جست‌وجوی استاد و پژوهشگر

Prasad Raghavendraمشاهده پروفایل

استاد

• Theory
• optimization
• complexity theory
• +۷ مورد دیگر

Prasad Raghavendra is a Professor in the Electrical Engineering and Computer Sciences (EECS) Department at the University of California, Berkeley. His research focuses on theoretical computer science, particularly in optimization, complexity theory, approximation algorithms, hardness of approximation, and statistics. He is affiliated with the Center for the Theoretical Foundations of Learning, Inference, Information, Intelligence, Mathematics and Microeconomics at Berkeley (CLIMB) and the Simons Institute for the Theory of Computing (SITC). PhD in Computer Science and Engineering, University of Washington, Seattle (2009) M.S. in Computer Science and Engineering, University of Washington, Seattle (2007) B.S. in Computer Science, Indian Institute of Technology, Madras, India (2005) Raghavendra's research spans theoretical computer science with a focus on optimization, complexity theory, approximation algorithms, and the hardness of approximation problems. He has made significant contributions to understanding Constraint Satisfaction Problems (CSPs), Sum-of-Squares SDP hierarchies, and their applications in high-dimensional statistics. His work bridges theoretical computer science with statistical inference, exploring computational-statistical gaps and developing efficient algorithms for problems in robust statistics, community detection, and tensor decomposition. Raghavendra's recent publications demonstrate a clear trajectory toward the intersection of theoretical computer science and high-dimensional statistics. His work increasingly focuses on Sum-of-Squares SDP hierarchies for statistical problems, robust algorithms for planted models, community detection in stochastic block models, and heavy-tailed statistics. The publications show a progression from foundational work on CSPs and approximation algorithms toward applications in machine learning and statistical inference, with particular attention to computational barriers and optimal algorithms in high-dimensional settings. Michael and Sheila Held Prize (2018) Okawa Research Grant (2015) NSF Faculty Early Career Development Award (CAREER) (2013) Sloan Research Fellow (2012) Raghavendra has advised numerous PhD students who have gone on to positions at institutions like Stanford Statistics, Google Research, and academic positions. His current and past students include David X. Wu, Sidhanth Mohanty, Tarun Kathuria, and others. His research has been supported by multiple grants including an NSF CAREER award and Okawa Research Grant, focusing on theoretical foundations of learning, inference, and computational complexity. He regularly teaches advanced courses including CS 270 (Combinatorial Algorithms and Data Structures), CS 294 (Constraint Satisfaction Problems), and CS 294 (Efficient Algorithms and Computational Complexity in Statistics). Raghavendra is affiliated with the Center for the Theoretical Foundations of Learning, Inference, Information, Intelligence, Mathematics and Microeconomics at Berkeley (CLIMB) and the Simons Institute for the Theory of Computing. His work often involves collaborations with researchers in theoretical computer science, statistics, and mathematics at Berkeley and beyond. His research group focuses on developing theoretical foundations for high-dimensional statistical problems and exploring computational barriers in inference tasks.

• Mathematics
• Computer Science
• Applied Mathematics
• +۶ مورد دیگر

Anthony Nouy is a Professor at the Department of Mathematics, Computer Science, and Biology (MIB) at École Centrale de Nantes. His research spans Mathematics, Computer Science, and Computational Biology, with a focus on approximation theory, tensor networks, and stochastic optimization. His work explores nonlinear manifold approximation dimension reduction in feature spaces stochastic gradient descent optimization moment methods for optimal transport and PDEs model reduction and tensor geometry Recent publications highlight trends in machine learning, numerical analysis, and computational mathematics, particularly through compositional polynomial networks, Poincaré inequality surrogates, and tree tensor formats. Collaborations include researchers like Alexandre Pasco, Philipp Trunschke, and Mazen Ali. He is affiliated with the Jean Leray Mathematics Laboratory and maintains a personal webpage for updates. Co-authors of recent publications include Antoine Bensalah, Joel Soffo, and Alexandre Pasco (2025) Robert Gruhlke, Philipp Trunschke (2024) Clément Cardoen, Swann Marx, Nicolas Seguin (2024) Mazen Ali, Antonio Falcó, Wolfgang Hackbusch (2023)

Karl Meerbergenمشاهده پروفایل

استاد

• Algebraic eigenvalue problems
• Algebraic model order reduction
• Preconditioning
• +۷ مورد دیگر

