جست‌وجوی استاد و پژوهشگر

Daniele Cavigliaمشاهده پروفایل

استاد

• Ultra-Low Power Electronics
• Biomedical Circuit Design
• Ultrasound Imaging Systems
• +۳ مورد دیگر

Daniele Caviglia serves as Full Professor in the Department of Naval, Electrical, Electronic and Telecommunications Engineering at the University of Genoa, Italy. He holds the position of Coordinator for the Master's Degree in Electronic Engineering and teaches advanced courses including Radio Frequency Electronics, Electronic Devices, and Electronic Systems for Telecommunication across both Bachelor's and Master's programs. His research program focuses on ultra-low-power electronics for biomedical and environmental applications, with three primary thrusts: (1) nW-scale circuit design for bio-signal processing and neural interfaces, (2) advanced beamforming techniques in medical ultrasound imaging, and (3) energy harvesting systems for autonomous environmental monitoring. His group has pioneered inverter-based OTAs achieving sub-10nW operation and developed novel genetic algorithm-optimized apodization methods for plane-wave ultrasound imaging. Recent publications (2024-2025) reveal strong thematic continuity with increasing emphasis on practical implementations - particularly sea wave energy harvesters for environmental buoys and satellite microwave link systems for rainfall monitoring in urban settings. The work consistently bridges fundamental circuit innovation with real-world medical and environmental applications, maintaining high impact in IEEE and Elsevier journals.

Maxim L. Yattselevمشاهده پروفایل

استاد

• Meromorphic Approximation
• Orthogonal Polynomials
• Integrable Systems
• +۳ مورد دیگر

Maxim L. Yattselev is a Professor in the Department of Mathematical Sciences at Indiana University–Purdue University Indianapolis (IUPUI) . He has held prior positions as a Visiting Assistant Professor at the University of Oregon and a Visiting Scholar at Vanderbilt University. Education: Ph.D. and M.S. in Mathematics from Vanderbilt University (2007, 2004), M.S. and B.S. from Dnepropetrovsk National University (2001, 2000). Research Interests focus on meromorphic approximation, orthogonal polynomials, integrable systems, random polynomials, and spectral theory. He explores connections between these areas and applications to Painlevé equations, random matrix theory, and convergence of rational approximants. Publications highlight advancements in strong asymptotics for Angelesco systems, spectral theory of Jacobi matrices on trees, universality in random polynomial roots, and convergence properties of Padé and Hermite–Padé approximants. His work often employs Riemann–Hilbert techniques and potential theory. Advising: Mentored Ph.D. students Hanan Aljubran and Ahmad Barhoumi on random polynomials and multiple orthogonal polynomials. Teaching includes advanced courses in complex analysis, real analysis, orthogonal polynomials, and differential equations at IUPUI, University of Oregon, and Vanderbilt University.

Tucker Carringtonمشاهده پروفایل

استاد

• Quantum Chemistry
• Spectroscopy
• Computational Molecular Science
• +۴ مورد دیگر

Tucker Carrington is a Full Professor in the Department of Chemistry at Queen's University, Kingston. He holds a cross-appointment in the Department of Physics, Engineering Physics and Astronomy within the Faculty of Arts and Science. His research focuses on developing computational methods for studying molecular vibrations and chemical reactions using quantum mechanics. Carrington earned his BSc from the University of Toronto (1981) and PhD from the University of California, Berkeley (1985). His research interests include quantum dynamics, potential energy surface construction, and applications of neural networks in chemistry. He leads projects on anharmonic vibrational spectra, van der Waals systems, and high-dimensional ab initio calculations. Current supervision includes postdoctoral researcher Robert Wodraszka. Key contributions involve iterative eigensolver methods, Smolyak interpolation, and rectangular collocation algorithms. His work bridges computational chemistry with experimental spectroscopy, addressing challenges in molecular energy levels and reaction dynamics. Affiliated with the Canada Research Chair in Computational Quantum Dynamics, he collaborates internationally and publishes in top journals like Journal of Chemical Physics and Physical Review . His research impacts drug design, energy storage, and climate science through advanced computational techniques.

