جست‌وجوی استاد و پژوهشگر

Yorie Nakahiraمشاهده پروفایل

استادیار

• Optimization Theory
• Control Systems
• Machine Learning
• +۵ مورد دیگر

Yorie Nakahira is an Assistant Professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University (CMU), within the College of Engineering. Her research focuses on the theoretical foundations of optimization, control, and learning, with applications to neuroscience, robotics, smart grids, and autonomous systems. She holds a Ph.D. from Caltech (2019) and a B.E. from Tokyo Institute of Technology (2012). Her education includes a Bachelor of Engineering in Control and Systems Engineering (Tokyo Institute of Technology, 2012) and a Ph.D. in Control and Dynamical Systems (California Institute of Technology, 2019). Her work bridges fundamental control theory with real-world applications, emphasizing safety, adaptability, and interdisciplinary collaboration. Research interests span optimization frameworks for layered architectures, safety guarantees in autonomous systems, and the integration of physics-informed learning. Notable trends in her recent publications include advancements in probabilistic safety certificates, stochastic control under uncertainty, and the application of neural networks to dynamical systems. She also explores energy-efficient computing in automated vehicles and human-robot collaboration dynamics. Selected articles highlight innovations in safety-critical systems, multi-agent coordination, and the theoretical limits of control under partial observability. These contributions underscore her commitment to bridging theoretical rigor with practical engineering challenges.

• Inverse problems
• Uncertainty quantification
• Numerical Analysis
• +۶ مورد دیگر

Zhang Wenlong is an Assistant Professor in the Department of Mathematics at the Southern University of Science and Technology (SUSTech). His research focuses on inverse problems, uncertainty quantification, numerical analysis, and their applications in fields such as electrical impedance tomography and partial differential equations. He holds a B.Sc. in Information and Computational Science from Nanjing University (2007-2011), a Ph.D. in Computational Mathematics from the Chinese Academy of Sciences (2011-2014), and a dual Ph.D. from the DMA at École Normale Supérieure and ETH Zürich under Professors Zhiming Chen and Habib Ammari. Prior to his current role, he was a postdoc at SUSTech (2017–2019). His research areas include inverse problems, hybrid imaging methods, homogenization theory, and numerical methods for PDEs. He has contributed to advancements in convexification techniques for inverse problems, stochastic convergence analysis, and data-driven model reduction methods. His work often intersects applied mathematics, computational physics, and biomedical imaging. Zhang has taught courses in linear algebra, ordinary differential equations, and finite element methods at SUSTech since 2017. His publications span top journals such as SIAM Journal on Imaging Sciences, Inverse Problems, and Journal of Computational Physics, reflecting his expertise in interdisciplinary computational and applied mathematics.

Ulrich Reifمشاهده پروفایل

استاد

• Geometric Modeling
• Numerical Analysis
• Approximation Theory
• +۳ مورد دیگر

Ulrich Reif is a Professor in the Department of Mathematics at Technische Universität Darmstadt. His research focuses on geometric modeling, numerical analysis, approximation theory, and computer-aided geometric design. He explores advanced topics such as isogeometric analysis, subdivision algorithms, and spline-based methods. Key research interests include the theoretical foundations of geometric modeling, computational methods for solving complex mathematical problems, and applications in engineering and computer graphics. His work addresses challenges in surface approximation, numerical stability, and algorithm development for geometric problems. Recent publications highlight contributions to Sobolev regularity analysis, subdivision surface techniques, and the application of deep learning in regression tasks on manifolds. His work often bridges pure mathematics and computational engineering, emphasizing practical implementations and theoretical rigor. No scientific awards or grants are explicitly listed in the provided materials. His academic contributions span over three decades, with a focus on advancing numerical methods and geometric modeling theories.

Daniele Panozzoمشاهده پروفایل

استادیار

• Geometry Processing
• Computer Graphics
• Digital Fabrication
• +۳ مورد دیگر

Daniele Panozzo is an Assistant Professor of Computer Science at the Courant Institute of Mathematical Sciences, New York University (NYU). Previously, he was a senior researcher at ETH Zurich's Interactive Geometry Lab. He holds a BSc and Ph.D. in Computer Science from the University of Genoa (2008, 2012). His research focuses on geometry processing, computer graphics, and digital fabrication, with notable contributions to meshing, finite element simulation, and computational geometry. Key achievements include the EUROGRAPHICS Annual Award for Best Ph.D. Thesis (2013), the Young Researcher Award (2015), an NSF CAREER Award (2017), and a Sloan Research Fellowship (2020). His work emphasizes robust algorithms for geometric computing, with applications in engineering, biology, and cultural heritage preservation. Publications span topics like microstructure optimization, contact dynamics, and 3D scanning, reflecting his interdisciplinary approach. He leads the Geometric Computing Lab at NYU, advancing open-source tools such as libigl for geometry processing. Ongoing projects include developing differentiable solvers and high-order meshing techniques for complex simulations.

