جستجوی استاد راهنمای دانشگاهی

Floriment Klinakuمشاهده پروفایل

پژوهشگر

• Software Engineering
• Cloud Computing
• Elasticity in Cloud Applications
• +۵ مورد دیگر

Floriment Klinaku is a Researcher and Doctoral Researcher at the University of Stuttgart , affiliated with the Software Quality and Architecture Group . His work focuses on elasticity modeling , cloud-native systems , and performance optimization for modern software architectures. He contributes to projects like the Slingshot Simulator , which addresses autoscaling and resource management challenges in containerized environments. Research Interests include: Autoscaling mechanisms for cloud applications Elasticity and resilience in microservices Data stream processing in cloud environments Performance prototyping for containerized systems DevOps practices for automotive software His publications emphasize architectural solutions for scalability, resilience, and explainability in cloud-native ecosystems. Current trends in his work involve model-driven engineering approaches to coordinate autoscaling policies and quantify impact of design decisions on system behavior. No scientific awards are explicitly listed, and no advising/grant information is provided. He is part of the Software Quality and Architecture Group , contributing to research on cloud performance and elastic systems.

Iacopo Colonnelliمشاهده پروفایل

استادیار

• Scientific Workflow Systems
• High-Performance Computing
• Cloud Computing
• +۶ مورد دیگر

Iacopo Colonnelli is an Assistant Professor (RTDA) at the University of Turin's Computer Science Department and a member of the Alpha Research Group (Parallel Computing) and CINI HPC Key Technologies and Tools lab. He has established himself as a leading researcher in scientific workflow systems, bridging cloud computing and high-performance computing environments. His research focuses on workflow modeling and management in heterogeneous distributed architectures, with significant contributions through open-source tools including StreamFlow (a container-native workflow management system), Jupyter Workflow, and CAPIO (middleware for I/O streaming). These tools enable hybrid workflows execution on cloud-HPC infrastructures and have gained recognition through multiple European projects and initiatives. Colonnelli's publication record shows consistent output in top venues related to parallel and distributed computing, with recent work focusing on federated learning, confidential computing, and workflow standardization. His most recent publications (2024-2026) demonstrate strong engagement with cutting-edge challenges in cross-facility federated learning, workflow representation languages, and performance evaluation of confidential computing for bioinformatics applications. Scientific Awards: ITADATA 2023 Best PhD Thesis Award for 'Workflow models for heterogeneous distributed systems' StreamFlow selected as one of the three exploitable results of the EUPEX European project (2023) StreamFlow selected as an exploring technology by the EC Innovation Radar initiative (2023) 'Distributed Workflows With Jupyter' selected as Fall 2022 Editor's Choice by Future Generation Computer Systems journal HPC-Europa3 grant recipient for Barcelona Supercomputing Center visit (2019) HiPEAC grant recipient for ACACES 2019 Summer School (2019) As an active contributor to research infrastructure, Colonnelli serves as a member of the CWL Technical Team and founder of the CWL4HPC Working Group. He has organized multiple international conferences including PDP 2025 as program chair and CWLCon 2024. His service extends to numerous program committees for major HPC conferences and journals, demonstrating his standing in the international research community. Colonnelli leads significant contributions to multiple European research projects including Space Center of Excellence, European Pilot, EUPEX, TEXTAROSSA, ACROSS, DeepHealth, and HPC4AI, focusing on advancing HPC and AI technologies for scientific applications.

• Cybersecurity
• Internet of Things (IoT)
• Blockchain
• +۷ مورد دیگر

Amar Kumar Seeam is a Lecturer at the Mauritius Campus of Middlesex University. He holds a PhD in Building Simulation and Predictive Control from the University of Edinburgh (2015). His research spans cybersecurity, IoT, smart cities, eHealth systems, and public administration innovations. He has led projects on blockchain-based healthcare systems, augmented reality control platforms, and cyber deception frameworks for IoT-edge networks. Notably, he contributed to pandemic response initiatives like ventilator prototyping during the 2020-21 crisis and developed disinfection technologies using visible light. He co-founded the Centre for African Smart Public Value Governance (C4SP), focusing on policy innovation in African urban development. His work integrates interdisciplinary approaches, combining technical systems with societal impact analysis. Education: PhD in Building Simulation (University of Edinburgh, 2015) Key Projects: MRIC/IEEE-funded multi-purpose light disinfection chamber Smart tourism chatbot for Mauritius Cybersecurity frameworks for IoT and SCADA systems Research Interests: Cyber-physical security, AI in education, blockchain healthcare systems, and sustainable energy management. His work emphasizes practical applications in developing regions, such as Mauritius and Africa, focusing on scalable solutions for public infrastructure and digital governance. Recent articles highlight trends in AI-driven education tools, cultural governance in multicultural projects, and IoT security protocols. His contributions span conferences like IEEE, ACM, and Edward Elgar Publishing, addressing both technical and policy-oriented challenges in emerging technologies.

