جست‌وجوی استاد و پژوهشگر

Elina Robevaمشاهده پروفایل

استادیار

• Mathematical Statistics
• Machine Learning
• Causal Inference
• +۶ مورد دیگر

Elina Robeva is an Assistant Professor in the Department of Mathematics at the University of British Columbia (UBC), effective until July 2025, when she will be promoted to Associate Professor. She is affiliated with the Institute of Applied Mathematics, CAIDA, and holds the titles of AMII Fellow and Canada CIFAR AI Chair. Her research focuses on the intersection of mathematical statistics, machine learning, combinatorics, and applied algebraic geometry, with an emphasis on causal inference, graphical models, and tensor decomposition. Education: She earned her PhD in Mathematics from UC Berkeley (2016), advised by Bernd Sturmfels, and a BS with Honors in Mathematics from Stanford University (2011). Prior to UBC, she was an NSF Postdoctoral Fellow and instructor at MIT (2016–2019). Research Interests: Her work bridges algebraic methods with statistical problems, including causal discovery in linear models, tensor decomposition applications in machine learning, and super-resolution imaging. She employs algebraic geometry and combinatorics to uncover structural insights in statistical problems. Awards: Recipient of the Andre Aisenstadt Prize (2024), Bernard Friedman Memorial Prize (2016), and Vanier Canada Graduate Scholarship (for student Pardis Semnani). She is also a Canada CIFAR AI Chair and AMII Fellow. Advising & Mentorship: She has mentored numerous students, including PhD candidates (e.g., Pardis Semnani), MSc students, and undergraduates through programs like NSERC USRA and MITACS. Notable collaborations include work on causal inference, log-concave density estimation, and tensor networks. Labs/Teams: Active in interdisciplinary research groups at UBC and collaborates internationally through programs like the Institute for Mathematical and Statistical Innovation (IMSI).

Denka Nikolova Kutzarova-Fordمشاهده پروفایل

استاد

• Functional Analysis
• Geometry of Banach Spaces
• Approximation Theory
• +۱ مورد دیگر

Denka Nikolova Kutzarova-Ford is a Professor of Mathematics at the University of Illinois Urbana-Champaign. Her office is located in 306 Altgeld Hall at 1409 W. Green Street, Urbana, IL 61801, and she can be reached at (217) 265-6756. Her primary research interests focus on Functional Analysis , specifically the geometry of Banach spaces and approximation theory. Her scholarly fingerprint reveals strong specialization in Banach Space Mathematics (100%), with significant contributions to Greedy Algorithms (37%), Asymptotics (23%), and Quasi-greedy Basis theory (20%). Her work frequently explores concepts of Uniform Convexity, Unit Vector properties, and Thresholding Greedy Algorithms. Kutzarova-Ford's recent publications demonstrate consistent scholarly output in top mathematical journals, with particular emphasis on subsymmetric sequences, renorming problems, and structural properties of Banach spaces. Her collaborative research network includes prominent mathematicians such as Stephen Dilworth, Peter Casazza, and Pavlos Motakis, indicating strong connections within the functional analysis community. With 92 total research outputs documented, her scholarly impact spans theoretical developments in sequence spaces, geometric properties of Banach spaces, and applications to approximation theory. Her work maintains strong theoretical foundations while addressing specific structural questions in functional analysis.

Gleb Pogudinمشاهده پروفایل

استادیار

• Symbolic computation
• Algorithms for differential equations
• Algorithms for difference equations
• +۵ مورد دیگر

Gleb Pogudin is an Assistant Professor at École Polytechnique, Institute Polytechnique de Paris, where he is a member of the MAX team within the Laboratoire d'informatique. His research focuses on the intersection of symbolic computation, differential equations, and algebraic methods with applications across multiple scientific domains. Dr. Pogudin's primary research interests span several interconnected areas: Symbolic computation and computer algebra algorithms Theory and applications of differential and difference equations Nonlinear algebra and polynomial systems Structural identifiability of dynamical models Model reduction techniques for complex systems His recent publications (2024-2025) demonstrate a strong focus on developing theoretical foundations for differential elimination, structural identifiability analysis, and model reduction. These works span applications in systems biology, epidemiology, pharmacology, and optics. Notably, his research bridges pure mathematical theory with practical computational implementations, creating tools that address real-world scientific challenges. Dr. Pogudin has developed several significant software tools that implement his theoretical advances: StructuralIdentifiability.jl: A Julia package for assessing structural identifiability CLUE: Software for exact model reduction of ODE models via constrained lumping SIAN: Software for structural identifiability analysis of ODE models His GitHub repositories contain numerous implementations of algorithms from his papers on differential elimination and related topics, demonstrating his commitment to making theoretical advances practically accessible to researchers across disciplines.

