جستجوی استاد راهنمای دانشگاهی

• Database Systems
• Privacy-Preserving Computation
• Indexing Techniques
• +۳ مورد دیگر

Dong Xie is an Assistant Professor in the field of Computer Science and Engineering , with a focus on database systems and privacy-preserving computation. His work spans indexing techniques, oblivious RAM, and high-throughput data processing. Key Research Areas: Encrypted databases, access pattern privacy, dynamic data structures, spatial analytics. Collaborations: Active in database optimization and security, with external partnerships reflected in recent publications. Over the past decade, Dong Xie has contributed to advancements in oblivious query processing , index dynamization , and spatial data management . His research addresses challenges in secure data access , concurrent updates , and storage efficiency , particularly for cloud and distributed environments. Recent publications highlight his work on low-latency transaction scheduling (2025), dynamic sampling indexes (2023), and SSD-based storage optimization (2022). His articles explore intersections of privacy , performance , and scalability .

Nectarios Kozirisمشاهده پروفایل

استاد

• Parallel and Distributed Systems
• Computer Architecture
• Systems Software
• +۱۰ مورد دیگر

Nectarios Koziris is a Professor at the Department of Computer Science , National Technical University of Athens (NTUA) , and former Dean of the School of Electrical and Computer Engineering . His research focuses on Parallel and Distributed Systems , Computer Architecture , and Cloud Computing . Key Research Themes: Compiler-OS-Architecture Interaction, Datacenter Hyperconvergence, Sparse Matrix Optimization, Quantum Computing, FPGA Virtualization Leadership: Founder of ~okeanos (Europe's largest public Cloud IaaS), Co-founder of GFOSS , Member of IEEE Computer Society Greece, Advisor to Arrikto Inc. His work has led to over 180 publications with 5800+ citations (h-index 33) , including two Best Paper Awards (IPDPS 2001, CCGRID 2013) and Intel Recognition (2015). He has supervised 12 PhD students and participated in 15+ EU projects as coordinator or consortium partner. Scientific Leadership: Program Co-Chair for Europar 2012 , Organizer for IPDPS , ICPP , SC conferences, and active member in Cloud Computing Expert Groups for the European Commission.

Giulia Rubinoمشاهده پروفایل

مدرس

• Indefinite Quantum Causality
• Integrated Photonics Technologies
• Quantum Thermodynamics
• +۵ مورد دیگر

Dr. Giulia Rubino is a Royal Commission 1851 Fellow and Proleptic Lecturer in the School of Physics at the University of Bristol. Her academic credentials include a BSc, MSc, and PhD, establishing her expertise in quantum physics and related disciplines. She maintains an active research presence with numerous publications and collaborations across international institutions. Dr. Rubino's educational background, though not detailed in specific institutions, demonstrates a strong foundation in physics through her BSc, MSc, and PhD qualifications. Her career progression has led to her current prestigious position at the University of Bristol, where she contributes significantly to quantum research. Her research program spans three interconnected pillars: Indefinite Quantum Causality , where she explores how causal structures might need to be both dynamic (as required by general relativity) and indefinite (due to quantum theory); Integrated Photonics Technologies , where she applies quantum optics principles to develop sophisticated photonic circuits; and Quantum Thermodynamics , where she addresses fundamental questions about defining work in quantum systems. Her work on indefinite causal structures challenges conventional space-time understanding, with significant implications for both foundational physics and quantum information processing. In experimental physics, she bridges quantum foundations and technologies through state-of-the-art photonic implementations. Her theoretical contributions to quantum thermodynamics address critical gaps in understanding work definitions for nanoscale quantum devices. Dr. Rubino's publication record reveals a clear research trajectory focused on quantum foundations and information processing. Her most recent work (2024-2025) shows increasing sophistication in quantum thermodynamics and causal structure experiments, with notable papers on quantum work fluctuation frameworks and quantum key distribution. The consistent theme across her publications is the exploration of quantum phenomena that challenge classical notions of causality, time, and thermodynamics, often with practical applications to quantum technologies. Her scientific recognition includes the prestigious Royal Commission 1851 Fellowship , awarded to exceptional early-career researchers with potential for significant contributions to their fields. This fellowship supports her innovative research at the University of Bristol and recognizes her standing in the quantum physics community. While specific student names aren't listed in the available information, Dr. Rubino's position as Lecturer suggests active involvement in mentoring graduate students. Her collaborative research approach is evident in her numerous co-authored publications with researchers from multiple institutions, indicating strong research networks. Her grant portfolio likely includes the Royal Commission fellowship and potentially other research council funding supporting her quantum physics investigations. Dr. Rubino is affiliated with the Quantum Engineering Technologies and Theoretical Physics research groups at the University of Bristol. Her work contributes to the university's strong reputation in quantum information science, particularly in the experimental realization of quantum phenomena that challenge conventional understanding of space-time and thermodynamics. She appears to be involved in sophisticated photonic circuit development and quantum information processing experiments that require specialized laboratory facilities.