Linus Wunderlichمشاهده پروفایل

مدرس

• Mathematical Finance
• Machine Learning
• Deep Neural Networks
• +۶ مورد دیگر

Dr. Linus Wunderlich is a Lecturer in Financial Mathematics at Queen Mary University of London (QMUL), affiliated with the School of Mathematical Sciences. He holds a PhD in Hybrid Finite Element Methods for Non-linear and Non-smooth Problems in Solid Mechanics from Technical University Munich. His research focuses on integrating machine learning with numerical techniques in mathematical finance, including solving high-dimensional PDEs via neural networks and accelerating risk computations through Chebyshev interpolation. He also explores the theoretical foundations of AI and its applications in finance, engineering, and education. Linus teaches courses such as Mathematical Tools for Asset Management and Masterclass in Business Analytics , emphasizing practical machine learning skills for business contexts. His work spans computational finance, numerical analysis, and interdisciplinary projects including Olympic medal prediction models and violin bridge modeling from CT scans. Research grants include a £10,800 project with Numerical Algorithms Group Ltd on efficient volatility interpolation (2018–2019). He collaborates with institutions like LSE, Birkbeck, and University College Dublin, presenting at conferences such as SIAM Financial Mathematics and the German Probability & Statistics Days. His activities include developing interactive art projects and teaching machine learning courses during the pandemic. No scientific awards are explicitly mentioned in the provided texts. His research interests and publications reflect a blend of mathematical rigor and applied innovation, bridging finance, engineering, and education.

Anthony P. Austinمشاهده پروفایل

استادیار

• Numerical Linear Algebra
• High-Performance Computing
• Approximation Theory
• +۳ مورد دیگر

Anthony P. Austin is an Assistant Professor in the Department of Applied Mathematics at the Naval Postgraduate School . His research focuses on numerical linear algebra, high-performance computing, approximation theory, and GPU-accelerated algorithms. He has held prior positions at Virginia Tech and Argonne National Laboratory as a J. H. Wilkinson Fellow. His work includes contributions to Chebfun, a software system for numerical computation. Education: D.Phil. in Mathematics , University of Oxford Mathematical Institute (2016) Ph.D. studies with active development in the Chebfun project Research Interests : Numerical methods for large-scale problems, high-order methods for PDEs, and computational techniques leveraging GPUs. His work emphasizes algorithmic innovation and stability in numerical simulations. Recent Contributions : Publications include advancements in spectral factorization, adversarial signal perturbations, and parallel algorithms for partial spectral computations. His seminar co-organization highlights collaboration in early-career faculty research initiatives. Professional Activities : Co-organizer of the NPS Junior Faculty Research Seminar (AY2022/Q3), focusing on fostering interdisciplinary collaboration among early-career faculty.

Melina Freitagمشاهده پروفایل

استاد

• Numerical Linear Algebra
• Data Assimilation
• Inverse Problems
• +۶ مورد دیگر

Melina Freitag is Professor for Data Assimilation at the Institute for Mathematics, University of Potsdam. Her research spans numerical linear algebra, inverse problems, and model order reduction, with applications in geophysics, image processing, and machine learning. She leads the Data Assimilation Group and contributes to the SFB 1294 Collaborative Research Center. Education : Prof. Freitag earned her Diplom in Mathematics at TU Chemnitz (2004) and PhD in Mathematical Sciences from University of Bath (2007). Research Themes : Her work focuses on Krylov subspace methods, low-rank approximations, and preconditioning for large-scale systems. She bridges classical numerical analysis with modern data assimilation, addressing challenges in: Bayesian inverse problems with unstable systems Optimized spectral sampling in X-ray imaging Physics-informed neural networks for Navier-Stokes inversion Parameter-dependent eigenvalue analysis Collaborations & Grants : She collaborates with institutions like KTH Stockholm and Arizona State University. Her group secures funding through SFB 1294 and participates in INI Cambridge networks. Leadership : Co-Chair of GAMM Activity Group on Applied Numerical Linear Algebra SIAM Activity Group on Linear Algebra Chair (2022) Teaching : Delivers courses on numerical optimization, matrix methods in data science, and inverse problems, integrating computational theory with real-world applications.