João Pimentel Nunesمشاهده پروفایل

استاد

• Mathematical Physics
• Gauge Theories
• String Theory
• +۶ مورد دیگر

João Pimentel Nunes is a Full Professor at the Department of Mathematics, Higher Technical Institute (Technical University of Lisbon). His research focuses on Mathematical Physics, Gauge Theories, and String Theory, with a particular emphasis on geometric quantization and symplectic geometry. He is affiliated with the Center for Mathematical Analysis, Geometry and Dynamical Systems. Habilitation in Mathematics (IST, 2012) Ph.D. in Physics (Brandeis University, 1996) M.A. in Physics (Brandeis University, 1994) Degree in Technological Physics Engineering (IST, 1991) His recent publications explore topics like Mabuchi rays, Kahler metrics, and geometric quantization techniques. Notable collaborations include work with J. Mourão, T. Baier, and W. Kirwin. Research applications span quantum field theory, symplectic geometry, and complex structures in physics. He has taught courses on Mathematical Analysis II/III, Differential Geometry, and Topology at IST, emphasizing differential calculus in R^n, integration on manifolds, and classical vector calculus theorems like Stokes' and Green's. His teaching materials include detailed class summaries and problem sets.

• Mathematical Logic
• Nonstandard Analysis
• Fixed Point Theory
• +۷ مورد دیگر

Eyvind Martol Briseid is an Associate Professor at the Department of Primary and Vocational Teacher Education, Faculty of Teacher Education and International Studies, Oslo Metropolitan University (OsloMet). His work bridges mathematical logic and mathematics education, with a focus on task design, proof, and argumentation in teaching. He is actively involved in research groups on task design in mathematics teaching and mathematics didactics. His research interests span Mathematical Logic , Nonstandard Analysis , Fixed Point Theory , and Mathematics Education . He investigates foundational aspects of mathematics, such as the limits of provability and rates of convergence, while also applying logical insights to pedagogical contexts. His work on task design emphasizes cognitive engagement, reasoning, and the integration of reading in mathematics. The recent publications reflect a dual trajectory: one in pure mathematics, particularly in logic and analysis, and another in educational research, especially on teacher training, proof, and curriculum. Trends include extracting computational content from proofs, functional interpretations in nonstandard arithmetic, and situational interest in word problems among prospective teachers. His interdisciplinary approach connects deep theoretical work with practical classroom applications. Scientific Awards: No awards listed in the provided text. Briseid advises on teacher education and curriculum development, particularly in mathematics. He has contributed to projects on reading in mathematics and guided schools in cross-curricular teaching. He has not listed formal PhD or master’s students in the provided materials. His research has been supported through institutional affiliations and collaborative projects, such as those under "Ungdomstrinn i utvikling." He is a member of research groups focused on Task design in mathematics teaching and Mathematics didactics: proof and argumentation . These groups engage in seminars, dissemination, and collaborative research with institutions like NTNU and the University of Agder. His work involves both theoretical development and practical implementation in teacher training and school development.

Melanie Schmidtمشاهده پروفایل

استاد

• Geometric Data Analysis
• k-means clustering
• Combinatorial Optimization
• +۳ مورد دیگر

