جستجوی استاد راهنمای دانشگاهی

Stefano Markidisمشاهده پروفایل

استاد

• High-Performance Computing
• Heterogeneous Systems
• Quantum Computing
• +۳ مورد دیگر

Stefano Markidis is a Professor of Computer Science specializing in high-performance computing systems at KTH Royal Institute of Technology in Sweden. He works in the Division of Computational Science and Technology, focusing on supercomputers, quantum computers, and computational methods for scientific simulations. His research spans multiple domains including plasma physics, computational fluid dynamics, and quantum computing. Markidis holds an MS degree from Politecnico di Torino and a PhD in Nuclear Engineering from the University of Illinois at Urbana-Champaign. Prior to joining KTH, he was a graduate research assistant at Los Alamos National Laboratory and Lawrence Berkeley National Laboratory, followed by a postdoc at KU Leuven. His academic journey reflects a strong foundation in both engineering and computational science. His primary research interests include High-Performance Computing , Heterogeneous Systems , and Quantum Computing . Markidis develops computational methods for plasma physics, particle-in-cell simulations, and fluid dynamics. His work bridges theoretical physics and practical computing, with applications in space physics, fusion energy, and materials science. He is particularly known for contributions to parallel computing, GPU acceleration, and the development of scalable simulation frameworks like Neko for computational fluid dynamics. His research increasingly integrates machine learning techniques with traditional numerical methods. Analysis of Markidis' recent publications reveals a strong focus on quantum-classical hybrid computing, advanced particle-in-cell methods, and high-fidelity computational fluid dynamics. His work demonstrates expertise in programming models for heterogeneous architectures including GPUs and quantum processors, with growing emphasis on AI-enhanced scientific computing. R&D100 award (2005) for the CartaBlanca project R&D100 award (2017) for the SHIELDS project Markidis teaches multiple courses at KTH including Applied GPU Programming, Quantum Computing for Computer Scientists, and High-performance Computing for Computational Scientists. He has supervised numerous degree projects across various specializations in computer science and electrical engineering. His research has been supported by various grants related to high-performance computing and quantum technologies, with applications spanning from space physics to medical treatments. Markidis leads research in computational science with a focus on developing frameworks like Neko for extreme-scale computational fluid dynamics. His team works on integrating traditional HPC methods with emerging technologies including quantum computing and AI, contributing to advancements in scientific simulation across multiple disciplines.

Michael Kuhnمشاهده پروفایل

استاد

• Parallel Computing
• High-Performance Computing
• Storage Systems
• +۳ مورد دیگر

Prof. Dr. Michael Kuhn is a Professor in the Faculty of Computer Science at Otto von Guericke University Magdeburg since 2020, leading the Parallel Computing and I/O Group. His work bridges theoretical parallel computing concepts with practical high-performance computing implementations. His academic journey includes: Bachelor's degree in Computer Science from Heidelberg University (2007) Master's degree in Computer Science from Heidelberg University (2009) Doctorate from Hamburg University (2015) with dissertation on "Dynamically Adaptable I/O Semantics for High Performance Computing" Prof. Kuhn's research tackles critical challenges in modern computing infrastructure where systems scale to millions of processor cores. He investigates fundamental improvements to storage architectures, I/O interfaces, and programming models that enable efficient data processing at exascale levels. His development of the JULEA storage framework provides dynamically adaptable solutions for high-performance computing environments, addressing the growing mismatch between computational power and data movement capabilities. As faculty public relations officer, he maintains the Faculty of Computer Science's digital presence while actively teaching courses that equip students with practical parallel programming skills. His group's work demonstrates how breaking computational problems into parallelizable components—like the matrix processing example reducing runtime to a quarter—enables scientific breakthroughs requiring massive computational resources. The Parallel Computing and I/O Group serves as a hub for advancing storage technologies and parallel processing methodologies, with applications spanning scientific computing, big data analytics, and next-generation supercomputer architectures.