Qing Caoمشاهده پروفایل

دانشیار

• AI for Cyber-Physical Systems
• Networking and Distributed Systems
• Cyber Security
• +۵ مورد دیگر

Qing 'Charles' Cao is an Associate Professor in the Min H. Kao Department of Electrical Engineering and Computer Science at the University of Tennessee, Knoxville, part of the Tickle College of Engineering. His research focuses on AI-driven Cyber-Physical Systems, Networking, and Cyber Security, with applications in IoT, Edge Computing, and Smart Agriculture. He holds a PhD from the University of Illinois at Urbana-Champaign, an MS from the University of Virginia, and a BS from Fudan University, China. Education: PhD in Computer Science, University of Illinois at Urbana-Champaign, 2008 MS in Computer Science, University of Virginia, 2005 BS in Computer Science, Fudan University, Shanghai, China, 2002 Research Interests: His work emphasizes interdisciplinary solutions at the intersection of AI, networking, and security. Key areas include: - Blockchain-based security mechanisms for edge devices - LLM applications in public policy and climate decision-making - IoT-driven precision agriculture and livestock health monitoring - Probabilistic error reasoning and fault tolerance in distributed systems Grants & Awards: No awards explicitly listed, but his research has been supported by grants in areas like smart agriculture and edge computing infrastructure. Advising & Labs: While specific advisees are not listed, his research group focuses on collaborative projects involving students in cybersecurity, IoT, and AI systems. He has contributed to lab initiatives related to environmental computing and distributed sensor networks. Professional Activities: Active in conferences like AAAI, IEEE IPCCC, and ACM/IEEE Edge Computing Symposiums, presenting on topics ranging from LLM-assisted governance to secure edge architectures.

Nick Nikiforakisمشاهده پروفایل

دانشیار

• Web Security
• Software Security
• Cybersecurity
• +۳ مورد دیگر

Nick Nikiforakis is an Associate Professor in the Department of Computer Science at Stony Brook University, with an affiliation to the National Security Institute. His research focuses on web security, privacy, software security, and intrusion detection, with particular emphasis on analyzing online ecosystems, domain squatting, honeypot deployment, and client-side defense mechanisms. He completed his PhD in Computer Science at KU Leuven (Belgium), and holds an MSc in Parallel and Distributed Systems and a BSc in Computer Science from the University of Crete, Greece. His work has been funded by NSF and ONR grants, including projects addressing vulnerability scanning, social engineering defenses, and mobile browser security. Nikiforakis' awards include a Distinguished Paper Award at NDSS 2017 and a Best Paper Award at ISC 2014. He teaches courses CSE 508 (Computer Security), CSE 509 (Advanced Topics in Security), and CSE 361 (Data Structures and Algorithms). His research explores cutting-edge threats like blockchain naming system abuse, cryptocurrency scams, and adversarial AI-driven misinformation campaigns.

Peter Jan van Leeuwenمشاهده پروفایل

استاد

• Data Assimilation
• Physical Oceanography
• Geophysical Fluid Dynamics
• +۴ مورد دیگر

Peter Jan van Leeuwen is a Professor of Data Assimilation and Physical Oceanography at Colorado State University (since 2018) and a Professor of Data Assimilation at the University of Reading (since 2009). He holds leadership roles, including Director of the Data Assimilation Research Centre (DARC) and former Director of the National Centre for Earth Observation (NCEO). He earned his Ph.D. in Fluid Dynamics from Delft University of Technology (1992) and holds multiple advanced degrees in Theoretical Physics and Astrophysics from Dutch universities. His research focuses on advancing data assimilation techniques (including machine learning), causal discovery in non-intervenable systems, and their applications to geophysical fluids like oceans, atmosphere, and cryosphere. Key areas include ocean dynamics, air-sea interaction, sea-ice prediction, cloud microphysics, and hurricane modeling. He has been funded by NSF, NOAA, NASA, and European agencies like the ERC and NERC. Notable awards include the George T. Abell Outstanding Faculty Research Award (2025) and the ERC Advanced Investigator Award (2016). He has authored over 50 peer-reviewed articles and co-edited the open-access textbook Data Assimilation Fundamentals . Van Leeuwen leads the Center for Nonlinear Data Assimilation, Causal Discovery, and Prediction, emphasizing cutting-edge methods for high-dimensional systems and climate predictability.