Giorgio Scorzelliمشاهده پروفایل

پژوهشگر

• Extreme Data Management
• Scientific Visualization
• Computational Topology
• +۶ مورد دیگر

Giorgio Scorzelli is a researcher at the University of Utah, serving as Director of Software Development for the Center for Extreme Data Management, Analysis, and Visualization (CEDMAV) and the National Science Data Fabric (NSDF) . He specializes in extreme data management, scientific visualization, and computational topology, with a focus on scalable solutions for climate science, materials science, and neuroscience datasets. His work emphasizes democratizing data access through platforms like OpenVisus , enabling efficient analysis of petascale and exascale data. Key contributions include orchestrating cyberinfrastructure, optimizing parallel I/O, and developing real-time visualization systems for heterogeneous resources. Notable scientific contributions include the NSF Grant #2127548 for NSDF development . His projects integrate cloud computing, geo-distributed storage, and FAIR digital objects to lower barriers to data democratization. Giorgio's research spans multi-resolution algorithms , computational topology , and 3D geometric modeling , with applications in infrastructure security, archaeological reconstruction, and biomedical imaging. His work bridges abstract mathematical frameworks (e.g., Boolean algebras, chain complexes) with practical software solutions.

• Nonlinear Filtering
• Multi-Object Tracking
• Deep Machine Learning
• +۳ مورد دیگر

Lennart Svensson is a Professor at Chalmers University of Technology in the Signal Processing research group. His work focuses on nonlinear filtering, multi-object tracking, Bayesian statistics, and deep machine learning with applications in autonomous systems and sensor fusion. Research Interests Nonlinear Filtering and Bayesian Inference Multi-Object Tracking and Sensor Fusion Deep Learning for Autonomous Systems Performance Metrics (GOSPA, T-GOSPA) Lidar-Camera Fusion and Radiance Fields 5G SLAM and mmWave Sensing Publications Trends Recent work emphasizes uncertainty-aware multi-object tracking metrics, trajectory estimation using Poisson Multi-Bernoulli Mixtures, and sensor fusion techniques for autonomous driving. His research integrates Bayesian methods with deep learning for applications in automotive radar, lidar, and 5G positioning systems. Contact Email: lennart.svensson@chalmers.se

Pedro M. B. Silva Girãoمشاهده پروفایل

استاد

• Instrumentation
• Transducers
• Wireless Sensor Networks
• +۶ مورد دیگر

Pedro M. B. Silva Girão is a Full Professor in the Department of Electrical Engineering at Instituto Superior Técnico (IST), University of Lisbon (UL), and a Senior Researcher at Instituto de Telecomunicações where he heads the Instrumentation and Measurements Group and coordinates the Basic Sciences and Enabling Technologies area. His dual institutional roles position him at the forefront of academic research and technological innovation in Portugal. His research program focuses on instrumentation, transducers, and measurement techniques with specialized applications in biomedical and environmental domains. Key interests include wireless sensor networks for health monitoring, metrology standards, and digital data processing methodologies. This work bridges engineering principles with real-world healthcare and ecological challenges, emphasizing practical implementations in diagnostic systems and environmental sensing. Analysis of his 2019-2024 publications reveals a strong thematic trajectory in IoT-enabled healthcare solutions and precision environmental monitoring. Recurring motifs include gait rehabilitation through mixed reality systems, advanced dosimetry for liver cancer radioembolization, microvascular reactivity assessment, and water quality sensor networks. His output demonstrates consistent interdisciplinary collaboration between engineering, medical, and environmental science communities. Dr. Girão's scientific recognition includes: IEEE Senior Member status IEEE IMS Distinguished Lecturer appointment Honorary Chairmanship of IMEKO TC19—Environmental Measurements As leader of the Instrumentation and Measurements Group at Instituto de Telecomunicações, he directs a multidisciplinary team developing next-generation measurement systems. Current initiatives integrate microwave Doppler radar, wearable biopotential sensors, and wireless networks for unobtrusive health monitoring and environmental assessment, with active partnerships across medical institutions and ecological agencies.

