جست‌وجوی استاد و پژوهشگر

• Optimizing Compilers
• Runtime Systems
• Embedded Systems
• +۵ مورد دیگر

Summary Role & Affiliation: Mahmut Taylan Kandemir is a Professor in the Department of Computer Science and Engineering at Pennsylvania State University. He is a member of the Microsystems Design Lab and has held roles such as Graduate Program Coordinator. His research focuses on optimizing compilers, runtime systems, embedded systems, and storage technologies. Education: B.S. and M.S. from Istanbul Technical University (Computer Engineering), Ph.D. from Syracuse University (Computer Science). Research Interests: Kandemir’s work spans compiler optimization, energy-efficient architectures, non-volatile memory systems, and cloud storage. His research has been funded by NSF, DARPA, and industry partners like Intel and Microsoft. Articles & Contributions: Over 150 journal papers and 650+ conference publications, covering topics like compiler-driven storage efficiency, 3D NAND SSD optimization, and approximate computing frameworks. Awards: NSF Career Award, Penn State Premier Research Award, IEEE Fellow, and multiple best paper awards. His contributions include compiler tools and storage systems for high-performance computing. Advising & Grants: Advised 32 Ph.D. and 20 M.S. students. Active in program committees for conferences like MICRO, ISCA, and HPCA. Current grants focus on cloud resource management and emerging memory technologies. Labs & Collaborations: Leads projects in collaboration with Argonne National Lab, NVIDIA, and Intel. His work bridges academia and industry, addressing challenges in multicore and embedded systems.

Yong Chenمشاهده پروفایل

استاد

• Data-Intensive Computing
• Parallel and Distributed Computing
• High-Performance Computing
• +۳ مورد دیگر

Dr. Yong Chen is a Professor and Interim Department Chair in the Computer Science Department at Texas Tech University (TTU), where he founded the Data-Intensive Scalable Computing Laboratory (DISCL). He also serves as Co-Director of the NSF Cloud and Autonomic Computing Center (CAC@TTU), focusing on data-intensive computing, high-performance computing (HPC), cloud systems, and parallel/distributed architectures. His research bridges hardware-software co-design for scientific and enterprise applications. Ph.D., Computer Science, Illinois Institute of Technology (2009) M.S., Computer Science, University of Science and Technology of China (2003) B.E., Computer Engineering, University of Science and Technology of China (2000) Dr. Chen's research spans data-intensive computing, HPC, cloud systems, and parallel architectures. He develops scalable solutions for scientific discovery and enterprise computing, emphasizing systems software, storage optimization, and hardware-software co-design. His work addresses challenges in metadata management, 3D-stacked memory, and efficient resource allocation in distributed environments. Recent publications include studies on 3D-stacked memory optimization (IEEE TC), metadata indexing (SC), and parallel file system reliability (ICS). His work is characterized by interdisciplinary collaboration and practical applications in HPC domains. NSF-TCPP Early Adopter Status Award Best Paper Award (IPDPS'21) Outstanding Teaching Assistant, IIT (2006) Dr. Chen advises students through graduate and undergraduate research assistantships at DISCL and CAC@TTU, offering financial support for qualified candidates. He has contributed to major conferences as Program Co-Chair (ICPP) and Committee Member (IPDPS, CCGrid, ISC, HPCAsia). Labs/Teams: Data-Intensive Scalable Computing Laboratory (DISCL), NSF Cloud and Autonomic Computing Center (CAC@TTU).

