جست‌وجوی استاد و پژوهشگر

Heiner Giefersمشاهده پروفایل

استاد

• Cloud Computing
• Hardware Acceleration
• Field-Programmable Gate Arrays
• +۷ مورد دیگر

Heiner Giefers is a Professor for Cloud Computing at the Department of Computer Science and Natural Sciences at Southwestphalia University of Applied Sciences since 2018. Prior to this position, he worked as a Research Staff Member at IBM Research - Zürich (2013-2018), focusing on hardware acceleration in cloud environments, implementation of big data algorithms on FPGAs, and development of hardware platforms for approximate and in-memory computing. Dr. Giefers received his doctorate (Dr. rer. nat.) from Universität Paderborn in 2012 with a dissertation titled "Design and Programming of Reconfigurable Mesh based Many-Cores." His academic journey at Universität Paderborn includes serving as an Academic Council Member (Akademischer Rat a.Z.) from 2008-2013 and as a Scientific Staff Member from 2006-2012, where he taught digital technology and computer architecture. Professor Giefers' research focuses on energy-efficient computing, particularly through hardware acceleration using FPGAs for cloud and AI workloads. His work spans cloud computing infrastructure, hardware-software co-design, approximate computing, in-memory computing, and energy-efficient implementations of machine learning algorithms. He has made significant contributions to the field of reconfigurable hardware for high-performance computing applications. His recent publications show a strong trend toward applying hardware acceleration techniques to artificial intelligence and machine learning workloads, with a particular focus on energy efficiency. His work bridges the gap between theoretical computer science and practical hardware implementation, often resulting in patented technologies that address real-world computing challenges in cloud environments. Best Paper Award for "Stochastic Matrix-Function Estimators: Scalable Big-Data Kernels with High Performance" (2016) Best Paper Award Nomination for "Energy-Efficient Stochastic Matrix Function Estimator for Graph Analytics on FPGA" (2016) Best Paper Award Nomination for "Analyzing the energy-efficiency of dense linear algebra kernels by power-profiling a hybrid CPU/FPGA system" (2014) Best Paper Award Nomination for "A Triple Hybrid Interconnect for Many-Cores: Reconfigurable Mesh, NoC and Barrier" (2010) Professor Giefers actively supervises numerous Bachelor's and Master's students, with over 50 completed theses covering topics from machine learning and cloud computing to IoT systems and hardware acceleration. He leads the "Energy-efficient AI" project (eki), which aims to increase the energy efficiency of AI systems through approximation techniques for FPGA implementation. Additionally, he collaborates with Prof. Dr. Christian Plessl on the "Digital teaching materials with Jupyter Notebooks" project, creating interactive learning materials that integrate teaching content, program code, and results into a single document. His work extends to practical applications through multiple patents related to FPGA implementations, neural networks, and memory systems, demonstrating his commitment to translating research into real-world solutions.

Mark Gatesمشاهده پروفایل

عضو هیئت علمی

• High Performance Computing
• Scientific Computing
• Linear Algebra
• +۳ مورد دیگر

Mark Gates is a Research Assistant Professor at the Innovative Computing Laboratory (ICL) at the University of Tennessee, Knoxville. His work focuses on high performance computing (HPC), scientific computing, and linear algebra, particularly leveraging multi-core and GPU architectures. He contributes to projects like SLATE, PLASMA, and MAGMA, advancing dense and sparse linear algebra libraries for modern parallel systems. His research emphasizes scalable algorithms, communication-avoiding techniques, and exascale computing. Education: Ph.D., M.S., and B.S. in Computer Science from the University of Illinois Urbana-Champaign (2011, 2007, 1998). Research interests include parallel algorithms, numerical stability, and GPU acceleration. Recent work highlights include task-based polar decomposition, mixed-precision algorithms, and efficient sparse linear algebra operations. His articles address challenges in distributed computing, library development, and performance optimization. Labs/Teams: Active in ICL, collaborating on HPC software for exascale systems. No scientific awards explicitly listed, though contributions to foundational HPC libraries are notable.

