جست‌وجوی استاد و پژوهشگر

• Continuum Mechanics
• Nonlinear Waves
• Solitons
• +۵ مورد دیگر

Andrus Salupere is a distinguished Professor and current Dean of the School of Science at Tallinn University of Technology. He holds a Tenured Full Professor position in the Department of Cybernetics, where he previously served as Director from 2017-2020. His academic career spans over three decades with significant leadership roles including Chairman of the Estonian National Committee for Mechanics since 2009 and representing Estonia in the IUTAM General Assembly. Salupere's educational background includes a Doctor's degree earned in 1991 from Tallinn University of Technology with his dissertation on "Optimal design of rigid-plastic stepped circular and annular plates" supervised by Ülo Lepik. His academic journey began at the University of Tartu where he completed his studies in Applied Mathematics (1976-1981) followed by PhD studies (1983-1986). His research focuses on continuum mechanics, nonlinear waves, and soliton theory, with significant contributions to numerical methods for solving nonlinear partial differential equations. Salupere's work bridges mathematical physics with practical applications in materials science and nondestructive testing. His recent publications demonstrate expertise in Haar wavelet methods for nonlinear PDEs, solitonic structures in hierarchical KdV systems, and applications of nonlinear wave phenomena in material characterization. His research portfolio reveals consistent exploration of wave propagation phenomena across multiple media types, from theoretical mathematical models to practical applications in carbon fiber composites and biological tissues. This interdisciplinary approach connects fundamental mathematical physics with engineering applications in nondestructive testing and materials characterization. Honors & Awards Order of the White Star, 4th Class (2013) Salupere maintains active involvement in academic governance as a member of TalTech Senate and its Committee for Academic Affairs. His international collaborations include visiting fellowships at Cambridge University (2002, 1998) and post-doctoral research at the University of Paris 6 (1993-1994). He has organized special issues on nonlinear wave complexity in the Proceedings of the Estonian Academy of Sciences and serves on multiple expert panels including the Estonian Research Council's Expert Panel for Natural Sciences.

Stefano Vigognaمشاهده پروفایل

دانشیار

• Machine Learning
• Statistical Learning Theory
• Harmonic Analysis

Stefano Vigogna is an Associate Professor in the Department of Mathematics at the University of Rome Tor Vergata with significant contributions to theoretical machine learning. He is affiliated with the Rome Center on Mathematics for Modeling and Data Sciences (RoMaDS), focusing on the mathematical foundations of learning algorithms. His research expertise spans: Machine Learning Statistical Learning Theory Harmonic Analysis Professor Vigogna's publication record demonstrates deep theoretical work connecting advanced mathematics to machine learning. His research investigates the spectral properties, geometric structure, and convergence behavior of neural networks using functional analysis and harmonic analysis techniques. Notable publications include his 2022 ICML paper on multiclass learning with exponential convergence rates and numerous works exploring the mathematical properties of deep learning systems through reproducing kernel spaces. He teaches Statistica for the Master's program in Environmental Biology and Statistical Learning for the Master's program in Pure and Applied Mathematics, reflecting his dual expertise in mathematical theory and practical data science applications. Professor Vigogna maintains active collaborations with leading researchers including Lorenzo Rosasco and Ernesto De Vito, advancing our fundamental understanding of learning algorithms through rigorous mathematical analysis. His work represents an essential bridge between pure mathematics and the theoretical foundations of modern artificial intelligence.

Ahmed Kaffelمشاهده پروفایل

استاد آموزشی

• Scientific Computing
• Partial Differential Equations
• Fluid Mechanics
• +۶ مورد دیگر

Dr. Ahmed Kaffel is a Teaching Professor in the Department of Mathematical Sciences at the University of Wisconsin-Milwaukee's College of Letters and Science. With a multidisciplinary background spanning mathematics and engineering, he holds a PhD in Mathematics from Virginia Tech and has held postdoctoral positions at Brown University and the University of Maryland. His research develops advanced numerical methods to solve physical problems in fluid mechanics, porous media, and biomedical systems. Core interests include: Computational fluid dynamics (Newtonian/viscoelastic fluids, free-surface flows) Multiscale modeling of transport in porous media Inverse problems for medical imaging reconstruction Applications of machine learning in computational biology High-performance scientific computing techniques (FEM, FVM, spectral methods) Recent publications demonstrate a shift toward medical imaging and data science applications, building on his foundational work in hydrodynamic stability. His 8 most recent articles (2010-2020) primarily appear in leading fluid dynamics and heat transfer journals. Dr. Kaffel has taught extensively across the mathematics curriculum, currently instructing courses in linear algebra, calculus, and differential equations at UW-Milwaukee. His pedagogical approach emphasizes connecting theoretical concepts to real-world applications in engineering and biosciences.

