جست‌وجوی استاد و پژوهشگر

Olivia Dasمشاهده پروفایل

دانشیار

• Distributed Systems
• Cloud Computing
• Mobile Cloud Computing
• +۵ مورد دیگر

Olivia Das is an Associate Professor in the Department of Electrical, Computer, and Biomedical Engineering at Toronto Metropolitan University. She holds a PhD, MSc, and BSc in Computer Engineering and Applied Mathematics, and is a licensed Professional Engineer (PEng). Her research focuses on performance and dependability evaluation of distributed systems, including cloud computing, mobile cloud, and IoT. Education: PhD, MSc, and BSc from Carleton University and the University of Calcutta. Das employs techniques like measurements, analytical models, simulation, and machine learning to study resource management in distributed software systems. Her work aims to eliminate loading errors and lags in cloud-based systems through proper backend server provisioning. She teaches courses such as Introduction to Software, Object-Oriented Engineering Analysis and Design, and Computer Systems Modelling. Scientific Awards: Teaching Excellence Award, Electrical and Computer Engineering, Toronto Metropolitan University (2011) Visiting Faculty, Indian Institute of Technology-Kanpur (2012) Visiting Faculty, International Institute of Information Technology-Hyderabad (2012) She leads the Dependable Adaptive Systems Group (DAS) and is a member of Professional Engineers Ontario (PEO) and IEEE. Das emphasizes interactive learning in her classroom and is available to supervise students.

• Distributed Computing
• Database Systems
• Big Data
• +۷ مورد دیگر

YongChul Kwon is a researcher affiliated with the University of Washington , focusing on distributed computing, database systems, and big data optimization. His work addresses critical challenges in MapReduce frameworks, including skew management, fault tolerance, and performance efficiency. Key research contributions include SkewTune for dynamic skew mitigation in MapReduce, SnipSuggest for SQL autocompletion, and comparative analyses of Hadoop's scalability. He has published in venues like VLDB , SIGMOD , and IEEE Data Engineering Bulletin , often collaborating with Magdalena Balazinska and Bill Howe. Recent work (2013–2016) explores iterative MapReduce frameworks, Hadoop's workload evolution, and parallel processing of scientific user-defined functions. Earlier contributions (2008–2010) include fault-tolerant stream processing, clustering algorithms for astrophysics, and collaborative query management systems. His research bridges distributed systems, database engineering, and scientific computing, with implications for cloud infrastructure and large-scale data analytics.

• Distributed Data Management
• Cloud Computing
• Digital Cultural Heritage
• +۴ مورد دیگر

Renata Słota is a Professor at the Institute of Computer Science within the Faculty of Computer Science at AGH University of Science and Technology in Kraków, where she serves as Vice-Dean of the Faculty of Development. Her office is located at D-17, ul. Kawiory 21, room II, 3.34, with contact details including phone +48 12 328 33 35 and email rena@agh.edu.pl. She also contributes to academic governance as a member of the Disciplinary Council for Technical Information Technology and Telecommunications. Her research centers on distributed data management and cloud computing , with pioneering work in digital cultural heritage systems , blockchain-based decentralized authority , and scientific workflow automation . Notable contributions include integrated frameworks for 3D Cultural Heritage objects management, consensus mechanisms for collaborative data sharing, and multi-cloud data access solutions. Her work bridges theoretical advances with practical applications in cultural preservation and scientific computing. Analysis of her 2020-2026 publications reveals dominant trends in decentralized data governance , cultural heritage digitization , and hybrid cloud workflow management . Key themes include blockchain applications for organizational structures, semantic interoperability for quality-oriented data access, and reproducibility frameworks for computational experiments. Her research consistently addresses real-world challenges in global data accessibility and fault tolerance. Professor Słota actively participates in major research initiatives including the Onedata project for global data federation and the Scalarm platform for distributed parameter studies, demonstrating leadership in data-intensive computing environments and collaborative infrastructure development.