Mykhailo Kuianمشاهده پروفایل

مدرس

• Applied Mathematics
• Numerical Analysis
• Linear Algebra
• +۳ مورد دیگر

Dr. Mykhailo Kuian is a Full-time Lecturer in the Department of Mathematics, Applied Mathematics, and Statistics at Case Western Reserve University , located in Cleveland, Ohio. He holds a Ph.D. (institution unspecified in provided data). His work bridges theoretical mathematics with practical engineering and computational applications. Education: Ph.D. (details not specified). Research Interests: Dr. Kuian’s research focuses on numerical analysis, applied mathematics, and optimization. Key areas include solving linear ill-posed operator equations, developing efficient matrix factorization algorithms for Vandermonde matrices, and enhancing multiparameter measurement systems. His work extends to electromagnetic design, incorporating uncertainty quantification and shape control in reflector systems. Recent Research Trends: His publications (2018–2025) emphasize numerical methods for inverse problems, matrix conditioning optimization, and interdisciplinary applications in optics and engineering. Notable contributions include fast computational techniques for Vandermonde-like matrices and accuracy improvements in polarized light microscopy systems. Awards/Grants: No awards or grants are mentioned in the provided information. Advising & Labs: No advisees, grants, or affiliated laboratories/teams are listed in the available data.

Narek Hovsepyanمشاهده پروفایل

استادیار

• Applied Mathematics
• Computational Mathematics
• Mathematical Physics
• +۵ مورد دیگر

Narek Hovsepyan is a Hill Assistant Professor in the Department of Mathematics at Rutgers University within the School of Arts and Sciences. His research spans applied mathematics, mathematical physics, and computational analysis, focusing on wave propagation, inverse problems, and analytic continuation techniques. Education: PhD from Temple University (advisor: Yury Grabovsky), MSc from University of Bonn (advisor: Juan J. L. Velázquez), BSc from Yerevan State University (advisor: Hakop Hakopian) Research: Scattering theory, nonlinear optical phenomena, quasiconvex analysis, and spectral methods Activities: Co-organizer of the Applied and Computational Mathematics Seminar since 2023, visiting researcher at Institut Fourier (Université Grenoble Alpes) in 2024 Contact: nh507@rutgers.edu, narek.hovsepyan@rutgers.edu

Walter W. Zhangمشاهده پروفایل

استادیار

• AI
• Personalization
• Targeting
• +۵ مورد دیگر

Walter W. Zhang is an Assistant Professor of Marketing at the Wharton School , University of Pennsylvania . His research bridges economic models and statistical methods to study marketing decision-making. Education : PhD and MBA from University of Chicago Booth School of Business, dual bachelor's in Physics and Economics from University of Chicago Research Focus : AI-driven personalization, causal inference, optimal transport in marketing His work addresses two key areas: (1) optimizing targeting strategies under regulatory constraints through Policy DNN and TEP methods, and (2) modeling consumer responses to personalization via hierarchical expenditure representations. Recent empirical studies leverage 6.5 million field data points and catalog mailing experiments . Key methodological contributions : CausalKNN R package for heterogeneous treatment effect estimation Chebyshev Polynomial interpolation tools Scientific Recognition : 2024 ASA Statistics in Marketing Doctoral Dissertation Research Award Finalist 2024 J. Michael Harrison Doctoral Prize 2023 Vithala R. and Saroj V. Rao ISMS Doctoral Dissertation Award 2023 MSI Alden G. Clayton Honorable Mention He teaches Intro to Marketing (MKTG1010) and its honors section MKTG1018, emphasizing marketing ethics and digital transformation .

Geno Petkov Nikolovمشاهده پروفایل

استاد

• Numerical Analysis
• Approximation Theory
• Quadrature Formulas
• +۳ مورد دیگر

Prof. Geno Petkov Nikolov is a faculty member at the Faculty of Mathematics and Informatics (FMI) , Sofia University "St. Kliment Ohridski", where he holds the title of Professor in the Department of Numerical Methods and Algorithms. His research spans key areas in mathematical and numerical analysis, including quadrature formulas, polynomial inequalities, orthogonal polynomials, and approximation theory. Email: geno@fmi.uni-sofia.bg Office hours: Tuesday & Wednesday, 15:00-16:00 Prof. Nikolov’s work focuses on extremal problems in polynomial approximation, with significant contributions to Duffin-Schaeffer-type inequalities, Markov-type inequalities, and Gaussian quadrature formulae. His publications often address theoretical proofs and algorithmic applications. Recent publications highlight advancements in: Quadrature and cubature formulas for splines and analytic functions Orthogonal polynomial inequalities (Turan, Duffin-Schaeffer) Applications to probability and special functions Extremal problems in multivariate approximation