Panagiotis Hadjidoukasمشاهده پروفایل

دانشیار

• Parallel Computing
• Distributed Systems
• High Performance Computing
• +۳ مورد دیگر

Panagiotis Hadjidoukas is an Associate Professor and Head of the Laboratory for Computing at the Computer Engineering and Informatics Department, University of Patras, within the School of Engineering. His work focuses on high-performance computing systems and parallel programming models. His research spans parallel and distributed computing , runtime support for parallel programming models , and automation of AI/ML workloads . Key contributions include developing the torc runtime system for task parallelism and pioneering work in extreme-scale scientific simulations. His interests bridge theoretical computer science with practical applications in scientific computing and AI acceleration. Notable achievements include the ACM Gordon Bell Prize Winner (2013) for 11 PFLOP/s cloud cavitation simulations and Finalist (2015) for in-silico lab-on-a-chip microfluidics. His software tools ( torc_lite , torcpy ) enable efficient parallelism across diverse architectures. Doctor of Philosophy (2003), University of Patras Master of Science (2001), University of Patras Diploma in Computer Engineering (1998), University of Patras As Head of the Laboratory for Computing, he leads infrastructure development while maintaining active research collaborations with IBM Research and ETH Zurich. His teaching portfolio includes graduate courses on high-performance computing for data sciences and parallel processing principles.

André Brinkmannمشاهده پروفایل

استاد

• Data Center Management
• Cloud Computing
• Storage Systems
• +۵ مورد دیگر

André Brinkmann is a full professor at the Department of Computer Science, Johannes Gutenberg University Mainz, leading the Efficient Computing and Storage Group. He previously served as head of the university's data center (2011–2021) and was an assistant professor at Paderborn University (2008–2011). He holds a Ph.D. in Electrical Engineering from Paderborn University (2004) and managed the Paderborn Centre for Parallel Computing (PC²). His research focuses on algorithm engineering for data center management, cloud computing, storage systems, and high-performance computing (HPC). Notable projects include: Development of ad hoc file systems like GekkoFS and IO-SEA for exascale architectures Optimization of storage systems (e.g., hybrid RAID, SSD garbage collection) Quantum computing compiler research for trapped-ion architectures Leadership in initiatives like the I/O Trace Initiative and BINARY (Big Data in Atmospheric Physics) He serves as Senior Associate Editor of ACM Transactions on Storage and co-chairs major conferences like FAST 2026 and ARCS 2026.

Nihat Altiparmakمشاهده پروفایل

دانشیار

• Data Storage Systems
• Parallel and Distributed Systems
• High Performance Computing
• +۲ مورد دیگر

Nihat Altiparmak is an Associate Professor in the Department of Computer Science and Engineering at the University of Louisville. He holds a B.S. in Computer Engineering from Bilkent University (2007) and a Ph.D. in Computer Science from the University of Texas at San Antonio (2013). His research focuses on data storage systems, parallel/distributed systems, high-performance computing, and cloud computing. He directs the Computer Systems Laboratory at the University of Louisville and has received NSF awards for his work on storage optimization and big data infrastructure. Education: B.S., Computer Engineering, Bilkent University, 2007 M.S., Computer Science, University of Texas at San Antonio, 2012 Ph.D., Computer Science, University of Texas at San Antonio, 2013 Research interests emphasize adaptive parallel storage systems and efficient data retrieval techniques. His work has been published in top-tier journals like IEEE Transactions on Computers and ACM Transactions on Storage. Notable contributions include frameworks for optimizing I/O performance in heterogeneous storage environments. Awards include NSF CRII (2017) and MRI (2018) grants for advancing storage technologies.

Greg Eisenhauerمشاهده پروفایل

پژوهشگر

• High Performance Computing
• Systems
• Enterprise Computing
• +۳ مورد دیگر

Dr. Greg Eisenhauer is a Senior Research Scientist at the Georgia Institute of Technology's School of Computer Science and affiliated with the Center for Experimental Research in Computer Systems (CERCS). His research focuses on high-performance computing (HPC), systems, and enterprise computing, with an emphasis on program monitoring, dynamic adaptation, performance evaluation, and I/O systems like ADIOS. Supported by NSF, DOE, DARPA, and industry grants, his work addresses challenges in HPC workflows, data management, and streaming analytics. He leads efforts in scalable data environments, metadata optimization, and exascale computing resilience.

