جست‌وجوی استاد و پژوهشگر

Steven Swansonمشاهده پروفایل

استاد

• Non-Volatile Memory
• Persistent Memory Systems
• Storage Systems
• +۱۲ مورد دیگر

Steven Swanson is a Professor in the Department of Computer Science and Engineering at the University of California, San Diego, within the Jacobs School of Engineering. He is the Director of the Non-Volatile Systems Laboratory (NVSL), where he leads cutting-edge research in non-volatile memory, storage systems, and hardware-software co-design. His work bridges computer architecture, systems, and software to develop efficient, reliable, and secure computing platforms. Ph.D., University of Washington, 2006 B.S., University of Puget Sound, 1999 Dr. Swanson's research centers on non-volatile and persistent memory systems , exploring how next-generation storage technologies can transform computing. His lab develops full-stack solutions including file systems like NOVA and Orion , programming models such as NV-Heaps , and hardware prototypes like Moneta and Onyx . The team also works on low-power co-processors (e.g., GreenDroid ) and tools for debugging and verifying persistent memory programs. Research spans system reliability, security, energy efficiency, and performance optimization. His recent publications reveal a strong focus on persistent memory safety , zero-copy I/O , RDMA-based distributed file systems , and real-world characterization of Intel Optane . These works appear in top venues including ASPLOS, FAST, MICRO, and USENIX ATC, demonstrating sustained innovation in storage and systems research. Scientific honors include: NSF CAREER Award Google Faculty Award Facebook Faculty Award NetApp Faculty Fellow Dr. Swanson has advised 15 PhD students and 4 postdocs , many now faculty or senior engineers at Google, Microsoft, Intel, and other leading tech firms. He has secured significant research funding and leads major community initiatives such as the annual Non-Volatile Memories Workshop and Persistent Programming In Real Life (PIRL) . His educational efforts include innovative courses on robotic system design, quadcopter building, and modern storage systems, emphasizing hands-on learning and real-world implementation. The Non-Volatile Systems Laboratory (NVSL) under his leadership fosters a collaborative, international research environment, hosting visitors and postdocs from around the world. The lab is recognized globally as a pioneer in storage systems research and a key contributor to the adoption of persistent memory technologies in industry.

Saugata Ghoseمشاهده پروفایل

استادیار

• Data-centric computing
• Processing-in-memory
• Memory and storage systems
• +۳ مورد دیگر

Saugata Ghose is an Assistant Professor in the Siebel School of Computing and Data Science at the University of Illinois Urbana-Champaign (UIUC), with affiliate appointments in the Coordinated Science Laboratory and the Department of Electrical and Computer Engineering. His research focuses on data-centric computing, processing-in-memory architectures, memory systems, and hardware-software co-design. He holds a Ph.D. and M.S. in Computer Engineering from Cornell University and dual B.S. degrees in Computer Engineering and Computer Science from SUNY Binghamton. His academic positions include roles at Carnegie Mellon University (2016–2020) and postdoctoral research at CMU (2014–2016). Ghose has received notable awards such as the 2024 HPCA Hall of Fame, 2023 Intel Rising Star Faculty Award, and the 2019 CMU Wimmer Faculty Fellowship. His work has been supported by grants from NSF, Samsung, and Sandia National Laboratories. Research Interests: His group (ARCANA) explores data-centric architectures, processing-in-memory (PIM), and emerging memory technologies. Key areas include architectures for smart cities, autonomous systems, and genomics. He teaches courses on computer architecture and systems organization. Awards: HPCA Hall of Fame (2024) Intel Rising Star Faculty Award (2023) CMU Wimmer Faculty Fellow (2019) Cornell ECE Teaching Assistant Award (2013) Grants & Projects: NSF $2M for semiconductor advancements Samsung/Sandia grants for PIM programming models UIUC/ZJU DREMES collaboration on neuromorphic PIM Labs/Teams: Leads the ARCANA Research Group, focusing on reimagining computing around new applications. Collaborates with ASAP and HYBRID centers for co-design tools and neuromorphic architectures.