Jerzy Proficzمشاهده پروفایل

دانشیار

• Computer Architecture
• Embedded Systems
• High-Performance Computing
• +۲ مورد دیگر

Jerzy Proficz serves as an Associate Professor at the Department of Computer Systems Architecture within Gdańsk University of Technology's Faculty of Electronics, Telecommunications and Informatics (inferred from workplace building designation), and concurrently holds the position of Director of the Computer Centre TASK. His academic and administrative roles are based in the Electronics, Telecommunications and Informatics Building (Gmach Elektroniki Telekomunikacji i Informatyki), Room 344 NE, with direct contact via phone 583486343. His research focuses on core computer systems disciplines including Computer Architecture, Embedded Systems design, High-Performance Computing infrastructure, Parallel Computing methodologies, and comprehensive Computer Systems development. These interests align with the department's specialization in hardware-software integration and computational systems engineering. As Director of Computer Centre TASK, Proficz oversees the university's central supercomputing facility, providing critical computational resources and technical infrastructure for research and academic activities across Gdańsk Tech. This dual appointment positions him at the intersection of academic research and institutional computing leadership.

• Performance Analysis and Engineering
• Energy Efficient Computing
• Parallel and Distributed Computer Architectures
• +۷ مورد دیگر

Vladimir Getov is a Professor at the University of Westminster with extensive international affiliations including Honorary Professor at Technical University of Sofia and Visiting Lab Fellow at Pacific Northwest National Laboratory. His leadership roles span the IEEE Computer Society (Governor since 2015), CoreGRID/ERCIM Working Group (Topic Leader 2009-2015), and multiple editorial positions including Area Editor for High Performance Computing at IEEE Computer. His research centers on Performance Analysis and Engineering, Energy Efficient Computing, and Parallel and Distributed Architectures with significant contributions to cloud computing, wireless sensor networks, and component-oriented design. Recent work focuses on energy-drain attack prevention in MAC protocols and application-specific thermal modeling for multi-core processors. Key awards include the Bulgarian National Scientific Award 'Pythagoras' (2009), IEEE Computer Society Certificates of Appreciation (2008, 2010), and CoreGRID Outstanding Contribution Award (2008). His publication record spans 35+ years with over 100 papers including the 2024 IEEE ICAI conference paper on electronic digital computing history. As leader of the Distributed and Intelligent Systems Research Group since 1996, he has supervised 7 PhD students to completion with frameworks like AGOCS cloud simulator and TunableMAC protocol. His EU project leadership includes CoreGRID (2004-2009) and GridCOMP (2006-2010), securing sustained research funding including Daiwa Foundation support (2017) for low-energy co-design methodology.

• Cloud Computing
• Machine Learning
• Computer Networks
• +۳ مورد دیگر

Hamed Hamzeh serves as Lecturer in Data Science at the School of Computer Science and Engineering, University of Westminster, and is affiliated with the Centre for Parallel Computing. His academic credentials include a Ph.D. in Cloud Computing from Bournemouth University and an MSc in Data Science from Istanbul Sehir University, Turkey. Dr. Hamzeh's research centers on cloud-native resource management, computer networks, and multi-agent systems, with groundbreaking work on fairness in cloud resource allocation. He developed novel algorithms including H-FFMRA and MRFS that address multi-resource scheduling in heterogeneous environments. His expertise spans AWS, Kubernetes, Python, and optimization techniques, bridging theoretical cloud computing with industrial applications in orchestration and resource management. Analysis of his 11 publications (2017-2023) reveals an evolving research trajectory: beginning with network bandwidth allocation (2017), advancing to cloud resource fairness (2018-2021), and culminating in cloud-to-things continuum orchestration (2023). This progression demonstrates increasing system complexity while maintaining core focus on fairness metrics across distributed environments. Dr. Hamzeh actively contributes to the academic community as technical committee member for IEEE ICCCS and Distributed AI conferences, and as reviewer for Springer's Journal of Grid Computing and Journal of Supercomputing. His service reflects recognition within cloud computing research circles. Prospective students receive supervision in Cloud Computing, Artificial Intelligence, Machine Learning, Software Engineering, and Computer Networks. Current research opportunities emphasize practical implementation of resource allocation algorithms in cloud-native environments through the Centre for Parallel Computing.

