جست‌وجوی استاد و پژوهشگر

Longxiu Huangمشاهده پروفایل

استادیار

• Data/Signal Processing
• Applied Harmonic Analysis
• Machine Learning
• +۴ مورد دیگر

Longxiu Huang is an Assistant Professor at Michigan State University (MSU), holding dual appointments in the Department of Computational Mathematics, Science and Engineering (CMSE) and the Department of Mathematics. Prior to this, she was an Assistant Adjunct Professor at UCLA's Department of Mathematics. Her research focuses on Data/Signal Processing, Applied Harmonic Analysis, Machine Learning, Random Matrix/Tensor Analysis, Dynamical Sampling, and Optimization. Education : Ph.D. in Mathematics, Vanderbilt University (2014-2019) M.S. in Mathematics, Fudan University (2012-2014) B.S. in Mathematics, Sun Yat-Sen University (2009-2012) B.S. in Software Engineering, Sun Yat-Sen University (2008-2009) Research Interests : Huang's work bridges theoretical mathematics and applied computational methods. She explores signal reconstruction in dynamical systems, tensor decompositions for high-dimensional data, robust matrix/tensor completion, and optimization algorithms for ill-conditioned problems. Her contributions include CUR-based methods for low-rank approximations and sampling strategies for sparse signals on graphs. Professional Activities : She co-organizes the One World MINDS Seminar, a global initiative in mathematical data science. Her research has been published in top journals like SIAM Journal on Imaging Sciences and Applied and Computational Harmonic Analysis, with a focus on theoretical guarantees and practical applications in imaging and machine learning. Lab/Teams : Active collaborator in interdisciplinary projects at MSU, particularly in computational mathematics and machine learning applications.

Haim Avronمشاهده پروفایل

استاد

• Numerical Computing
• High Performance Computing
• Machine Learning
• +۳ مورد دیگر

Haim Avron is a Professor in the Department of Applied Mathematics at Tel Aviv University's School of Mathematical Sciences, where he has been employed since 2015. His research focuses on numerical computing, high-performance computing, and their applications in scientific computing and machine learning. Education: Completed PhD in Computer Science at Tel Aviv University under Prof. Sivan Toledo, followed by postdoctoral research at IBM T.J. Watson Research Center. Research interests: Foundations of numerical linear algebra and randomized algorithms Tensor-tensor algebra for multiway data representation High-performance computational methods for machine learning Optimization techniques for large-scale systems Recent publications demonstrate strong focus on tensor algebra, randomized numerical methods, and machine learning optimization, with applications ranging from quantum computing to deep learning architectures. Awards: SIAM Activity Group on Computational Science and Engineering Best Paper Prize 2025 Software contributions include development of numerical libraries such as libSkylark for matrix sketching and Blendenpik for least-squares problems.

Patrick Kürschnerمشاهده پروفایل

دانشیار

• Numerical Linear Algebra
• Model Order Reduction
• Eigenvalue Problems
• +۸ مورد دیگر

Patrick Kürschner is an Associate Professor for Numerical Mathematics and Linear Algebra at Leipzig University of Applied Sciences (HTWK Leipzig) . He previously held postdoctoral positions at the Max Planck Institute for Dynamics of Complex Technical Systems and KU Leuven's Kulak Kortrijk Campus. His work focuses on computational methods in systems and control theory. PhD in Mathematics (Otto-von-Guericke University, 2015) MSc in Mathematics (Chemnitz University of Technology, 2010) BSc in Financial Mathematics (Chemnitz University of Technology, 2008) His research interests span numerical linear algebra, matrix equations and functions, eigenvalue problems, preconditioning, model order reduction, tensor methods, and numerical algorithms for digital signal processing. He develops efficient computational techniques for large-scale systems in control theory and data science applications. Recent publications include advancements in low-rank ADI iteration, time-limited balanced truncation, inexact linear solvers, and tensor decompositions for polynomial systems. His work addresses challenges in computational neuroscience, mechanical simulations, and optimal control.