• Biomedical Engineering
• Computer Vision
• Medical Imaging
• +۳ مورد دیگر

Dr. Mirosław Socha is a Lecturer at the Department of Metrology and Electronics within the Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering at AGH University of Science and Technology in Kraków. His office is located in building B-1, room 206, where he conducts research and academic activities. Contact is available via phone (+48 12 617 28 28) or institutional email (socha@agh.edu.pl). His research encompasses several interconnected domains: Biomedical engineering with focus on medical imaging reconstruction and visualization techniques Computer vision applications for surgical navigation and diagnostics Deep learning methodologies for medical implant design and cranial defect solutions Metrology systems including flow calibration and friction measurement Signal processing for EEG and neural interface systems Analysis of his 15 most recent publications reveals strong emphasis on computational methods in healthcare, particularly deep learning-based medical reconstruction (2022-2023), measurement instrumentation (2019-2021), and medical visualization systems (2012-2015). His work consistently bridges theoretical concepts with clinical and industrial applications. No scientific awards, prizes, or fellowships are documented in available sources. Information regarding research grants, laboratory leadership, supervised students, or collaborative teams is unavailable in the provided materials.

• Computer Networks
• Software-Defined Networking (SDN)
• Network Security
• +۵ مورد دیگر

Fernando Ramos is an Associate Professor at the Higher Technical Institute (University of Lisbon) in the Department of Computer Engineering . His work focuses on Software-Defined Networking (SDN) , Network Security , and Machine Learning applications in networking . Teaches courses: Master's Dissertation in Telecommunications and Computer Engineering Introduction to Computer Architecture Computer Organization Advanced Topics in Networks and Distributed Systems Ramos is actively involved in research on malicious traffic detection , network virtualization , and fault-tolerant systems . Recent projects include Peregrine (ML-based security) and FlowLens (efficient traffic classification). His publications highlight trends in programmable data planes , multi-cloud security , and energy-efficient networking , with notable work on Random Linear Network Coding and Secure Network Monitoring .

• Embedded Systems Security
• Side-Channel Attacks
• Secure Architectures
• +۳ مورد دیگر

Guillaume Duc is an Associate Professor (Maître de Conférences) at Télécom Paris, affiliated with the Laboratoire Traitement et Communication de l'Information (LTCI) and the Systèmes Embarqués Critiques Autonomes team within the Computer Science and Networks Department. His expertise lies in embedded systems security, side-channel attacks, and secure architectures. Research Interests: His work spans embedded systems security , focusing on securing communication buses and memory architectures against espionage. He explores hardware support for software security , side-channel attacks (notably through the DPA Contest ), and secure memory encryption techniques. His research also delves into integrity checking , fault detection , and distributed security for multiprocessor systems. Publications & Patents: He has published extensively in top-tier journals and conferences, including Journal of Cryptographic Engineering , ACSAC , and CARDIS . His work covers topics like AES side-channel attacks, secure memory architectures, and Java Card vulnerabilities. He also holds multiple patents in secure memory allocation and electronic equipment monitoring. Teaching & Supervision: He leads several courses at Télécom Paris, including INF107: From Logic Gates to Operating Systems and SE203: Tools, Languages, and Practices for Microprocessor Systems . He is also involved in advanced Rust programming modules and has supervised PhD students. Labs & Teams: He is a key member of the Systèmes Embarqués Critiques Autonomes team at LTCI, contributing to cutting-edge research in secure and autonomous embedded systems.

• Computer Graphics
• Human-Machine Interaction
• Virtual Reality
• +۶ مورد دیگر

Andrea Sanna is a Full Professor at the Department of Control and Computer Engineering (DAUIN) of the Polytechnic University of Turin, Italy. His research focuses on 3D computer graphics , virtual and augmented reality , human-machine interaction , and scientific visualization . He has led numerous teaching initiatives, including courses on Computer Animation , Human-Machine Interaction , and 3D Graphics in Biometric Applications , with a strong emphasis on education technology and curriculum design. Scientific Branch: Information Processing Systems (Area 0009 - Industrial and Information Engineering) ERC Sectors: PE6_8 (Computer Graphics), PE6_9 (Human-Computer Interaction), PE6_11 (Machine Learning) His research spans peer-to-peer architectures , cloud solutions , and deep learning for 3D object reconstruction , with applications in augmented maintenance and synthetic dataset generation . He has contributed to European projects like OASIS and regional initiatives EASE-R3 and HUMANS. Recent publications highlight his work in AI-driven industrial design , VR/AR for sports analytics , and multimodal HMI systems . Awards include three Best Paper Awards (UCMEDIA 2009, HUCAPP 2017, SalentoAVR 2018) and a CNR fellowship (2002-2003). Advising : Supervising PhD candidate Luca Francesco Rossi on "Who’s What: From Recognition to Identification" and mentoring via the Mentoring Polito Project (M2P). Grants include funding for projects like Impressed: 3D Body Angle Calculation and Human-Robot Collaboration via AR .