Nelly Villamizarمشاهده پروفایل

دانشیار

• Applied Algebraic Geometry
• Commutative Algebra
• Spline Approximation
• +۲ مورد دیگر

Dr. Nelly Villamizar is an Associate Professor in the Department of Mathematics at Swansea University, UK. She specializes in Applied Algebraic Geometry and Commutative Algebra, with applications in Geometric Modeling and Computer-Aided Geometric Design (CAGD). Her work bridges abstract algebraic theories with practical computational methods for geometric problems. Previously, she served as a Postdoctoral Researcher at RICAM (Linz, Austria) from 2013 to 2017. Teaching Responsibilities include advanced modules such as MA-233 Projective Geometry MA-231 Advanced Geometry (covering conic sections and non-Euclidean geometry) MA-312 Higher Algebra (module theory and category theory) MA-212 Groups and Rings Research interests focus on spline approximation and algebraic spline geometry, with ongoing contributions to geometric design methodologies. She is actively involved in postgraduate supervision and maintains an ORCID profile (https://orcid.org/0000-0002-8741-7225).

Mohamed Omarمشاهده پروفایل

استاد

• Discrete Mathematics
• Combinatorics
• Pure Mathematics
• +۳ مورد دیگر

Mohamed Omar is a Full Professor in the Department of Mathematics and Statistics at York University, located in Toronto, Canada. His research focuses on Discrete Mathematics, Combinatorics, and Pure Mathematics, with a particular interest in the application of polynomials to discrete structures. He holds a Ph.D. from the University of California, Davis, and an M.Sc. from the University of Waterloo. His notable awards include the AMS Claytor-Gilmer Fellowship, Karen EDGE Fellowship, and Henry L. Alder Award. His work spans algebraic combinatorics, graph theory, and neural coding, with contributions to permutation patterns, polynomial applications, and discrete geometry. Recent research highlights include studies on permutation descent words, partition lattice analysis, and sparse neural codes. He has authored books such as Graph Theory You Need Before Undergrad Research and Number Theory Toward RSA Cryptography , reflecting his commitment to undergraduate education and mathematical exposition.

Richard G. Baraniukمشاهده پروفایل

استاد

• Signal Processing
• Data Science
• Neuroengineering
• +۴ مورد دیگر

Richard G. Baraniuk is the C. Sidney Burrus Professor of Electrical and Computer Engineering at Rice University, with concurrent appointments in Statistics and Computer Science. He holds the IEEE Jack S. Kilby Medal (2025) and the Harold W. McGraw Prize in Education (2022). His research spans multiscale signal processing, compressive sensing, and AI-driven education initiatives like OpenStax. He earned B.Sc., M.Sc., and Ph.D. degrees in Electrical Engineering from the University of Manitoba (1987), University of Wisconsin-Madison (1988), and University of Illinois at Urbana-Champaign (1992). Research focuses on: Multiscale computational signal/image processing Open access scholarly publishing Data science and neuroengineering Generative AI safety (e.g., self-consuming feedback loops) Notable contributions include the single-pixel camera, compressive sensing ADCs, and SafeInsights AI ethics framework. He advises 5+ graduate students and leads the DSP Group at Rice, collaborating with Adobe Research and others. His work on AI self-improvement mechanisms (SIMS) addresses model autophagy challenges in generative systems. Awards include NAE membership (2022), McGraw Prize for educational innovation, and 2025 IEEE medal for signal processing contributions. His OpenStax initiative provides free textbooks used by millions globally.

Lisa Maria Kreusserمشاهده پروفایل

دانشیار

• Partial Differential Equations
• Machine Learning
• Data Analysis
• +۴ مورد دیگر

Dr. Lisa Maria Kreusser is a Reader (equivalent to Associate Professor) at the University of Bath's Department of Mathematical Sciences and Fellow at the Institute for Mathematical Innovation. Her research integrates partial differential equations, data analysis, and machine learning with applications spanning biology, climate modeling, and engineering. Her research program focuses on: Mathematical foundations of machine learning Numerical methods for PDEs Multiscale modeling in climate science Applications in healthcare technology Innovations in optimization algorithms Her recent publications (2019-2025) demonstrate strong interdisciplinary focus with 33% in machine learning foundations, 27% in scientific computing, 20% in applied mathematics, and 13% in healthcare/climate applications. Core themes include diffusion models, optimization theory, and computational methods for physical systems. Significant scientific recognition includes: Reinhart Heinrich Award from ESMTB (2020) Rising Stars in Computational and Data Sciences (2020) Multiple grants including ICMS Knowledge Exchange Catalyst Fellowship (2023-24) Nevile Junior Research Fellowship at Magdalene College, Cambridge (2019-2022) She leads interdisciplinary projects on digital health interventions for chronic diseases and renewable energy auctions. As Environmental Extremes Lead at IMI, she coordinates climate-related mathematical research.