Guillaume Rabusseauمشاهده پروفایل

دانشیار

• Machine Learning
• Multilinear Algebra
• Tensor Networks
• +۴ مورد دیگر

Guillaume Rabusseau is an Associate Professor at Mila and the Department of Computer Science and Operations Research (DIRO) at Université de Montréal , holding a Canada CIFAR AI Chair since 2019. His research spans machine learning, theoretical computer science, and multilinear algebra. Education : PhD in Computer Science (2016) from Aix-Marseille Université , MSc in Fundamental Computer Science from AMU, BSc in Computer Science (distance learning) from AMU. Research Interests : Tensor methods for machine learning, spectral learning algorithms, connections between weighted automata, tensor networks, and RNNs, low-rank regression, and nonlinear computational models on structured data. Publication Trends : Recent work focuses on tensor train decomposition, temporal graph benchmarks, quantum-inspired ML, spectral regularization, and formal methods for sequence modeling. Collaborative papers address dynamic graphs, foundational models for molecular learning, and high-order pooling in GNNs. Scientific Awards : Canada CIFAR AI Chair (2019–present, renewed) Advising : Supervises PhD students like Maude Lizaire and Pascal Tikeng Notsawo, and MSc students such as Soroush Omranpour. Past advisees include Andy Huang (now at Oxford) and Tianyu Li (Samsung).

Olga Kloppمشاهده پروفایل

استاد

• Nonparametric Estimation
• High-Dimensional Inference
• Network Models
• +۴ مورد دیگر

Olga Klopp is a Professor of Statistics at ESSEC Business School and a permanent member of the CREST research center. Her work focuses on nonparametric estimation, high-dimensional inference, network models, and matrix completion. Research spans theoretical and applied statistics Specializes in sparse/high-dimensional data and network analysis Develops algorithms for matrix completion and graphon estimation Recent research includes tensor decomposition for economic networks, detection of change-points in dynamic networks, and handling missing data in epidemic modeling. She has received ERC and ANR grants for innovative projects. PhD Students: Solenne Gaucher, Mokhtar Alaya, Guillermo Martin Key methodological contributions in low-rank modeling and robust estimation

• Tensor Networks
• Low-Rank Approximations
• Numerical Linear Algebra
• +۴ مورد دیگر

Sebastian Krämer is a researcher at the Institute for Geometry and Practical Mathematics at RWTH Aachen University, working under the supervision of Prof. Markus Bachmayr and Prof. Lars Grasedyck. His research focuses on tensor networks, low-rank approximations, and numerical methods for high-dimensional problems. He has made significant contributions to the field of tensor train formats and rank minimization techniques. Dr. Krämer's research interests span tensor networks, low-rank approximations, numerical linear algebra, high-dimensional approximation, tensor train formats, and machine learning optimization. His work centers on developing efficient algorithms for tensor decompositions, particularly focusing on alternating least squares methods, iteratively reweighted least squares approaches, and geometric constraints for tensor singular values. His research bridges theoretical numerical analysis with practical applications in high-dimensional data processing and scientific computing. His publication record shows a consistent output of high-quality work in top numerical analysis journals, with recent publications in 2024 demonstrating ongoing active research. His work demonstrates expertise in both theoretical aspects of tensor decompositions and practical implementation of numerical algorithms. He has developed several open-source toolboxes for tensor network arithmetic and tensor train feasibility problems, which have been widely used by the research community. Dr. Krämer has been actively involved in teaching, serving as a lecturer and assistant for various mathematics courses at RWTH Aachen, including Numerical Mathematics for mathematicians and civil engineers. He has also contributed to specialized research schools on high-dimensional approximation and deep learning, developing course materials and providing instruction. His professional activities include regular participation in the GAMM conference since 2017 and peer review activities for SIAM journals since 2015.

