جست‌وجوی استاد و پژوهشگر

Donsub Rimمشاهده پروفایل

استادیار

• Numerical Analysis
• Partial Differential Equations (PDEs)
• Model Reduction
• +۶ مورد دیگر

Donsub Rim is an Assistant Professor in the Department of Mathematics at Washington University in St. Louis. He holds a PhD from the University of Washington, advised by Randall J. LeVeque and Gunther Uhlmann. His research focuses on numerical analysis of PDEs, inverse problems, and low-rank neural network representations (LRNRs) for real-time solutions in geophysics, medical imaging, and plasma physics. Key areas include stability analysis of neural networks for tsunami early warning, fast inversion of the approximate discrete Radon transform (ADRT), and computational applications in geophysical hazard assessment. Rim is on leave during 2024-2025 as a Visiting Scholar at the University of Washington. Previously, he was a visiting scholar at Tohoku University's IRIDeS (2023) and affiliated with the Courant Institute and Columbia University. His work bridges mathematical theory with practical tools like the 'adrt' Python library for signal processing. Research Interests: Numerical methods for PDEs, model reduction techniques, neural network applications in high-consequence domains (e.g., tsunami forecasting), and ADRT-based algorithms for dimensional splitting and sparse approximations. Collaborations include projects on probabilistic tsunami hazard assessment (PTHA), earthquake kinematic effects on tsunami propagation, and meta-learning approaches for physics-informed networks. Publications highlight contributions to real-time prediction systems, stability analysis of neural networks, and manifold approximations for transport-dominated problems. His software developments, such as the ADRT Python package, emphasize open-source tools for scientific computing. Rim has advised or collaborated with researchers at institutions including Columbia University, Tohoku University, and the University of Washington. Grants and future work involve advancing LRNR frameworks for nonlinear hyperbolic problems, improving tsunami early warning systems via GNSS data, and exploring applications of sparse physics-informed backpropagation in geophysics and plasma physics.

T.W. Kornerمشاهده پروفایل

استاد

• Mathematical Analysis
• Fourier Analysis
• Measure Theory
• +۳ مورد دیگر

Professor T.W. Korner is a retired academic affiliated with the Department of Pure Mathematics and Mathematical Statistics (DPMMS) at the University of Cambridge's Faculty of Mathematics. His research focuses on advanced mathematical analysis, including Fourier analysis, measure theory, and functional analysis. He maintains an active presence through his personal homepage and has contributed to seminal works on topics like convolutions, Hausdorff dimensions, and Berge equilibria in game theory. Key research themes include exploring the interplay between Fourier transforms and algebraic structures, properties of critical points in calculus, and theoretical foundations of mathematical logic. His publications often bridge pure mathematics with applications in spectral analysis and geometric measure theory. Professor Korner's work reflects a deep engagement with foundational questions in analysis, with notable contributions to Menšov's theorem adjustments and investigations into differentiability on sparse sets like the rationals. Though no specific awards are listed, his sustained output since the 1990s underscores his scholarly impact.

Michael Kapralovمشاهده پروفایل

دانشیار

• Theoretical Computer Science
• Sublinear Algorithms
• Big Data Analysis
• +۹ مورد دیگر

Michael Kapralov is an Associate Professor in the School of Computer and Communication Sciences at École Polytechnique Fédérale de Lausanne (EPFL) , where he leads research at the Theory Group . His work spans multiple departments including the Laboratory of Theory of Computation 4 and the Doctoral Program in Computer Science and Communications . Kapralov's research focuses on theoretical computer science , particularly sublinear algorithms for big data analysis , with applications in streaming , sketching , sparse recovery , and Fourier sampling . University: EPFL School: School of Computer and Communication Sciences Department: Theory Group Academic Rank: Associate Professor Education: Kapralov earned his Ph.D. in Computer Science from Stanford iCME under the supervision of Ashish Goel . He subsequently held postdoctoral positions at the Mit CSAIL Theory of Computation Group with Piotr Indyk and as a Herman Goldstine Postdoctoral Fellow at IBM T. J. Watson Research Center . Ph.D.: Stanford iCME (2012), advisor: Ashish Goel Postdoctoral: MIT CSAIL (2012-2014), IBM Watson (2014) Research Interests: Kapralov's work addresses fundamental challenges in processing large-scale data through rigorous mathematical models. His contributions include advancements in sublinear algorithms , streaming complexity , spectral sparsification , sparse Fourier transforms , and differential privacy . He has developed techniques for dimension-independent signal processing , kernel ridge regression , and graph spanners , with theoretical guarantees and practical implications for machine learning and data analysis. Scientific Awards: Kapralov received the ERC Starting Grant SUBLINEAR (2018-2023) and the Gene H. Golub Dissertation Award (2012). Advising: He has supervised numerous Ph.D. students and postdoctoral researchers, including Ekaterina Kochetkova , Grzegorz Gluch , Kshiteej Sheth , and Amir Zandieh , many of whom have taken academic or industry positions at institutions like UC Berkeley, National University of Singapore, and Google Zurich. Collaborations and Teaching: Kapralov co-organizes the Turing Course for high school students, leads the Reading Group on Foundations of Deep Learning , and contributes to academic initiatives such as Theory Coffee and the Swiss Winter School on Theoretical Computer Science . He teaches courses like Sublinear Algorithms for Big Data Analysis and Algorithms II , focusing on advanced algorithm design and analysis.

