highlight advancements in primal-dual splitting , inertial dynamics , and stochastic optimization , with applications to GANs , phase retrieval , and structured convex minimization . Scientific Awards include the 2003 University Prize of Chemnitz University of Technology , 1998 award for graduating with the best grade from Babeş-Bolyai University, and fellowships during his Ph.D. and pre-Ph.D. research stays. Editorial Roles include Editor-in-Chief of SIAM Journal on Optimization (2026–2029) and editorial board membership for journals like Mathematical Programming , Computational Optimization and Applications , and Journal of Optimization Theory and Applications .

Armin Iskeمشاهده پروفایل

استاد

• Numerical Approximation
• Applied Mathematics
• Kernel Methods
• +۴ مورد دیگر

Prof. Dr. Armin Iske is a Full Professor of Numerical Approximation at the University of Hamburg's Department of Mathematics, within the Faculty of Mathematics, Computer Science and Natural Sciences. He holds a PhD from the University of Göttingen (1994) and habilitation from TU Munich (2002). His research focuses on numerical approximation, kernel-based methods, computational fluid dynamics, and medical imaging. He has held academic positions globally, including visiting roles at ANU (Australia) and the University of Leicester (UK). Research interests include scattered data approximation, adaptive particle methods for flow simulation, and high-dimensional data analysis. He has authored 118+ publications, including works on kernel interpolation, medical imaging reconstruction, and machine learning applications. He serves on editorial boards for journals like Advances in Computational Mathematics and Sampling Theory . His contributions span interdisciplinary projects, such as SFB/TRR 181 on energy transfer in atmosphere and ocean, and collaborations in nanotechnology for brain interfaces. His work bridges theoretical mathematics with practical applications in engineering and biosciences.

• Numerics of Partial Differential Equations (PDEs)
• Finite Element Methods (FEM)
• Boundary Element Methods (BEM)
• +۳ مورد دیگر

Markus Faustmann is a Senior Scientist at the Institute of Analysis and Scientific Computing (E 101) at TU Wien. He holds a Dipl.-Ing. Dr.techn. degree in Technical Mathematics. His research focuses on numerical methods for partial differential equations (PDEs), finite element methods (FEM), boundary element methods (BEM), hierarchical matrices, elliptic regularity, and fractional differential operators. Faustmann has been recognized with the TU Best Teacher Award (2022) and TU Best Paper Award (2022) from TU Wien's Faculty of Mathematics and Geoinformation. Education: PhD in Mathematics from TU Wien (2015), supervised by J.M. Melenk; Diploma in Technical Mathematics (2011) and Bachelor of Science in Mathematics (2009), both from TU Wien. Research Interests: Development and analysis of numerical methods for non-local operators, including fractional Laplacian problems, hp-FEM, matrix compression techniques, and error estimation. His work emphasizes efficient discretization strategies and theoretical foundations for complex PDE systems. Teaching: Current courses include Numerical Methods for PDEs (Summer 2025), Non-local Operators (Winter 2024/25), and Scientific Programming for Interdisciplinary Mathematics . He has also supervised numerous bachelor's and master's theses in numerical analysis and computational mathematics. Awards: His research contributions have been highlighted through prestigious awards, reflecting his expertise in both teaching and applied numerical analysis.