• Distributed Computing
• Computer Networks
• Parallel and Distributed Algorithms
• +۲ مورد دیگر

Prof. Boaz Patt-Shamir is a faculty member at Tel Aviv University , affiliated with the Iby and Aladar Fleischman Faculty of Engineering and the School of Electrical Engineering . His research focuses on distributed computing, computer networks, parallel algorithms, and recommender systems. Email: boaz@tauex.tau.ac.il Office: Computer and Software Engineering, Room 213 Phone: 03-6407036, 03-6409416 Fax: 03-6407095 Research Interests include: Distributed Computing Computer Networks Parallel Algorithms Collaborative Filtering Recent Work explores vertex recoloring algorithms for dynamic cloud job placement, contributing to online algorithm design and graph theory. Scientific Awards : SIROCCO Prize

• Distributed Systems
• AI/ML Systems
• Databases
• +۷ مورد دیگر

Paolo Romano is a Senior Researcher at the Distributed Systems Group, INESC-ID, and an Associate Professor at the Department of Computer Engineering, Instituto Superior Técnico (IST), Universidade Técnica de Lisboa, Portugal. He holds a PhD in Computer Engineering (2007) and has contributed extensively to research in distributed systems, transactional memory, and autonomic computing. PhD in Computer Engineering, Sapienza Rome University (2007) Master Degree in Computer Engineering, University of Rome Tor Vergata (2002), Summa Cum Laude Certificate of Advanced English, Cambridge University (1997) His research interests span: Dependable Distributed Systems: Fault-tolerance in multi-tier systems, formal verification of distributed protocols, and reliable AI systems. Parallel/Distributed/High-Performance Computing: Transactional memory, GPU programming, and efficient AI/ML systems. Autonomic Systems: Self-optimization of complex systems and adaptive concurrency control schemes. Performance Modeling: Machine-learning-based prediction for complex systems and QoS in cloud platforms. Scientific accolades include: Best Paper Award at ICAC 2014, ICDCS 2012, and NETYS 2013 Best Paper Awards at IEEE Symposium on NCA 2008 and 2007 Best INESC-ID Young Researcher (2011) Distinguished Member, INSTICC He has supervised numerous PhD and MSc students, including current advisees Maria Casimiro, Daniel Castro, and Shady Alaa. Former students include Shady Issa (PostDoc at INESC-ID), Nuno Diegues (Software Engineer at Feedzai), and Diego Didona (PostDoc at EPFL). His research is supported by projects like ARISTOS, Cloud-TM, and Euro-TM, with total funding exceeding 2.4 million euros. Paolo Romano is affiliated with the Distributed Systems Group at INESC-ID, a research laboratory associated with IST, Universidade Técnica de Lisboa.

Sümeyye KAYNAKمشاهده پروفایل

استادیار

• Artificial Intelligence
• Cloud Computing
• Environmental Informatics
• +۳ مورد دیگر