Thorsten Raaschمشاهده پروفایل

استاد

• Numerical Mathematics
• Adaptive Discretization Methods
• Partial Differential Equations
• +۷ مورد دیگر

Thorsten Raasch is a Professor of Numerical Mathematics at the Institute of Mathematics, Johannes Gutenberg University Mainz, Germany. He is affiliated with the Department of Physics, Mathematics, and Computer Science, and leads research in numerical analysis, particularly adaptive methods for PDEs and inverse problems. Research Interests: His work focuses on adaptive discretization methods for partial differential equations and inverse problems, non-smooth numerical optimization , wavelet systems , multilevel frames , and GPGPU computing . These interests are deeply rooted in applied and computational mathematics, with applications in scientific computing and engineering. The most recent articles show a strong trend in developing adaptive wavelet-based numerical schemes for solving inverse and stochastic PDEs, with a particular emphasis on sparsity, convergence analysis, and preconditioning. His publications span high-impact journals in numerical analysis and inverse problems, indicating sustained scholarly activity. Teaching: He has taught a variety of courses including Fundamentals of Numerics, Convex Optimization, Numerical PDEs, Spectral Methods, and advanced seminars on topics like semi-smooth Newton methods and reduced basis methods. Thorsten Raasch earned his doctorate in 2007 from Philipps University of Marburg and has held academic positions at JGU Mainz, the University of Siegen, and the University of Rostock. He was appointed W2 Professor at JGU Mainz in 2015 and previously held a junior professorship there. No scientific awards are mentioned in the provided texts. Advising and Grants: While no students or grants are explicitly listed, his role as a professor and active researcher suggests involvement in supervising graduate students and participating in research projects, possibly within the 'Mathematics of Computation' research network. Labs and Teams: He is part of the Numerical Mathematics Working Group at JGU Mainz and contributes to the 'Mathematics of Computation' research network, indicating collaboration within a structured research environment.

Maryam Fazelمشاهده پروفایل

دانشیار

• Mathematical Optimization
• Machine Learning
• Control Theory
• +۷ مورد دیگر

Maryam Fazel is the Moorthy Family Inspiration Career Development Professor and Associate Professor of Electrical Engineering at the University of Washington, with adjunct appointments in Computer Science, Mathematics, and Statistics. She earned her PhD and MS in Electrical Engineering from Stanford University and a BS from Sharif University of Technology in Iran, followed by a postdoctoral fellowship at Caltech. Research Focus: Mathematical optimization, machine learning, control theory, and data science applications. Her work bridges convex optimization, reinforcement learning, and low-rank matrix methods. Honors: NSF CAREER Award (2009), UWEE Outstanding Teaching Award (2009), UAI Best Student Paper Award (2014), ScienceWatch Fast Breaking Paper (2011), and the Moorthy Family Professorship (2020). Leadership: Director of the Institute for Foundations of Data Science (IFDS), a multi-university NSF TRIPODS Phase II institute. Former co-director of Algorithmic Foundations of Data Science Institute (ADSI), Phase I. Students: Mentored award-winning advisees including Zhihan Xiong (Meta AI Fellowship), Avinandan Bose (Meta AI Fellowship), and K. Dvijotham (UAI Best Student Paper). Former group alumni include Brian Hutchinson (Full Professor at Western Washington University) and Ting Kei Pong (Full Professor at Hong Kong Polytechnic University). Publications: Recent work spans optimization in machine learning, control theory for nonlinear systems, and submodular maximization. Key themes include policy optimization, robustness in AI, and low-rank modeling. She serves on editorial boards for the Journal of Machine Learning Research (JMLR) and the MOS-SIAM Book Series on Optimization, and co-organizes cross-campus seminars like the Distinguished Seminars in Optimization and Data. Her TRIPODS+X grants (2018) focus on neuro/geoscience applications via hack weeks, while her DARPA Lagrange grant (2018) explores control of uncertain dynamical systems.