François Trahayمشاهده پروفایل

استاد

• High Performance Computing
• Runtime Systems
• Performance Analysis
• +۴ مورد دیگر

François Trahay is a Full Professor in the Computer Science department at Télécom SudParis (Institut Mines-Télécom) and a member of the Benagil Inria team. He leads research in high-performance systems, runtime systems, and performance analysis for HPC and distributed systems. He holds an HDR from Institut Polytechnique de Paris and a PhD from University of Bordeaux (2009). His work includes the EZTrace framework for performance analysis and contributions to storage systems optimization. Education: 2021: Habilitation à Diriger des Recherches (HDR), Institut Polytechnique de Paris 2010: PostDoc at Riken, University of Tokyo 2009: PhD in Computer Science, University of Bordeaux 2006: MS in Computer Science, University of Bordeaux Research focuses on runtime system design, HPC trace analysis, and storage efficiency. Recent projects include PALLAS trace format (IPDPS 2025) and GPU performance prediction (Euro-Par 2024). He advises 4 current PhD students and has supervised 2 former students now in industry. Key contributions include: Co-developer of EZTrace performance analysis framework Co-author of 60+ peer-reviewed papers in top venues (IPDPS, IEEE Cluster, ICPP) Technical leadership in Inria's Benagil team and Samovar Lab

Francois Trahayمشاهده پروفایل

دانشیار

• High Performance Computing
• Storage Systems
• Parallel and Distributed Systems
• +۲ مورد دیگر

François Trahay is an Associate Professor at Telecom SudParis, affiliated with the SAMOVAR research laboratory. His research focuses on high-performance computing systems, storage optimization, and performance analysis tools for parallel and distributed environments. He completed his PhD at Université Bordeaux I (2009) and Habilitation (HDR) at Institut Polytechnique de Paris (2021). Research Interests: Trahay specializes in optimizing storage systems (SSDs, RAID configurations), developing performance analysis frameworks (e.g., EZTrace, NumaMMA), and enhancing energy efficiency in HPC. Key areas include I/O performance, parallel runtime systems, and adaptive computing for machine learning workloads. Publication Trends: His recent work (2018–2025) demonstrates strong focus on: (1) SSD/RAID management techniques for modern storage hardware, (2) HPC performance tools for tracing and analysis, and (3) optimization strategies for distributed deep learning systems. Laboratory Affiliation: Member of SAMOVAR Laboratory (UMR CNRS), conducting research in distributed systems, networks, and computational efficiency.

Alan J. Smithمشاهده پروفایل

استاد

• Computer Architecture
• Computer Engineering
• Operating Systems
• +۴ مورد دیگر

Alan J. Smith is a Professor in the Electrical Engineering and Computer Sciences department at the University of California, Berkeley, within the College of Engineering. With a distinguished career spanning several decades, he has made significant contributions to computer architecture, system performance analysis, and memory systems. Dr. Smith received his S. B. from MIT and a Ph.D. from Stanford University. His educational background provided the foundation for his extensive research in computer systems. Professor Smith's research focuses on computer architecture and engineering, particularly in system performance analysis, I/O systems, cache memories, and memory systems. His work has profoundly influenced how computer systems are designed and evaluated, with particular emphasis on optimizing storage systems, cache performance, and energy efficiency in computing platforms. His research has bridged theoretical analysis with practical implementation, resulting in numerous influential publications and real-world applications. His publication record shows a consistent focus on computer system performance, evolving from early work on cache memory and paging algorithms to more recent research on multimedia workloads, energy management, and storage systems. The breadth of his work spans fundamental computer architecture principles to applied system design, with particular emphasis on measurement-based analysis and optimization techniques. Harry Goode Award of the IEEE Computer Society (2006) IEEE Reynold B. Johnson Information Storage Systems Award (2008) A. A. Michelson Award of the Computer Measurement Group (2003) Fellow of the American Association for the Advancement of Science (2001) Fellow of the ACM (2000) Fellow of the IEEE (1988) Throughout his career, Professor Smith has actively contributed to the academic community through editorial roles, conference organization, and professional society leadership. He has served as Subject Area Editor for the Journal of Parallel and Distributed Computing since 1989, chaired ACM SIGARCH and SIGOPS, and participated in numerous technical committees. His research has been supported by various grants that enabled extensive experimental work and system development. Professor Smith has been instrumental in developing benchmarking methodologies and performance analysis techniques that have become standard practices in computer system evaluation. His work on cache memory systems, in particular his influential 1982 Computing Surveys paper "Cache Memories," has shaped the field for decades.