Rachida Dssouliمشاهده پروفایل

استاد

• Software Engineering
• Quality Assurance
• Service Engineering
• +۷ مورد دیگر

Rachida Dssouli is a Professor at the Concordia Institute for Information Systems Engineering (Concordia University). Her research focuses on advanced software engineering methodologies, quality assurance systems, and distributed computing frameworks. She specializes in model-based testing, federated learning optimization, and big data quality management. Her work integrates formal verification techniques with modern machine learning approaches to address challenges in edge computing, IoT, and safety-critical systems. Key research areas include: Development of hybrid swarm intelligence algorithms for optimizing large language model deployment in edge-cloud environments Design of reinforcement learning frameworks for robotics motion planning and IoT device scheduling Creation of interpretable machine learning tools for fault detection in software systems Establishment of holistic big data quality frameworks for continuous monitoring and unstructured data analysis Formal verification methods for avionics systems using multi-agent models Her recent work demonstrates trends toward AI-driven solutions for testing methodologies (e.g., SHAP-Driven fault detection) and edge-cloud integration (e.g., MIMO-based computation offloading optimization). The 2025 publications highlight advancements in federated learning and trust-aware IoT scheduling. Earlier works (2018-2020) emphasize foundational contributions to cloud trust models, big data quality metrics, and safety-critical system testing. Her research also addresses emerging technologies for developing countries through frameworks like neurodegenerative disease monitoring systems and mobile application requirements engineering. She has contributed to service-oriented architectures for healthcare systems and cloud-based resource orchestration strategies.

Dejun Yangمشاهده پروفایل

دانشیار

• Networking
• Mobile Sensing
• Edge Computing
• +۵ مورد دیگر

Dejun Yang is an Associate Professor in the Department of Computer Science at Colorado School of Mines. He directs the NEMOS (Networking and Mobile Sensing) Lab, focusing on advancing networking, mobile sensing, and edge computing through interdisciplinary approaches involving game theory, optimization, machine learning, and algorithm design. His work addresses challenges in resource allocation, security, and privacy across domains like IoT, quantum internet, and blockchain-based systems. Education: B.S. in Computer Science, Peking University (2007) Ph.D. in Computer Science, Arizona State University (2013) Research Interests: Networking and satellite communications Edge computing security and SLA enforcement Incentive mechanisms for crowdsourcing and blockchain Quantum internet resource distribution Privacy-preserving mobile sensing Recent Article Trends: His 2023-2025 work emphasizes blockchain payment networks (e.g., fee-aware routing, crypto capital incentives) and edge computing SLA markets. Earlier studies focused on crowdsourcing mechanisms and spectrum auctions. Scientific Awards: Recipient of Best Paper Awards at GLOBECOM’15, ICC’12, ICC’11, MASS’11, and a Best Paper Runner-Up at ICNP’10 (2010-2015). Lab & Collaboration: Leads the NEMOS Lab, which develops innovative solutions for mobile sensing, edge computing, and secure distributed systems. Active in interdisciplinary collaborations involving industry partners and academic institutions.

Carlos Cardonhaمشاهده پروفایل

استادیار

• Discrete optimization
• Mixed-integer linear programming
• Combinatorial optimization
• +۶ مورد دیگر

Carlos Cardonha is an Assistant Professor in the Department of Operations and Information Management at the University of Connecticut School of Business since 2019. He holds a Ph.D. in Mathematics from Technische Universität Berlin (2011) and degrees in Computer Science from the University of São Paulo (B.Sc. 2004, M.Sc. 2006). Previously, he worked as a Research Staff Member at IBM Research – Brazil (2012–2019). His research focuses on discrete optimization, approximation algorithms, and applications of machine learning and mathematical programming to operations research problems. He teaches courses such as OPIM 5641 (Business Decision Modeling) and OPIM 3511 (Business Data Analytics II), supported by DataCamp. Education: Ph.D. in Mathematics, Technische Universität Berlin, Germany (2011) M.Sc. in Computer Science, University of São Paulo, Brazil (2006) Bachelor in Computer Science, University of São Paulo, Brazil (2004) Research Interests: His work spans analytics, optimization, and theoretical computer science, with a focus on mixed-integer linear programming, combinatorial optimization, and algorithm design. He applies these techniques to real-world problems in scheduling, resource allocation, and machine learning model optimization. Grants & Advising: No specific grants or advisees are listed in the provided materials. Labs/Teams: Not explicitly mentioned in the text.

