جست‌وجوی استاد و پژوهشگر

• Computational Mathematics
• Numerical Analysis
• Computational Fluid Dynamics
• +۴ مورد دیگر

Dr. Arash Ghasemi serves as an Adjunct Faculty member in the Department of Engineering Management and Technology within the College of Engineering and Computer Science at the University of Tennessee at Chattanooga (UTC). His academic role bridges computational mathematics and engineering applications through teaching and research activities focused on advanced numerical methods. His research spans computational mathematics, numerical analysis, and scientific computing with emphasis on high-order spectral/finite element methods, mesh generation, and preconditioning techniques. Key application areas include computational fluid dynamics (particularly compressible flow), structural engineering (crane mechanisms, non-destructive testing), climate modeling for infrastructure, and electromagnetic simulations. His methodological innovations consistently target improved accuracy, efficiency, and stability in engineering computations across civil, mechanical, and aerospace domains. Dr. Ghasemi's publication trajectory from 2006 to 2024 reveals sustained contributions to computational methods. Early work established foundations in wireless sensor networks for infrastructure and high-order ODE solvers, while his 2016 'spectral hull' breakthroughs revolutionized degree-of-freedom reduction in conservation laws. Recent publications (2021-2024) demonstrate expanding applications in polynomial approximation, data interpolation, and climate modeling, maintaining his signature focus on mathematical rigor coupled with practical engineering impact across fluid dynamics, structural mechanics, and environmental systems.

• Theoretical Physics
• Quantum Physics
• High Energy Physics
• +۳ مورد دیگر

Rafael Nepomechie is a Professor in the Department of Physics, College of Arts and Sciences, University of Miami. His research focuses on theoretical physics, quantum information science, and integrable systems, with key contributions to quantum spin chains, Bethe ansatz methods, and quantum entanglement analysis. University of Miami, College of Arts and Sciences, Department of Physics Research spans quantum computing, orthogonal polynomials, and boundary integrability Collaborates with Luc Vinet, David Raveh, and Nicolas Crampé Research Interests: His work addresses exact solutions of integrable models, quantum state preparation algorithms, and entanglement entropy in inhomogeneous systems. Recent projects include deterministic Dicke state generation, VQE-based Bethe root estimation, and symmetry analysis of spin chains. Publication Trends: Articles emphasize quantum information applications (e.g., Dicke states, quantum circuits), mathematical physics (Bethe ansatz, quantum groups), and entanglement in low-dimensional systems. Subfields include quantum computing, statistical mechanics, and algebraic combinatorics. Advising & Collaboration: No formal students listed, but collaborates closely with researchers like David Raveh on quantum computing implementations and Luc Vinet on orthogonal polynomial-based models. Labs & Teams: Engaged in the University of Miami's Physics Department research groups, particularly those focused on quantum field theory and quantum integrability.

Ahmet Nihat Simsekمشاهده پروفایل

پژوهشگر

• Neuroinformatics
• Big Data Management
• Software Development
• +۳ مورد دیگر

Dr. Ahmet Nihat Simsek is a Researcher at the Structural and Functional Organization of the Brain (INM-1) department within the Institute of Neuroscience and Medicine at Forschungszentrum Jülich GmbH . His work focuses on Neuroinformatics , Big Data Management , and Software Development for scientific applications. Specializes in computational tools for neuroscientific research Active in Big Data Analytics Working Group Develops software infrastructure like siibra-python Location: Building 15.9 / Room 3027, Jülich, Germany His research combines biophysical modeling with data science , spanning from neuroimaging informatics to molecular adhesion mechanics . Recent publications demonstrate expertise in: Quantifying bacterial adhesion dynamics under shear stress Developing advanced traction force microscopy algorithms Mathematical modeling of fractal systems Specific work includes: Investigating Type IV pili in Pseudomonas aeruginosa Creating self-stabilizing mechanisms for biomolecular adhesions Advancing Bayesian parameter selection in microscopy Studying substrate-rigidity effects on bacterial migration Mathematical analysis of Jacobi matrices for fractal measures