Chengming Zhangمشاهده پروفایل

استادیار

• Efficient machine learning systems
• AI algorithm-hardware Co-design
• Effective efficiency algorithms
• +۵ مورد دیگر

Chengming Zhang is a tenure-track assistant professor in the Computer Science Department at the University of Houston. He recently completed his Ph.D. in Computer Engineering from Indiana University in May 2024, where he was a member of the HiPDAC group working on building efficient and scalable deep learning systems under the advisement of Prof. Dingwen Tao. He has extensive industry research experience with multiple projects at Microsoft Research, Meta Reality Labs, and Argonne National Laboratory. His educational background includes: Ph.D. in Computer Engineering, Indiana University (May 2024) Dr. Zhang's research focuses on creating efficient machine learning systems that can operate effectively across diverse hardware platforms. His work bridges the gap between theoretical algorithms and practical implementation with particular emphasis on efficient machine learning systems for training and inference on parallel, distributed, and heterogeneous hardware; AI algorithm-hardware co-design, particularly for GPU architectures; effective efficiency algorithms including model compression, data efficiency, and parameter-efficient tuning; and large-scale deep learning applications such as Large Language Models, Agents, and Image/Video Generation systems. His publication record demonstrates a consistent focus on optimizing deep learning systems through hardware-aware approaches. The majority of his work centers around making deep learning more efficient through techniques like model compression, hardware-algorithm co-design, and memory optimization. His research spans both theoretical algorithm development and practical system implementation, with publications in top-tier conferences including the International Conference on Supercomputing, PPoPP, and AAAI. A notable trend in his work is the emphasis on practical efficiency - not just theoretical improvements but solutions that deliver real-world performance gains on actual hardware. Dr. Zhang is actively building his research group at the University of Houston and has secured significant computational resources for his lab, including multiple high-performance computing servers worth a total of $130K (two 8-Ada6000 servers and one dual-4090 servers).

Wei-keng Liaoمشاهده پروفایل

استاد پژوهشی

• Parallel and distributed file I/O
• Storage system design
• Data mining algorithms
• +۵ مورد دیگر

Wei-keng Liao is a Research Professor in the Department of Electrical Engineering and Computer Science at Northwestern University's McCormick School of Engineering. His research spans high-performance computing with a focus on parallel and distributed systems. Dr. Liao's research interests include parallel and distributed file I/O and storage system design, data mining algorithm design and their parallelization, data management for large-scale scientific applications, and computational model design for large-scale applications on parallel and distributed environments. He is a key contributor to the Parallel netCDF project, which provides parallel I/O capabilities for scientific applications. His recent work shows strong trends in high-performance computing infrastructure, particularly in optimizing I/O systems for scientific applications, parallel data clustering algorithms, and machine learning acceleration in distributed environments. His publications span computational science, parallel computing, and data-intensive applications across various scientific domains including materials science, astrophysics, and healthcare. Best Paper Award at IEEE International Conference on Cluster Computing (2016) for Parallel DTFE Surface Density Field Reconstruction Dr. Liao has supervised numerous research projects funded by DOE, NSF, NASA, and Argonne National Laboratory, with recent work focusing on data libraries for exascale science, machine learning-driven resilience for extreme-scale systems, and scalable data clustering for scientific computing. He leads research on the Parallel K-means Data Clustering software package and is a principal developer of Parallel netCDF. His work connects multiple research groups through the Center for Ultra-scale Computing and Information Security (CUCIS) at Northwestern University, where he collaborates with scientists across disciplines to develop scalable computing solutions for complex scientific problems.