Tao Yangمشاهده پروفایل

استاد

• Web Search and Mining
• Information Retrieval
• Database and Information Systems
• +۴ مورد دیگر

Tao Yang is a Professor in the Department of Computer Science at the University of California, Santa Barbara, where he has been a faculty member since 1993. His research spans web search and mining, database and information systems, machine learning and data mining, parallel and distributed systems, and cloud computing. He serves as an active educator, teaching courses including CS170 Operating Systems (Spring 2024), CS291A Neural Information Retrieval (Fall 2024), and CS140 Parallel Computing (Winter 2025). PhD in Computer Science, Rutgers University ME in Artificial Intelligence, Zhejiang University MS in Computer Science, Rutgers University BS in Computer Science, Zhejiang University Professor Yang's research focuses on advancing the field of information retrieval with particular emphasis on neural approaches to search and ranking. His recent work explores neural document ranking, privacy-aware search systems, and versioned data search. He has led significant projects including Neptune clustering infrastructure, Sorrento self-organizing storage cluster, and TMPI for MPI execution optimization. His research bridges theoretical advances with practical implementations, particularly in scaling search architectures to handle billions of documents while maintaining relevancy, performance, and freshness. His publication record shows a clear evolution from foundational work in parallel and distributed systems toward contemporary research in neural information retrieval. Recent publications demonstrate expertise in optimizing both sparse and dense retrieval methods, with particular focus on efficiency improvements for multi-vector representations. His work consistently addresses real-world challenges in search scalability and privacy preservation. Faculty Research Award, Google Research Research Initiation Award, NSF (1994) UC Regents' Junior Faculty Award (1994) Computer Science Faculty Teacher Award (1995) CAREER Award, NSF (1997) Noble Jeeviant Award, AskJeeves (2002) Professor Yang has supervised numerous graduate students, many of whom have gone on to prominent positions at companies like Google, Apple, and Coursera, or academic positions at universities worldwide. His industry experience as Chief Scientist for Ask.com (2001-2010) and founding Chief Scientist for Teoma (2000-2001) has informed his research direction and provided valuable practical context for his academic work. He has served on program committees for major conferences including WWW, SIGIR, KDD, WSDM, CIKM, ECIR, and EMNLP. His research group maintains active projects in neural information retrieval, privacy-aware search, similarity computing, and parallel computing systems. The group collaborates closely with industry partners, particularly in the search technology space, and has developed systems that power major search engines serving over 100 million users.

• Large-scale Scientific Computing
• Computational Linear Algebra
• Parallel and Distributed Processing
• +۴ مورد دیگر

Professor Efstratios Gallopoulos is a faculty member at the Department of Computer Engineering & Informatics , University of Patras, where he holds the Division of Computer Software . He currently serves as Deputy Department Chair and Director of the High Performance Information Systems Laboratory (HPCLab) . His academic career spans multiple institutions including the University of Illinois at Urbana-Champaign, University of California Santa Barbara, and collaborations with INRIA Rennes and NASA Goddard Space Flight Center. Education : B.Sc. in Mathematics (First Class Honours) from Imperial College London (1979) Ph.D. in Computer Science from University of Illinois at Urbana-Champaign (1985) Research Focus : His work centers on Large-scale Scientific Computing with emphasis on Computational Linear Algebra , Parallel/Distributed Processing , and Data Mining . Recent publications highlight innovations in Randomized Numerical Linear Algebra , Heterogeneous Cluster Scheduling , and GPU-Accelerated Inversion Techniques . Article Trends : His research bridges High-Performance Computing with Data Science , focusing on scalable algorithms for Matrix Computations , Recommender Systems , and Biomarker Analysis . The work spans theoretical advancements (e.g., Givens Rotations ) and practical implementations (e.g., pylspack library). Scientific Awards : NASA Group Achievement Award for Massively Parallel Processor (MPP) development ACM SIGWEB Hypertext Ted Nelson Newcomer Award (2012) Advising and Grants : He has advised numerous research projects funded by European Research Council , Hellenic Foundation for Research and Innovation (HFRI) , and international bodies like the US National Science Foundation. Notably, he co-organized the 2015 Gene Golub SIAM Summer School and served as Chair of the SIAM Gene Golub Summer School Committee (2020-24). Labs and Teams : He leads the High Performance Information Systems Laboratory (HPCLab) and co-directs the interdisciplinary graduate program Data Driven Computing and Decision Making . His teams have contributed to the Cedar vector multiprocessor project at UIUC and Text-to-Matrix Generator (TMG) tools for data mining.

