جست‌وجوی استاد و پژوهشگر

Stergios Anastasiadisمشاهده پروفایل

استاد

• Operating Systems
• Distributed Storage
• File Systems
• +۲ مورد دیگر

Stergios Anastasiadis is a Professor at the University of Ioannina's Department of Computer Science and Engineering, with visiting appointments at Cambridge and EPFL. His research focuses on core systems innovation spanning operating systems, distributed storage, and cloud infrastructure. Research interests include multitenant isolation, concurrent data structures, storage I/O optimization, and filesystem design. His work develops novel approaches to resource management in cloud environments, emphasizing security, efficiency, and reliability in distributed systems. Recent publications reveal sustained focus on storage subsystem optimization, with contributions to journaling techniques, isolation mechanisms, and I/O performance tuning. Key innovations include lock-free concurrency models, client-side storage innovations, and cross-platform memory management techniques. Educational background includes Ph.D. from University of Toronto and international research collaborations. No specific advising relationships or scientific awards are documented. Laboratory affiliations include distributed systems research groups at University of Ioannina, with collaborations extending to cloud computing initiatives across Europe.

• Computational Fluid Dynamics
• Aerodynamic Design
• Ship Hull Reconstruction
• +۴ مورد دیگر

Nikolos Ioannis serves as a Professor at the School of Production Engineering and Management, Technical University of Crete, currently on leave but maintaining active research status. His work spans computational fluid dynamics, aerodynamics, and marine engineering within the Department of Production Engineering and Management. His primary research focuses on computational fluid dynamics with emphasis on ship hull reconstruction from 2D drawings, hypersonic flow simulation, and wind turbine optimization. Recent publications demonstrate expertise in geometric modeling , rarefied gas dynamics , and renewable energy systems . The 2025-2020 article portfolio reveals consistent contributions to naval architecture, aerodynamic shape optimization, and laser-matter interactions with strong methodological focus on B-spline functions, DSMC methods, and differential evolution algorithms. Professor Ioannis maintains an active email contact ( inikolos@tuc.gr ) and office location at D4.107 in the MPD Building. His work shows significant interdisciplinary connections between production engineering, computational physics, and marine applications with particular strength in translating theoretical models to practical engineering solutions.

Bradford L. Chamberlainمشاهده پروفایل

استاد مدعو

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

Bradford L. Chamberlain is a Distinguished Technologist at Hewlett Packard Enterprise and an Affiliate Professor in the Paul G. Allen School of Computer Science and Engineering at the University of Washington. With over two decades of experience in high-performance computing, he has made significant contributions to parallel programming languages, particularly as the technical lead of the Chapel programming language project since 2006. His educational background includes: Ph.D. in Computer Science & Engineering from the University of Washington (2001) M.S. in Computer Science & Engineering from the University of Washington (1995) B.S. in Computer Science from Stanford University (1992) Chamberlain's research focuses on improving programmer productivity for high-performance computing through innovative language design, compiler techniques, and runtime systems. His work centers around the Chapel programming language, which aims to provide a multiresolution programming model that allows developers to express parallelism at varying levels of abstraction while maintaining performance across diverse architectures from laptops to supercomputers. His research spans parallel language design, compiler optimization, data distribution strategies, locality management, and performance portability. Chapel builds on his earlier work with the ZPL language, where he developed region-based approaches for sparse parallel computing. His publication record over the past fifteen years demonstrates a consistent focus on practical approaches to parallel programming, with recent work emphasizing data locality, heterogeneous architectures, and performance portability. His articles show an evolution from foundational language design concepts to increasingly sophisticated implementations addressing real-world HPC challenges, particularly in the areas of domain mapping, iterator abstractions, and memory management for large-scale systems. As a Distinguished Technologist at HPE, Chamberlain has played a key role in growing the Chapel project from a modest effort to one involving nearly 20 full-time developers. His leadership has positioned Chapel as one of the most promising languages for addressing the challenge of productive parallel programming at scale. He has secured funding, established collaborations with academia and industry, and performed extensive outreach through talks, tutorials, and research visits. At the University of Washington, Chamberlain serves as a liaison between academia and industry, participating in student committees, teaching graduate courses like Parallel Computation, and fostering communication between the department and HPE/Cray. His teaching experience spans from undergraduate data structures to graduate seminars on parallel programming environments. He has also volunteered as a tutor for underrepresented students in computer science.