Renaud Pacaletمشاهده پروفایل

پژوهشگر

• Hardware Security
• Side-Channel Attacks
• Embedded Systems
• +۷ مورد دیگر

Renaud Pacalet is a Researcher at Institut Mines-Télécom – Télécom Paris , affiliated with the Communications and Electronics (Comelec) Department and the System on Chip (LabSoc) research team under the Information Processing and Communication Laboratory (LTCI). His work spans hardware security, embedded systems, and software-defined radio (SDR) architectures. Current Research: Hardware security, side-channel attacks (power, timing, fault injection), RISC-V security analysis using gem5, FPGA scheduling for cloud data centers, and model-driven design methodologies. Past Research: Hardware acceleration for ray tracing, SDR front-end processing, SoC security, and memory bus protection (SecBus project). Teaching: Courses on Digital Systems, Computer Architecture, and Hardware Security at EURECOM, including lab sessions on side-channel attacks and fault analysis. Email: renaud.pacalet@telecom-paris.fr Contact: Télécom ParisTech, Campus SophiaTech, 450 route des Chappes 06410 Biot, France

• Information Processing Systems
• Internet of Things (IoT)
• Edge Computing
• +۴ مورد دیگر

Marcello Pietri is a Researcher (td art. 24 c. 3 lett. A) and Contract Professor at the Department of Engineering Sciences and Methods (DISMI) at the University of Modena and Reggio Emilia. His work focuses on Information Processing Systems, with expertise in IoT, Edge Computing, and Digital Twins. He teaches courses like 'Sistemi Informativi' (Information Systems) for the Engineering Management program, emphasizing database design, SQL, and web application development with Python. His research explores advanced topics including Digital Twin integration in Industry 5.0, Fluid Computing in IoT ecosystems, and Smart City data fusion using 5G MEC architectures. He collaborates with institutions like the DIPI Lab (Distributed and Pervasive Intelligence Group), contributing to projects on telecom security, energy forecasting, and human-centric manufacturing systems. Recent publications highlight innovations in operator digital twins for workplace well-being, distributed data mesh models for IoT-edge-cloud systems, and adaptive monitoring algorithms for large-scale cloud environments. His work bridges theoretical advancements with practical implementations, addressing challenges in scalability, real-time data processing, and interdisciplinary collaboration.

Michalis Mountantonakisمشاهده پروفایل

پژوهشگر

• Large-Scale Semantic Data Integration
• Linked Open Data
• Semantic Web
• +۴ مورد دیگر

Michalis Mountantonakis is a Postdoctoral Researcher at FORTH and Laboratory Teaching Staff in the Department of Computer Science at the University of Crete, Greece. He holds a PhD (2020), MSc (2016), and BSc (2014) in Computer Science from the University of Crete, all with top grades. His research focuses on Large-Scale Semantic Data Integration, Linked Open Data, and Semantic Web technologies, with over 45 publications in top venues like ACM VLDB, ISWC, and ECML. He has been awarded the prestigious SWSA Distinguished Dissertation Award (2020) and the Maria Michael Manasaki Fellowship (2020). His work includes tools like LODsyndesis and LODChain, addressing challenges in knowledge graph connectivity and validation of AI-generated content. Education: PhD in Computer Science (2016-2020), University of Crete (Excellent GPA 9.74/10) MSc in Computer Science (2014-2016), University of Crete (Excellent GPA 9.87/10) BSc in Computer Science (2010-2014), University of Crete (2nd in class with GPA 8.42/10) Research Interests: His work bridges semantic web technologies with modern AI challenges, emphasizing large-scale data integration, knowledge graph applications, and validation frameworks. He has contributed to cultural heritage informatics, machine learning-augmented semantic systems, and cross-lingual NLP solutions. Recent trends include leveraging LLMs for query generation and semantic enrichment while ensuring factual accuracy through knowledge graph-driven validation. Key Achievements: Developed LODsyndesis, a global-scale semantic integration service Pioneered real-time validation of ChatGPT responses using RDF knowledge graphs Won Best Paper Award (ISWC 2022) for entity enrichment techniques Recipient of Stelios Orphanoudakis Undergraduate Fellowship (2013-2014) Participated in Roche Continents 2019 (top 100 European science students) Grants & Labs: His research has been supported by GSRT/HFRI. He collaborates with FORTH-ICS and leads projects in EU-funded initiatives like iMarine and BlueBridge. Current work focuses on governance models for ontologies, interoperable thesaurus creation (e.g., FoodEx2), and semantic analytics for cultural heritage datasets.