Karl Meerbergen is a Full Professor in the Department of Computer Science at KU Leuven, Faculty of Engineering Sciences. He leads research in numerical analysis and applied mathematics with a focus on eigenvalue problems, model order reduction, and computational linear algebra. He is a member of the Numerical Analysis and Applied Mathematics (NUMA) research unit and holds affiliations with multiple KU Leuven institutes including iSi Health, Leuven.AI, Leuven.AM, and the LGI Gravitation Institute. His research spans several key areas of numerical mathematics with emphasis on algebraic eigenvalue problems, algebraic model order reduction, preconditioning techniques, computational acoustics, tensor computations, exascale computing, generic programming, and parallel computing. His work bridges theoretical numerical analysis with practical applications in engineering and scientific computing. Analysis of his recent publications shows a strong focus on advanced numerical methods for eigenvalue problems, model order reduction techniques, and parallel computing approaches. His work frequently addresses challenges in large-scale scientific computing, with applications in structural dynamics, acoustics, and optimization problems. The research demonstrates increasing sophistication in handling nonlinear and parametric systems through rational approximation methods and specialized preconditioning techniques. Dr. Meerbergen actively contributes to the academic community through his teaching responsibilities and supervision of graduate students. His work has significant implications for computational science and engineering applications requiring efficient numerical solutions to complex mathematical problems.

• Analysis
• Applied Mathematics
• Probability
• +۴ مورد دیگر

Nicholas F. Marshall is an Assistant Professor in the Department of Mathematics at Oregon State University's College of Science. His academic journey includes a Ph.D. in Applied Mathematics from Yale University (2019) and a B.S. in Mathematics from Clarkson University (2014), with additional research experience at Princeton University as an NSF Postdoc. Ph.D. in Applied Mathematics, Yale University, 2019 B.S. in Mathematics, Clarkson University, 2014 His research focuses on the interplay between analysis, geometry, and probability, particularly as applied to data science challenges. Current investigations include harmonic analysis on geometric domains, randomized algorithms for linear systems, and mathematical frameworks for cryo-electron microscopy. His work bridges pure mathematical theory with computational applications in imaging and machine learning. Analysis of his recent publications reveals strong trends in computational harmonic analysis, with significant contributions to fast algorithms for spherical and disk harmonics, randomized linear solvers with momentum acceleration, and geometric approaches to hyperdimensional computing. His work consistently connects abstract mathematical concepts to practical computational problems in imaging and data science. Dr. Marshall actively mentors graduate students including Wyatt Whiting, Peter Cowal, and Heather Fogarty, and has supervised notable undergraduate research projects leading to publications in SIAM journals. His current teaching portfolio includes advanced courses in probability theory, numerical linear algebra, and data science mathematics. He maintains active research collaborations with institutions including Princeton University and Yale, focusing on applications in cryo-EM imaging and computational geometry. Personal interests include skiing (learned in Vermont) and kayaking along the Oregon Coast.

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.

Randal Burnsمشاهده پروفایل

استاد

• Data Science
• Machine Learning
• Cloud Storage Systems
• +۴ مورد دیگر

Randal Burns is a Professor and the Bill and Lisa Stromberg Head of the Department of Computer Science in the Whiting School of Engineering at Johns Hopkins University. He is also affiliated with the Data Science and AI Institute and co-founder of NeuroData. His research focuses on scalable data systems for scientific applications, spanning storage technologies, cloud infrastructure, and graph/sparse-matrix engines for machine learning. Key themes include data-intensive science, neuroscience imaging analysis, and adaptive performance management. Recent publications highlight trends in edge-parallel graph encoders masked matrix operations for sparsity decentralized foundation model training storage optimization for evolving AI models batch-parallel data structures turbulence data analysis These reflect his work on scalable systems for machine learning and scientific computing. Scientific awards include NSF CAREER Award DOE Early Career Principal Investigator Kavli Fellowship IBM Outstanding Innovation Award Advising: Mentors PhD students in scalable data systems and machine learning. Grants: Supported by NSF, DOE, and DARPA initiatives. Labs: Core member of the Johns Hopkins Turbulence Database Group and NeuroData team, developing open platforms for neuroscience and turbulence research.