David Greggمشاهده پروفایل

استاد

• Software performance optimization
• Embedded systems
• Accelerating deep neural networks on embedded systems
• +۱۲ مورد دیگر

David Gregg is a Professor in the Department of Computer Science at Trinity College Dublin's School of Computer Science and Statistics, where he serves as Global Director for Computer Science (since 2020) and previously as Head of the Discipline of Software and Systems (2018-2022). His research focuses on software performance optimization and embedded systems, with particular expertise in accelerating deep neural networks on resource-constrained platforms. He teaches Systems Programming (CS2014/5) and Concurrent Systems I (CS3014). Gregg's research spans multiple areas of computer systems including: Compiler optimization and program analysis Processor microarchitecture and parallelism (multi-core, vector, instruction-level) Computer arithmetic and domain-specific languages Low-energy embedded systems and FPGA implementations Deep neural network acceleration He has served on numerous program committees including PACT, PLDI, CC, and other major computer systems conferences, and has been on the Board of Distinguished Reviewers for ACM TACO multiple times. Professor Gregg has advised numerous PhD and MSc students, many of whom have gone on to prominent positions at companies like Intel-Movidius, Google, Amazon, and Synopsys. He leads the triNNity project which includes optimized libraries and compilers for implementing convolutional neural networks on CPUs.

Saday Sadayappanمشاهده پروفایل

استاد

• Compiler Optimization
• High-Performance Computing
• Sparse/Dense Matrix Computations
• +۳ مورد دیگر

P. (Saday) Sadayappan is a Professor at the University of Utah's School of Computing, specializing in high-performance computing and compiler optimizations. His research focuses on performance optimization for parallel systems, particularly for tensor computations, sparse matrix operations, and machine learning workloads. He leads multiple NSF and DARPA-funded projects focused on GPU optimization, tensor computations, and scalable machine learning frameworks. Research Interests: Dr. Sadayappan's work spans compiler optimizations for high-performance systems, optimization of sparse/dense matrix/tensor computations, scalable machine learning, and algorithm-architecture co-design. His recent projects include developing performance-portable frameworks for tensor applications and optimizing data locality for scientific computing. Publication Trends: His recent publications (2020-2022) predominantly focus on GPU acceleration of machine learning workloads (especially CNNs), automated I/O complexity analysis, and optimization techniques for sparse matrix/tensor operations. Earlier work (2018-2019) established foundations in GPU code generation for tensor contractions and cache optimization. Awards and Honors: ACM SIGPLAN Most Influential PLDI Paper Award (2018) for A Practical Automatic Polyhedral Parallelizer and Locality Optimizer Active Grants and Projects: NSF: A Comprehensive Framework for Efficient, Scalable, and Performance-Portable Tensor Applications (2022-2027) NIH SBIR: Enabling next generation machine learning for large scale image analysis (2023-2025) NSF: AI Institute for Intelligent CyberInfrastructure (ICICLE) (2021-2026) NSF: Data Locality Optimization for Sparse Matrix/Tensor Computations (2020-2024) DARPA SBIR: Performance Portable Framework for Developing Graph Applications (2017-2022) Teaching: He currently teaches CS 4230/6230 (Parallel and High-Performance Computing) at the University of Utah.

Leonidas Akritidisمشاهده پروفایل

مدرس

• Natural Language Processing
• Deep Machine Learning
• Data Mining
• +۵ مورد دیگر

Leonidas Akritidis serves as a Lecturer at the Department of Science and Technology within the School of Science and Technology at International Hellenic University. He holds a PhD and Diploma in Electrical and Computer Engineering from University of Thessaly and Aristotle University of Thessaloniki respectively. His educational background includes: PhD in Electrical and Computer Engineering, University of Thessaly (2007-2013) Diploma in Electrical and Computer Engineering, Aristotle University of Thessaloniki (1997-2003) Akritidis specializes in Natural Language Processing, Deep Machine Learning, and Data Mining with particular expertise in short text clustering, dimensionality reduction, and rank aggregation techniques. His research addresses challenges in sparse data environments and high-dimensional feature spaces, developing novel algorithms for text representation and processing. He has contributed significantly to parallel and distributed computing approaches for big data analytics. His publication trends reveal a consistent focus on NLP and machine learning applications, with recent work expanding into generative models for tabular data, medical image analysis, and advanced fault detection systems. The research demonstrates progression from foundational text processing techniques to more complex applications in healthcare, e-commerce, and software engineering. Akritidis has taught multiple courses including Big Data and Cloud Computing, Machine Learning Principles and Concepts, Mobile Application Development, and Web Programming. His research projects include CyberPi (intelligent cyber threat detection), NANOTRIM (transistor sizing optimization), and iMuSe (virtual museum platform). He collaborates extensively with researchers like Panayiotis Bozanis, Miltiadis Alamaniotis, and Athanasios Fevgas, primarily within the Greek academic community.