Florian Bossmannمشاهده پروفایل

دانشیار

• Applied Signal Processing
• Inverse Problems
• Greedy Algorithms
• +۸ مورد دیگر

Florian Bossmann is an Associate Professor and Doctoral Supervisor at the School of Mathematics, Harbin Institute of Technology. His research focuses on applied signal processing with an emphasis on algorithm design and applications in inverse problems, sparsity, and compressed sensing. His work spans seismic exploration, ptychography, and video processing. Education: PhD (Mathematics, University of Göttingen, 2010-2013), Diploma in Mathematics (University of Duisburg-Essen, 2005-2009). Research interests include greedy methods, structured sparsity, and data denoising. His recent articles address topics like object reconstruction via K-approximation graphs and phase retrieval in ptychographic imaging. No scientific awards explicitly stated. Active in mentoring doctoral students and collaborations in applied mathematics and engineering. Labs/Teams: Engaged in interdisciplinary research groups focused on signal processing and mathematical modeling.

Gero Frieseckeمشاهده پروفایل

استاد

• Electronic structure of atoms and molecules
• Calculus of variations
• Molecular dynamics
• +۳ مورد دیگر

Prof. Gero Friesecke is a Professor of Mathematics at the Technical University of Munich (TUM), leading the Chair of Analysis within the Department of Mathematics. He is affiliated with the TUM School of Computation, Information and Technology. His research focuses on electronic structure theory, calculus of variations, molecular dynamics, multiscale methods, and optimal transport. He has held academic positions at institutions like the University of Oxford and the University of Warwick before joining TUM in 2004. Education includes a PhD from Heriot-Watt University (1993) and postdoctoral work at Carnegie Mellon University, Universität Freiburg, and ETH Zurich. Notable awards include the Whitehead Prize (1999) and the Oberwolfach Prize (1995). His work spans theoretical and applied mathematics, with contributions to quantum chemistry, materials science, and optimal transport algorithms. He co-edits journals like the Archive for Rational Mechanics and Analysis and serves on scientific advisory boards, including the Max Planck Institute for Mathematics in the Sciences. Recent research emphasizes optimal transport applications in data science and quantum chemistry, with publications on topics like Wasserstein barycenters and self-assembly processes. His leadership roles include Dean of the Faculty of Mathematics at TUM (2012-2015) and co-founding the GAMM activity group on molecular systems modeling.

Radu-Lucian LUPŞAمشاهده پروفایل

مدرس

• Image Processing
• Computer Graphics
• Graph Algorithms
• +۳ مورد دیگر

Radu-Lucian LUPŞA serves as a Lecturer at Babeş-Bolyai University's Faculty of Mathematics and Computer Science in Romania. His academic career spans over two decades with a consistent focus on computer science, particularly in image processing and graph algorithms. His teaching responsibilities include courses on Graph Algorithms and Parallel and Distributed Programming, demonstrating his expertise across theoretical and applied computer science domains. LUPŞA's research interests center around image processing, with significant contributions to compression methods, dithering algorithms, and multiresolution analysis. His work bridges theoretical computer science with practical applications in graphics and healthcare optimization. The evolution of his research shows a progression from fundamental image compression techniques in the late 1990s to more specialized applications in medical imaging and transportation logistics by the late 2000s. Analysis of his publications reveals strong expertise in computational geometry, algorithm design, and optimization problems. His research demonstrates consistent application of mathematical rigor to practical computing challenges, particularly in the areas of image representation and processing. The interdisciplinary nature of his work is evident in publications spanning computer graphics, healthcare logistics, and mathematical optimization. LUPŞA completed his PhD in 2006 with the dissertation titled Contributions to the analysis, processing and representation of images , which serves as a foundation for his subsequent research trajectory. His academic output shows a period of high productivity between 1996-2008 with publications in respected venues including IEEE conferences and academic journals. As an educator, LUPŞA has developed comprehensive teaching materials for graph algorithms, including lecture notes, practical assignments, and evaluation methods. His teaching approach emphasizes both theoretical foundations and practical implementation, as evidenced by the detailed laboratory assignments and programming tasks he has designed for students.