• High Performance Computing
• Distributed Systems
• Deep Learning
• +۴ مورد دیگر

Muhammad Mustafa Rafique is an Associate Professor in the Department of Computer Science at Rochester Institute of Technology's Golisano College of Computing and Information Sciences. His research specializes in optimizing large-scale computing systems, with focus areas including: High-performance computing (HPC) resource management Distributed deep learning acceleration Fault-tolerant cloud architectures GPU-accelerated checkpointing systems Serverless computing frameworks Dr. Rafique's work demonstrates consistent innovation in improving computational efficiency for containerized HPC workflows, multi-GPU scheduling, memory optimization, and distributed training pipelines. His publications frequently appear in premier IEEE/ACM conferences, reflecting contributions to systems performance engineering. While no awards or student advisees are mentioned in available materials, his research collaborations include co-authors from institutions worldwide, indicating active engagement in the high-performance computing research community. Current work explores emerging memory technologies like CXL and advanced containerization techniques for next-generation datacenters.

Alfonso González Brionesمشاهده پروفایل

دانشیار

• Ubiquitous Computing
• Ambient Intelligence
• Smart Cities
• +۸ مورد دیگر

Dr. Alfonso González Briones is an Associate Professor in the Department of Computer Science and Automation at the University of Salamanca, where he conducts cutting-edge research in intelligent systems and their applications. He is a prominent member of the BISITE Research Group and has also worked with the GRASIA Research Group at Complutense University of Madrid as a 'Juan De La Cierva' postdoc. His academic journey at the University of Salamanca includes a Bachelor of Technical Engineering in Computer Engineering (2012), a Bachelor's Degree in Computer Engineering (2013), a Master's Degree in Intelligent Systems (2014), and a PhD in Computer Engineering (2018). His research focuses on Ubiquitous Computing and Ambient Intelligence for developing smarter, more energy-efficient cities that improve social welfare and promote sustainable development. His work spans Multiple Agent Systems (MAS), energy optimization, smart cities infrastructure, Industry 4.0 applications, and machine learning techniques for various domains including social networks, transportation, and agricultural systems. Dr. González Briones has published extensively with over 30 journal articles and 60 conference proceedings publications, demonstrating consistent productivity across multiple domains of computer science and artificial intelligence. His research trends show a clear progression from foundational work in multi-agent systems toward increasingly sophisticated applications in smart cities, energy management, and Industry 4.0 contexts, with a growing emphasis on practical implementations that address real-world challenges. 2nd place in 1st SENSORS+CIRTI Award for best national thesis in Smart Cities (CAEPIA 2018) Juan de la Cierva State Program Grant in ICT - Information and Communication Technologies (2018) Member of scientific committees for Advances in Distributed Computing and Artificial Intelligence Journal (ADCAIJ) and British Journal of Applied Science and Technology (BJAST) Reviewer for prestigious journals including Supercomputing Journal, Journal of King Saud University, Energies, Sensors, Electronics, and Applied Sciences As an active researcher, Dr. González Briones has participated in 10 international research projects and served on technical committees for prestigious international conferences including AIPES, HAIS, FODERTICS, PAAMS, and KDIR. His work bridges academic research with practical industry applications, particularly in energy optimization systems, IoT, and Machine Learning solutions for real-world problems. He has also collaborated with private research centers including Virtual Power Solutions in Portugal and AIR Institute, where he worked as Project Manager in Industry 4.0 and IoT projects. His research infrastructure includes work with the BISITE Research Group, where he develops and implements multi-agent architectures for optimizing energy consumption and other complex systems. His laboratory work spans smart home energy management, intelligent transportation systems, semantic analysis for Industry 4.0, and social network analysis applications.