جست‌وجوی استاد و پژوهشگر

Shungeng Zhangمشاهده پروفایل

استادیار

• Distributed Systems
• Cloud Computing
• Cybersecurity
• +۴ مورد دیگر

Shungeng Zhang serves as an Assistant Professor in the Department of Computer & Cyber Sciences at Augusta University's School of Computer and Cyber Sciences. He holds a Ph.D. in Computer Science from Louisiana State University (2021) and a B.E. in Computer Engineering from Huazhong University of Science and Technology (2014). His educational background includes: Ph.D. in Computer Science, Louisiana State University, 2021 B.E. in Computer Engineering, Huazhong University of Science and Technology, 2014 Dr. Zhang's research spans distributed systems, cloud computing, and cybersecurity with a focus on enhancing performance and scalability of web applications and IoT stream processing in cloud environments. He employs sophisticated timeline analysis and fine-grained monitoring to identify transient bottlenecks causing long-tail latency problems, addressing propagation effects in complex dependency chains among application components. His publication record (2017-2022) reveals consistent contributions to cloud systems performance, particularly in n-tier architectures, concurrency control, and latency optimization. Key themes include stream processing synchronization, fanout query performance, adaptive concurrency for SLO compliance, and mitigation of transient resource contention attacks across distributed systems. No scientific awards were mentioned in the provided information. Dr. Zhang actively seeks graduate students with strong computer systems backgrounds for research collaboration. His departmental service includes Faculty Assembly participation (2021-2022) and faculty interviewing duties. Professionally, he serves as a reviewer for ACM SoCC'23, ACM TOIT, SmartCom 2023, The Journal of Supercomputing, and ACM SoCC'22. Teaching responsibilities include AIST 4720 (Enterprise System Architectures), CSCI 1200 (Introduction to Computers and Programming), and graduate courses such as CSCI 8940 (Dissertation Research) and AIST 3310 (Advanced Networking). No dedicated research labs or teams were specified in the available documentation.

Shiva Nejatiمشاهده پروفایل

استاد

• Software Engineering
• Cyber-Physical Systems
• Autonomous Vehicles
• +۷ مورد دیگر

Shiva Nejati is a Professor at the University of Ottawa 's School of Electrical Engineering and Computer Science . He holds a PhD in Computer Science from the University of Toronto and previously worked as a Senior Scientist (2012-2019) and Scientist (2009-2012) at the SnT Centre (University of Luxembourg) and Simula Research Laboratory. Research focus: Software engineering for cyber-physical systems (autonomous vehicles, IoT), blending formal verification, machine learning, and search-based testing Key tools developed: ARIsTEO, SOCRaTEs, SimCoTest, EPIcuRus Editorial roles: Associate Editor for EMSE Journal (2025–), ASE Journal (2025–), IEEE Transactions on Software Engineering (2020–2024) His work combines formal methods , empirical software engineering , and AI/ML to address verification challenges in complex systems, particularly through evolutionary algorithms and surrogate modeling . Notable collaborations include industry partners in telecommunications, automotive, and aerospace sectors. Recent publications emphasize large language models for requirements analysis, adversarial testing of vision systems, and multi-objective optimization for test generation. His Sedna Research Lab actively trains graduate students in these cutting-edge methodologies.

Jon Weissmanمشاهده پروفایل

استاد

• Distributed Systems
• Cloud Computing
• Edge Computing
• +۷ مورد دیگر

Jon Weissman is a Professor of Computer Science at the University of Minnesota, Twin Cities. His research focuses on distributed systems, edge and cloud computing, and high-performance computing (HPC), aiming to enhance performance, reliability, and energy efficiency. Education: Ph.D. in Computer Science, University of Virginia (1995) M.S. in Computer Science, University of Virginia (1989) B.S. in Applied Mathematics and Computer Science, Carnegie-Mellon University (1984) His research explores edge and cloud computing, IoT, and HPC, including subtopics like storage systems, resource management, and security. Publications highlight trends in adaptive prefetching, compressed sensing for medical devices, and IoT-informed autoscaling. Scientific Awards: NSF CAREER Award (1995) Senior Member, IEEE He has advised Ph.D. students like Albert Jonathan, Kwangsung Oh, and Francis Liu. His lab is located in 4-204A Keller Hall, and he serves on steering committees for conferences like HPDC.

