جست‌وجوی استاد و پژوهشگر

Jack Marquezمشاهده پروفایل

عضو هیئت علمی

• High Performance Computing (HPC)
• Cloud Computing
• Heterogeneous Storage Systems
• +۳ مورد دیگر

Jack Marquez is a Research Assistant Professor at the University of Tennessee, Knoxville, affiliated with the College of Engineering and the Department of Electrical Engineering and Computer Science. He is a member of the Global Computing Laboratory directed by Michela Taufer. PhD in Software Engineering (2022), Universidad Autónoma de Occidente, Colombia BS in Software Engineering (2013), Universidad Autónoma de Occidente, Colombia His research focuses on High Performance Computing (HPC) and Cloud Computing, with a particular emphasis on heterogeneous storage systems, scientific workflows, and performance optimization techniques. He has developed scalable algorithms for motif counting, dynamic simulation management, and data fabric integration, bridging computational efficiency with data-intensive science. His recent publications highlight expertise in: Optimizing HPC performance in cloud environments In-situ analysis for molecular dynamics simulations Machine learning-driven resource allocation Checkpointing and fault tolerance mechanisms Community-driven scientific data infrastructure Previously, he served as a researcher and adjunct professor at Universidad Autónoma de Occidente (2013–2016), contributing to software engineering pedagogy and industry collaboration as a software developer. He is based in the Min H. Kao Building, Room 351, and can be reached at jmarque4@utk.edu .

Gerald Quentin Maguire Jr.مشاهده پروفایل

استاد

• Data Communications
• Medical Image Processing
• Mobile Computing and Communication
• +۳ مورد دیگر

Gerald Quentin Maguire Jr. is a Professor (emeritus) at KTH Royal Institute of Technology, Sweden, specializing in Computer Science and Communication Systems. He holds a Ph.D. in Computer Science from the University of Utah and a B.A. in Physics from Indiana University of Pennsylvania. His academic roles include Professor of Computer Communication at KTH since 1994, and prior positions at Columbia University and the U.S. National Science Foundation. He has contributed to fields like data communications, medical imaging, and mobile computing. Education: Ph.D. in Computer Science, University of Utah, 1983 M.S. in Computer Science, University of Utah, 1981 B.A. in Physics (magna cum laude), Indiana University of Pennsylvania, 1975 Research Interests: Focuses on network protocols, mobile computing, medical imaging integration, and high-performance computing. His work includes cognitive radio systems, GPU-centric networking, and CT/PET fusion for medical diagnostics. Awards: IEEE Fellow (2013) Teacher of the Year (2006/2007, KTH) Giovanni DiChiro Award (1997) Multiple honors in medical imaging and network research Advising & Grants: Supervised 8 doctoral students and over 75 master's students. Authored 5 books and numerous papers, with over 200 invited lectures globally. His research has been supported by grants from Swedish and international funding bodies. Labs & Teams: Formerly led the Computer Communication Systems Lab (CCSlab) at KTH and collaborated with Karolinska Institutet on medical imaging projects.

Matthew Knepleyمشاهده پروفایل

استاد

• High-Performance Computing
• Geophysical Modeling
• Scientific Library Development
• +۳ مورد دیگر

Matthew Knepley is a Professor and Co-Director of Graduate Studies in the Department of Computer Science and Engineering at the University at Buffalo, affiliated with the School of Engineering and Applied Sciences. He also holds an affiliation with the Department of Mechanical and Aerospace Engineering within the same school. His research focuses on scalable numerical methods for multiphysics problems, including geophysical modeling (e.g., crustal deformation, mantle convection), scientific library development (e.g., PETSc, PyLith), and high-performance computing techniques for GPUs and exascale systems. Dr. Knepley earned his PhD in Computer Science from Purdue University (2000), MS in Computer Science from the University of Minnesota (1996), and BS in Mathematical Physics from Case Western Reserve University (1994). His research interests include the development of efficient solvers for partial differential equations, mesh adaptation, and the integration of advanced numerical methods into computational frameworks. He leads the Engineering Laboratory for Advanced Computational Science, advancing tools like PyLith for crustal dynamics and PETSc for parallel scientific computing. Recent publications highlight advancements in mesh transformations, finite difference stencils, and landau collision operators, reflecting his commitment to computational efficiency and portability. His work bridges theoretical numerical methods with real-world applications in geophysics, fluid dynamics, and climate modeling.