Piotr Indykمشاهده پروفایل

استاد

• Algorithms
• Theoretical Computer Science
• Data Mining
• +۳ مورد دیگر

Piotr Indyk is a Professor in the Department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology (MIT), School of Engineering, specializing in efficient algorithms for high-dimensional geometric problems and massive data analysis. His seminal contributions include: Co-inventing locality sensitive hashing for approximate nearest neighbor search, revolutionizing applications in data mining and computer vision by enabling similarity detection without full dataset scans. Pioneering sublinear algorithms for massive data streams that operate with minimal memory, addressing scalability challenges in big data. Developing accelerated sparse Fourier transform algorithms that outperform FFT for signals with sparse spectral representations.

Jan Hamaekersمشاهده پروفایل

پژوهشگر

• Numerical Algorithms
• High-Dimensional Problems
• Sparse Grids
• +۷ مورد دیگر

Dr. Jan Hamaekers is a researcher at the Fraunhofer Institute for Algorithms and Scientific Computing (SCAI) in Bonn, Germany. He is affiliated with the development of advanced numerical methods for high-dimensional problems in computational science, particularly in quantum mechanics and molecular dynamics. His work is centered on efficient algorithms such as HCFFT, ESPACK, and TREMOLO, and he collaborates on major research projects funded by DFG and EU programs. Research Interests: Development of hierarchical methods for high-dimensional problems Sparse grid and fast Fourier transform techniques Quantum mechanical and molecular mechanical (QM/MM) methods Numerical algorithms for the electronic Schrödinger equation Parallel software for nanoscale dynamics simulations His recent publications reflect a strong trend in computational materials science, multiscale modeling, and machine learning applications in chemistry. He has contributed to software packages like TREMOLO and ATK-ForceField, and his work bridges applied mathematics, physics, and engineering. He teaches practical labs in numerical simulation, indicating an active academic engagement. Scientific Projects: Molecular Dynamics Simulations for Material Science Applications CODICE - Computationally Driven design of Innovative Cement-based materials (EU FP7) Development of efficient algorithms for nanoscale dynamics simulations Multiscale QM/MM simulations of nanotubes and composites (DFG) Dr. Hamaekers has advised or collaborated with multiple researchers, as seen in his extensive co-authorship. He has not received any explicitly mentioned awards in the provided text. He is involved in software development and algorithmic innovation, with a focus on scalability and efficiency in scientific computing.

Felix Krahmerمشاهده پروفایل

دانشیار

• Optimization and Data Analysis
• Compressed Sensing
• Unlimited Sampling
• +۷ مورد دیگر

Prof. Dr. Felix Krahmer is an Associate Professor for Optimization and Data Analysis at the Department of Mathematics, Technical University of Munich (TUM) . His research focuses on mathematical foundations of data science, with expertise in Compressed Sensing Unlimited Sampling Quantization of Signals and Images Neural Networks Uncertainty Quantification Low-Rank Recovery He leads the Data Science research group within TUM's School of Computation, Information and Technology, collaborating with institutions like TU Berlin, RWTH Aachen, and Technion. His recent publications span topics like Robust blind deconvolution under adversarial noise Kronecker-structured dimensionality reduction Hysteresis in unlimited sampling systems Efficient sparse FFT algorithms Notable scientific awards include two August-Wilhelm-Scheer Visiting Professorships (2016, 2019) for hosting international collaborators. Former and current students include Olga Graf Dominik Stöger Claudio Verdun Felipe Pagginelli Patricio Juana Kostin He has co-organized workshops at SAMPTA, Oberwolfach, and ISIT, serving as associate editor for journals including SIAM Journal on Matrix Analysis and Frontiers in Signal Processing.

Gennady Pekhimenkoمشاهده پروفایل

دانشیار

• Computer Architecture
• Systems Research
• Applied Machine Learning
• +۳ مورد دیگر

Dr. Gennady Pekhimenko is an Associate Professor in the Department of Computer Science and cross-appointed to the Electrical and Computer Engineering department at the University of Toronto. He leads the EcoSystem research group and serves as CEO/Co-Founder of CentML, with affiliations at CIFAR and Vector Institute. PhD in Computer Science from Carnegie Mellon University (2016) His research spans computer architecture, systems optimization, and applied machine learning, with a focus on memory hierarchy efficiency, hardware acceleration, and compiler design. Recent work emphasizes large language model training, GPU virtualization, and processing-in-memory systems. Key article trends include: 1) Memory optimization for transformers and sparse models (2024-2025), 2) GPU resource management (2022-2024), 3) Prompt programming languages (2025), and 4) Deep learning compilation frameworks (2021-2023). Major Awards: NVIDIA Graduate Fellowship, Microsoft Research PhD Fellowship, ISCA Hall of Fame, AWS/Google/VMware research grants Key Collaborations: Onur Mutlu (CMU), Todd Mowry (CMU), Vector Institute researchers