Rigoberto Florezمشاهده پروفایل

استاد

• Combinatorics
• Graph Theory
• Matroid Theory
• +۳ مورد دیگر

Dr. Rigoberto Florez is a Professor of Mathematics at The Citadel, The Military College of South Carolina, within the Department of Mathematical Sciences, part of the Swain Family School of Science and Mathematics. He holds a Ph.D. in Mathematics from Binghamton University (SUNY), an M.S. from Universidad Nacional de Colombia, and a B.A. from Universidad de Antioquia in Colombia. His research focuses on combinatorics, particularly graphs, algebraically representable matroids, harmonic matroids, biased graphs, enumerative combinatorics, and elementary number theory. He actively involves undergraduate and graduate students in research projects, which have led to conference presentations and awards. He co-founded the Carolina Math Seminar to foster collaborative research and education. Dr. Florez’s recent publications explore topics like Fibonacci identities, Dyck paths, matroid projectivity, and asymmetric graph families. His work bridges theoretical mathematics with pedagogical innovation, emphasizing student-driven problem-solving and visual proofs through experiments like the Möbius strip. He serves in roles that enhance academic excellence and leadership, aligning with The Citadel’s mission. His research and teaching highlight interdisciplinary connections between combinatorics, algebra, and geometry.

Andreas Kleefeldمشاهده پروفایل

استاد

• Boundary Integral Equations
• Non-linear Eigenvalue Problems
• Acoustic Scattering
• +۴ مورد دیگر

Prof. Dr. Andreas Kleefeld is a Professor at the University of Applied Sciences Aachen (Campus Jülich) in the Faculty of Medical Engineering and Technomathematics, where he teaches courses such as Analysis and Stochastics. He also leads the Algorithm, Tools and Methods Lab (Numerical and Statistical Methods) at the Jülich Supercomputing Centre (JSC), part of the Institute for Advanced Simulation (IAS) at Forschungszentrum Jülich. His research focuses on boundary integral equations, non-linear eigenvalue problems, acoustic and electromagnetic scattering, and inverse problems. He is a Principal Investigator in the Helmholtz Information Program 1, Topic 1. Research Interests: Kleefeld’s work bridges theoretical and applied mathematics, with emphasis on numerical methods for partial differential equations, scattering theory, and resilience modeling in economic systems. His contributions include advancements in direct sampling methods for inverse scattering, spectral Galerkin schemes for stochastic PDEs, and applications of mathematical morphology in color image processing. Recent Publications: His 2025 works explore economic resilience in global supply networks and fourth-order numerical schemes for reaction-diffusion systems. He has also published extensively on inverse scattering techniques for anisotropic materials and boundary value problems. Labs & Teams: As Group Leader at JSC, he oversees the development of numerical algorithms and statistical methods applied to high-performance computing challenges. His lab collaborates on projects within the Helmholtz Association, focusing on information science and simulation infrastructure.

Lixin Shenمشاهده پروفایل

استاد

• Applied and Computational Harmonic Analysis
• Optimization
• Imaging Science
• +۴ مورد دیگر

Lixin Shen is a Professor in the Department of Mathematics at Syracuse University, part of the College of Arts and Sciences. His research focuses on applied and computational harmonic analysis, optimization, imaging science, and information processing. Shen holds a Ph.D. in Mathematics from Sun Yat-Sen University (1996), an M.Sc. from Peking University (1990), and a B.Sc. from Peking University (1987). Education: Ph.D., Mathematics, Sun Yat-Sen University, 1996 M.Sc., Mathematics, Peking University, 1990 B.Sc., Mathematics, Peking University, 1987 Research Interests: Shen’s work emphasizes sparse optimization, image and signal processing, computational mathematics, and their applications. Notable areas include wavelet analysis, tensor decomposition, and robust algorithms for noise removal and high-resolution imaging. Grants & Awards: NSF Grant: Collaborative Research: Sparse Machine Learning and Sparse Optimization (2022–2025) Excellence in Graduate Education Faculty Recognition Award, Syracuse University (2024) Air Force Summer Faculty Fellowship (2024, 2023, 2021, 2020) Service & Leadership: Chair of Graduate Committee, Mathematics Department (2024–2025) Editor, Frontiers in Applied Mathematics and Statistics Organizer, SIAM Conferences on Optimization and Imaging (2023–2020) Teaching: Recent courses include Numerical Linear Algebra, Numerical Methods with Programming, Sparse Optimization, and Partial Differential Equations.