• Computer Graphics
• Real-Time Rendering
• Parallel Computing
• +۳ مورد دیگر

Erwan Leria is a Doctoral Researcher in the Department of Information Technology at Mid-Sweden University (2021–2025). His research focuses on real-time rendering techniques, multi-GPU computing, and light field technologies. Key interests include optimizing path tracing algorithms for high-performance graphics processing units (GPUs), developing scalable open-source rendering solutions, and advancing real-time applications in virtual/augmented reality environments. His work emphasizes interdisciplinary collaboration, with contributions to projects like the Tauray open-source path tracer for stereo and light field displays. Research topics span multi-source spatial reprojection, denoising techniques, and parallel computing architectures. Publications highlight trends in next-generation rendering systems, particularly in balancing computational efficiency with visual fidelity. Collaborations involve international teams exploring emerging technologies for immersive display systems.

Hariprasath Ganesanمشاهده پروفایل

پژوهشگر

• Atomistic Simulations
• Nanomechanics
• Materials Informatics
• +۳ مورد دیگر

Dr. Hariprasath Ganesan is a Senior Scientist at the Research Center Jülich, affiliated with the Institute for Advanced Simulation (IAS) and the Materials Data Science and Informatics (IAS-9) department. His expertise spans Atomistic Simulations, Nanomechanics, and Materials Informatics, focusing on computational modeling of materials' behavior under extreme conditions. He leads research into high-temperature deformation mechanisms in advanced alloys like TiAl, leveraging multiscale simulation techniques such as molecular dynamics and Monte Carlo methods. His work emphasizes GPU-accelerated computational approaches for atomistic simulations, improving efficiency in studying solute segregation, interface dynamics, and thermal-mechanical properties. Recent studies include ultrafast laser-material interactions and creep behavior in nanomaterials. Collaborations bridge theoretical models with practical applications in materials design and additive manufacturing. Publications highlight contributions to understanding lamellar interface stability, Cottrell atmosphere formation, and parallel computing frameworks for scale-bridging simulations. His research aligns with Helmholtz energy and materials science initiatives, advancing computational tools for next-generation materials discovery.

François Faureمشاهده پروفایل

استاد

• Physical Simulation for Computer Graphics
• Deformable Models
• Contact Detection and Modeling
• +۴ مورد دیگر

François Faure is a Professor of Computer Science at University Joseph Fourier, Grenoble, and a researcher at INRIA and LJK-CNRS. His work focuses on physical simulation for computer graphics, including deformable models, collision detection, and simulation software architecture. He leads the development of the SOFA simulation library, widely used in medical and entertainment applications. Education: M.Sc. Computer Science, University Paris XI (1992) Ph.D. Computer Graphics, University of Grenoble (1997) Habilitation in Computer Science, University of Grenoble (2008) Research Interests: Combines physics-based modeling with computational efficiency, emphasizing GPU acceleration and software modularity. Key topics include meshless deformation models, contact detection algorithms, and high-performance simulation frameworks. Publications: Focus on advancing simulation accuracy and efficiency, with notable work on stable constrained dynamics and frame-based deformation. Recent contributions address real-time interaction and anatomical modeling. Advising & Labs: Supervises PhD students in computer graphics and medical simulation. Active in the ANIMA and IMAGINE teams at INRIA, collaborating on virtual reality and biomechanical systems.

Xing Caiمشاهده پروفایل

استاد

• Parallel programming
• High-performance computing
• Performance modeling
• +۸ مورد دیگر

Xing Cai is a Professor at the Department of Informatics, University of Oslo, specializing in Scientific Computing and Machine Learning. His academic career spans several decades with a consistent focus on high-performance computing and its applications to complex scientific problems. He maintains an active research profile with numerous publications in top-tier journals and conferences. Professor Cai's research interests encompass parallel programming and high-performance computing, performance modeling and optimization, automated code generation, heterogeneous computing, and numerical methods for solving partial differential equations. His work extends to specialized applications in computational cardiology, computational geoscience, and biomedical computing. His research bridges theoretical computer science with practical applications in medicine and earth sciences, demonstrating exceptional interdisciplinary reach. An analysis of his recent publications (2019-2024) reveals a strong trend toward leveraging novel hardware architectures (GPUs, AI processors, specialized accelerators) for scientific computing, with particular emphasis on cardiac modeling applications. His work shows increasing sophistication in hardware-aware algorithm design, with publications spanning from fundamental performance modeling to domain-specific applications. The interdisciplinary nature of his work is evident in the diverse range of journals and conferences where he publishes, from computer science venues to specialized medical and geoscience publications. Professor Cai leads or participates in several significant research projects including the EuroHPC Centre of Excellence: Numerical Modeling of Cardiac Electrophysiology at the Cellular Scale (MICROCARD-2), High resolution simulation of cardiac electrophysiology on realistic whole-heart geometries, Maelstrom Associate Team, ODISSEE, Simula-Berkeley Education and Research collaboration (SIMBER), and aCG eX3: Experimental Infrastructure for Exploration of Exascale Computing. These projects reflect his leadership in both computational methodology development and domain-specific applications. His research group maintains strong collaborations with medical researchers, particularly in cardiac electrophysiology, and with geoscientists working on reservoir simulation. The publications list demonstrates consistent mentorship of junior researchers, with frequent co-authorship patterns suggesting an active supervision of PhD and postdoctoral researchers. His work on the EMI model for cardiac tissue represents a significant contribution to computational cardiology with potential clinical applications. The laboratory environment surrounding Professor Cai's work appears to be well-equipped for high-performance computing research, with access to advanced hardware platforms including GPU clusters, AI processors, and specialized accelerators. His publications on the use of Graphcore IPUs, Xeon Phi processors, and NVIDIA architectures indicate a well-resourced research environment capable of experimenting with cutting-edge hardware.