Hadj Batatiaمشاهده پروفایل

دانشیار

• Computational Imaging
• Machine Learning
• Medical Imaging
• +۶ مورد دیگر

Hadj Batatia is an Associate Professor at the School of Mathematical and Computer Sciences (MACS) at Heriot-Watt University Dubai (HWUD) since 2020, serving as Associate Director of Research. He previously held roles at the National Polytechnic Institute of Toulouse (INP Toulouse), Conservatoire National des Arts et Métiers (CNAM Toulouse), and University of Toulouse, where he led research teams and departments. His academic journey includes a PhD in Computer Vision from the University of Toulouse (1992) and an MSc in Artificial Intelligence (1987), with undergraduate studies at the University of Algiers. His research focuses on computational imaging, machine learning, and statistical modeling applied to medical imaging and computer vision. Key areas include deep learning, inverse problems, and biomedical image processing. He has supervised 15 PhD theses and contributed to 82 research outputs, achieving an h-index of 26. Notable grants include the ESMS project (2021-2023) for healthcare smart home systems and CAMM4D (2008-2012) for skin oncology imaging. Dr. Batatia teaches Advanced Machine Learning, Biologically Inspired Computation, and Big Data Management. He has led international collaborations across Europe, Latin America, and Asia, addressing challenges in medical imaging, cybersecurity, and education. His work aligns with UN SDGs, particularly in health and innovation. Awards include qualifications to professorship in Computer Science and Engineering.

Stephen M. Wattمشاهده پروفایل

استاد

• Computer Algebra
• Symbolic Computation
• Programming Languages
• +۹ مورد دیگر

Stephen M. Watt is a Distinguished University Professor and Dean of the Faculty of Mathematics at the University of Waterloo, holding a position in the David R. Cheriton School of Computer Science. He earned his PhD and DSc (h.c.) in computer science and has contributed significantly to fields such as computer algebra, programming languages, and mathematical computing interfaces. His research focuses on symbolic computation, pen-based mathematical recognition, and compiler optimization. He is a co-creator of Maple and Aldor, and a key contributor to standards like MathML and InkML. Watt’s academic journey includes roles at the University of Western Ontario and IBM Research. His awards include the IBM Outstanding Innovation Award (1992), NSERC Synergy Award (2004), and IWAY Award for New Technology Development (2002). He has supervised numerous graduate students, advancing research in areas like approximate polynomial algorithms, mathematical collaboration tools, and handwriting recognition. His leadership extends to directing the Ontario Research Center for Computer Algebra (ORCCA) and pioneering projects like MONET (Mathematical Web Services) and PenMath (pen-based interfaces). Current research explores AI applications in STEM assessment, GPU-optimized arithmetic, and hybrid symbolic-numeric methods.

• Finite Element Methods
• Inverse Problems
• Numerical Analysis
• +۲ مورد دیگر

Robert Luce is an HDR-qualified Lecturer in Mathematics at the University of Pau, specializing in numerical methods for partial differential equations. His research evolved from parameter identification to developing pseudo-conforming finite elements for complex geometries. Current work focuses on NXFEM methods for interface problems and posteriori error estimators. He has supervised 6 PhD students on topics ranging from nonlinear pseudoparabolic problems to fracture modeling in porous media. Collaborative projects include international partnerships through the Zaragoza-Pau Mathematics Conferences.

Ying Liangمشاهده پروفایل

استادیار

• Inverse problems
• Mathematical imaging
• Scattering theory
• +۳ مورد دیگر

Ying Liang is a Phillip Griffiths Assistant Research Professor of Mathematics at Duke University, affiliated with the Department of Mathematics within Trinity College of Arts & Sciences. She holds a Ph.D. in Mathematics from The Chinese University of Hong Kong (2021) and a BSc from Wuhan University (2016). Her research focuses on inverse problems, mathematical imaging, scattering theory, and scientific machine learning. She has contributed to theoretical and numerical studies of inverse source problems, wave equations, and electromagnetic diffraction. Her work combines mathematical rigor with computational techniques to address challenges in applied mathematics and engineering. Liang has taught courses such as MATH 563 (Applied Computational Analysis) and MATH 561 (Numerical Linear Algebra) at Duke, and previously instructed multiple courses at Purdue University and The Chinese University of Hong Kong. Her academic journey includes roles as a Golomb Visiting Assistant Professor (Purdue, 2021–2024) and a Research Assistant (CUHK, 2016–2021). Her publications span topics like stability analysis of inverse problems, electromagnetic diffraction theory, and numerical methods for partial differential equations. She actively participates in conferences and workshops, presenting on neural network approaches to inverse problems and scientific computing innovations.