• Computer Architecture
• Memory Systems
• Solid-State Drives
• +۲ مورد دیگر

Dr. Seyyedmohammad Sadrosadati is a Lecturer in the Department of Information Technology and Electrical Engineering at ETH Zurich. His expertise centers on storage systems and computer architecture, with particular focus on solid-state drive (SSD) design and optimization. Research focuses on: Modern SSD architecture and performance enhancement Memory hierarchy optimization Hardware accelerators for data-intensive applications Energy-efficient storage solutions His teaching includes courses on SSD design and advanced computer architecture.

Janki Bhimaniمشاهده پروفایل

استاد

• Emerging Memories
• Flash-Based Storage Systems
• Machine Learning
• +۷ مورد دیگر

Janki Bhimani is a Professor and Director of the Data Management Research Lab (DaMRL) at the School of Computing and Information Science, Florida International University (FIU). Her research focuses on Memory and Storage Systems, Cloud Computing, Performance Modeling, and Applied Machine Learning. She holds a Ph.D. in Computer Engineering from Northeastern University (2019), an M.S. in Electrical and Computer Engineering (2016), and a B.S. in Electrical and Electronics Engineering from GITAM University (2013). Prior to FIU, she taught at Northeastern University and collaborated with Samsung Semiconductor Research Labs on flash-based SSDs. Her research interests include emerging memory technologies, high-performance computing, and datacenter reliability management. She leads innovative projects like Heimdall (machine learning for storage I/O optimization) and MoKE (modular key-value storage emulation). Awards include FIU Top Scholar and KFSCIS Excellence in Applied Research. Teaching highlights include CIS 3530 (Data Structures), CIS 5346 (Storage Systems), and EECE 2560 (Engineering Algorithms). Her work emphasizes bridging theory and practice, with patents on storage system optimization and machine learning integration.

Bryan S. Kimمشاهده پروفایل

استادیار

• Data Storage Systems
• Computer Architecture
• Operating Systems
• +۴ مورد دیگر

Bryan S. Kim is an Assistant Professor in the Department of Electrical Engineering and Computer Science at Syracuse University. His research focuses on computer systems, particularly data storage systems, emphasizing performance, reliability, and scalability in the context of heterogeneous hardware. He holds a Ph.D. and M.S. in Computer Science and Engineering from Seoul National University and a B.S. in Electrical Engineering and Computer Science from UC Berkeley. Prior to academia, he worked as a postdoctoral researcher at Seoul National University and as a manager at SK Telecom. Key research interests include SSD reliability, storage system design, and overcoming hardware limitations through innovative architectures. Notable recent work includes projects on capacity-variant storage systems, CXL-enabled SSDs, and RAID adaptations for heterogeneous SSDs. He has been awarded two NSF grants: the DESC proposal (CHIPLETS360) in 2025 and a CAREER award for bridging memory/storage gaps in 2025. Education: Ph.D. in Computer Science and Engineering, Seoul National University M.S. in Computer Science and Engineering, Seoul National University B.S. in Electrical Engineering and Computer Science, UC Berkeley Recent Awards: NSF DESC Proposal Award (2025) NSF CAREER Proposal Award (2025) Teaching: CSE486: Design of Operating Systems CIS341: Computer Organization & Programming Systems CIS700: Storage Systems for Big Data His students include Shao-Peng Yang, Xiangqun Zhang, and Omkar Desai. He advises on projects related to storage systems, and his work has been published in top-tier conferences like FAST, ATC, EuroSys, and OSDI.