Nachiket Kapreمشاهده پروفایل

دانشیار

• Concurrent and Spatial Architectures
• Parallel Processing
• Heterogeneous Architectures
• +۴ مورد دیگر

Nachiket Kapre is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Waterloo, Canada (2023–present). He previously held positions as Associate Professor (2016–2021) and on leave as Research Director at Xilinx Labs (AMD Research, Singapore, 2022–2023). Before that, he was an Assistant Professor at Nanyang Technological University (NTU), Singapore (2012–2016) and a Junior Research Fellow at Imperial College London (2010–2012). He earned a Ph.D. and two M.S. degrees from the California Institute of Technology (2010), and a B.E. from the University of Pune (2002). His research focuses on Concurrent and Spatial Architectures , Parallel Processing , and Communication-Centric Design , with a strong emphasis on FPGA-based acceleration for applications like machine learning, graph algorithms, and embedded systems. Key contributions include Hoplite NoC architectures and CaffePresso for deep learning acceleration. Notable awards include the FPT Best Paper Award (2024), TRETS Best Paper Award (2023), and the CASES 2016 Best Paper Award. He has led multiple grants, including NSERC Discovery Grants and A*STAR-funded projects. His advising spans 15+ students across PhD and MSc levels, contributing to FPGA toolflows, NoC design, and hardware acceleration. He has authored over 50 publications in top venues like FPGA, FPL, and ACM TRETS, and serves as a program chair for FCCM 2023. His work bridges theory and practice, emphasizing energy-efficient computing and reconfigurable systems.

John Strattonمشاهده پروفایل

دانشیار

• Software Performance Optimization
• High-Performance Computing
• Parallel Processing
• +۵ مورد دیگر

John Stratton is an Associate Professor of Computer Science and Director of the 3-2 Engineering Program at Whitman College. He holds a Ph.D., M.S., and B.S. in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign (2006–2013). His research focuses on software performance optimization, tools for efficient scientific applications, and compiler optimizations for parallel and GPU-based systems. He leads the Whitman Optimization Laboratory (OptiLab), involving student researchers in projects like biological simulation, image analysis, and programmer productivity tools. Stratton’s work emphasizes bridging hardware and software efficiency, with contributions to CUDA compilation for FPGAs, parallel thread scheduling, and memory optimization. His pedagogical innovations include group oral presentations in algorithms courses. He has published extensively on topics ranging from kernel fusion in OpenCL to performance portability on shared-memory systems. Professional contributions include the SPEC ACCEL benchmark suite and the Parboil throughput computing benchmarks. He mentors student researchers and has advised projects on multi-threaded memory allocation, dynamic type acceleration, and thread-block management in multi-core systems.

• Evolutionary Algorithms
• Swarm Robotics
• Optimization
• +۳ مورد دیگر

Prof. Ben Paechter is a leading academic at the School of Computing Engineering and the Built Environment , Edinburgh Napier University, specializing in evolutionary algorithms and swarm robotics. As a Professor , he has shaped research directions in computational optimization and adaptive systems for over two decades. Research Themes : Evolutionary Swarm Robotics, Multi-Objective Optimization, Hyper-heuristics, Neural Architecture Search Key Projects : FOCAS (European Commission), KTP: Intelligent Agents (Innovate UK), PerAda (Self-Aware Systems) Collaborations : CAVES Research Group, Centre for Algorithms, Visualisation and Evolving Systems His article trends demonstrate expertise in cross-domain hyper-heuristics, swarm behavior tree evolution, and neural network architecture optimization. Recent works (2023-2024) focus on GPU-accelerated evolutionary algorithms and hierarchical swarm control systems. Scientific Awards : Fellow of the British Computer Society (FBCS) Chartered IT Professional (CITP) GECCO Best Paper Nomination (2018) Advising & Grants : Supervised 5 PhD students including Paul Lapok (planar mechanisms) and Andreas Steyven (swarm diversity). Secured £2.2M+ in funding across 13 projects, including EU FOCAS (£639,999) and Innovate UK grants.