Francis Y. Yanمشاهده پروفایل

استادیار

• Networked systems
• Cloud infrastructure & resource management
• Machine learning for systems and networks
• +۳ مورد دیگر

Francis Y. Yan is an Assistant Professor of Computer Science at the University of Illinois Urbana-Champaign (UIUC), holding an affiliate appointment in Electrical & Computer Engineering within the Grainger College of Engineering. He leads the Illinois Networked Systems and AI (NSAI) research group, focusing on building intelligent networked systems that are safe, robust, and performance-optimized through practical machine learning integration. Prior to joining UIUC in January 2025, he served as a Senior Researcher at Microsoft Research Redmond under Victor Bahl. His educational background includes: Ph.D. in Computer Science from Stanford University (2020), advised by Keith Winstein and Philip Levis B.S. in Computer Science (Yao Class) and B.A. in Economics from Tsinghua University (2015) Additional undergraduate studies at MIT Yan's research adopts a holistic approach to practical machine learning for networked systems, emphasizing judicious application rather than indiscriminate use. He builds real-world systems and research platforms to lay ML foundations, devises deployable algorithms using domain insights, and validates performance through extensive empirical evidence. His work consistently addresses operator concerns regarding ML deployment—focusing on safety, robustness, generalization, and efficiency—while strategically combining ML with classical networking and systems techniques. Analysis of his 15 most recent publications (2023-2025) reveals dominant themes in resource allocation for microservices (DeDe, Autothrottle), real-time video optimization (Mowgli, GRACE), and LLM-driven network algorithm design. His work bridges theoretical advances with industrial deployment, evidenced by platforms like Puffer (400,000+ users) and OpenNetLab that have become community standards for validating congestion control algorithms. His research has been recognized with top honors: USENIX NSDI Outstanding Paper Award (2024) for Autothrottle APNet Best Paper Award (2022) IRTF Applied Networking Research Prize (2021) USENIX NSDI Community Award (2020) USENIX ATC Best Paper Award (2018) for Pantheon Yan actively recruits master's and undergraduate researchers for his NSAI group, prioritizing self-motivated students for projects in networked systems and AI. His research is supported by industry collaborations (notably Microsoft) and manifests in deployable platforms like Puffer—which has enabled award-winning research at NSDI and SIGCOMM—and OpenNetLab for real-time communications. His work directly impacts production systems including Microsoft Teams and Bing. He founded and directs the Illinois Networked Systems and AI (NSAI) research group, which operates critical infrastructure including Puffer (a live TV service and research platform) and OpenNetLab. These platforms facilitate community-wide validation of novel algorithms, with Puffer alone supporting multiple best-paper awards at top conferences. Current workstreams span cloud resource management (Teal, Autothrottle, DeDe), low-latency video (Puffer, Tambur, Mowgli), and LLM-augmented systems (Nada, Designing Network Algorithms via LLMs).

• Software Architecture
• Microservices
• Cloud Computing
• +۷ مورد دیگر

Uwe Zdun is a Professor at the Faculty of Computer Science, University of Vienna, where he serves as Vice-Director of Studies for Computer Science and Head of the Research Group Software Architecture. His teaching portfolio includes core courses such as Software Engineering 2, Advanced Software Engineering, and Practical Software Courses for Bachelor's and Master's theses across multiple semesters (2024W-2025S). His research spans software architecture with emphasis on microservices, cloud computing, and DevOps. Key focus areas include architectural design decisions, infrastructure-as-code conformance, security in distributed systems, and the integration of machine learning operations (MLOps/RLOps). He investigates cognitive aspects of architecture practices through controlled experiments and develops model-driven approaches for quality assessment in complex systems. Recent publications (2024-2026) reveal three dominant trends: (1) Security and coupling analysis in infrastructure-as-code deployments, (2) MLOps/RLOps integration for Industry 4.0 cyber-physical systems, and (3) Performance optimization patterns for CI/CD pipelines and autoscaling. His work bridges theoretical architecture models with industrial practice, particularly in microservice ecosystems and reinforcement learning applications. Professor Zdun leads the Research Group Software Architecture at the University of Vienna's Faculty of Computer Science. The group focuses on empirical validation of architectural patterns, tool development for conformance checking, and advancing design decision methodologies in cloud-native and AI-driven systems.

Theodora Varvarigouمشاهده پروفایل

استاد

• Cloud Computing
• Multimedia Content Processing
• Social Networking Technologies
• +۳ مورد دیگر

Theodora Varvarigou is a Professor in the Department of Electrical and Computer Engineering at the National Technical University of Athens (NTUA). She holds a B.Eng. from NTUA and M.Eng. and Ph.D. degrees from Stanford University. Her career includes research at AT&T Bell Labs and roles at the Technical University of Crete. From 2008-2012, she served as director of NTUA's 'Technoeconomic Systems' postgraduate program. Her research focuses on Cloud Computing, Multimedia Content Processing, Social Networking Technologies, and emerging areas like blockchain, edge computing, and cybersecurity. She has published over 200 papers and led numerous European research projects, emphasizing scalable systems, data management, and smart infrastructure applications. Her work spans technical innovations such as intrusion detection systems, edge resource optimization, and blockchain-based solutions for IoT, healthcare, and smart cities. Recent publications highlight advancements in AI-driven resource allocation, privacy-preserving blockchain designs, and predictive analytics for edge computing environments. Professor Varvarigou has contributed to interdisciplinary initiatives, including cohort data harmonization in biomedical research and social media analytics for urban planning. Her teaching includes courses on digital systems, network programming, and fault-tolerant systems.