• Numerical Analysis
• Computational Mathematics
• Sparse Signal Processing
• +۴ مورد دیگر

Dr. Toni Volkmer is a researcher affiliated with the Faculty of Mathematics at Chemnitz University of Technology. His work focuses on computational mathematics and signal processing, particularly in high-dimensional data analysis and sparse Fourier transforms. His research intersects numerical analysis, approximation theory, and algorithm design for efficient data processing. Key research areas include Sparse spectral estimation Rank-1 lattice sampling Multivariate function approximation High-dimensional data analysis Fast matrix-vector operations Graph Laplacian computations His publications demonstrate expertise in developing sublinear-time algorithms for harmonic analysis and creating efficient numerical methods applicable to modern data science challenges. While no formal awards are listed, his contributions to scientific computing have been documented in multiple peer-reviewed publications since 2012.

David Monniauxمشاهده پروفایل

پژوهشگر

• program verification
• abstract interpretation
• static analysis
• +۸ مورد دیگر

David Monniaux is a senior researcher (directeur de recherche) at CNRS and an adjunct professor at École polytechnique. He works at VERIMAG, a computer science laboratory jointly operated by CNRS and the University of Grenoble. Dr. Monniaux obtained his PhD in 2001 from Université Paris Dauphine under Professor Patrick Cousot, with a dissertation on the static analysis of probabilistic programs by abstract interpretation. He later earned his habilitation in computer science in 2009 from Université Joseph Fourier, Grenoble, and also holds an agrégation in mathematics. Monniaux's research focuses on program verification, with particular emphasis on proving software correctness. His work spans theoretical foundations in computability theory and practical applications in safety-critical systems. He has made significant contributions to abstract interpretation, static analysis, and the verification of numerical properties in programs. His research bridges computer science theory with practical engineering challenges, particularly in the context of critical embedded systems where software failures can have severe consequences. His work connects to diverse fields including game theory, algebra, and convex optimization. His recent publications demonstrate a strong focus on improving the precision and efficiency of static analysis techniques. Key themes include polyhedral approximation, program analysis with local policy iteration, abstraction of arrays and maps, synthesis of ranking functions, and computing worst-case execution times. These works collectively advance the field of program verification by addressing challenges in handling nonlinear constraints, branching, and complex data structures while maintaining computational feasibility. Monniaux has supervised several students including Julien Henry, Alexis Fouilhé, George (Egor) Karpenkov, and Alexandre Maréchal (now at LIP6), with current students Hang Yu and Valentin Touzeau. He has led significant research projects including VERASCO (2012-2015), which aimed at integrating a static analyzer into the CompCert certified compiler, and STATOR (2012-2017), an ERC starting investigator grant exploring advanced techniques for automatic inference of program invariants. At VERIMAG, Monniaux is part of a vibrant research community focused on critical systems. His work connects with broader efforts in formal methods, with applications in aviation, automotive systems, and other safety-critical domains where software reliability is paramount.

Shihao Yangمشاهده پروفایل

استادیار

• Statistics
• Machine Learning
• Data Science
• +۵ مورد دیگر

Shihao Yang serves as a Harold E. Smalley Early Career Professor and tenure-track Assistant Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology. He maintains critical affiliations with the Institute for Data Engineering and Science (IDEaS), Center for Machine Learning at Georgia Tech (ML@GT), and Institute of People and Technology (IPaT). His academic credentials include: Ph.D. in Statistics (2019), Harvard University A.M. in Statistics (2016), Harvard University B.Sc. in Actuarial Science (2014), University of Hong Kong Professor Yang's research program centers on harnessing big data through methodological development, computational tools, and probabilistic modeling to solve real-world problems. His expertise spans statistics, machine learning, and data science with concentrated applications in infectious disease modeling, dynamic system inference (parameter estimation in differential equations), and electronic medical records mining. His work consistently bridges theoretical innovation with practical healthcare implementations. Analysis of his recent publications reveals dominant themes in time series forecasting, causal inference, and physics-informed machine learning. His research integrates deep learning with statistical methods to enhance dynamic system analysis and healthcare applications, particularly in electronic health records interpretation and infectious disease prediction using internet search data. This synthesis drives methodological advances in handling noisy, sparse real-world datasets. His notable recognition includes: Harold E. Smalley Early Career Professorship Professor Yang actively mentors undergraduate, master's, and PhD students, encouraging prospective candidates to contact him with CVs and transcripts. His research receives substantial institutional support through Georgia Tech's interdisciplinary centers. He leads methodological development for real-world data challenges while maintaining strong industry and healthcare partnerships. As a core member of IDEaS, ML@GT, and IPaT, he drives cross-disciplinary collaboration between engineering, computing, and social sciences to advance data-driven solutions for complex societal problems.