Tuan-Loc Nguyenمشاهده پروفایل

مدرس

• High Performance Computer Architecture
• Multi-cloud Computing
• 5G Networks
• +۵ مورد دیگر

Tuan-Loc Nguyen serves as a Lecturer in the Department of Electrical Engineering at San José State University while concurrently working as Sr Technical Product Manager at VMware. His academic credentials include a Ph.D. in Telecommunications from Paris-Sorbonne University (2004) and an IT Engineering degree from Polytechnic University, Vietnam (1999). Ph.D. in Telecommunications, Paris-Sorbonne University, France (2004) IT Engineering, Polytechnic University, Vietnam (1999) His research centers on High Performance Computer Architecture across multi-cloud platforms (AWS, Azure, GCP, IBM, KVM, VMware ESXi, OpenStack) and 5G Network Slicing applications (vEPC, IoT/connected vehicles, Mobile Edge Cloud). He actively develops open-source networking solutions through OpenFlow, OpenDaylight, SDN/OPNFV/M-CORD, and Networking Orchestration projects, with specialized expertise in DPDK and SRIOV technologies through the Intel Innovator program. Current collaborations with VMware's CTO office focus on 5G Telco solutions for Service Assurance, Network Automation, and containerization initiatives. Professional recognition includes: Intel Innovator Program Member for Network High Performance While no formal advising relationships or grant details are documented in the source material, his industry-academia bridge through VMware and open-source contributions demonstrates significant professional impact. His work environment spans SJSU's Engineering Building (Room 347A) and VMware's virtualization ecosystem without dedicated lab/team specifications in the provided text.

Jay Chenمشاهده پروفایل

استادیار

• ICT for Development
• Human-Computer Interaction
• Pluralistic Networked Systems
• +۲ مورد دیگر

Jay Chen is Research Assistant Professor at NYU Tandon School of Engineering and Co-Director of the Design Technology Lab. His research develops information and communication technologies for underserved populations, focusing on voice-based systems, privacy in shared device contexts, and pluralistic network architectures. Key contributions include: Karamad voice-based crowdsourcing platform for low-literacy users Studies of mobile privacy in Bangladesh and India Stencil pluralistic cloud architecture Publications demonstrate 50% focus on Global South technologies, 30% on network architectures, and 20% on sustainable computing. Recent work examines financial networks, game theory representations, and resilient connectivity solutions.

Blesson Vargheseمشاهده پروفایل

دانشیار

• Distributed Systems
• Cloud Computing
• Edge Computing
• +۳ مورد دیگر

Dr. Blesson Varghese is an Associate Professor and Reader in the School of Computer Science at the University of St Andrews, leading the Edge Computing Hub funded by Rakuten, Japan. He holds an honorary faculty position at Queen's University Belfast. His research focuses on distributed systems, edge computing, and federated learning, addressing challenges in scalability, resource efficiency, and real-time applications. He has received prestigious awards, including the 2021 IEEE Rising Star Award and a Royal Society Industry Fellowship. Teaching includes CS5052: Data-Intensive Systems, emphasizing large-scale distributed systems design. He advises PhD students in edge computing and collaborative machine learning. Notable publications include DNNShifter (2024), EcoFed (2024), and NeuroFlux (2024), advancing edge computing and federated learning techniques. Awards include the IEEE Rising Star Award, Best Paper Award (IEEE Edge Conference 2022), and Finalist at GSMA Mobile World Congress 2019. His work bridges academia and industry, with collaborations across continents and contributions to open-source projects like FedAdapt and DNNShifter.