• Cloud Computing
• Data Center Optimization
• Resource Allocation
• +۴ مورد دیگر

Bhuvan Urgaonkar is a Professor in the Department of Computer Science and Engineering at Penn State University's College of Engineering. His research centers on optimizing cloud computing systems through innovative approaches to resource allocation, cost efficiency, and energy management. Current research focuses on Burstable Instance Scaling Serverless Computing Optimization Distributed Storage Systems Multi-resource Fair Allocation Cloud Economics Recent publications highlight advancements in autoscaling techniques, serverless architecture design, and trace modeling for high-load scenarios. These works emphasize practical solutions for cost-effective resource utilization in public cloud environments. Scientific Awards: CNS: Core: Small: Consistent, Geo-Distributed Data Stores on the Public Cloud (NSF, 2022-2025) CNS Core: Small: Principled Methodologies for Automated Cost-Effective Service Blending (NSF, 2021-2024) PPoSS: Cross-Layer Design for HPC in the Cloud (NSF, 2020-2022) CSR: Burstable Instances for Cost-Efficacy (NSF, 2017-2020) CSR: Student Travel Support for SIGMETRICS (NSF, 2016-2017)

Mauro Pezzèمشاهده پروفایل

استاد

• Software Engineering
• Software Testing and Analysis
• Self-Adaptive Systems
• +۵ مورد دیگر

Mauro Pezzè is a Full Professor of Software Engineering at the Università della Svizzera italiana (USI) and Università di Milano Bicocca, leading the STAR research group since 2006. He holds a laurea from the University of Pisa and a PhD from Politecnico di Milano. His research focuses on software testing, analysis, self-adaptive systems, and cloud systems. He has held editorial roles, including Editor-in-Chief of ACM Transactions on Software Engineering and Methodologies (TOSEM), and served on numerous program committees. Education: Laurea (Pisa), PhD (Politecnico di Milano). Professional roles include Dean of the Faculty of Informatics at USI (2009-2013), visiting scientist at UC Irvine and Edinburgh, and technical lead for international projects. He co-authored a seminal book on software testing (Wiley, 2007), with over 670 citations. Research Interests: Software Testing, Self-Adaptive Systems, Cloud Computing, AI in SE, Sustainable Software. Projects include work on field-based testing, failure prediction in distributed systems, and neuro-symbolic approaches for test oracles. Grants and Advising: Led STAR Lab projects in self-healing systems, GUI testing, and semantic matching. Advised numerous PhD/postdoc students (e.g., Ciniselli, Di Grazia, Qiu). Collaborations with European tech firms on R&D initiatives. Labs/Teams: STAR Group at USI/Constructor Institute, Bicocca, and Politecnico di Milano. Current members include postdocs and PhD students working on AI-driven testing and cloud reliability.

• Dependable computing
• Networking algorithms
• Cloud computing
• +۳ مورد دیگر

Alex X. Liu is a Professor in the Department of Computer Science & Engineering at Michigan State University (2016-2022), currently serving as Chief Information Security Officer and President of Midea Software Engineering Institute. His academic career includes roles as Associate Professor (2012-2016) and Assistant Professor (2006-2012) at the same institution. He holds a Ph.D. and M.S. in Computer Science from The University of Texas at Austin, and a B.S. in Computer Science from Jilin University, China. Education Ph.D. in Computer Science (UT Austin, 2006) M.S. in Computer Science (UT Austin, 2002) B.S. in Computer Science (Jilin University, 1996) Liu's research focuses on Dependable computing , Networking algorithms , Cloud computing , Mobile computing , Privacy computing , and Computer/network security . His work spans secure systems, network protocols, and resource optimization in distributed environments. Recent publications address quantum neural networks , microservices autoscaling , RFID tag recognition , network traffic classification , and hybrid physical-layer authentication , demonstrating expertise at the intersection of AI and network security. Key trends include deep learning applications for cloud systems and robust security protocols. Scientific Awards IET Fellow (2021) IEEE Fellow (2019) ACM Distinguished Scientist (2019) Withrow Distinguished Scholar Awards (Senior 2019, Junior 2011) NSF CAREER Award (2009) IEEE & IFIP William C. Carter Award (2004)