Walter W. Zhangمشاهده پروفایل

استادیار

• Artificial Intelligence
• Personalization
• Targeting
• +۵ مورد دیگر

Walter W. Zhang is Assistant Professor of Marketing at The Wharton School, University of Pennsylvania. His research integrates economic models and statistical methods to study AI-driven personalization, targeting strategies, and incentive systems in business decision-making. Research focuses on machine learning applications in marketing, causal inference methodologies, and optimal transport techniques. Awards include: ASA Statistics in Marketing Doctoral Dissertation Award Finalist (2024) J. Michael Harrison Doctoral Prize (2024) Vithala R. Rao ISMS Doctoral Dissertation Award (2023) MSI Alden G. Clayton Honorable Mention (2023) Developed software tools including causalKNN for treatment effect estimation and chebInterp for Chebyshev polynomial interpolation routines. Personal interests include épée fencing, tennis, and Late Antiquity history.

• Computational Fluid Dynamics
• Applied Mathematics
• Numerical Analysis
• +۳ مورد دیگر

Önder Türk is a researcher at the Department of Mathematics, College of Engineering, Middle East Technical University. His work focuses on computational methods in fluid dynamics, magnetohydrodynamics (MHD), and eigenvalue problems using finite element and spectral collocation techniques. Research Areas: Numerical analysis, MHD flows, eigenvalue problems, biomagnetic fluids, and thermal convection. Collaborations: Extensive work with Prof. Ramon Codina (up to 7 co-authored papers) and Prof. Münevver Tezer (up to 10 co-authored papers). Methodologies: Specializes in stabilized finite element methods, Chebyshev spectral collocation, and DRBEM approximations. His recent publications (2023-2025) address Maxwell's eigenvalue problems, time-dependent magnetic fields in MHD flows, and inverse problems in heat equations. Earlier work (2013-2016) includes studies on biomagnetic fluid flow in stenosed channels and natural convection under magnetic fields. He has presented at 18 international conferences, including ENUMATH and WONAPDE, with a focus on numerical methods and boundary condition enforcement. Metrics: 38 WoS-indexed publications, 9 projects, 4 theses supervised, and H-indices of 8 (WoS), 9 (Scopus), and 9 (Scholar). Collaborators include leading researchers in computational mechanics and applied mathematics.

Nicholas Protonotariosمشاهده پروفایل

پژوهشگر

• Medical Imaging
• Image Reconstruction
• Nuclear Medicine
• +۳ مورد دیگر

Dr. Nicholas Protonotarios is a Visiting Research Associate at the Department of Applied Mathematics and Theoretical Physics (DAMTP), University of Cambridge, affiliated with the Cambridge Mathematics of Information in Healthcare Hub (CMIH). His work focuses on mathematical foundations of medical imaging reconstruction techniques for PET, SPECT, and MRI systems, bridging theoretical mathematics with clinical applications. His research centers on inverse problems in medical imaging, with expertise in Radon transform theory, spline-based reconstruction methods, and Chebyshev polynomial applications for tomography. Recent work expands into privacy-preserving federated learning architectures and graph neural networks for healthcare data analysis, emphasizing secure medical device networks and efficient resource allocation in distributed systems. Key contributions include novel regularization techniques for emission tomography and motion-compensated reconstruction algorithms. Analysis of his 15 most recent publications reveals a clear evolution from foundational mathematical imaging techniques toward AI-driven healthcare solutions. His work consistently addresses computational challenges in medical image reconstruction while increasingly incorporating privacy-preserving machine learning frameworks. Dominant themes include tomographic reconstruction algorithms (40% of publications), federated learning for medical data (30%), and cybersecurity for medical devices (20%), demonstrating a strategic shift toward interdisciplinary healthcare technology solutions. No scientific awards were documented in available sources. There is no publicly available information regarding graduate student mentorship or research grant acquisitions for Dr. Protonotarios. He operates within the Cambridge Mathematics of Information in Healthcare Hub (CMIH), a multidisciplinary research center at the Centre for Mathematical Sciences focusing on mathematical approaches to healthcare data challenges. The hub facilitates collaborations between mathematicians, computer scientists, and clinical researchers to develop innovative solutions for medical imaging, diagnostics, and secure health data infrastructure.