Noh Jae-chunمشاهده پروفایل

استاد

• Cloud Computing and Virtualization
• Distributed File Systems
• Parallel Storage Systems
• +۲ مورد دیگر

Professor Noh Jae-chun serves in the Department of Computer Engineering at Sejong University, South Korea, leading research in systems engineering with a focus on modern computing infrastructure. His research program centers on four critical domains of computer systems: Cloud Computing and Virtualization techniques for resource optimization Distributed and Parallel File Systems architecture for scalable storage solutions Big Data Platform Construction methodologies for data-intensive applications SSD (Solid State Drive) technologies and performance optimization He directs the Systems Engineering Lab where active development occurs in these areas, maintaining regular laboratory interview hours on Tuesdays and Thursdays from 2:00 PM to 4:00 PM for student collaboration and research discussions.

Xiaojun Ruanمشاهده پروفایل

دانشیار

• Energy-Efficient Systems
• Cloud Computing
• Storage Systems Optimization
• +۷ مورد دیگر

Xiaojun Ruan is an Associate Professor in the Department of Computer Science at California State University, East Bay. He holds a Ph.D. in Computer Science from Auburn University (2011) and a B.E. in Computer Science and Technology from Shandong University (2005). His primary research focuses on energy-efficient systems, cloud computing optimization, storage systems, and security-aware resource management. He has extensive experience in thermal modeling, parallel I/O performance, and distributed deep learning frameworks. Dr. Ruan’s work emphasizes balancing energy efficiency, reliability, and performance in storage and cloud environments. Notable projects include DuoFS (hybrid storage system), energy-aware VM allocation strategies, and securing cloud infrastructure against co-residence attacks. His research bridges hardware-software co-design principles with practical system optimizations. His publications span topics from NVMe SSD performance optimization to text augmentation for spam detection, reflecting a blend of storage systems and machine learning applications. He has actively contributed to improving Shuffle I/O in big data processing, thermal management in clusters, and secure virtualization techniques. Dr. Ruan collaborates on interdisciplinary projects involving distributed computing, cybersecurity, and real-time systems. His lab focuses on deploying energy-efficient solutions while maintaining robust reliability, evidenced by over 50 peer-reviewed articles and ongoing contributions to academic conferences.

Willy Zwaenepoelمشاهده پروفایل

استاد

• Operating Systems
• Distributed Systems
• Database Replication
• +۴ مورد دیگر

Willy Zwaenepoel is a Professor and Dean of the Faculty of Engineering at the University of Sydney. He holds a B.S. from the University of Gent and M.S./Ph.D. from Stanford University. Previously, he served as Dean of the School of Computer and Communication Sciences at EPFL and was a faculty member at Rice University. His expertise spans operating systems, distributed systems, and high-performance computing. Education: B.S., University of Ghent, Belgium (1979) M.S., Stanford University (1980) Ph.D., Stanford University (1984) Research Interests: Dr. Zwaenepoel focuses on distributed systems, operating systems, and their applications in database replication, virtual machine performance, and software update mechanisms. His work includes foundational contributions to distributed shared memory (e.g., Treadmarks) and startups like iMimic Networking. Awards: ACM Fellow (2000) IEEE Fellow (1998) Fellow of the Australian Academy of Technical Sciences and Engineering (2020) Recipient of the IEEE Tsutomu Kanai Award (2007) Key Contributions: His research addresses challenges in distributed systems performance, such as latency reduction in key-value stores and efficient graph processing. Current projects explore I/O optimization in virtualized environments and causal consistency for geo-replicated systems. Students/Advising: Advises Ph.D. students and postdocs, including William in database replication. His mentorship led to the Rice University Teaching Award (2000).

• 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).

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.