Purushotham V. Bangaloreمشاهده پروفایل

استاد

• High-Performance Computing
• Parallel Computing
• Distributed Systems
• +۷ مورد دیگر

Dr. Purushotham V. Bangalore serves as the James R. Cudworth Professor in the Department of Computer Science at the University of Alabama's College of Engineering and holds the position of Associate Director for the Center for Understandable, Performant Exascale Communication Systems (CUP-ECS), a Predictive Science Academic Alliance Program (PSAAP) Focused Investigatory Center. His academic credentials include: B.E. in Computer Science and Engineering from Bangalore University (1991) M.S. in Computer Science from Mississippi State University (1995) Ph.D. in Computational Engineering from Mississippi State University (2003) Dr. Bangalore's research centers on High-Performance Computing (HPC) with emphasis on designing abstraction layers for heterogeneous architectures, predictive performance modeling, and portability. His work extends to fault-tolerant message-passing middleware, exascale storage security, and reliability frameworks. Additional expertise spans data analytics, object-oriented numerical libraries, grid computing environments, and adaptive systems development through three decades of HPC and cloud computing innovation. Analysis of his 2021-2025 publications reveals dominant themes in HPC security architecture, containerization for scientific workloads, and MPI communication advancements. Key application areas include hydrological modeling (NextGen framework), GPU-accelerated communication protocols, and data provenance systems for exascale platforms, reflecting interdisciplinary approaches to computational challenges. Dr. Bangalore has secured approximately $20 million in research funding as PI/Co-PI from NSF, NIH, DoE, and industry partners, resulting in over 90 peer-reviewed publications. His academic service includes editorial roles for IEEE Transactions on Parallel and Distributed Systems, MPI Forum contributions to the MPI-4.0 standard, and organization of DoD-sponsored HPC training workshops. He leads research initiatives through CUP-ECS while maintaining active participation in the MPI Forum. His team develops frameworks for exascale communication systems with focus on security posture analysis, performance portability, and fault tolerance in next-generation computing environments.

Rainer Kellerمشاهده پروفایل

استاد

• Operating Systems
• Distributed Systems
• Parallel Programming
• +۴ مورد دیگر

Prof. Dr.-Ing. Rainer Keller serves as Vice Dean of the School of Computer Science and Information Technology at Esslingen University of Applied Sciences. He concurrently holds Laboratory Management roles for both the Operating Systems Laboratory and Information Technology Laboratory, coordinates the Applied Computer Science (Master) program, and serves on the Environmental Committee for his school. His academic journey includes a Doctorate in Engineering (with distinction), Research Associate and Group Leader positions at HLRS, University of Stuttgart (leading the 9-member "Applications, Models and Tools" group), PostDoc tenure at Oak Ridge National Laboratory (ORNL), and prior appointments at Stuttgart University of Applied Sciences. Keller's research centers on operating systems, distributed systems, and HPC with specialized expertise in Linux-based parallel programming tools and file I/O optimization. His work bridges theoretical system models with practical performance tuning, particularly in high-performance computing environments where syscall tracing and storage optimization are critical. Recent publications reveal consistent focus on system-level performance analysis, evolving from foundational file I/O profiling (2020) to advanced syscall tracing mechanisms (2022) and forward-looking access optimization frameworks (2025). These works demonstrate applied research in Linux ecosystems with direct relevance to HPC infrastructure. As Program Coordinator for the Applied Computer Science (Master) program and laboratory manager for two key facilities, he oversees academic development and hands-on technical training. His consultation hours (Tuesdays 1-2 PM by appointment) support student engagement in systems research. He actively contributes to national HPC infrastructure as a Member of the State User Committee (LNA) bwHPC, influencing regional high-performance computing strategies while maintaining his laboratory management responsibilities at Esslingen.