Ahmed I. Zayedمشاهده پروفایل

استاد

• Sampling Theory
• Wavelets
• Fractional Fourier Transform
• +۸ مورد دیگر

Ahmed I. Zayed is a Full Professor and Chair of the Department of Mathematical Sciences at DePaul University's College of Science and Health in Chicago, Illinois. He joined DePaul University in September 2001 after serving as Associate Chair for Graduate Studies at the University of Central Florida. His academic journey began with a PhD in Mathematical Sciences from the University of Wisconsin-Milwaukee in 1979. Dr. Zayed's research spans the breadth of applied harmonic analysis, with particular expertise in Sampling Theory, Wavelets, Fractional Fourier Transform, Sinc Approximations, Special Functions, and Integral Transforms. His work bridges theoretical mathematics with practical applications in signal and image processing. Over his distinguished career, he has published 9 books, 17 book chapters, and 106 research articles, establishing himself as a leading authority in his fields of interest. An analysis of his 15 most recent publications reveals a consistent focus on advanced mathematical transformations and their applications. His research demonstrates strong connections between classical harmonic analysis and modern signal processing techniques, with particular emphasis on fractional transforms, wavelet theory, and energy concentration problems. The publications show a clear trajectory toward increasingly sophisticated mathematical frameworks for signal analysis and reconstruction. Dr. Zayed has served on the editorial boards of prestigious journals including the Journal of Integral Transforms and Special Functions and the International Journal of Fractional Calculus and Applied Analysis. He has received multiple awards for excellence in research and online course design. Currently, he serves as Chair of the SampTA Steering Committee, an international organization dedicated to Sampling Theory and its Applications. His collaborative network is extensive and global, spanning 19 countries with 45 documented collaborators. This international reach has significantly influenced the direction and impact of his research. Dr. Zayed teaches a wide range of courses from college algebra to graduate-level topics in functional analysis, special functions, wavelets, and the history of mathematics, which he delivers as a hybrid online course.

Smita Krishnaswamyمشاهده پروفایل

دانشیار

• Unsupervised Machine Learning
• Manifold Learning
• Deep Learning
• +۷ مورد دیگر

Smita Krishnaswamy is an Associate Professor of Genetics and Computer Science at Yale University with joint appointments in both departments. She is affiliated with multiple interdisciplinary programs including the Applied Mathematics Program, Computational Biology and Bioinformatics Program, Yale Center for Biomedical Data Science, Yale Cancer Center, and the Wu Tsai Institute. Her research bridges computational methods development with biomedical applications, focusing on unsupervised machine learning approaches for high-dimensional data analysis. Associate Professor of Genetics, Yale School of Medicine Associate Professor of Computer Science, Yale University Affiliated Faculty, Applied Mathematics Program Affiliated Faculty, Computational Biology and Bioinformatics Member, Yale Center for Biomedical Data Science Member, Yale Cancer Center Member, Wu Tsai Institute Dr. Krishnaswamy's research focuses on developing unsupervised machine learning techniques, particularly manifold learning and deep learning methods, to analyze high-dimensional biomedical data. Her lab creates algorithms for non-linear dimensionality reduction, data geometry learning, denoising, imputation, and inference of multi-granular structures from complex datasets. These methods are applied to diverse data types including single-cell RNA-sequencing, mass cytometry, electronic health records, and connectomic data across multiple biological systems. Her work spans several key application areas including immunology and immunotherapy, cancer research, neuroscience, developmental biology, and health outcomes analysis. The lab employs approaches from geometric deep learning, multiscale graph signal processing, and topological data analysis to extract meaningful biological insights from complex datasets. Recent publications demonstrate the lab's leadership in developing methods for spatial transcriptomics, brain-state trajectory modeling, and organ donation prediction. Excellence in Science Early-Career Investigator Award from FASEB (2022) Yale Cancer Center Class of '61 Cancer Research Award (2025) Dr. Krishnaswamy maintains active collaborations across Yale and secures research funding supporting her work in computational biomedicine. She advises students through multiple programs including Genetics, Computer Science, and the Biological and Biomedical Sciences Graduate Program, fostering interdisciplinary training at the intersection of computation and biomedicine. The Krishnaswamy Lab operates at the forefront of computational biomedicine, developing mathematical approaches that enable new biological discoveries from complex datasets.