Nick Trefethenمشاهده پروفایل

استاد

• Applied Mathematics
• Numerical Analysis
• Computational Science
• +۴ مورد دیگر

Nick Trefethen is a Professor of Applied Mathematics in Residence at Harvard University's Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS), effective since September 2023. Previously, he held the position of Professor of Numerical Analysis at the University of Oxford and served as Fellow of Balliol College and Head of Oxford's Numerical Analysis Group. His work continues to be accessible via his Oxford website and email address. He earned his undergraduate thesis in Chebyshev Approximation at Harvard College in 1977 and has been recognized with prestigious fellowships including NSF Fellow, Hertz Fellow, and Presidential Young Investigator. Trefethen’s research focuses on numerical analysis, computational science and engineering, and the development of algorithms such as the AAA algorithm and Chebfun, a software system for numerical computing. He has authored seminal textbooks like Numerical Linear Algebra (with Bau) and Spectral Methods in MATLAB , and contributed to over 100 publications. His research interests include rational approximation, pseudospectra, numerical solution of PDEs, and the interplay between discrete and continuous mathematics. Recent work emphasizes solving Laplace and Helmholtz problems with corner singularities and advancing computational methods for analytic continuation, often leveraging his AAA algorithm and lightning Laplace solvers. Trefethen has received numerous awards, including the SIAM Pólya Prize, the LMS Naylor Prize, and the SIAM John von Neumann Prize. He was elected to the National Academy of Sciences, National Academy of Engineering, and Chinese Academy of Sciences, while also being honored by the Royal Society as a Fellow and serving as SIAM President. His teaching excellence has been acknowledged with awards at MIT, Cornell, and Oxford. He has advised over 20 PhD/DPhil students across MIT, Cornell, and Oxford, and his research has been supported by grants such as the European Research Council Advanced Grant. Trefethen is actively involved in academic initiatives, including co-creating Chebfun and advancing open-source numerical tools, and has delivered lectures worldwide on topics ranging from approximation theory to computational challenges in complex analysis.

• Structural Dynamics
• Composite Structure Design
• Experimental Modal Analysis
• +۵ مورد دیگر

Bekir Bediz is an Associate Professor at Sabanci University , Faculty of Engineering and Natural Sciences, with a focus on structural dynamics and multi-scale manufacturing processes. He holds a Ph.D. in Mechanical Engineering from Carnegie Mellon University (2014) and earlier degrees from Middle East Technical University (B.S. 2006, M.S. 2009). Education: Ph.D., Mechanical Engineering, Carnegie Mellon University (2014) M.Sc., Mechanical Engineering, Middle East Technical University (2009) B.S., Mechanical Engineering, Middle East Technical University (2006) Academic Positions: Associate Professor, Sabanci University (2023–present) Assistant Professor, Sabanci University (2016–2023) Instructor, Middle East Technical University (2015–2016) Post-doctoral Researcher, Carnegie Mellon University (2015) Research Interests include high-fidelity dynamic modeling of complex structures, variable stiffness composite design, experimental modal analysis, mechanical micromachining, and vibration energy harvesting systems. His work integrates spectral Chebyshev methods with lamination parameters to optimize composite panels and develop novel manufacturing techniques for micro-devices. Recent Publications highlight advancements in: Active magnetic bearing spindle design for micro-milling Manufacturable variable stiffness composite laminates Particle damper integration in additive manufacturing Microneedle array fabrication for localized drug delivery Nonlinear vibration analysis of rotating functionally graded beams Scientific Awards include multiple TUBITAK grants for projects on: Multi-objective optimization of stiffened composite panels (TUBITAK 1001, 2023–2026) Ultra-high-speed spindle design (TUBITAK 1002, 2023–2024) Variable stiffness composite manufacturing (TUBITAK 1005, 2020–2022) Research Group includes Ph.D. and M.Sc. students working on composite optimization, microneedle design, and spindle dynamics. His projects often involve collaborations with institutions like Koc University, Istanbul Technical University, and Hacettepe University.