Haesun Parkمشاهده پروفایل

استاد

• Data analysis
• Visual analytics
• Numerical computing
• +۴ مورد دیگر

Dr. Haesun Park is a Regents' Professor and Chair in the School of Computational Science and Engineering at Georgia Institute of Technology, Atlanta, Georgia. She has held this position since August 2020, after becoming a Regents' Professor in July 2019 and a Professor since July 2005. Prior to joining Georgia Tech, she served as a Professor in the Department of Computer Science and Engineering at the University of Minnesota from 1987 to 2005 (Assistant Professor 1987-1993, Associate Professor 1993-1998, Professor 1998-2005) and as a Program Director at the National Science Foundation from 2003-2005. Education: Ph.D. in Computer Science (minor: Mathematics), Cornell University, 1987 M.S. in Computer Science, Cornell University, 1985 B.S. (Summa Cum Laude) in Mathematics, Seoul National University, 1981 (with Presidential Medal for top graduate) Dr. Park's research spans multiple domains within computational science, with particular focus on data analysis, visual analytics, numerical computing, text mining, social media mining, parallel computing, and bioinformatics. Her work integrates mathematical foundations with practical applications across various fields, developing innovative algorithms for large-scale data analysis. She has made significant contributions to nonnegative matrix factorization techniques and their applications in diverse domains including healthcare, social media analysis, and scientific computing. Her recent publication trends show a strong focus on parallel and distributed implementations of matrix factorization algorithms, applications in healthcare data analysis (particularly patient profiling and clustering), and development of scalable frameworks for handling large-scale datasets. The work demonstrates increasing interdisciplinary collaboration, especially with medical researchers, and a growing emphasis on practical implementations that can handle real-world data challenges. Scientific Awards: SIAM Fellow (2013) IEEE Fellow (2016) ACM Fellow (2020) Dr. Park has held significant leadership roles including Executive Director of the Center for Data Analytics (2013-2015) and Director of the NSF/DHS FODAVA-Lead Center (2008-2014). She has served as conference co-chair for the SIAM International Conference on Data Mining in 2008 and 2009, and as an editorial board member for leading journals including IEEE Transactions on Pattern Analysis and Machine Intelligence, SIAM Journal on Matrix Analysis and Applications, and SIAM Journal on Scientific Computing. She has been a plenary keynote speaker at major international conferences including SIAM Conference on Applied Linear Algebra in 1997 and 2015, and SIAM International Conference on Data Mining in 2011. Dr. Park is affiliated with multiple research centers at Georgia Tech including the Algorithms and Randomness Center (ARC) and the Institute for Data Engineering and Science (IDEaS). She has maintained a long-term collaboration with the Korea Institute for Advanced Study (KIAS) as a KIAS Scholar from 2008-2018, demonstrating her international research impact.

Caroline Fossatiمشاهده پروفایل

استاد

• Signal Processing
• Hyperspectral Imaging
• Medical Imaging
• +۲ مورد دیگر

Caroline Fossati is a Full Professor at Ecole Centrale de Marseille and a member of the Institut Fresnel - GSM Signal Processing lab. Her work bridges signal processing, hyperspectral imaging, and medical diagnostics, with a focus on Alzheimer's disease detection using PET imaging and tensor-based noise reduction techniques. Current Affiliations: Ecole Centrale de Marseille, Institut Fresnel Past Affiliations: Université Paul Cézanne (1996-1997) Her research spans: Medical Imaging: Developing advanced algorithms for Alzheimer's diagnosis through 18F-FDG PET scans and multi-level feature extraction. Hyperspectral Processing: Innovating tensor-based methods for noise reduction, dimensionality reduction, and target detection in complex imaging environments. Optical Engineering: Investigating CMOS sensor design, photolithography simulations, and defect characterization in optical materials. Publications demonstrate expertise in multidimensional signal processing, array modeling, and biomedical applications. While no specific awards are documented in the provided text, her extensive co-authorship network and technical contributions across disciplines underscore her academic impact.

Coralia Cartisمشاهده پروفایل

استاد

• Numerical Optimization
• Algorithm Design and Analysis
• Nonlinear Optimization
• +۶ مورد دیگر

Professor Coralia Cartis holds a Professorship in Numerical Optimization at the University of Oxford's Mathematical Institute, where she also serves as a Tutorial Fellow at Balliol College. Additionally, she is a Turing Fellow at The Alan Turing Institute in London, focusing on advanced optimization research with applications spanning machine learning and climate science. She obtained her PhD in Mathematics from the University of Cambridge in 2005, establishing the foundation for her expertise in algorithmic optimization. Research Focus: Her work centers on designing and analyzing algorithms for linear and nonlinear optimization problems, both convex and nonconvex, with emphasis on complexity theory and dynamical systems connections. Key application areas include compressed sensing, sparse approximation, machine learning, and inverse problems in climate modeling, where she develops methods for ocean biogeochemical model optimization and data assimilation. Publication Evolution: Recent publications (2024-2025) demonstrate expansion into high-order tensor methods and quartic regularization models, while maintaining her foundational work on cubic regularization. Her research consistently bridges theoretical complexity analysis with practical climate science applications, showing increasing interdisciplinary collaboration. Honors: Leslie Fox Prize in Numerical Analysis (2005, second place) Mathematical Programming Computation Journal Best Paper Prize (2019) INFORMS Simulation Society Outstanding Paper Prize (2021) ICM Invited Speaker (2022) EUROPT Fellow (2023) SIAM Fellow (2023) Academic Leadership: She actively contributes to Oxford's Numerical Analysis and Machine Learning research groups, supervises graduate students (though specific names aren't listed), and secures research funding for projects involving climate modeling and optimization theory. Her work with the Alan Turing Institute facilitates cross-institutional collaborations in data science. Research Infrastructure: Her optimization research is conducted within Oxford's Mathematical Institute, leveraging resources from both the Numerical Analysis group and the Machine Learning and Data Science research cluster, with additional computational support through The Alan Turing Institute.