Sümeyye KAYNAK serves as an Assistant Professor in the Department of Computer Engineering at Sakarya University's Faculty of Computer and Information Sciences. Her academic profile demonstrates interdisciplinary expertise bridging computer science with environmental sustainability and educational technology through computational modeling and data-driven solutions. Her educational foundation includes: Doctorate (2019) from Sakarya University Institute of Science/Computer and Information Engineering with thesis on solar energy potential modeling using 3D data Master's degree (2014) from same institution focusing on AI-based student counseling infrastructure Bachelor's degree (2012) in Computer Engineering from Sakarya University Faculty of Engineering Dr. KAYNAK's research integrates advanced computational methodologies across multiple domains. Her primary focus areas include Artificial Intelligence applications for resource allocation in cloud manufacturing (utilizing genetic algorithms and AHP), Environmental Informatics for flood modeling and hydrological analysis, and Sustainable Energy systems through solar potential estimation tools. Recent work demonstrates significant expansion into geospatial web components for earth science agencies and digital twin frameworks for urban infrastructure resilience, reflecting growing emphasis on climate change adaptation technologies. Publication analysis from 2012-2025 reveals a clear research evolution: early work (2012-2018) concentrated on educational AI systems and solar energy tools, while current output (2023-2025) prioritizes environmental applications with city-scale flood impact modeling, hydroinformatics, and open data frameworks. This trajectory shows increasing sophistication in real-time data analytics and cross-domain integration, particularly between manufacturing systems and environmental monitoring. No scientific awards were documented in the source materials. Regarding academic mentoring, the available documentation contains no references to graduate student supervision or grant-funded research projects. No laboratory affiliations or research team leadership roles were specified in the provided information.

• Cryptography
• Computer Science
• High-Performance Computing (HPC)
• +۳ مورد دیگر

Dr. Michał Klisowski is a Lecturer at the Fundacja Uniwersytetu Marii Curie-Skłodowskiej , affiliated with the Faculty of Mathematics, Physics and Computer Science and the Department of Information Systems Software . His research focuses on cryptography, high-performance computing, and media truth detection. Research Highlights: Cryptographic protocols, energy-efficient computation, media credibility analysis Email: michal.klisowski@mail.umcs.pl His recent publications address topics such as multicore key exchange protocols , RAPL power monitoring , and fake news detection using machine learning. He holds an ORCID identifier and has published in information and communication technology domains. Collaboration & Tools: Utilizes ORCID and Scopus for academic visibility, with a focus on integrating theoretical cryptography with practical computational efficiency.

Kovalchuk Myroslav Lyubomyrovychمشاهده پروفایل

دانشیار

• Neural networks
• database organization
• creation of information systems
• +۱ مورد دیگر

Myroslav Lyubomyrovych Kovalchuk serves as an Associate Professor in the Department of Computer Science at Yuriy Fedkovych Chernivtsi National University. His academic foundation includes degrees in Electronics and Microelectronics, culminating in a Candidate of Physical and Mathematical Sciences qualification. His educational background comprises: Bachelor of Electronics, Yuriy Fedkovych Chernivtsi National University (2001) Engineer-physicist in Microelectronics and Semiconductor Devices, Yuriy Fedkovych Chernivtsi National University (2002) Candidate of Physical and Mathematical Sciences, Specialty 01.04.01 (2010) Kovalchuk's research centers on neural networks, database systems, information system development, and electronics engineering. His work bridges theoretical computer science with practical engineering applications, particularly in AI-driven solutions for real-world problems. He has developed specialized systems for image analysis, financial forecasting, and tourism infrastructure, demonstrating consistent innovation in computational methodologies. Over the past five years, his publication record reveals a strategic focus on neural network optimization, distributed database systems, and intelligent information architectures. His textbooks and research articles show increasing integration of cloud computing with traditional AI techniques, reflecting industry trends toward scalable, accessible computational solutions across finance, healthcare, and tourism sectors. Kovalchuk actively contributes to the Bukovina Information Technology Cluster (since 2019) and provides scientific consulting to KM TRADE LLC on security systems. He maintains professional currency through Sigma Software University's Teachers' Smart Up program and international training initiatives in Warsaw and Suceava, emphasizing modern pedagogical approaches for technical education.