• Cloud Computing
• Distributed Systems
• High-Performance Computing
• +۵ مورد دیگر

Raja Appuswamy is an Assistant Professor in the Department of Data Science at EURECOM, focusing on data management on modern hardware and molecular information storage. His research integrates cutting-edge storage technologies with bioinformatics, particularly in DNA-based data storage. He teaches courses in cloud computing, distributed systems, and databases. His work spans Optimizing storage hierarchies with DNA archival systems Hardware-conscious algorithms for GPUs and heterogeneous architectures Error correction in molecular storage Long-term database preservation strategies Key research interests include Cross-architecture data joins (e.g., OneJoin, XJoin) Cold storage innovation with CMOSS and OligoArchive Integration of bio-inspired storage with traditional computing and explores interdisciplinary challenges at the intersection of computer science and molecular biology. Publications emphasize scalable solutions for DNA storage density, error tolerance, and archival systems. Awards highlight contributions to DNA-based image encoding, storage reliability, and page cache checksums. Current projects focus on enzymatic DNA ligations for storage density improvement, motif-based error correction, and hardware acceleration for knowledge graphs. His work targets both theoretical advancements and practical implementations in storage systems.

John Taylorمشاهده پروفایل

استاد

• Climate Modeling
• Machine Learning
• Image Analysis
• +۳ مورد دیگر

Prof. John Taylor is a prominent academic at The Australian National University (ANU), affiliated with the School of Computing. His research focuses on interdisciplinary areas spanning climate science, machine learning, and high-performance computing. He has made significant contributions to atmospheric modeling, GPU-accelerated algorithms, and data-driven environmental predictions. Research Interests Climate Modeling and Regional Climate Simulations Machine Learning Applications in Meteorology Deep Learning for Image Analysis and Semantic Segmentation High-Performance Computing and GPU Optimization Statistical Downscaling of Climate Variables Cloud-Based Scientific Workflows Collaborations Prof. Taylor collaborates extensively with institutions globally, including work on projects like the Earth Virtualization Engines (EVE) and the PAUNet precipitation prediction framework. His research often bridges computational methods with real-world environmental challenges, such as heat extremes and hydrological modeling. Key Achievements Over 5000 citations and an h-index of 50 Developed innovative frameworks for climate data assimilation and GPU-accelerated algorithms Leader in applying machine learning to environmental monitoring and prediction Labs/Teams He leads research groups at ANU focused on computational climate science and advanced imaging techniques. His team has developed platforms like the Cloud-Based Image Analysis Toolbox and the DCM software for 3D materials modeling.

James Demmelمشاهده پروفایل

استاد

• Numerical Analysis
• Linear Algebra
• Scientific Computing
• +۴ مورد دیگر

James Demmel is a Professor of Computer Science and Mathematics at the University of California, Berkeley, with joint appointments since 1990. His research focuses on numerical linear algebra, high-performance computing, and parallel algorithms. He co-developed widely used libraries like LAPACK and ScaLAPACK. Demmel holds ACM, SIAM, and IEEE Fellowships, and is a member of both the National Academy of Engineering and Sciences. Education: Ph.D. in Computer Science, UC Berkeley, 1983 B.S. in Mathematics, Caltech, 1975 Research Interests: Demmel’s work emphasizes numerical methods for linear algebra, including algorithms for eigenvalue problems, matrix factorizations, and high-performance computing architectures. His contributions bridge theory and practice, addressing challenges in floating-point arithmetic and algorithm scalability. Publications & Awards: Over 150+ publications, including foundational papers on LAPACK and iterative methods. Recipient of the SIAM Activity Group Linear Algebra Best Paper Prize (1988, 1991), Wilkinson Prize (1993), and ACM Supercomputing Test of Time Award (2019). Labs & Teams: Demmel collaborates with groups like Berkeley Benchmarking and Optimization Group (BeBOP) and CLIMB, focusing on algorithmic efficiency and exascale computing.