Adam Chlipalaمشاهده پروفایل

استاد

• Programming Languages
• Formal Methods
• Computer Systems
• +۵ مورد دیگر

Adam Chlipala is a Professor at the Massachusetts Institute of Technology working at the intersection of programming languages, formal methods, and computer systems. His research focuses on building practical verified systems with end-to-end machine-checked proofs, particularly using the Coq proof assistant. His educational background includes a Computer Science undergraduate degree from Carnegie Mellon University (2003) and a PhD in Computer Science from the University of California, Berkeley (2007). Following a postdoctoral position at Harvard University through 2011, he joined MIT as faculty. Chlipala's research spans multiple domains with strong emphasis on dependent types , verified compilation , and hardware-software co-verification . His work consistently bridges theoretical foundations with practical implementation, as evidenced by his development of the Ur/Web programming language and his focus on creating clean-slate hardware-software stacks with formal guarantees. Key research thrusts include cryptographic constant-time verification, side-channel security, and verified tensor compilation. His recent publications (2020-2025) reveal a clear trajectory toward increasingly complex verified systems, with growing emphasis on hardware-software integration, cryptographic implementations, and performance-critical applications. The work consistently leverages Coq for machine-checked proofs while addressing real-world constraints like timing channels and hardware interfaces. Chlipala is the author of the influential textbook Certified Programming with Dependent Types , which serves as a primary educational resource for Coq at numerous institutions worldwide. His professional activities include significant service to the PL community through program committees for major conferences including PLDI, POPL, ICFP, and CPP. He leads research initiatives connecting hardware and software verification, most notably through the DeepSpec project which aims to build fully verified computing stacks. His current work focuses on practical applications of dependent types for business applications through Ur/Web and verified cryptographic implementations.

Gilbert Bernsteinمشاهده پروفایل

استادیار

• Computer Graphics
• Programming Languages
• High-Performance DSLs
• +۷ مورد دیگر

Gilbert Bernstein is an Assistant Professor in the Computer Science & Engineering department within the College of Engineering at the University of Washington. His research bridges computer graphics and programming languages, with a focus on high-performance domain-specific languages. Previously, he was a post-doctoral scholar at UC Berkeley and MIT working with Jonathan Ragan-Kelley, and received his PhD from Stanford University under Pat Hanrahan. His research interests span Computer Graphics, Programming Languages, High-Performance DSLs, Physical Simulation, Geometry & Topology, Differentiable Programming, Hardware DSLs, Tools for Artists, Fabrication, and Human-Computer Interaction. Bernstein develops languages and compilers that enable efficient computation for creative applications, physical simulations, and graphics rendering systems. His recent publications reveal strong trends in differentiable programming for graphics applications, domain-specific languages for hardware acceleration, and computational approaches to traditional crafts like quilting and knitting. His work consistently combines formal language theory with practical applications in graphics and fabrication. Bernstein actively mentors students across multiple institutions including current advisees Felix Hahnlein (UW Postdoc), Ryan Zambrotta (UW PhD), Haoran Peng (UW PhD), and previous students including Alex Reinking (UC Berkeley PhD 2022, now at Qualcomm) and MacKenzie Leake (Stanford PhD 2021, now at Adobe Research). His lab works on diverse projects including debugging CAD programs, compilers for finite element methods, semantics for knitting machines, algebraic scheduling of tensor programs, and exocompilers for hardware accelerators. Bernstein also collaborates on DSLs for networking, Counterstrike bots, gradient-based optimization, memory management, hardware design, and garment design tools.