Nikola Georgiev Naidenovمشاهده پروفایل

دانشیار

• Numerical Methods
• Algorithms
• Approximation Theory
• +۳ مورد دیگر

Nikola Georgiev Naidenov is an Associate Professor in the Faculty of Mathematics and Informatics at the University of Sofia. His email is nikola@fmi.uni-sofia.bg . He focuses on numerical methods, approximation theory, and inequality analysis. Research areas: Oscillating polynomials, Markov inequalities, Tchebycheff systems, extremal points in polynomial spaces. Collaborated with B. Bojanov and L. Milev on numerous influential papers. Investigated weighted polynomials, integral norms, and asymptotic behavior of polynomial derivatives.

Parvan Evtimov Parvanovمشاهده پروفایل

دانشیار

• Mathematical Analysis
• Approximation Theory
• Numerical Analysis
• +۴ مورد دیگر

Parvan Evtimov Parvanov is an Associate Professor at the Faculty of Mathematics and Informatics , University of Sofia, Bulgaria. His research focuses on approximation theory, operator theory, and numerical methods, particularly in the context of weighted approximation and multivariate functions. Academic Rank: Associate Professor University: University of Sofia School: Faculty of Mathematics and Informatics Email: pparvan@fmi.uni-sofia.bg Parvanov’s work primarily investigates the theoretical foundations of best approximation using operators like Bernstein-type, Baskakov-type, and Meyer-König-Zeller-type. His studies emphasize error estimation, K-functionals, and moduli of smoothness to characterize approximation rates. His publications (1994–2011) demonstrate expertise in: Weighted approximation techniques Trigonometric and spline approximations Operator theory in numerical analysis Exact constants in quadrature formulae Moduli of smoothness in multivariate approximation Polynomial and functional inequalities

• Approximation Theory
• Sampling Theory
• Function Spaces
• +۳ مورد دیگر

Dr. Yurii Kolomoitsev serves as a Research Fellow at the Institute of Numerical and Applied Mathematics, University of Göttingen. His research focuses on approximation theory and sampling processes, with active involvement in multiple DFG-funded projects including "Spezielle Glattheitsmaße und Funktionenräume für Sampling und Interpolation" (2025-2027, project KO 5804/3-1) and the Research Training Group 2088 "Discovering structure in complex data" (2015-2024). His primary research interests span approximation theory, sampling theory, function spaces, Fourier analysis, compressed sensing, and inverse problems. Kolomoitsev investigates fundamental properties of quasi-interpolation operators, moduli of smoothness, and Lebesgue constants, with significant contributions to multivariate approximation and high-dimensional computational methods. His work bridges theoretical analysis with practical applications in data science and signal processing. Analysis of his 15 most recent publications (2020-2023) reveals consistent focus on asymptotic behavior in approximation processes, particularly examining error estimates in Lp spaces, sparse grid methods, and the interplay between function smoothness and approximation quality. Key trends include the extension of classical results to non-standard spaces (0 Dr. Kolomoitsev holds substantial research funding through the German Research Foundation (DFG), currently leading project KO 5804/3-1 and participating in RTG 2088 until 2024. He actively contributes to the academic community through workshop organization including the annual RTG Workshop series and specialized events on inverse problems and clinical MRI data analysis. His teaching portfolio includes graduate-level courses such as "An Introduction to Compressed Sensing" (2023/24) and "Fourier Analysis and Approximation" (2022).

Martin Siebenbornمشاهده پروفایل

استاد

• Shape Optimization
• Computational Mathematics
• Fluid Dynamics
• +۳ مورد دیگر

Martin Siebenborn is a Junior Professor (W1) for Optimization and Approximation at the Department of Mathematics, University of Hamburg. His research focuses on shape optimization with applications in fluid dynamics, structural mechanics, and partial differential equations (PDEs), emphasizing scalable high-performance algorithms and multigrid methods. Education: PhD in Mathematics (2014, Universität Trier), Diploma in Mathematics (2010, Universität Trier). His research integrates algorithmic scalability for PDE-constrained optimization, leveraging multigrid preconditioners and nonlinear extension operators. Recent projects include simulation-based design optimization under uncertainties and scalable shape optimization for fluid dynamics. Onyshkevych, Pinzon Escobar, and Wyschka are part of his research team. He has developed open-source software tools like MinFEM, MGSS, and MultigridShapeOpt for teaching and high-dimensional data analysis.