Professor Melanie Schmidt is a faculty member in the Department of Computer Science at Heinrich-Heine-Universität Düsseldorf, where she leads the Algorithms and Data Structures research group. Previously, she was affiliated with the University of Bonn's Institute of Computer Science, where she completed her PhD under Prof. Dr. Heiko Röglin and headed a subgroup on "clustering for big data" within his research group. Current Position: Professor at Heinrich-Heine-Universität Düsseldorf Previous Position: Researcher and lecturer at University of Bonn PhD Advisor: Prof. Dr. Heiko Röglin Her research focuses on geometric data analysis, particularly k-means clustering in data streams, combinatorial optimization, and approximation algorithms. Her work bridges theoretical computer science with practical applications in big data processing. She has made significant contributions to understanding the theoretical foundations of clustering algorithms while developing efficient implementations for real-world applications. Professor Schmidt's publication record shows a consistent evolution from theoretical analysis of k-means to practical implementations for big data environments. Her recent work explores fairness in clustering, privacy-preserving techniques, and efficient algorithms for high-dimensional data. She has published in top-tier conferences including SODA, ICALP, ESA, and ITCS, demonstrating both theoretical rigor and practical relevance. Best Student Paper Award at ESA 2012 (joint work with Martin Groß, Jan-Philipp W. Kappmeier, and Daniel Schmidt) She actively supervises numerous Master's and Bachelor's students, with current advisees including Lena Carta, Lukas Drexler, and Anna Arutyunova. Her research group includes members such as Anja Rey, Julian Wargalla, and Annika Hennes. She teaches advanced courses in algorithms and data structures, with a focus on randomized algorithms and efficient algorithm design for big data problems. Professor Schmidt leads the Algorithms and Data Structures research group at Heinrich-Heine-Universität Düsseldorf, which focuses on developing and analyzing efficient algorithms for fundamental computational problems, with particular emphasis on clustering, geometric data analysis, and big data applications.

Daniel Peralta-Salasمشاهده پروفایل

استاد

• Dynamical Systems
• Partial Differential Equations
• Differential Geometry
• +۱۳ مورد دیگر

Daniel Peralta-Salas is a CSIC Research Professor and Chair of the Group 'Differential Geometry and Geometric Mechanics' at the Institute of Mathematical Sciences (ICMAT) in Madrid, Spain. ICMAT is a joint research institute of the Spanish National Research Council (CSIC) and several Madrid universities. He is an active researcher with leadership roles in the ICMAT board and multiple international advisory boards. PhD in Mathematics, Universidad Complutense de Madrid, 2006 His research lies at the intersection of dynamical systems , partial differential equations , and differential geometry , with applications in fluid mechanics, plasma physics, quantum mechanics, and mathematical physics. He has developed a unifying theory to study geometrically complex structures in physical models. His work includes proving long-standing conjectures by Arnold, Lord Kelvin, and Michael Berry, and constructing fluid flows that simulate universal Turing machines, thereby demonstrating undecidable dynamics in fluid particle paths. The most recent articles highlight his ongoing focus on steady Euler flows , topological obstructions in magnetic fields , optimal eigenvalue problems , and Turing universality in fluid dynamics . His publications span geometric analysis, spectral theory, and mathematical physics, often combining deep topological insight with PDE techniques. He has developed novel methods such as global approximation and inverse localization, which have broad implications across mathematical physics. His scientific awards include: ERC Starting Grant (2014–2019) Barcelona Dynamical Systems Prize (2015) Plenary Speaker, European Congress of Mathematics (2016) The Floer Lectures (2019) MINT Distinguished Lectures, Tel Aviv (2020) Plenary Speaker at major national and international mathematical societies (2021–2024) EMS Distinguished Speaker (2023) Peralta-Salas advises and collaborates extensively with researchers including Alberto Enciso, Robert Cardona, Eva Miranda, and others. His work has been supported by prestigious grants such as the ERC Starting Grant. He has published over 100 papers in top journals including Annals of Mathematics , Acta Mathematica , PNAS , and Physical Review Letters . He is a member of editorial boards for journals such as Revista Matemática Iberoamericana and Journal of Dynamics and Differential Equations , and serves on the scientific advisory board of IMTECH (UPC-Barcelona Tech). He leads the research group on Differential Geometry and Geometric Mechanics at ICMAT, which focuses on geometric structures in dynamical systems and PDEs. The group explores topics like Beltrami fields, vortex dynamics, and topological aspects of eigenfunctions. Their work is highly collaborative and interdisciplinary, bridging pure mathematics and theoretical physics.