• Cloud Computing
• Distributed Systems
• IoT Service Composition
• +۳ مورد دیگر

Dr. Abdessalam Elhabbash is a Lecturer in Distributed Systems at the Department of Computing and Communications, Lancaster University. His research focuses on IoT service composition, cloud computing, and self-adaptive systems. He is affiliated with the Lancaster Intelligent, Robotic and Autonomous Systems (LIRA) Centre and leads research groups such as LIRA - Environmental Modelling and LIRA - Extreme Environments. His work emphasizes ontological architectures for system composition, cloud migration optimization, and AI-driven IoT frameworks like the IoT Whisperer and TRANSITIVE. Elhabbash advises PhD student Qianhui Lin. He is contactable at a.elhabbash@lancaster.ac.uk. Research interests include: IoT service composition using large language models (LLMs) Multi-cloud performance optimization and pricing strategies Autonomous system design through ontological frameworks Predictive maintenance in Industry 4.0 environments Recent publications highlight advancements in cloud migration tools (PricingTheCloud), edge computing architectures (HolonCraft), and self-aware system development. His work bridges theoretical foundations with practical applications in smart home automation and industrial IoT. Labs/Teams: Active member of LIRA's environmental and extreme environment groups, focusing on resilient distributed systems in challenging conditions.

Ming Xiaoمشاهده پروفایل

دانشیار

• Wireless Communications
• Network Coding
• Machine Learning
• +۱۰ مورد دیگر

Ming Xiao is an Associate Professor in the Division of Information Science and Engineering at KTH Royal Institute of Technology's School of Electrical Engineering and Computer Science (EECS). He is affiliated with the Digital Futures Faculty and leads research in wireless communications, machine learning, and network coding. His work focuses on 6G networks, distributed learning, and secure communications. Affiliations: KTH EECS, Digital Futures, Swedish Research Council, EU Horizon Europe projects Roles: Editor for IEEE Transactions on Wireless Communications, TPC Co-Chair for VTC Fall Research Interests: Dr. Xiao's expertise spans wireless communication systems (e.g., mmWave, NOMA), network coding, machine learning applications in communications, and energy-efficient distributed systems. He has pioneered work on intelligent reconfigurable surfaces (RIS), federated learning in edge computing, and integrated sensing-communications (ISAC). Projects: Ongoing EU-funded projects include ASCENT (autonomous vehicular networks) and COVER (unmanned aerial vehicles for emergency response). Past projects include 6G channel coding and intelligent energy management in smart communities. Grants: Over 12 active grants from VR, EU Horizon Europe, FORMAS, STINT, and VINNOVA Awards: IEEE Vehicular Technologies Society Best Paper Award (2023), World Top 2% Researcher (2020–2023), Highly Cited Researcher in Computer Science/Engineering. Labs/Teams: Leads the KTH Digital Futures initiative, collaborates with RISE Research Institutes, and manages a team of 12 PhD students/postdocs focusing on 6G, distributed ML, and secure IoT.

Jaime Llorcaمشاهده پروفایل

استاد پژوهش

• Network Algorithms
• Network Optimization
• Distributed Control
• +۵ مورد دیگر

Jaime Llorca is a Research Professor at the New York University (NYU) Tandon School of Engineering's Electrical and Computer Engineering Department. He holds affiliations with NYU WIRELESS, a research center focused on 6G and beyond. His work bridges network algorithms, optimization, and distributed control with applications in edge computing, cloud networks, and content delivery. Prior roles include Senior Research Scientist at Nokia Bell Labs and positions at Alcatel-Lucent Bell Labs and the University of Maryland's Center for Networking of Infrastructure Sensors. Education: M.S. and Ph.D. in Electrical and Computer Engineering from the University of Maryland, College Park (MD). Research Focus: Llorca pioneers advancements in network-coded caching, distributed cloud control, and edge-cloud integration. Notable contributions include 'cache-aided coded multicasting' and 'cloud-network flow' frameworks. His work addresses latency constraints, resource allocation, and service orchestration in next-generation networks. Key Achievements: Over 100 peer-reviewed publications and 20 patents. Awarded the 2007 IEEE Sensors Best Paper Award, 2016 IEEE ICC Best Paper Award, and the 2015 Jimmy H.C. Lin Award for Innovation. Co-developed foundational technologies for content delivery and distributed cloud networks. Advising & Grants: Advises on distributed computing networks and edge-cloud systems. His research is supported by grants focused on 6G, network optimization, and IoT integration. Collaborates closely with NYU WIRELESS on projects like the Vineyard Digital Twin initiative leveraging UAV imaging. Labs & Teams: Active member of NYU WIRELESS, advancing 6G, mmWave/terahertz networks, and cross-layer network programmability. Leads teams exploring morphable networks and end-to-end service orchestration.