Ahmad Abdelfattahمشاهده پروفایل

عضو هیئت علمی

• High Performance Computing
• Parallel Numerical Algorithms
• GPU Computing
• +۳ مورد دیگر

Dr. Ahmad Abdelfattah is a Research Assistant Professor at the University of Tennessee's Innovative Computing Laboratory (ICL). He holds a PhD in Computer Science from KAUST (2015), an MS in Computer Engineering from Ain Shams University (2010), and a BS in Computer Engineering (2006). His research focuses on high-performance computing, parallel numerical algorithms, and GPU acceleration, particularly in dense and sparse linear algebra libraries. He leads the MAGMA library and has contributed to NVIDIA's cuBLAS and AMD's rocBLAS libraries. His work emphasizes exascale computing, mixed-precision arithmetic, and optimizing batched operations on GPUs. His research spans algorithm design for GPUs, including batched matrix operations, sparse linear algebra, and high-order finite element methods. He has developed libraries like libCEED and SLATE, targeting exascale systems. His contributions address challenges in memory-bound computations, irregular patterns in matrices, and efficient use of tensor cores. Key trends in his publications include optimizing GPU kernels for small matrices, exploring mixed-precision techniques for solving linear systems, and enabling portable performance across diverse architectures. His work bridges theoretical numerical methods with practical implementation on modern hardware, aiming to accelerate scientific applications. Awards and recognitions include acknowledgments from NVIDIA and AMD for contributions to their BLAS libraries. His advising and grants involve advancing exascale computing through collaborations with ECP (Exascale Computing Project), focusing on CEED-enabled applications and performance tuning. He leads initiatives to enhance software tools for high-order discretizations and scalable linear algebra solutions. Abdelfattah is affiliated with the Innovative Computing Laboratory, contributing to both academic and industrial partnerships. His research impacts fields like computational science, computer vision, and robotics through optimized algorithms and libraries.

• Numerical Analysis
• High-Performance Computing
• Parallel Computing
• +۵ مورد دیگر

Carl C. Kjelgaard Mikkelsen is an Associate Professor at the Department of Computing Science, Umeå University. He specializes in numerical analysis and high-performance parallel computing, focusing on robust algorithms that cannot fail. He co-authored the StarNEig library for nonsymmetric eigenvalue problems and works on parallel constraint solvers for molecular dynamics and ultra-sparse linear systems. Research Interests: Numerical analysis, parallel computing, eigenvalue problems, linear systems, algorithm design. His recent publications emphasize robust task-based algorithms, GPU acceleration, and precision requirements in Newton's method. He teaches numerical analysis and scientific computing, emphasizing finite precision arithmetic and software documentation.

• High-Performance Computing
• Parallel Computing
• Deep Learning
• +۳ مورد دیگر

Enrique S. Quintana-Ortí is a Professor at the Technical University of Valencia and Jaume I University , Spain, specializing in Computer Science of Systems and Computers . His work bridges High-Performance Computing (HPC) , Parallel Computing , and Deep Learning , with a focus on optimizing Matrix Algorithms for modern architectures. Key research areas: Quantized Inference , GEMM-Based Convolutions , GPU Acceleration , and Performance Portability across ARM, RISC-V, and NVIDIA processors. Recent projects include RED-SEA (European interconnect solutions), GreenLightningAI (decoupled AI systems), and Ginkgo (GPU-based linear algebra frameworks). His publications (2023–2025) emphasize edge computing , mixed-precision techniques , and energy-efficient AI . Collaborative efforts span institutions like Xilinx , Fujitsu , and co-authors such as Adrián Castelló , Héctor Martínez , and Francisco D. Igual .