Laura Emilia Maria Ricciمشاهده پروفایل

استاد

• Blockchain Technology
• Layer-2 Solutions for Blockchain
• Cryptographic Techniques
• +۳ مورد دیگر

Laura Emilia Maria Ricci is a Full Professor at the University of Pisa's Department of Computer Science, leading the Pisa Distributed Ledger Laboratory (Pisa DLT Lab). Her research focuses on blockchain technology, layer-2 solutions, cryptographic techniques, and self-sovereign identity frameworks. She coordinates the National PhD program in Blockchain and Distributed Ledger Technologies and leads the PRIN research project 'AWESOME' (2023-2025). Ricci serves as an associate editor for the ACM Distributed Ledger Technologies: Research and Practice journal and the Springer Nature SN Computer Science section on blockchain innovations. Her research emphasizes blockchain scalability, transaction analysis, and social network dynamics. Recent work includes studies on NFT architectures, post-quantum cryptography in Ethereum, and query authentication protocols. She co-organized the 7th IEEE International Conference on Blockchain and Cryptocurrencies (2025) and actively participates in global blockchain initiatives. Ricci has been awarded Best Paper Awards for her contributions to decentralized cloud scheduling, hybrid architectures for online games, and distributed virtual environments. She advises numerous PhD students and oversees grants like the H2020 'HELIOS' project. Ricci's academic roles include teaching blockchain, peer-to-peer systems, and web scraping at the University of Pisa. Her lab collaborates on projects such as the AQuSDIT grant (2024-2025) and the Ethereum Foundation's 'Cross Chain Authenticated Queries.' She also chairs conferences like IEEE Blockchain and co-edits special issues on blockchain-based pervasive systems and social media analysis.

Mike Feeleyمشاهده پروفایل

دانشیار

• Distributed Systems
• Operating Systems
• Cloud Storage
• +۴ مورد دیگر

Mike Feeley is an Associate Professor in the Department of Computer Science at the University of British Columbia (UBC), located in Vancouver, Canada. He is a member of the Distributed Systems Group and has held this academic position since at least 2014. His work is centered on operating systems and distributed systems, with a focus on scalable and reliable file systems, cloud storage, mobile computing, and system software for clusters. He has been actively involved in teaching courses such as CPSC_V 416 (Distributed Systems) and CPSC_V 213/313 (Computer Systems and Hardware) across multiple terms from 2014 to 2025. Feeley has received several teaching awards, including the 2014 UBC Computer Science Undergraduate Team Award and multiple CS Department Teaching Awards. His research projects include Elephant (a file system), NetVM (user-mode RDMA), and GMS (global memory for PC clusters), along with work on peer-to-peer file systems and mobile ad hoc networks. He is affiliated with UBC’s Department of Computer Science, located in the ICICS/CS Building. His office is in room 393 (CISR 393), and he maintains a personal webpage and Google Scholar profile. Beyond academia, he shares his family life, including two children and residential addresses in Vancouver.

Divya Mahajanمشاهده پروفایل

استادیار

• Computer Architecture
• Distributed Systems
• Machine Learning Systems
• +۲ مورد دیگر

Divya Mahajan is an Assistant Professor holding dual appointments in the College of Computing and College of Engineering at Georgia Institute of Technology. She directs the Systems Infrastructure and Architecture Research Lab, focusing on sustainable computing platforms for large-scale AI, machine learning, and data storage systems. Her research integrates computer architecture, distributed systems, and database technologies to optimize end-to-end data pipelines. Mahajan earned her PhD from Georgia Tech and Bachelor's from IIT Ropar (President's Gold Medal). Prior to academia, she was a Senior Researcher at Microsoft Azure, leading communication collective designs for distributed DNN training. Awards include NCWIT Collegiate Award (2017) and HPCA Distinguished Paper Award (2016). Research spans hardware-software co-design for ML systems, efficient recommendation architectures, federated learning optimization, and PIM/NPU acceleration. Recent projects address challenges in large-scale model serving, edge-cloud integration, and sustainable computing. Student advisees include PhD and MS researchers in systems and architecture. Publications demonstrate consistent innovation in accelerating ML workloads through novel hardware/software techniques, with work appearing in ISCA, MICRO, ASPLOS, NeurIPS, and VLDB. Current projects explore energy-efficient AI infrastructure and domain-specialized systems for emerging economies.