• Signal and Image Processing
• Statistical Machine Learning
• Low-Rank Approximations
• +۱۵ مورد دیگر

Matthieu Puigt is a Professor at Université du Littoral Côte d'Opale, specializing in signal and image processing with a focus on statistical machine learning , low-rank approximations , and sparse component analysis . His research extends to hyperspectral data fusion, unmixing, and restoration, as well as applications in chemistry and computational imaging . He leads the SPECIFI research team. Research Themes : Blind source separation, compressive learning, sensor calibration, and big data analysis Applications : Audio signal processing, environmental monitoring via drones, and urban air quality assessment His recent work includes developing VAE-based hyperspectral image emulators, tensor decomposition methods for multisensor data, and frameworks for butterfly species recognition. He advised Valentin Mullet's 2022 thesis on blockchain traceability systems and actively contributes to IEEE and GRETSI conferences.

• Matrix and Tensor Factorizations
• Riemannian Optimization
• Cancer Classification
• +۵ مورد دیگر

Max Pfeffer is an Assistant Professor at the Institute for Numerical and Applied Mathematics within Georg-August-Universität Göttingen (since 2023). He previously held research and adjunct positions at TU Chemnitz, SimulaMet Oslo, Johannes-Gutenberg-Universität Mainz, and MPI MiS Leipzig. His work bridges numerical mathematics with data science applications. Current affiliations: Universität Göttingen (Junior Professor), TU Chemnitz (Adjunct Professor) Collaborators: Martin Stoll (TU Chemnitz), Evrim Acar Ataman (SimulaMet), Markus Bachmayr (Mainz), Bernd Sturmfels (MPI MiS) Research Focus Matrix/Tensor factorizations for high-dimensional data Riemannian optimization on manifolds Cancer classification through machine learning Quantum chemistry numerical methods for matrix product states Parametric PDE solutions for biomedical applications Recent Publications Highlight His 2023-2025 publications demonstrate cross-disciplinary impact: tensor decompositions for temporal data analysis, Gaussian process acceleration with tensor structures, and biomedical applications in melanoma gene selection. The work intersects numerical mathematics, machine learning, and quantum computing. Academic Background PhD in Mathematics (2018), M.Sc. (2014), B.Sc. (2011) from TU Berlin DFG-funded project on constrained matrix/tensor factorizations (2021-2023)

James E. Fowlerمشاهده پروفایل

استاد

• Hyperspectral Imagery Analysis
• Image and Video Coding
• Compressed Sensing
• +۳ مورد دیگر

James E. Fowler is the William L. Giles Distinguished Professor and holder of the Billie J. Ball Endowed Professorship in the Department of Electrical & Computer Engineering at Mississippi State University. As of January 2022, he serves as a Program Director in the Communications and Information Foundations (CIF) cluster at the National Science Foundation (NSF). His primary affiliations include Mississippi State University and NSF, with visiting roles at Télécom ParisTech and Polytech Nantes. He earned his B.S., M.S., and Ph.D. in Electrical Engineering from The Ohio State University. Dr. Fowler's research focuses on hyperspectral imagery analysis, compressed sensing, and image/video coding. His work integrates signal processing, machine learning, and computational imaging. Notable contributions include innovations in hyperspectral compression, random projections, and deep learning frameworks for remote sensing applications. Education: Ph.D., Electrical Engineering, The Ohio State University, 1996 M.S., Electrical Engineering, The Ohio State University, 1992 B.S., Computer and Information Science Engineering, The Ohio State University, 1990 Awards & Roles: Fellow of the IEEE Editor-in-Chief of IEEE Signal Processing Letters (2017–2019) General Co-Chair of the Data Compression Conference and IEEE International Conference on Image Processing Research Labs: High Performance Computing Collaboratory (HPC2) at Mississippi State University.