Chen Dingمشاهده پروفایل

استاد

• Computer memory
• Locality theory
• Cache optimization
• +۵ مورد دیگر

Chen Ding is a Professor and Chair of the Computer Science Department at the University of Rochester, where he leads research in computer memory systems and locality theory. His work focuses on optimizing memory hierarchy performance across computing platforms from handheld devices to supercomputers. Ph.D. from Rice University (2000) M.S. from MTU (1996) B.S. from Beijing University (1994) Professor Ding's research centers on the scientific foundation of computer memory, particularly locality theory and its applications for minimizing data movement - the primary bottleneck in modern computing systems. His work has established that data movement, data reuse, and working set are mathematically related manifestations of the same underlying phenomenon. His research spans parallel program locality, data movement complexity, relational theory of locality, reference affinity, and whole-program locality analysis. His recent publications demonstrate a continued focus on memory system optimization, with particular emphasis on lease caching, symmetric locality modeling, and continuous-time analysis of Zipfian workloads. His work bridges theoretical foundations with practical implementations across CPU/GPU cache modeling, cache sharing optimization, and key-value memory caching systems. NSF CAREER award (2003) DOE Young Investigator Award (2002) Professor Ding has supervised numerous graduate students, including Lavaee who contributed to the "Hardness of data packing" research presented at POPL'16. His work has received consistent funding from NSF and DOE, supporting his research in memory systems and parallel programming. He has served in leadership roles including General Chair for ISMM 2020 and committee positions for major conferences like PLDI and PPoPP. His research group maintains the roclocality.org resource and develops tools like SLO (suggestions of locality optimizations) for analyzing and improving program locality. The group collaborates with industry partners including Microsoft Research, where Ding served as a Visiting Researcher, and academic institutions worldwide.

Erez Petrankمشاهده پروفایل

استاد

• concurrent computing
• programming languages
• memory management
• +۵ مورد دیگر

Erez Petrank is a Professor of Computer Science at the Technion - Israel Institute of Technology, where he holds the Andrew and Erna Viterbi Chair. His academic career spans decades with significant contributions to systems research, particularly in memory management and concurrent programming. He has maintained continuous academic service through leadership roles in major conferences including SPAA'24, ISMM 2023, and PPOPP 2021. His research focuses on concurrent computing, programming languages, and systems with special emphasis on memory management. Additional interests include parallelism, cryptography, data structures, approximation algorithms, and distributed computing. Petrank's work bridges theoretical foundations with practical systems implementation, particularly evident in his persistent memory research and garbage collection innovations. Petrank's publication record shows consistent contributions to ACM SIGPLAN conferences over two decades, with recent work focusing on non-volatile memory systems, lock-free data structures, and memory reclamation techniques. His research demonstrates a clear trajectory from foundational memory management concepts toward modern persistent memory architectures, reflecting adaptability to evolving hardware paradigms while maintaining theoretical rigor. H-index: 41 (Google Scholar) Erdos number: 2 62 co-authors across diverse research collaborations Petrank has mentored numerous researchers through his academic position and conference leadership roles, serving on program committees for major venues including PLDI, PPoPP, and ISMM. His professional service includes executive committee membership in ACM SIGPLAN (2009-2012) and steering committee roles for multiple conferences. He maintains active research collaborations with institutions worldwide, as evidenced by his extensive co-author list spanning theoretical computer science to practical systems implementation. His academic lineage traces back through Oded Goldreich, Shimon Even, and Hao Wang to intellectual giants including Isaac Newton and Galileo Galilei, reflecting deep roots in theoretical computer science and mathematics.

• Software Engineering
• Cloud Computing
• Internet of Things
• +۳ مورد دیگر

Ευριπίδης Γ.Μ Πετράκης is a Professor & Laboratory Director at the School of Electrical and Computer Engineering, Technical University of Crete (TUC) since 1999. He holds a Ph.D. in Computer Science (University of Crete, 1993) and a B.Sc. in Physics (National University of Athens, 1985). His research focuses on Cloud Computing, IoT, Semantic Web, and Image Indexing, with over 175 publications and top international recognition including the 2024 AINA Best Paper Award and Stanford's Top 2% Global Impact ranking (2021-2023). Key projects include leading roles in Horizon 2020-funded BorderUAS (2020-2023), MARS smart farming initiative (2018-2021), and FP7-funded FI-STAR (2013-2016). He has pioneered solutions for IoT security (iBoT), edge-cloud computing (DeFog), and semantic service frameworks (OASL). His academic contributions span advanced distributed systems, including Apache Flink-based real-time video processing (Video2Flink) and Kubernetes optimization strategies. He also actively contributes to standards development for IoT interoperability and semantic web services.

• Stochastic Optimization
• Numerical Simulation
• Environmental Engineering
• +۳ مورد دیگر

Prof. Saridakis Yiannis is a Professor in the School of Production Engineering and Management at the Technical University of Crete. His research focuses on stochastic optimization, numerical simulation, and mathematical modeling of environmental systems and biomedical processes. He develops advanced numerical methods such as the Fokas transform, Hermite collocation, and Runge-Kutta schemes for solving reaction-diffusion equations, groundwater management challenges, and tumor growth models. His work integrates computational techniques like GPU acceleration and parallel algorithms for efficient problem-solving. Key research areas include: Stochastic optimization for coastal aquifer management Numerical treatment of discontinuous systems in environmental and biomedical models Development of high-performance computing strategies for complex PDEs Prof. Saridakis has contributed to over 50 peer-reviewed articles since 1985, exploring topics ranging from soliton cellular automata to modern GPU-based iterative methods. His research bridges applied mathematics, environmental engineering, and computational biology. His publications highlight innovative approaches in: Groundwater sustainability through stochastic algorithms Mathematical modeling of tumor invasion dynamics Parallel computing architectures for scientific problems Despite extensive contributions, no scientific awards are explicitly listed in the provided materials. His academic profile emphasizes technical expertise in numerical methods and their applications in interdisciplinary fields.