Galen Reevesمشاهده پروفایل

استادیار

• Information Theory
• Machine Learning
• Statistical Signal Processing
• +۳ مورد دیگر

Galen Reeves is an Assistant Professor at Duke University with a joint appointment in the Department of Electrical and Computer Engineering and Department of Statistical Science since Fall 2013, reflecting his interdisciplinary expertise bridging engineering and mathematical sciences. His research establishes rigorous theoretical frameworks at the intersection of information theory, machine learning, and statistical signal processing, focusing on fundamental limits in high-dimensional inference problems. His academic background features elite training across top institutions: PhD in Electrical Engineering and Computer Sciences, University of California, Berkeley (2011) MS in Electrical Engineering, University of California, Berkeley (2007) BS in Electrical and Computer Engineering, Cornell University (2005) Reeves' research centers on mathematical foundations of data science, with seminal contributions to compressed sensing, tensor estimation, and coding theory. He investigates information-theoretic bounds for estimation problems, develops efficient algorithms like approximate message passing, and analyzes generative AI model behavior under recursive training conditions. His work demonstrates how statistical physics approaches solve complex problems in communication theory and high-dimensional statistics. Analysis of his 2021-2025 publications reveals three dominant trends: breakthroughs in channel capacity using Reed-Muller codes, theoretical analysis of generative models and diffusion sampling, and fundamental limits in tensor/matrix estimation. These works consistently integrate information theory with machine learning, emphasizing scalability challenges in high dimensions and algorithmic robustness under heteroskedasticity. His scientific recognition includes: NSF VIGRE fellowship supporting postdoctoral research at Stanford University (2011-2013) NSF CAREER award (2018) for Theoretical Foundations for Probabilistic Models with Dense Random Matrices While no specific students are documented in the source text, his faculty position entails graduate mentorship in both ECE and Statistical Science departments. Research funding primarily stems from the NSF CAREER grant advancing probabilistic modeling, complemented by earlier fellowship support. His collaborations span Stanford University, EPFL, TU Delft, and Microsoft Research. Though no dedicated lab is mentioned, his joint appointment fosters cross-departmental research at Duke, particularly in projects like 'Modeling Traffic with Self Driving Cars' which applies statistical learning to autonomous systems. His work maintains strong ties to industry through past Microsoft Research internships and ongoing computational applications in communications and AI.

Krzysztof Podgórskiمشاهده پروفایل

استاد

• Stochastic Processes
• Random Spatio-temporal Fields
• Non-Gaussian Stochastic Models
• +۵ مورد دیگر

Krzysztof Podgórski is a Professor and Head of the Department of Statistics at Lund University School of Economics and Management (LUSEM). His research spans applied probability, statistics, and interdisciplinary applications in engineering, finance, and environmental sciences. Key Research Areas: Multivariate non-Gaussian stochastic models Statistical analysis of spatio-temporal random fields Distributions at random crossing events Applications: Mechanical engineering (road modeling) Ocean engineering (wave/ship reliability) Financial econometrics (market linkages, risk analysis) Actuarial sciences (non-Gaussian claims) Methodological Contributions: Include ergodic theory of stochastic processes, extreme value theory, and computational statistics. His recent work explores functional data analysis with periodic splines and matrix variate distributions. Collaborative Networks: Extensive partnerships across theoretical and applied disciplines, with projects involving road dynamics, stochastic fields, and uncertainty quantification.