• Parallel Computing
• Middleware for Clouds and Big Data
• High Performance Computing (HPC)
• +۶ مورد دیگر

Christophe Cérin is a Full Professor at Université Sorbonne Paris Nord in the Laboratoire d'Informatique de Paris Nord (LIPN) UMR CNRS 7030 . He is also a member of the Data Intelligence Institute of Paris (DiiP) and chairs the MAGI cluster for high-performance computing at Sorbonne Paris Nord. His research focuses on Parallel Computing , Middleware for Clouds and Big Data , and High Performance Computing (HPC) . His work includes the EcoIndex metric for measuring the environmental performance of web requests, using techniques like Random Projection , Locality Sensitive Hashing (LSH) , and Self-Organizing Maps (SOM) . He has developed tools to analyze energy consumption and emissions related to HTTP requests, integrating data from ARCEP and ENEDIS . He has served as co-chair for the 19th Annual IFIP International Conference on Network and Parallel Computing (NPC 2022) and the International Conference on Future Computational Paradigms (FCP-2022) . He was an Editorial Board Member of IEEE Transactions on Computers (2019-2023) and Associate Editor for Springer Journal of Cloud Computing (2021--). Scientific Awards Recipient of the 2020 IEEE Technical Committee on Cloud Computing Research Innovation Award . He leads initiatives like #ACESinFrance (Global Perspective on Cybersecurity) and collaborates with institutions in Japan, Poland, and China. He also organizes seminars for users of the MAGI cluster and contributes to cybersecurity education through partnerships with Hexatrust , WALLIX , and Nexedi .

Liming Zhuمشاهده پروفایل

استاد

• Software Architecture
• Distributed Systems
• DevOps
• +۶ مورد دیگر

Dr. Liming Zhu is a Conjoint Full Professor at the School of Computer Science and Engineering, University of New South Wales, and leads the Software and Computational Systems Research Program at Data61, CSIRO. This research program comprises over 200 personnel working across key technology domains including big data analytics infrastructure, computational science platforms, trustworthy systems, distributed systems, business process management, legal informatics, provenance tracking, behavior analytics, blockchains, and software engineering. His research expertise spans software architecture in enterprise and embedded systems, dependable and secure distributed systems, DevOps and continuous deployment methodologies, big data analytics infrastructure and pipelines, blockchain applications, software ecosystems, and model-driven development. His work intersects with multiple Fields of Research including Computer Software, Distributed Computing, Software Engineering, Computer System Security, and Data Security. Zhu's publication record demonstrates significant scholarly impact with 97 journal articles, 186 conference papers, 39 preprints, 7 book chapters, and 1 authored book. His research program at Data61 focuses on translating theoretical advances into practical systems that address real-world challenges in data management, system security, and software development processes. The research program has particular strength in developing infrastructure for computational sciences, including specialized applications in imaging processing and bioinformatics/life sciences. Software and Computational Systems Research Program, Data61, CSIRO (Leadership role) School of Computer Science and Engineering, University of New South Wales (Conjoint Full Professor) Dr. Zhu actively supervises PhD students at UNSW, having successfully guided 6 doctoral candidates to completion as primary supervisor. His teaching focuses on software architecture courses, connecting academic theory with industry practice. The research program he leads serves as a bridge between academic inquiry and practical application, working closely with industry partners to develop innovative solutions in data platforms, trustworthy systems, and software engineering practices.

• Computer Architecture
• Distributed Systems
• Machine Learning
• +۵ مورد دیگر

Chitaranjan Das is a Professor in the Department of Computer Science and Engineering at Penn State University . His work aligns with the United Nations Sustainable Development Goals , particularly contributing to advanced computing infrastructure and global technological innovation. Das actively collaborates with researchers like Anand Sivasubramaniam , Mahmut Kandemir , and Vijay Narayanan on cutting-edge projects. Key Research Areas: Computer Architecture, Network-on-Chip (NoC) systems, Quality of Service (QoS), Spiking Neural Networks (SNNs), Cloud Computing, and Bioinformatics. Das leads projects funded by the National Science Foundation (NSF), including SHF: Medium: Exploring an Edge Platform Design Trajectory for Next Generation XR Applications and CNS Core: Small: Embracing cross stack heterogeneity in next-generation cloud platforms . His research outputs span over 300 publications, with recent works focusing on Edge Computing, Serverless Computing, and hardware acceleration for genomic data analysis. Recent Publications (2024-2025) highlight his interdisciplinary approach, integrating Machine Learning with FPGA-based acceleration for nanopore sequencing, optimizing VR streaming on edge devices, and enhancing serverless computing on heterogeneous hardware. His projects often bridge the gap between system design and algorithmic innovation , with a strong emphasis on real-world applications in cloud platforms and neural networks.