Nima Anariمشاهده پروفایل

استادیار

• Sampling Algorithms
• Markov Chains
• Parallel Algorithms
• +۲ مورد دیگر

Nima Anari is an Assistant Professor of Computer Science at Stanford University and member of the Theory Group. He holds a Ph.D. in Computer Science from UC Berkeley (2015) and dual B.Sc. degrees in Computer Engineering and Mathematics from Sharif University of Technology (2010). His research focuses on theoretical computer science with emphasis on sampling algorithms, Markov chains, and combinatorial optimization. Research explores parallel sampling methods, log-concave polynomials, high-dimensional expanders, and applications in machine learning. Recent publications develop efficient algorithms for determinantal point processes, diffusion models, and optimization on discrete domains. Honors include the Michael and Sheila Held Prize (2025), Frontiers of Science Award (2023), Sloan Research Fellowship (2021), NSF CAREER Award (2021), and Best Paper Award at STOC 2019. He mentors 13 graduate and undergraduate students in theoretical computer science.

Zheng Zhangمشاهده پروفایل

پژوهشگر

• Radio Cosmology
• Astrophysics
• Astronomical Instrumentation
• +۵ مورد دیگر

Zheng Zhang is a research associate in Radio Cosmology at the Astronomy and Astrophysics Theory Group. His expertise spans cosmology, astrophysics, and astronomical instrumentation. Primary affiliation: Astronomy and Astrophysics Theory Group Key research areas: 21cm Cosmology, Cosmic Reionization, Antenna Design, Power Spectra Analysis Zheng Zhang's research focuses on advancing understanding of the early universe through radio cosmology techniques. His work involves: Developing novel antenna systems for 21cm global signal detection Modeling spinning dust radiation Improving calibration methods for radio arrays Statistical analysis of cosmic structures His publications highlight technical innovations in radio astronomy instrumentation and fundamental studies of cosmic reionization. Key projects include: RHINO antenna development Spinning dust modeling (SpyDust) Murchison Widefield Array analysis 21cm power spectra constraints

Ying Zhangمشاهده پروفایل

استاد

• Artificial Intelligence
• Wireless Sensor Networks
• Internet of Things (IoT)
• +۷ مورد دیگر

Dr. Ying Zhang is a Professor in the School of Electrical and Computer Engineering at Georgia Institute of Technology and serves as Senior Associate Chair. She directs the Sensors and Intelligent Systems Laboratory. Her research focuses on interdisciplinary systems engineering, including wireless sensor networks, IoT, biomedical engineering, structural health monitoring, and intelligent diagnostic systems. She holds a B.S. from Tongji University and advanced degrees from the University of Illinois at Chicago, University of Massachusetts Lowell, and Ph.D. in Systems Engineering from UC Berkeley. Education: B.S., Materials Science and Engineering, Tongji University M.S., Materials Engineering, University of Illinois at Chicago M.S., Electrical Engineering, University of Massachusetts Lowell Ph.D., Systems Engineering, University of California, Berkeley Research Interests: Her work spans AI-enabled systems, MEMS, smart materials, thermal management of bioimplants, and energy-efficient wireless sensor networks. Recent projects include adaptive thermal management for implantable devices and human-in-the-loop control for HVAC systems. Awards: NSF CAREER Award (2013) Lockheed Martin Dean's Excellence in Teaching Award (2012) Hesburgh Award Teaching Fellow (2017) TechConnect Innovation Awards (2017) Grants & Labs: Leads projects funded by NSF, including adaptive thermal management for bioimplants and supercapacitor-based power management. Directs the Sensors and Intelligent Systems Lab, which collaborates on structural health monitoring, biomedical sensors, and energy harvesting.