Kai Liuمشاهده پروفایل

استادیار

• Machine Learning
• Optimization
• Data Mining
• +۴ مورد دیگر

Kai Liu is an Assistant Professor in the Computer Science Division at Clemson University . He holds a Ph.D. in Computer Science from the Colorado School of Mines (adviser: Dr. Hua Wang). His research focuses on the intersection of Machine Learning and Optimization , particularly in designing robust, efficient, and scalable algorithms for data mining applications. Key areas include non-negative matrix factorization, tensor completion, and healthcare analytics such as Alzheimer's disease analysis. Teaching: Liu has taught advanced courses including Optimization for Machine Learning , Advanced Machine Learning , and Applied Data Science at Clemson University. He also contributed to undergraduate courses like Discrete Structures . Research Contributions: His work emphasizes theoretical guarantees for algorithms, with applications in bioinformatics (e.g., HIV-1 drug resistance prediction), computer vision, and healthcare. Notable methods include spherical principal component analysis and accelerated tensor completion techniques. Professional Service: Liu serves on program committees for top conferences including ICML, NeurIPS, AAAI, and CVPR. He is a recognized expert in first-order optimization methods and high-dimensional statistics. Personal Interests: Beyond academia, Liu engages in traditional Chinese culture (calligraphy, painting, poetry), enjoys hiking, and is an avid reader of biographies. He is also a marathon runner and long-time supporter of Real Madrid.

Bruno Carpentieriمشاهده پروفایل

دانشیار

• Numerical Linear Algebra
• High-Performance Computing
• Computational Electromagnetics
• +۵ مورد دیگر

Bruno Carpentieri is an Associate Professor in Applied Mathematics at the Faculty of Computer Science , Free University of Bozen-Bolzano , Italy. His work focuses on Numerical Linear Algebra , High-Performance Computing , and Preconditioning Techniques for large-scale scientific problems. Education : Laurea in Applied Mathematics (1997, University of Bari Aldo Moro); PhD in Computer Science (National Polytechnic Institute Toulouse). Research Interests : Sparse linear systems, Krylov subspace methods, computational electromagnetics, PageRank problems, and fractional calculus applications in nonlinear engineering. His publications (1998–2025) emphasize efficient solvers for multi-shifted systems, parallel computing, and preconditioning strategies. Recent works (2025) explore fractional-order methods and multilayer network centrality . He has collaborated on ITER tokamak simulations (F4E/MIUR grants) and biomedical finite element models . Grants : F4E-2008-OPE-06-06-11 (ITER analysis); MIUR PRIN 2010SPS9B3. Key Contributions : VBARMS preconditioner (2014), hybrid block GMRES variants (2018), and chaos-enhanced fractional solvers (2025).

Alexander Tichaiمشاهده پروفایل

استاد پژوهشی

• Nuclear Physics
• Many-Body Theory
• Theoretical Physics
• +۳ مورد دیگر

Alexander Tichai is an ERC Group Leader at the Technical University of Darmstadt's Institute of Nuclear Physics, leading the DeformedNuclei research group. His academic journey includes a PhD in Physics (2017) and dual bachelor's and master's degrees in Physics and Mathematics from TU Darmstadt. Prior to his current role, he held postdoctoral positions at CEA Saclay/Université Paris-Saclay (2018-2019) and TU Darmstadt (2019-2025). His research focuses on nuclear many-body theory , including open-shell nuclei, symmetry-broken correlation expansions, and tensor factorization techniques. He specializes in ab initio methods for nuclear structure and dynamics, with applications to neutron stars, neutrinos, and dark matter. Key techniques include coupled-cluster theory, Bogoliubov perturbation methods, and low-rank matrix decompositions. His work bridges theoretical physics and computational science, addressing challenges in heavy-nuclei simulations and precision nuclear modeling. Collaborations include the EUSTRONG and ChiralEFT4DM projects, supported by ERC funding. Tichai’s research also explores interdisciplinary applications like eigenvector continuation and quantum information entropy. He advises the DeformedNuclei research group at TU Darmstadt and contributes to the STRONGINT initiative. His publications emphasize computational advancements for nuclear many-body problems, including data compression and numerical stability in large-scale simulations.