Hrushikesh Mhaskarمشاهده پروفایل

استاد پژوهشی

• Approximation Theory
• Computational Harmonic Analysis
• Machine Learning
• +۳ مورد دیگر

Hrushikesh Mhaskar is a Research Professor of Mathematics at Claremont Graduate University (CGU) since 2012, with a prior 32-year tenure at California State University, Los Angeles. He holds a PhD in Mathematics from Ohio State University, alongside an MS in Computer Science and an MSc from the Indian Institute of Technology, Mumbai. His research focuses on approximation theory, computational harmonic analysis, machine learning, and signal processing, with significant contributions to neural network theory and kernel-based methods. Mhaskar has authored over 150 papers, two books, and five edited volumes. His work includes pioneering studies on weighted polynomial approximation, Fourier domain conversions, and manifold learning. He currently serves on editorial boards for journals like Applied and Computational Harmonic Analysis and Journal of Approximation Theory , and collaborates with institutions like the University of California, Santa Barbara. Awards include five Alexander von Humboldt Fellowships and a John von Neumann Distinguished Professorship. His research is supported by the NSF and previously by the U.S. Air Force and intelligence agencies. Notable contributions include developing eignets for function approximation on manifolds and analyzing deep vs. shallow networks' approximation capabilities. His work bridges theoretical mathematics with practical applications in biomedical data analysis (e.g., blood glucose prediction) and signal processing.

Konrad Aguilarمشاهده پروفایل

استادیار

• Noncommutative Geometry
• C*-algebras
• Quantum Metric Spaces
• +۴ مورد دیگر

Konrad Aguilar is an Assistant Professor of Mathematics and Statistics at Pomona College, currently on sabbatical for the 2025-2026 academic year. He holds a PhD in Mathematics from the University of Denver (2015), an M.S. in Mathematics from the same institution, and a B.S. in Applied Mathematics from California State Polytechnic University, Pomona. His research focuses on noncommutative/quantum metric geometry, blending tools from functional analysis, operator algebras, and metric geometry to study convergence of operator algebras and finite-dimensional approximations. Research Interests include compact quantum metric spaces, Quantum Gromov-Hausdorff convergence, finite-dimensional approximations, and compact quantum groups. His work has led to contributions in areas such as AF algebras, Podleś spheres, and quantum ultrametrics. He has collaborated with leading mathematicians like Frédéric Latrémolière and Jens Kaad, producing influential papers in Studia Mathematica , Journal of Operator Theory , and Journal of Geometry and Physics . Awards include the MAA Project NExT fellowship (2021-2022) and the AMS-Simons Travel Grant (2019-2022). He actively mentors undergraduates in matrix analysis and finite-dimensional algebraic structures, bridging foundational theory with advanced research. His articles explore topics such as quantum metric spaces, spectral triples, and convergence in noncommutative geometry, reflecting a deep engagement with both pure mathematics and its applications in quantum frameworks. Recent work includes studies on Podleś sphere spectra and quantum metrics on tensor products of C*-algebras.

• Computational Mechanics
• Numerical Simulation
• Shape Optimization
• +۱۲ مورد دیگر

Stefanie Elgeti is Associate Professor and Private Lecturer at the Chair for Computational Analysis of Technical Systems (CATS), Faculty of Mechanical Engineering, RWTH Aachen University. She previously held a professorship in lightweight design at TU Vienna starting in 2019. Her research integrates computational mechanics with manufacturing process optimization, focusing on plastics extrusion, injection molding, and high-pressure die casting. Diploma in Mechanical Engineering, majoring in 'Manufacturing Techniques for Microsystems' PhD (2011): 'Free-Surface Flows in Shape Optimization of Extrusion Dies' Habilitation (2016): 'CAD-Conforming Finite Element Methods in Engineering Design' Her research centers on solving inverse problems in manufacturing through numerical simulation. She employs advanced techniques such as free-surface flow modeling, non-Newtonian material models, spline-based finite elements, and PDE-constrained shape optimization. Her group simulates entire process chains from filling to solidification and warpage prediction, enabling design optimization of cavities and cooling systems. The recent publications (2022–2024) reveal a strong trend toward integrating artificial intelligence—particularly physics-informed neural networks and Bayesian optimization—into traditional simulation workflows. There is increasing emphasis on warpage compensation, shape optimization of extrusion dies, and modeling of biomedical and environmental systems, showcasing a broadening scope from industrial manufacturing to interdisciplinary applications. She is actively involved in academic service, having served as vice-spokesperson of GAMM-Juniors (2013–2014) and currently co-chairing the ECCOMAS Young Investigator Group. While no formal awards are listed, her leadership roles and editorial contributions reflect significant recognition in the computational mechanics community. Prof. Elgeti advises students and leads multiple research initiatives at CATS, including work groups focused on production engineering, fluid-structure interaction, and INTERESST. Her team develops model hierarchies and digital twins for industrial processes, aiming to bridge simulation and real-world manufacturing through intelligent, adaptive systems.