Nick Vannieuwenhovenمشاهده پروفایل

استادیار

• Tensor decompositions
• Numerical analysis
• Riemannian optimization methods
• +۲ مورد دیگر

Nick Vannieuwenhoven is an Assistant Professor at KU Leuven, affiliated with the Department of Computer Science and the NUMA Division. He serves as the Exchange Coordinator for the Master in Mathematical Engineering and is an Associate Editor for The Electronic Journal of Linear Algebra and SIAM Journal on Applied Algebra and Geometry . His research focuses on tensor decompositions, numerical analysis, Riemannian optimization, and applications in data science. He obtained his PhD in 2015 under Professors Karl Meerbergen and Raf Vandebril, funded by the FWO (Research Foundation Flanders). His postdoctoral research (2015–2021) was also supported by FWO fellowships. His research group investigates tensor decompositions, multilinear algebra, and numerical techniques for data science, with a focus on condition number analysis and Riemannian optimization. Collaborators include experts like Carlos Beltrán, Paul Breiding, and Simon Telen. Current students include Jana Jovcheva, Bram Leys, and David Thorsteinsson, working on manifold-valued function approximation, group-invariant networks, and data-based engineering. Key awards include FWO fellowships for his PhD and postdoctoral studies. Grants include support for postdoctoral researchers via MSCA and FWO schemes. Notable projects involve Tucker compression libraries (ATC) and geometric analysis of tensor networks. His work bridges algebraic geometry, numerical analysis, and machine learning, emphasizing stability and computational efficiency.

George Labahnمشاهده پروفایل

استاد

• Computer Algebra
• Symbolic-Numeric Computation
• Computational Finance
• +۲ مورد دیگر

George Labahn is Professor and Director of the Symbolic Computation Group at the University of Waterloo's David R. Cheriton School of Computer Science. His research spans computer algebra, computational finance, and pen-based mathematical interfaces. Key contributions include fundamental algorithms for matrix normal forms (Hermite/Smith forms), symbolic-numeric polynomial computation, and development of the MathBrush system for handwritten mathematics recognition. Research focuses on efficient algorithms for polynomial matrix arithmetic, rational approximation, and solving differential equations with elliptic coefficients. Computational finance work develops numerical methods for option pricing under jump diffusion models. Current projects include fast Hermite form computation and rank-sensitive matrix algorithms. Serves as associate editor for Journal of Symbolic Computation (JSC) and previously for ACM Transactions on Mathematical Software (TOMS). Authored core Maple programming guides and implemented key packages for symbolic integration, differential equations, and graphics in Maple.

Leili Rafiee Sevyeriمشاهده پروفایل

استادیار

• Hybrid Symbolic-Numeric Computation
• Scientific Computing
• Computer Algebra
• +۳ مورد دیگر

Leili Rafiee Sevyeri is an Assistant Professor in the Teaching Stream at the University of Waterloo, affiliated with the David R. Cheriton School of Computer Science. Her research focuses on hybrid symbolic-numeric computation, scientific computing, and computer algebra. She holds a PhD and MSc in Applied Mathematics from Western University (2020 and 2016, respectively). Her teaching spans theoretical computer science, numerical methods in finance/business, and introductory computer science courses. She is based in location MC 6431 on campus and can be reached at leili.rafiee.sevyeri@uwaterloo.ca . Research interests include developing computational methods at the intersection of symbolic and numeric techniques, with applications in linear algebra, polynomial systems, and matrix theory. Recent work emphasizes interpolation methods, Bohemian matrix analysis, and approximate GCD algorithms. Her publications (e.g., Google Scholar ) reflect a focus on advancing computational tools for scientific and mathematical problems. No scientific awards are explicitly listed in the provided information. Advising and grant details are not mentioned, but her academic webpage provides further details on ongoing and past projects. She is actively involved in computational mathematics education and research within the university's Faculty of Mathematics.