Khanh Luongمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Data Mining
• Multi-Aspect Data Clustering
• +۷ مورد دیگر

Dr. Khanh Luong is a Research Fellow at the School of Communication within the Faculty of Creative Industries, Education & Social Justice at Queensland University of Technology (QUT). She holds a PhD in Computer Science specializing in Data Science (2019) from QUT. Her research focuses on multi-aspect data, addressing challenges in high-dimensional, sparse datasets through innovative machine learning and data mining methods. She contributes to real-world applications in diverse domains like text, image, and bioinformatics data. Teaching roles include tutoring in Discrete Mathematics, Programming Principles, and Database Management for undergraduate/postgraduate students. She also designed the online unit 'Introduction to Programming' for the Master of IT program. Research interests span machine learning, data mining, multi-aspect data clustering, dimensionality reduction, and unsupervised learning. She works on the Applied Data Science project at QUT's Centre for Data Science, developing solutions for complex industry datasets. Publications emphasize multi-view clustering, manifold learning, and domain adaptation. Collaborations include roles in organizing the 2021 AusDM conference and reviewing for IEEE journals.

King-Wah Chuمشاهده پروفایل

پژوهشگر ارشد

• Numerical Analysis
• Numerical Linear Algebra
• Control Theory
• +۳ مورد دیگر

King-Wah Chu is an Adjunct Senior Research Fellow at the School of Mathematics, Monash University. He has been working in Melbourne, Australia for 30 years. His research focuses on numerical analysis, numerical linear algebra, eigenvalue problems, control theory, and matrix equations. He serves as an Associate Editor for the SIAM Journal on Matrix Analysis and Applications since 2015. Education: Hong Kong and England Affiliations: Monash University, SIAM Journal His research interests include perturbation and numerical solutions of eigenvalue problems, inverse eigenvalue problems, Riccati equations, and large-scale structured computations. He has contributed to the development of algorithms for control systems and tensor computations. Key Projects: Research visit to the International Symposium on Mathematical Theory of Networks & Systems (2000) Advising & Grants: Chu has advised on multiple projects but no specific students are listed. His work involves collaborations on large-scale algebraic Riccati equations and numerical methods. Labs/Teams: Active in the School of Mathematics at Monash University, focusing on numerical linear algebra and control systems.

Raymond Ka Wai Wongمشاهده پروفایل

دانشیار

• Statistical Learning
• Causal Inference
• Functional Data Analysis
• +۴ مورد دیگر

Raymond Ka Wai Wong is an Associate Professor and Director of the PhD Program in the Department of Statistics at Texas A&M University. He holds a PhD in Statistics from the University of California, Davis (2014), an MPhil from The Chinese University of Hong Kong (2010), and a BSc with minors in Mathematics and Risk Management Science (2008). His research focuses on causal inference, functional data analysis, low-rank modeling, reinforcement learning, and statistical learning with applications in astronomy, brain imaging, and genomics. Wong's professional roles include Associate Editor for Journal of Computational and Graphical Statistics , Journal of the American Statistical Association , and member of the Research Institute for Foundations of Interdisciplinary Data Science (FIDS). He has secured grants from NSF, NASA, and NIH, including leadership in projects like Virtual Assistant for Spacecraft Anomaly Treatment and phenomic selection in maize hybrids. His awards include Top Reviewer distinctions at NeurIPS (2023) and ICML (2020), and a 2016 Discussion Paper in the Annals of Applied Statistics. He has advised numerous doctoral students, with notable advisees receiving awards such as the ICSA Student Paper Award and Emanuel Parzen Fellowship. Wong’s recent work emphasizes methodological advancements in reinforcement learning (e.g., distributional off-policy evaluation) and matrix/tensor completion under informative missingness. His research bridges theoretical statistics with practical applications in interdisciplinary domains such as neuroscience, agriculture, and space exploration.