Matheus Venturyne Xavier Ferreiraمشاهده پروفایل

استادیار

• AI
• Algorithmic Economics
• Security
• +۳ مورد دیگر

Matheus Venturyne Xavier Ferreira is an Assistant Professor of Computer Science at the University of Virginia. His research focuses on AI, Algorithmic Economics, Security, and Cryptography, with particular emphasis on creating secure and transparent platforms using artificial intelligence and theoretical computer science tools. He previously held a Postdoctoral Fellowship at Harvard University under David C. Parkes and completed his Ph.D. (2022) and M.A. (2018) at Princeton University under S. Matthew Weinberg. He earned his B.S. in Computer Engineering from the Federal University of Itajubá (2016). Education: Ph.D. in Computer Science, Princeton University (2022) M.A. in Computer Science, Princeton University (2018) B.Sc. in Computer Engineering, Federal University of Itajubá (2016) His research interests include multi-agent AI systems, blockchain technology, and the application of algorithmic economics to decentralized finance. He has presented widely on topics such as credible decentralized exchange design, blockchain transaction fee mechanisms, and cryptographic protocols for secure consensus. Notable venues include the ACM EC conference, SIAM Financial Mathematics, and the MIT Media Lab. Matheus has co-organized events like the Oxford-Harvard Conference on Decentralized Finance and Market Microstructure, and his work often bridges theoretical computer science with practical applications in security and decentralized systems. He is actively involved in academic leadership, including panel discussions on inclusion in engineering and workshops on blockchain innovation.

• Numerical Analysis
• High-Dimensional Problems
• Low-Rank Matrix and Tensor Techniques
• +۴ مورد دیگر

Bart Vandereycken is an Associate Professor in the Mathematics Department at the University of Geneva, specializing in numerical analysis and scientific computing. His research focuses on large-scale and high-dimensional problems solved using low-rank matrix and tensor techniques, with applications in numerical linear algebra, optimization, and nonlinear eigenvalue problems. He previously held positions as an instructor at Princeton University and postdoctoral researcher at EPF Lausanne and ETH Zurich, and earned his PhD from KU Leuven in 2010. His research interests include Riemannian optimization algorithms, multilevel preconditioning, and machine learning applications. He serves as an associate editor for SIAM Journal on Matrix Analysis and Applications and Linear Algebra and its Applications . Bart organizes the Numerical Analysis seminar with colleagues, and advises students interested in numerical analysis or numerical linear algebra. Recent work emphasizes convexity structures in matrix decompositions, robust preconditioning techniques, and scalable low-rank algorithms for high-dimensional PDEs. His 2024–2025 publications explore advancements in Riemannian optimization schemes, subspace iteration methods, and distributed computing applications of matrix decompositions. Key themes include improving convergence guarantees and developing geodesic-based optimization frameworks for challenging numerical problems.

Robert Rahmمشاهده پروفایل

دانشیار

• Functional Analysis
• Harmonic Analysis
• Operator Theory
• +۳ مورد دیگر

Robert Rahm is an Instructional Associate Professor of Mathematics at Texas A&M University, serving as a Faculty Advisor for APMT. His research focuses on functional analysis, harmonic analysis, and operator theory with applications to mathematical physics and partial differential equations. He holds an office at BLOC 332C and can be contacted via math-advising@tamu.edu. Rahm's work emphasizes weighted norm inequalities, sparse operators, and spectral theory. His recent studies explore connections between perturbed potentials and the Riemann zeta function, as well as inverse problems in Schrödinger equations. He has contributed to advancements in Bergman space projections, entropy conditions for operators, and wavelet theory applications. No scientific awards are explicitly listed in the provided materials. His advising role suggests active engagement with student mentorship, though specific advisee names are not documented here. Research collaborations likely involve advanced mathematical modeling and analytical techniques in both pure and applied contexts.