Dianne P. O'Learyمشاهده پروفایل

استاد

• Scientific Computing
• Numerical Analysis
• Computational Science
• +۶ مورد دیگر

Dianne P. O'Leary is a Professor affiliated with the Department of Computer Science at the University of Maryland. Her work focuses on scientific computing, numerical analysis, and computational science, with a particular emphasis on matrix factorizations, differential equations, and Monte Carlo methods. She authored the textbook *Scientific Computing with Case Studies* (SIAM Press, 2009), which integrates theoretical concepts with practical case studies across physics, engineering, epidemiology, and biology. Her research explores topics such as image deblurring, blind deconvolution, elastoplastic torsion, and iterative methods for solving large linear systems. The book includes nineteen case studies that illustrate algorithm design, mathematical modeling, and software development principles. Notably, the text emphasizes stability, error analysis, and MATLAB implementation grounded in machine arithmetic and memory management. While no explicit scientific awards or student advisees are mentioned in the provided texts, her contributions to computational education and algorithm development are central to her work. Supplementary materials include solution manuals, MATLAB code repositories, and detailed notes on topics like sensitivity analysis, computer memory management, and finite element methods. Her case studies often bridge theory and application, such as modeling epidemics, robot control systems, and structural engineering problems. The book’s structure reflects a commitment to hands-on learning, with units dedicated to dense matrix computations, optimization, Monte Carlo simulations, and sparse matrix techniques for partial differential equations.

Thomas Pockمشاهده پروفایل

استاد

• Computer Vision
• Image Processing
• Mathematical Optimization
• +۷ مورد دیگر

Thomas Pock is a Professor of Computer Science at Graz University of Technology, holding the AIT Stiftungsprofessur for Mobile Computer Vision. He is affiliated with the Institute for Computer Graphics and Vision (ICG) within the Faculty of Computer Science and serves as a principal scientist at the Austrian Institute of Technology (AIT), Center for Vision, Automation & Control. He leads the Vision, Learning and Optimization (VLO) research group, which focuses on mathematical modeling and optimization in computer vision. His research interests lie at the intersection of computer vision, image processing, and mathematical optimization. Specifically, he develops mathematical models for computer vision and efficient convex and non-smooth optimization algorithms , particularly for mobile scenarios. His recent work increasingly integrates variational methods with deep learning, especially in solving inverse problems in imaging such as medical reconstruction and deblurring. The trends in his recent publications show a strong emphasis on deep learning for inverse problems , variational networks , and learned optimization . His group explores how to combine classical mathematical models with data-driven deep learning approaches to achieve stable, interpretable, and high-performance solutions in image reconstruction and processing. His scientific achievements have been recognized with several prestigious awards: START Prize, Austrian Science Fund (FWF), 2013 German Pattern Recognition Award, DAGM, 2013 ERC Starting Grant, European Research Council, 2014 Thomas Pock actively mentors students and leads a research group of 10 PhD students and 2 postdocs. He has secured significant research grants, including the ERC Starting Grant, which supports his foundational work. He is also engaged in scientific communication, giving invited talks at international venues such as SIAM and co-organizing the IMAGINE One World seminar series to foster global collaboration in imaging and inverse problems. He leads the Vision, Learning and Optimization (VLO) group at the Institute for Computer Graphics and Vision. The group develops mathematical models and efficient algorithms for computer vision and image processing, with a focus on mobile applications. The team includes multiple PhD students and postdoctoral researchers and has produced notable software and publications in top venues.