• Production Systems
• Business Processes
• Supply Chain Optimization
• +۴ مورد دیگر

George Ioannou is a Professor of Production Management and Business Processes at the Athens University of Economics and Business, where he leads the MBA International Postgraduate Program and the Center for Business Processes and ERP Systems at the Laboratory of Management Science. Previously, he served as Assistant Professor at Virginia Tech's Department of Production and Systems Engineering. His academic journey includes a B.Sc. in Mechanical Engineering from National Technical University of Athens, an M.Sc./DIC in Industrial Robotics/Production Automation from Imperial College London, and a Ph.D. in Mechanical Engineering from the University of Maryland. Current Academic Roles: Professor, Director of MBA Program, Head of ERP Systems Center Prior Academic Roles: Assistant Professor at Virginia Tech Education: B.Sc., M.Sc./DIC, Ph.D. His research focuses on integrating web-based technologies with operational research to optimize production systems, business processes, and supply chains. This spans applications in plant spatial planning, ERP systems, and cultural heritage digitization. He has secured research funding from global organizations including NSF, European Commission, and private sector leaders like Microsoft. Scientific awards include Microsoft's Excellence in Education Award and multiple teaching excellence recognitions for MBA programs. Ioannou serves on the Technical Chamber of Greece (TEE) and the editorial board of Production Planning & Control , with extensive consulting experience for public and private sector entities.

• Computer Architecture
• Memory Systems
• Hardware/OS Interaction
• +۷ مورد دیگر

Vasileios Karakostas is an Assistant Professor at the Department of Informatics and Telecommunications, National and Kapodistrian University of Athens. He is a member of the Computer Architecture Lab and focuses on computer architecture, memory systems, and resource management. Previously, he was a postdoctoral researcher at the National Technical University of Athens' Computing Systems Lab. He holds a PhD in Computer Architecture from Universitat Politècnica de Catalunya and Barcelona Supercomputing Center. Education: Ph.D. in Computer Architecture, Universitat Politècnica de Catalunya (2016) M.Sc. in Computer Architecture, Networks, and Systems, UPC (2012) B.Eng. in Electrical and Computer Engineering, NTUA (2009) Research Interests: Memory systems (virtual memory, NVM) Hardware/OS interaction Resource management in data centers Parallel systems and serverless computing RISC-V architectures and cloud infrastructure Projects: Active in Horizon Europe projects Vitamin-V, Neuropuls, and REBECCA. Previously contributed to DAPHNE and ACTiCLOUD (EU H2020). Awards: 2024: Distinguished Artifact Award (ASPLOS) 2011: Best Paper Award (ICPE) Selected for IEEE Micro's Top Picks (2015, 2016) Teaching: Courses include Logic Design, Parallel Systems, and Large-scale Computing Systems at undergraduate and graduate levels. Labs/Teams: Leads research in the Computer Architecture Lab, collaborating on resilient architectures and cloud computing innovations.

• High-Performance Computing
• Parallel Processing
• Code Optimization
• +۳ مورد دیگر

Georgios Goumas is an Associate Professor in the Division of Computer Science at the School of Electrical and Computer Engineering, National Technical University of Athens. His research focuses on high-performance computing, parallel systems, and efficient resource management for modern computing platforms. Research domains: Serverless computing scalability and memory management Quantum algorithm design for classification Virtual memory optimization Hardware acceleration for sparse computations NUMA-aware concurrent data structures Recent innovations include DaeMon for disaggregated systems, eBPF-based Linux memory management, and multi-GPU matrix multiplication optimizations. Publications demonstrate consistent advancement in low-level system efficiency across quantum, serverless, and heterogeneous computing domains. Dr. Goumas teaches undergraduate and graduate courses on Operating Systems, Parallel Processing, and Code Optimization Techniques. His lab develops open-source tools like Paralia runtime for auto-tuning linear algebra and FPGA-based accelerators for scientific computing.