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

Michael Swiftمشاهده پروفایل

استاد

• Operating Systems
• Computer Architecture
• Device Drivers
• +۴ مورد دیگر

Michael Swift is Professor of Computer Sciences at the University of Wisconsin-Madison within the College of Letters and Sciences. He leads the Sonar research group and co-leads the Multifacet architecture group with Mark Hill, while contributing to the Wisconsin Institute on Software-defined Datacenters (WISDoM). His research centers on operating system/hardware interaction, with seminal work in device driver reliability (Nooks, Shadow Drivers), memory systems (storage-class memory integration), and cloud security. Projects like Microdrivers and Carburizer address driver complexity and fault tolerance, while recent work explores learned OS policies and heterogeneous processor support. Analysis of his 2023-2025 publications reveals intensifying focus on memory tiering, security in cloud environments, and hardware-accelerated systems, with growing integration of machine learning for system optimization. Key awards include: Best paper award at OSDI 2004 Best paper award at SOSP 2003 Best paper award at the 3rd Asia-Pacific Workshop on Networking (2019) Professor Swift mentors numerous PhD students (current and past) and secures research funding through NSF grants CNS-0915363 and CNS-0745517 plus Google support. He serves as Instructional Program Director and chairs both the Space committee and Diversity, Equity, and Inclusion committee. The Sonar group drives innovation in system support for emerging hardware technologies, while Multifacet focuses on computer architecture advancements, particularly in heterogeneous computing and memory systems.

Reza Salkhordehمشاهده پروفایل

مدرس

• Operating Systems
• Solid-State Drives
• Data Storage Systems
• +۵ مورد دیگر

Reza Salkhordeh is a Lecturer and Postdoctoral Researcher at Johannes Gutenberg University Mainz, Germany, where he leads the Efficient Computing and Storage Group. He holds a Ph.D. in Computer Engineering from Sharif University of Technology (2018) and has been affiliated with Ferdowsi University of Mashhad and Sharif University of Technology. His research focuses on operating systems, storage systems, and non-volatile memory technologies, with a particular emphasis on high-performance computing and I/O optimization. He has taught courses such as Storage Systems and Advanced Topics in Operating Systems since 2020. His academic journey includes a B.Sc. from Ferdowsi University (2011), M.Sc. and Ph.D. from Sharif University (2013, 2018). He has held roles such as Technical Lead for the High-Performance Data Storage System (HPDS) project in Tehran, Iran, and has mentored 4 M.Sc. and 6 B.Sc. students. His work spans patented technologies like Reconfigurable Cache Architectures and Load Balancers for I/O caching systems. Research interests include heterogeneous memory management, storage system design, and optimizing I/O performance in distributed environments. His recent publications address challenges in NVMM utilization, garbage collection in SSDs, and I/O tracing for HPC systems. He is actively involved in conference committees (e.g., FAST, ARCS, SC) and has reviewed for top journals like IEEE TPDS and ACM Transactions on Storage. Notable recognitions include membership in Iran’s National Elites Foundation (2012–2015) and top rankings in national exams (3rd in PhD, 35th in MSc). His contributions to storage systems have advanced enterprise-grade architectures and decentralized file systems, with a focus on practical implementations for modern computing challenges.

Salvatore DANIELEمشاهده پروفایل

عضو هیئت علمی

• Analytical Chemistry
• Electrochemistry
• Material Science
• +۴ مورد دیگر

Salvatore Daniele is a Senior Researcher at Ca' Foscari University of Venice, affiliated with the Department of Molecular Sciences and Nanosystems and the Research Institute for Complexity Communications. His work focuses on analytical and electrochemical methodologies, with applications in material science, environmental monitoring, and cultural heritage preservation. He holds a SSD qualification in Analytical Chemistry (CHIM/01). Key research interests include electrochemical sensor development, nanomaterials for energy storage, corrosion studies, and non-invasive analysis of archaeological artifacts. Daniele has authored over 200 publications, with recent contributions on micro-supercapacitors for biosensors, X-ray tomography for Roman coins, and electrochemical characterization of metallic surfaces. His research often bridges fundamental electrochemistry with applied challenges in health, environment, and heritage conservation. Collaborations involve interdisciplinary teams addressing topics like SARS-CoV-2 detection, corrosion protection of artworks, and advanced catalytic materials.