• Computational Mechanics
• Finite Element Analysis
• Structural Dynamics
• +۹ مورد دیگر

Prof. Duc Nguyen is a Professor in the Department of Civil & Environmental Engineering at Old Dominion University (ODU), affiliated with the Batten College of Engineering and Technology. His expertise spans Computational Mechanics, Finite Element Analysis (FEA), Structural Dynamics, and Parallel Computing. He has held numerous prestigious roles, including NASA-ASEE Fellowships and the 2010 ODU Shining Star Award. Nguyen holds a Ph.D. from the University of Iowa (1982), M.S. from the University of California (1976), and B.S. from Northeastern University (1974). Research focuses on large-scale algorithms for parallel supercomputing, design sensitivity analysis, sparse linear solvers, and multiphysics CAD-based optimization. He has led grants totaling over $2 million, including projects on numerical methods, engineering education, and structural analysis. Notable publications address parallel finite element methods, structural/acoustic coupling, and optimization using genetic algorithms. Awarded honors include recognition for citation impact (2004), teaching excellence (2001), and computational performance (1989). His work bridges computational efficiency with engineering applications, emphasizing interdisciplinary collaboration. Current research explores MPI-enabled algorithms, multi-hazard risk assessment, and coastal infrastructure resilience.

• Numerical Analysis
• High Performance Parallel Computing
• Algorithm Design
• +۵ مورد دیگر

Carl C Kjelgaard Mikkelsen is an Associate Professor at the Department of Computing Science, Umeå University, Sweden. His research focuses on developing robust numerical algorithms that cannot fail, particularly in the context of finite precision arithmetic used by computers. His work bridges theoretical numerical analysis with practical high-performance computing applications. Dr. Mikkelsen's research interests center around numerical analysis and high performance parallel computing, with special emphasis on the worst case analysis of algorithms. He investigates the profound differences between exact arithmetic used by humans and finite precision arithmetic used by computers. His current projects include developing parallel constraint solvers for constrained molecular dynamics and parallel solvers for ultra-sparse linear systems. He is a co-author of the StarNEig library for solving dense nonsymmetric standard and generalized eigenvalue problems in parallel. His recent publications demonstrate a strong focus on accuracy requirements in numerical methods, particularly Newton's method, and the development of robust parallel algorithms for linear algebra problems. His work spans from theoretical analysis of numerical stability to practical implementation in high-performance computing environments. Dr. Mikkelsen teaches numerical analysis and scientific computing, emphasizing algorithm design, analysis, and implementation. He focuses on teaching students the critical differences between exact and finite precision arithmetic, and invests significant time in teaching software development practices and technical writing.

Ümit V. Çatalyürekمشاهده پروفایل

استاد

• High-Performance Computing
• Combinatorial Scientific Computing
• Biomedical Informatics
• +۴ مورد دیگر

Ümit V. Çatalyürek is a Professor in the School of Computational Science and Engineering at Georgia Institute of Technology's College of Computing. Previously, he held positions as a Professor and Vice Chair in Biomedical Informatics at Ohio State University. He earned his Ph.D., M.S., and B.S. in Computer Engineering from Bilkent University, Turkey. His research focuses on High-Performance Computing, Combinatorial Scientific Computing, and Biomedical Informatics. Education: Ph.D. (2000), M.S. (1994), B.S. (1992) in Computer Engineering from Bilkent University Research interests include parallel computing, graph algorithms, and genomic data analysis. He has authored over 200 peer-reviewed articles and leads the TDA research group at Georgia Tech. Notable contributions include scalable graph partitioning methods and genome assembly tools like BOA. Awards include IEEE and SIAM Fellowships and an NSF CAREER Award. He serves as Editor-in-Chief of Parallel Computing and has held leadership roles in ACM SIGBio and IEEE TCPP. His grants include funding from DOE, NIH, and NSF for projects in computational science and biomedical informatics. Advising involves mentoring students in computational methods and large-scale data analysis. He leads the TDA lab and collaborates on interdisciplinary initiatives in bioinformatics and quantum computing.