Martijn C. van Beurdenمشاهده پروفایل

استاد

• Computational Electromagnetics
• Inverse Scattering
• Antenna Design
• +۲ مورد دیگر

Martijn van Beurden is a Full Professor in the Electromagnetics group at Eindhoven University of Technology's Department of Electrical Engineering. His research focuses on computational electromagnetics for high-tech systems, specializing in inverse scattering and antenna design. He holds a cum laude MSc (1997) and PhD (2003) from TU/e, where he received the C.I.V.I. Prize and ASML Prize for his thesis work. His research interests center on developing advanced numerical methods for electromagnetic modeling, with applications in wave behavior analysis, inverse scattering problems, and antenna optimization. Current projects include electromagnetic field modeling in stochastic environments and periodic structures. Recent publications demonstrate strong focus on computational efficiency in electromagnetic simulations, with innovations in Green's function discretization and FFT-accelerated integral equations applied to biomedical and scattering problems. Awards: C.I.V.I. Prize for Electrical Engineering (MSc thesis) ASML Prize for Best PhD Thesis in Applied Research He leads multiple projects including INNOSTAR and MAX META-XT, focusing on meta-lens design and electromagnetic scattering analysis. Manages the Electromagnetics research group and Center for Wireless Technology Eindhoven.

Lexing Yingمشاهده پروفایل

استاد

• Applied Mathematics
• Computational Science
• Quantum Computing
• +۷ مورد دیگر

Lexing Ying is a Professor of Mathematics at Stanford University. His research focuses on applied mathematics, computational science, and interdisciplinary areas such as quantum computing, machine learning, and numerical analysis. He has made significant contributions to high-dimensional problems, inverse problems, and the development of efficient algorithms for complex systems. His work spans theoretical advancements and practical applications, including quantum signal processing, tensor-based numerical methods, and probabilistic modeling. Key themes in his research include the analysis of diffusion models, stochastic systems, and optimization algorithms. Recent publications highlight innovations in solving high-dimensional PDEs, improving sampling techniques, and advancing quantum algorithms. Notably, Ying has developed methods for operator learning, functional hierarchical tensors, and robust phase estimation. His research often bridges mathematical theory and computational practice, addressing challenges in areas like statistical inference and numerical simulation.

• Fourier analysis
• Harmonic analysis
• Real analysis
• +۷ مورد دیگر

Joris Roos is an Assistant Professor at the University of Massachusetts Lowell within the Department of Mathematics and Statistics, part of the Kennedy College of Sciences. He holds a Ph.D. in Mathematics from the University of Bonn (2017). His research focuses on Fourier analysis, real analysis, and computer-assisted proofs, with particular emphasis on harmonic analysis applications in combinatorics, number theory, PDEs, and fractal geometry. He has been supported by grants including the NSF DMS-2154835 (2022-2025) and a Simons Foundation Grant (2021-2022). Roos has also participated in fellowships at the Hausdorff Research Institute for Mathematics and Oberwolfach. His work bridges pure analysis with computational methods, addressing problems in multilinear inequalities, maximal functions, and sparse domination techniques. Education: Ph.D. in Mathematics, University of Bonn, 2017 Research Interests: Fourier and harmonic analysis on Euclidean spaces Oscillatory integrals, maximal functions, singular integrals Discrete analogues in harmonic analysis Applications to combinatorics, number theory, dispersive PDEs Formalization of mathematics using interval arithmetic Grants & Fellowships: National Science Foundation Grant DMS-2154835 (2022-2025) Simons Foundation Grant ID 855692 (2021-2022) American Institute of Mathematics SQuaRE projects (2021-2025) Hausdorff Research Institute for Mathematics Fellowships (2021, 2022, 2024) Oberwolfach Research Fellowship (2023) Current Activity: Leading the 2024 HIM Trimester Program on Boolean Analysis in Computer Science and co-organizing the MLHA 2024 Spring School on multilinear singular integrals.