Stephen Wattمشاهده پروفایل

استاد

• Symbolic Computation
• Computer Algebra Systems
• Programming Languages
• +۵ مورد دیگر

Stephen Watt is a Professor at the University of Waterloo, affiliated with the Department of Computer Science within the Faculty of Mathematics. He holds a PhD in Computer Science (1986), MMath in Applied Mathematics (1981), and BSc in Honors Mathematics and Physics (1979). His research focuses on enabling computers to handle mathematics intelligently, with key areas including programming languages for mathematical software, symbolic computation algorithms, computer algebra system interoperability, mathematical knowledge management, and STEM education technology. His work spans theoretical and applied domains, such as developing algorithms for block matrices and handwriting recognition systems like the Legendre-Sobolev approach. He has contributed to initiatives like the International Mathematical Knowledge Trust (IMKT) and collaborated on projects like InkML for mathematical collaboration. Recent publications explore generative AI in STEM assessments, software portability, and GPU-optimized arithmetic operations. Dr. Watt’s research also addresses historical perspectives on symbolic computation and its evolution, alongside modern challenges in mathematical collaboration tools and digital ink compression. His contributions span conferences such as ISSAC, CICM, and SYNASC, reflecting interdisciplinary engagement with both theoretical and applied computer science.

Burak Hatinogluمشاهده پروفایل

استادیار مهمان

• Spectral Theory
• Mathematical Physics
• Complex Function Theory
• +۳ مورد دیگر

Burak Hatinoglu is a Visiting Assistant Professor in the Department of Mathematics at Michigan State University (MSU). Previously, he held similar positions at Georgia Institute of Technology (with Svetlana Jitomirskaya) and the University of California, Santa Cruz. His research focuses on spectral theory, complex function theory, approximation theory, and mathematical physics. Hatinoglu earned his Ph.D. from Texas A&M University in 2020 under Alexei Poltoratski, following M.S. and B.S. degrees in Mathematics from Bilkent University. His academic accolades include the MSU Postdoctoral Excellence in Mentoring and Teaching Award (2025), Postdoctoral Award for Excellence in Teaching (2025), and Outstanding Teaching Assistant Award (2019). He has also received scholarships from TÜBİTAK and Texas A&M University’s Dean’s office. Research interests span spectral properties of operators (e.g., Jacobi, Schrödinger), inverse spectral problems, and applications of complex analysis to approximation theory. Recent work explores uniqueness theorems for meromorphic functions, spectral bounds for periodic systems, and analytic methods for inverse problems. His contributions include studies on elastic beam lattices, discrete Schrödinger operators, and Widom factors in approximation theory. Current activities emphasize bridging spectral theory with mathematical physics, with a focus on operator-based models.

Jeffrey C. Lagariasمشاهده پروفایل

استاد

• Number Theory
• Computational Complexity Theory
• Cryptography
• +۶ مورد دیگر

Jeffrey C. Lagarias is the Harold Mead Stark Distinguished University Professor of Mathematics at the University of Michigan. His research spans Number Theory, Computational Complexity, Cryptography, and Discrete Geometry. He advises numerous graduate students, including those focusing on topics like Tetrahedral Packings, Polynomial Equations, and Zeta Functions. His work includes groundbreaking contributions to packing problems, dynamical systems, and the 3x+1 conjecture. Notable publications include 'Mysteries in Packing Regular Tetrahedra' (2012) and 'Li Coefficients for Automorphic L-Functions' (2007). He collaborates extensively with researchers like Yang Wang and Chuanming Zong. No awards are explicitly listed, though his academic stature reflects significant recognition. Lagarias teaches advanced mathematics courses and maintains an active research group. His lab focuses on interdisciplinary topics blending pure mathematics with applications in cryptography and optimization.