• Heterogeneous computing systems
• High-level synthesis (HLS)
• Hardware specialization for machine learning
• +۳ مورد دیگر

Zhiru Zhang is a Professor at Cornell University in the School of Electrical and Computer Engineering , leading research at the Computer Systems Laboratory . His work focuses on algorithms, methodologies, and design automation tools for heterogeneous computing systems. Recent publications emphasize high-level synthesis (HLS), hardware specialization for machine learning, and programming models for software-defined FPGAs. Education: Ph.D. in Computer Science, UCLA B.S. in Computer Science, Peking University M.S. in Computer Science, UCLA Research Interests: Heterogeneous computing systems High-level synthesis (HLS) optimization FPGA-based hardware acceleration Sparse data format compilers Machine learning for electronic design automation Scientific Awards: IEEE Fellow Intel Outstanding Researcher Award NSF CAREER Award DARPA Young Faculty Award Multiple Best Paper Awards at ASPLOS, ISPD, FPGA, AutoML, FCCM Research Group: Mentors 12 current students including Jordan Dotzel, Jie Liu, and Grace Dinh, with 14 alumni now at institutions like AWS AI, NVIDIA, Google DeepMind, and Microsoft. His lab develops tools like UniSparse for sparse format customization, presented at OOPSLA'24 and IEEE CAL.

Kirshanthan Sundararajahمشاهده پروفایل

استادیار

• Compilers
• Programming Languages
• High-Performance Computing
• +۳ مورد دیگر

Kirshanthan Sundararajah is an Assistant Professor in the Department of Computer Science at Virginia Tech's College of Engineering. His research focuses on compilers, programming languages, and high-performance computing, with an emphasis on optimizing algorithms for complex data structures like sparse tensors and tree traversals. He holds a Ph.D. and M.S. in Electrical and Computer Engineering from Purdue University (2022) and a B.S. in Electronics and Telecommunication Engineering from the University of Moratuwa, Sri Lanka (2014). His work spans compiler optimization techniques, parallel computing strategies, and efficient execution models for heterogeneous architectures. Key contributions include frameworks like Orchard for heterogeneous parallelism, SparseAuto for sparse tensor computations, and SABLE for blocked evaluation of sparse matrices. He also explores dynamic symbolic execution and secure multi-party computation through metaprogramming (HACCLE). No scientific awards were explicitly mentioned in the provided text. His advising and grant activities are not detailed here, though his research likely involves collaborative projects in high-performance computing and compiler design. He is affiliated with the Department of Computer Science at Virginia Tech and contributes to academic initiatives in parallel algorithms and computational efficiency.

• Statistics
• Machine Learning
• Tensor Methods
• +۳ مورد دیگر

Yuchen Zhou is an Assistant Professor in the Department of Statistics at the University of Illinois. His research focuses on high-dimensional statistics, tensor methods, and optimization, with applications in data analysis and machine learning. He has contributed to advancements in areas such as heteroskedastic PCA, low-rank tensor inference, and sparse group lasso regularization. His work emphasizes statistical theory and methodology, addressing challenges in high-dimensional data structures and computational efficiency. Collaborations and research outputs highlight interests in matrix and tensor decomposition, regularization techniques, and non-asymptotic analysis. Zhou's publications span reputable journals like the Annals of Statistics and IEEE Transactions on Information Theory, reflecting his expertise in both theoretical and applied statistical problems. He has no listed academic awards or current advisees.

Joshua Agterbergمشاهده پروفایل

استادیار

• High-Dimensional Statistics
• Matrix and Tensor Analysis
• Nonconvex Optimization
• +۴ مورد دیگر

Joshua Agterberg is an Assistant Professor in the Department of Statistics at the University of Illinois, College of Liberal Arts & Sciences. His research focuses on high-dimensional statistics, matrix and tensor analysis, nonconvex optimization, and statistical network analysis. He holds a B.S. in Actuarial Science and Mathematics from the University of Wisconsin-Madison, followed by M.S. and Ph.D. degrees in Statistics from Johns Hopkins University. After a postdoctoral fellowship at the University of Pennsylvania, he returned to the Midwest to join UIUC. Education: Bachelor of Science in Actuarial Science and Mathematics, University of Wisconsin-Madison Master of Science in Statistics, Johns Hopkins University Doctor of Philosophy in Statistics, Johns Hopkins University Research Interests: His work bridges theoretical foundations and practical applications, addressing problems in nonconvex matrix sensing, multilayer network analysis, and statistical-computational gaps. He aims to develop methodologies for analyzing complex data structures, particularly in neuroscience, sociology, and engineering. Recent projects include advancing spectral methods, nonconvex optimization, and hypothesis testing frameworks for networks. Teaching Philosophy: Agterberg emphasizes statistical intuition over formulaic approaches, encouraging students to tackle problems from first principles. He teaches courses on theoretical statistics and hopes to develop a high-dimensional statistics curriculum for PhD students. Future Goals: He seeks to create a principled pipeline for network data analysis and to enhance understanding of machine learning algorithms' theoretical underpinnings. Collaborations include work on dynamic networks and statistical methodologies for connectomics.