Anne BENOITمشاهده پروفایل

دانشیار

• High-Performance Computing (HPC)
• Multi-criteria scheduling algorithms
• Resource optimization
• +۴ مورد دیگر

Anne BENOIT is an Associate Professor of Computer Science at École normale supérieure de Lyon (ENS de Lyon), affiliated with the Parallel Computation Laboratory (LIP) and the ROMA project. She holds leadership roles, including Chair of the IEEE Technical Community on Parallel Processing (2020–2024) and IUF Senior Membership (2023–2028). Her research focuses on multi-criteria scheduling algorithms for HPC, addressing performance, cost, energy, and reliability challenges. She earned a PhD in Systems and Communications from INPG Grenoble (2003) and an HDR (2009). Research Interests : Her work integrates scheduling optimization, fault tolerance, and energy efficiency in HPC systems. She designs resilient protocols to manage failures and uncertainties, particularly in heterogeneous environments. Key topics include checkpointing strategies, workflow scheduling on edge platforms, and balancing conflicting optimization criteria. Awards & Roles : Senior Member of the Institut Universitaire de France (2023) Guest Professor at Georgia Tech (2017–2018) Associate Editor-in-Chief of Elsevier JPDC and ParCo IEEE Senior Member Professional Contributions : Chair of major conferences like IPDPS and HiPC. Authored A Guide to Algorithm Design (CRC Press, 2013). Leads the Computer Science Department at ENS Lyon since 2022. Labs & Projects : Active in the ROMA project (Resource Optimization) and the LIP laboratory, focusing on parallel computation and algorithmic efficiency.

Iacopo Colonnelliمشاهده پروفایل

استادیار

• Scientific Workflow Systems
• High-Performance Computing
• Cloud Computing
• +۶ مورد دیگر

Iacopo Colonnelli is an Assistant Professor (RTDA) at the University of Turin's Computer Science Department and a member of the Alpha Research Group (Parallel Computing) and CINI HPC Key Technologies and Tools lab. He has established himself as a leading researcher in scientific workflow systems, bridging cloud computing and high-performance computing environments. His research focuses on workflow modeling and management in heterogeneous distributed architectures, with significant contributions through open-source tools including StreamFlow (a container-native workflow management system), Jupyter Workflow, and CAPIO (middleware for I/O streaming). These tools enable hybrid workflows execution on cloud-HPC infrastructures and have gained recognition through multiple European projects and initiatives. Colonnelli's publication record shows consistent output in top venues related to parallel and distributed computing, with recent work focusing on federated learning, confidential computing, and workflow standardization. His most recent publications (2024-2026) demonstrate strong engagement with cutting-edge challenges in cross-facility federated learning, workflow representation languages, and performance evaluation of confidential computing for bioinformatics applications. Scientific Awards: ITADATA 2023 Best PhD Thesis Award for 'Workflow models for heterogeneous distributed systems' StreamFlow selected as one of the three exploitable results of the EUPEX European project (2023) StreamFlow selected as an exploring technology by the EC Innovation Radar initiative (2023) 'Distributed Workflows With Jupyter' selected as Fall 2022 Editor's Choice by Future Generation Computer Systems journal HPC-Europa3 grant recipient for Barcelona Supercomputing Center visit (2019) HiPEAC grant recipient for ACACES 2019 Summer School (2019) As an active contributor to research infrastructure, Colonnelli serves as a member of the CWL Technical Team and founder of the CWL4HPC Working Group. He has organized multiple international conferences including PDP 2025 as program chair and CWLCon 2024. His service extends to numerous program committees for major HPC conferences and journals, demonstrating his standing in the international research community. Colonnelli leads significant contributions to multiple European research projects including Space Center of Excellence, European Pilot, EUPEX, TEXTAROSSA, ACROSS, DeepHealth, and HPC4AI, focusing on advancing HPC and AI technologies for scientific applications.