• Mobile Generative AI
• Edge General Intelligence
• Quantum Computing and Networking
• +۳ مورد دیگر

Roles & Affiliations: Professor and President's Chair Professor in Computer Science and Engineering at Nanyang Technological University (NTU), Singapore. Member of the IEEE and IET, with roles in editorial boards of major journals like IEEE Transactions on Network Science and Engineering and IEEE Communications Surveys & Tutorials. Current positions: Editor-in-Chief of IEEE Transactions on Network Science and Engineering, Area Editor in multiple journals including IEEE Communications Surveys and Tutorials. Education: Ph.D. (2008) and M.Sc. (2005) in Electrical and Computer Engineering from the University of Manitoba, Canada. B.Eng. (1999) in Computer Engineering from King Mongkut's Institute of Technology Ladkrabang, Thailand. Research Interests: Focuses on mobile generative AI, edge general intelligence, quantum networking, incentive mechanisms, and wireless communication systems. Explores applications in 6G, IoT, UAV networks, and secure communication. Recent Articles & Trends: 15 most recent articles highlight advancements in generative AI for wireless networks, quantum computing integration, secure communication protocols, and AI-driven network optimization. Key themes include semantic communication frameworks, RIS-aided systems, and federated learning in edge networks. Awards: Over 20+ prestigious awards including IEEE Fellow (2017), IET Fellow (2022), Stuart Meyer Memorial Award (2024), and multiple best paper awards at top conferences like IEEE WCNC and IEEE ICC. Advising & Grants: Advisor to numerous students (not listed explicitly). Active in funding initiatives for AI in wireless systems, quantum networks, and low-altitude economy networking. Leads projects on GenAI for networking and 6G innovations. Labs & Teams: Research focuses on CCDS labs at NTU, collaborating on projects like GenAI for ISAC, mobile edge computing, and secure federated learning systems.

Vladimir Vlassovمشاهده پروفایل

استاد

• Machine Learning
• Data-intensive computing
• Cloud computing
• +۷ مورد دیگر

Dr. Vladimir Vlassov is a full Professor in Computer Systems at the Division of Software and Computer Systems (SCS) , Department of Computer Science (CS) , School of Electrical Engineering and Computer Science (EECS) , KTH Royal Institute of Technology , Stockholm, Sweden. He leads the AVA project in ALEC2, an AI-powered system for mental health care. He is a member of the Distributed Computing research group (DC@KTH) . Education & Roles: Holds a PhD and is a member of ACM and IEEE. Previously visited MIT (1998) and UMass Amherst (2004). Teaches courses on Data Mining , Distributed Systems , and Concurrent Programming . Research Interests: Focus on scalable AI, Cloud computing, distributed systems, and NLP for mental health. Projects include ExtremeEarth (Copernicus data analytics) and EMJD-DC (distributed computing PhD program). Grants & Projects: Principal Investigator in ALEC2 (adaptive mental health care) and ExtremeEarth (EU H2020). Led EU projects like ENCORE (manycore systems) and PaPP (embedded systems). Labs & Teams: Directs the Distributed Computing group, contributing to Hopsworks (machine learning feature store) and Maggy (hyperparameter optimization).

Pieter Simoensمشاهده پروفایل

استادیار

• Distributed Artificial Intelligence
• Edge Computing
• Algorithms for Neuromorphic Computing
• +۲ مورد دیگر

Pieter Simoens is an Assistant Professor at Ghent University and affiliated with the imec research institute. He works at the intersection of distributed artificial intelligence, edge computing, and collective intelligence, with a focus on AI applications for resource-constrained environments and robotic systems. His research explores innovative approaches to machine learning deployment in heterogeneous infrastructures, task planning for IoT-integrated robotics, and modeling collective decision-making processes. He has contributed to frameworks like DIANNE for distributed deep learning and developed methods for cognitive modeling in reinforcement learning scenarios. With over 100 publications, his recent work spans adaptive neural networks, privacy-preserving surveillance, UAV hyperspectral data analysis, and computational fairness in AI systems. He leads research initiatives within the Internet Technology and Data Science Lab (IDLab) and contributes to educational programs in software engineering and applied machine learning. Responsible for courses on software engineering, mobile development, system design, and applied machine learning Active in edge computing and neuromorphic algorithms research Develops AI solutions for robotics, surveillance, and industrial IoT applications