Andrew P. Murrayمشاهده پروفایل

استاد

• Machine Design
• Kinematic Synthesis
• Robotics
• +۷ مورد دیگر

Andrew P. Murray is a Professor and Director of Mechanical Engineering Graduate Programs in the Department of Mechanical and Aerospace Engineering at the University of Dayton's School of Engineering. He has been a full-time faculty member since 1997 and leads the Design of Innovative Machines Lab (DIMLab), focusing on machine design and kinematic synthesis theory. Ph.D. in Engineering, University of California, Irvine (1996) M.S. in Engineering, University of California, Irvine (1993) B.S. in Mechanical Engineering, Rose-Hulman Institute of Technology (1989) His research spans machine design, kinematics, robotics, and biomechanics , with applications in continuum robots, assistive tricycles for FES-stimulated riders, variable-geometry dies, and strain-energy mechanisms. He employs advanced geometric and computational methods to solve complex motion synthesis and mechanism design problems. His collaborative work includes long-standing partnerships with Dave Myszka and researchers at the Laboratoire d’Informatique, de Robotique et de Microélectronique in Montpellier, France. The recent trend in his publications (2016–2024) reflects a strong focus on kinematic synthesis of planar and spatial mechanisms , particularly four-bar and six-bar linkages, with increasing emphasis on biologically inspired and medical applications such as FES cycling and continuum robotics. His work combines theoretical rigor with experimental validation, often published in top-tier journals like the ASME Journal of Mechanisms and Robotics and IEEE Robotics and Automation Letters . His scientific honors include: ASME Fellow (2014) A.T. Yang Memorial Award in Kinematics (2023) University of Dayton Alumni Award for Faculty Teaching (2013) Best Paper Award, ASME Mechanisms and Robotics Conference (2006) Multiple teaching excellence awards from student organizations Dr. Murray has advised numerous students and mentored research in the DIMLab. He has secured research grants supporting innovation in mechanical design and has served in key academic roles, including General Program Chair for the ASME International Design Engineering Technical Conferences (2010) and Associate Editor for the ASME Journal of Mechanisms and Robotics since 2009. He teaches graduate and undergraduate courses such as Theory of Machines, Kinematics of Mechanisms and Robots, and Computational Methods for Design. He directs the Design of Innovative Machines Lab (DIMLab) , which fosters interdisciplinary research in mechanism design, combining theoretical development with practical prototyping and application in assistive technology, aerospace, and manufacturing.

Ingrid Daubechiesمشاهده پروفایل

استاد

• Applied Mathematics
• Signal Processing
• Image Compression
• +۳ مورد دیگر

Ingrid Daubechies is a prominent mathematician and physicist known for her foundational work in wavelet theory. Born in Belgium, she earned her B.S. and Ph.D. from the Free University of Brussels. She transitioned from a physics background to applied mathematics, becoming a leading authority on wavelets while at AT&T Bell Laboratories. In 1993, she became the first woman tenured professor of mathematics at Princeton University, later joining Duke University as the James B. Duke Professor of Mathematics. Her research spans signal processing, image compression, and interdisciplinary applications in biology and art restoration. Daubechies' academic journey includes roles at Bell Labs, Princeton, and Duke, alongside extensive collaborations in computational biology and mathematics education. She has pioneered wavelet-based algorithms for data compression (JPEG 2000), developed metrics for biological morphology analysis, and contributed to art restoration techniques using mathematical imaging. Her leadership roles include presidency of the International Mathematical Union (2011–2014) and advocacy for women in mathematics. Recognized with prestigious awards like the National Academy of Sciences Award, John von Neumann Lecture Prize, and Nemmers Prize, her work bridges pure mathematics and applied sciences. She has supervised numerous research projects and authored influential texts, including Ten Lectures on Wavelets . Her legacy includes advancing computational tools for diverse fields, from medical imaging to evolutionary biology.