Vivek Sarinمشاهده پروفایل

دانشیار

• Numerical Algorithms
• Parallel Computing
• Computational Science
• +۳ مورد دیگر

Vivek Sarin is an Associate Professor in the Department of Computer Science at Texas A&M University. His research focuses on numerical algorithms, parallel computing, computational science, and data mining. He holds a Ph.D. from the University of Illinois at Urbana-Champaign (1997), an M.S. from the University of Minnesota (1993), and a B.Tech. from the Indian Institute of Technology Delhi (1990). Research Interests: Numerical algorithms for scientific/engineering problems (e.g., protein structure modeling, VLSI circuit analysis, coastal modeling) High-performance parallel computing and distributed numerical algorithms Data mining and large-scale data analysis (e.g., protein-nucleic acid interactions, traffic flow modeling) Grants & Awards : NSF CAREER Award (2000–2004), IBM Faculty Partnership Award (2002), TEES Select Young Faculty (2002), and TEES Special Research Fellow (2001). He has led over $4M in funded research, including projects on coastal modeling, VLSI design, and computational fluid dynamics. Teaching : Courses include Parallel Computing, Computational Linear Algebra, and Data Mining. He has advised numerous PhD and MS students, contributing to their work in areas like inductance extraction, fluid dynamics, and iterative methods. Professional Contributions : Served on NSF panels, conference program committees (e.g., ICPP, Supercomputing), and editorial/review boards for journals like Parallel Computing and SIAM Journal on Scientific Computing.

Michał Derezińskiمشاهده پروفایل

استادیار

• Theoretical Computer Science
• Randomized Numerical Linear Algebra (RandNLA)
• Stochastic Optimization
• +۳ مورد دیگر

Michał Dereziński is an Assistant Professor of Computer Science and Engineering at the University of Michigan, affiliated with the College of Engineering. His research focuses on the theoretical foundations of randomized algorithms for numerical linear algebra, machine learning, optimization, and data science. Previously, he held postdoctoral positions at UC Berkeley (Department of Statistics) and the Simons Institute for the Theory of Computing, and completed his Ph.D. in Computer Science at UC Santa Cruz under Manfred Warmuth, alongside Master's degrees in Mathematics and Computer Science from the University of Warsaw. He teaches courses such as Foundations of Computer Science (EECS 376), Algorithms for Data Science (EECS 474), and specialized RandNLA courses. His work emphasizes bridging theory and practice in RandNLA, with applications to ML and optimization. Key achievements include an NSF CAREER Award and a NeurIPS 2020 Best Paper Award for work on column subset selection. Current advisees include Sachin Garg, Jiaming Yang, and Shabarish Chenakkod. His research explores topics like sketching algorithms, stochastic optimization, and random matrix theory, with publications in top venues such as NeurIPS, COLT, and SIAM journals. He actively contributes to the RandNLA community through tutorials and software development efforts like RandBLAS/RandLAPACK initiatives.

José R. Herreroمشاهده پروفایل

دانشیار

• High Performance Computing
• Parallel Computing
• Dense Linear Algebra Algorithms
• +۳ مورد دیگر

José R. Herrero is an Associate Professor in the Computer Architecture Department (DAC) at the Universitat Politècnica de Catalunya (UPC), affiliated with the Barcelona School of Informatics (FIB). His primary research area is High Performance Computing (HPC), focusing on parallel algorithms, dense linear algebra, and multicore processor optimization. He has held administrative roles including Vice-Dean for Academic Studies (2010–2013) and Vice-Dean for Institutional and International Relations (2013–2015) at FIB. Education: PhD in Computer Science from UPC (2006), Postgraduate in Training of University Professors (1997), and a Computer Science degree (1993). Teaching experience includes over 15 courses in Computer Architecture, Operating Systems, and Parallel Programming at both undergraduate and graduate levels. Research interests span HPC, parallel computing, and algorithm design. His work emphasizes optimizing matrix computations and linear algebra libraries for modern architectures. Notable contributions include advancements in sparse Cholesky factorization, task-parallel algorithms, and energy-efficient computing. Recognition includes UPC's Teaching Mention for Outstanding Quality (2009, 2014, 2019) and a Certificate of Merit for contributions to scientific computing workshops. He advises numerous PhD students and collaborates internationally, including visiting roles at GeorgiaTech and École Polytechnique de Montréal.