Raluca Ada Popaمشاهده پروفایل

دانشیار

• Computer Security
• Systems Security
• Applied Cryptography
• +۴ مورد دیگر

Raluca Ada Popa is an associate professor in the Department of Electrical Engineering and Computer Science at UC Berkeley, holding the Robert E. and Beverly A. Brooks Endowed Chair. She leads the Frontier Security team at Google DeepMind and co-founded the RISELab, SkyLab, and DARE diversity program. Her research focuses on systems security, cryptographic computing, and privacy-preserving machine learning. She received her PhD from MIT in 2014, alongside BS and MS degrees in Computer Science and Mathematics from MIT. Education: PhD in Computer Science, MIT (2014) MS in Computer Science, MIT (2010) BS in Computer Science and Mathematics, MIT (2009) Research Interests: Popa designs systems that compute securely on encrypted data, enabling confidentiality in cloud computing, AI, and collaborative analytics. Her work bridges cryptography and systems, with contributions to secure multi-party computation, hardware enclaves, and decentralized trust mechanisms. Key innovations include CryptDB, Opaque, and Helen, which have influenced industry standards like Microsoft SQL's Always Encrypted service. Publications & Awards: Over 50+ peer-reviewed papers, including influential work at top venues like USENIX Security, SOSP, and IEEE S&P. Notable awards include the ACM Grace Murray Hopper Award (2021), Sloan Research Fellowship (2018), and MIT Technology Review's TR35 (2019). Her work has been recognized for advancing secure computing and privacy-preserving technologies. Advising & Grants: Advisor to over 20 PhD students and postdocs, many of whom hold faculty positions or lead industry research. Recipient of major grants including NSF CAREER Award (2020), Bakar Fellowship (2019), and a $10M NSF Expedition for RISELab. Co-founded Opaque Systems (2021) and PreVeil (2015), with significant industry impact. Labs & Teams: Co-director of RISELab (Real-time, Intelligent, Secure Systems) and SkyLab (secure cloud and edge computing). Founded DARE to promote diversity in EECS research, connecting undergraduates with faculty mentors.

Sophia Shaoمشاهده پروفایل

دانشیار

• Computer Architecture
• Integrated Circuits
• Domain-Specific Accelerators
• +۵ مورد دیگر

Sophia Shao is an Associate Professor in the Department of Electrical Engineering and Computer Sciences (EECS) at the University of California, Berkeley. She was promoted to Associate Professor with tenure in July 2024 after serving as an Assistant Professor from August 2019 to June 2024. Previously, she worked as a Senior Research Scientist at NVIDIA from October 2018 to July 2019. Her academic journey began with a Bachelor of Electrical Engineering from Zhejiang University, China (2005-2009), followed by a Master of Science (2009-2014) and Ph.D. (2009-2016) in Computer Science from Harvard University. Shao's research focuses on computer architecture, with special emphasis on domain-specific accelerators, heterogeneous architecture, and agile VLSI design methodology. Her work bridges the gap between hardware and software through systematic approaches to accelerator design and evaluation. She leads research efforts in the Agile Design of Efficient Processing Technologies (ADEPT), Berkeley Emerging Technologies Research (BETR), Berkeley Wireless Research Center (BWRC), and SpeciaLIzed Computing Ecosystems (SLICE) centers. Her recent publications reveal a strong trend toward holistic hardware-software co-design for machine learning acceleration, with particular focus on efficient neural network accelerators, multi-tenant execution environments, and automated design methodologies. Her research spans from low-level circuit design to system-level architecture, demonstrating expertise across the entire computing stack. Notably, her work on Gemmini, Stellar, and Virgo represents significant contributions to the field of domain-specific accelerator design and integration. Sloan Research Fellowship (2024) Anita Borg Early Career Award (2024) NSF CAREER Award (2023) Google Research Scholar Award (2023) IEEE TCCA Young Computer Architect Award (2022) Intel Rising Star Faculty Award (2022) ISCA Distinguished Artifact Award (2023, 2024) Best Paper Award at DAC 2021 Professor Shao actively mentors a large group of graduate and undergraduate students, with several former students now at leading technology companies including NVIDIA, Microsoft, and Apple. Her research has been supported by multiple grants from NSF, Google, Intel, and other organizations. She leads several open-source research projects including Gemmini, Stellar, Chipyard, and RoSÉ, which have gained significant traction in both academic and industrial research communities. Her lab, part of the SLICE ecosystem, focuses on creating next-generation computing platforms through vertically integrated hardware-software approaches.