Steffen Beckerمشاهده پروفایل

استاد

• Software Engineering
• Cloud Systems
• Model-Driven Engineering
• +۷ مورد دیگر

Steffen Becker is a Professor at the University of Stuttgart's Faculty of Computer Science, Electrical Engineering and Information Technology, affiliated with the Institute for Software Engineering's Software Quality and Architecture group. His work focuses on software engineering, cloud systems, model-driven engineering, and cybersecurity. He leads research in architectural modeling tools like Slingshot, GUI testing frameworks (ViMoTest), and hardware security analysis. Recent studies explore AI integration in testing, education, and automotive systems (CARISMA). Research interests include elasticity modeling, self-adaptive systems, and educational technology. His 2025 publications address issues like end-user hardware comprehension, FPGA security, and LLM-driven test generation. Notable tools developed include the Slingshot Simulator for cloud-native systems and Gropius for cross-component issue management. Becker contributes to both theoretical advancements and practical implementations in software quality, security, and cloud infrastructure. Key Areas: Software Architecture, Cyber-Physical Systems, Testing, Reverse Engineering Tool Developments: ViMoTest, Slingshot, Gropius Education Focus: Online programming pedagogy and curriculum innovation His work bridges foundational research with industry applications, addressing challenges in automotive computing, cloud elasticity, and human-centric security awareness. Recent efforts emphasize explainable hardware (XHW) and AI's role in qualitative analysis automation.

Per-Olov Östbergمشاهده پروفایل

دانشیار

• Distributed Systems
• Cloud Computing
• Autonomous Resource Management
• +۷ مورد دیگر

Per-Olov Östberg is an Associate Professor at the Department of Computing Science, Umeå University, and a research leader in the Autonomous Distributed Systems Lab (ADSLab). His work focuses on resource management for distributed cloud environments using AI/ML-based techniques, with a particular emphasis on ethical reasoning integration for responsible AI solutions. Research Themes: Cloud-edge continuum optimization, serverless frameworks, 6G computing challenges, data fabric architectures, and energy-aware systems Projects: COGNIT (cognitive serverless framework), WARA Common Information Bridge (data-driven cloud operations), De facto Center of Excellence in Autonomous Distributed Systems His publications (2011-2024) demonstrate consistent contributions to cloud resource management, including fairshare scheduling, decentralized prioritization, and power-performance tradeoffs. He has collaborated on interdisciplinary projects with institutions across Europe. Scientific Awards: None explicitly stated in provided information.

• Distributed Computing
• Distributed Algorithms
• Fault-Tolerance
• +۸ مورد دیگر

Marina Papatriantafilou is an Associate Professor in the Department of Computer Science and Engineering at Chalmers University of Technology and University of Gothenburg. Her research focuses on distributed computing, fault-tolerance, parallel algorithms, and concurrency control. She has contributed to methods for fault-tolerant distributed systems, visualization tools for distributed algorithms, and scalable overlay networks. Her academic roles include teaching advanced courses on distributed systems, computer communication, and operating systems. She advises graduate students in areas like distributed algorithms and parallel computing. Key research interests include lock-free synchronization, memory reclamation, and self-stabilizing systems. She has authored over 100 publications in top-tier conferences and journals, with recent work on data streaming frameworks, energy-sharing optimization, and vehicular network processing. Professional involvement includes roles in program committees for conferences like OPODIS, SWAT, and SSS, plus membership in research evaluation boards for Swedish and European funding agencies. She pioneered educational tools like the Lydian environment for distributed algorithm visualization.

Dinsha Vinodمشاهده پروفایل

پژوهشگر

• Control Systems
• Artificial Intelligence
• Robotics
• +۳ مورد دیگر

Dinsha Vinod is a Postdoctoral Researcher at the University of Agder, affiliated with the Department of Engineering Sciences. His research focuses on control systems, fog computing, and autonomous robotics, with a particular emphasis on integrating artificial intelligence and event-triggered control mechanisms. He has contributed to advancements in fog-cloud platforms for mobile robotics applications, model predictive control (MPC), and robust control design. Education details are not explicitly provided in the text, but his work spans academic and industrial collaborations. He is part of the Robotics and Automation research group at UiA. Key research areas include: Event-based control strategies for LPV systems Fog computing autoscaling with service guarantees MPC applications in mobile robotics Integration of AI inference into control systems Publications since 2017 reflect a trajectory from aerospace control systems to fog computing and robotics. Recent work emphasizes real-time systems and autonomous decision-making frameworks.

• Distributed Systems
• Cloud Computing
• Edge Computing
• +۴ مورد دیگر

Thomas Werner Pusztai is a Researcher at the Department of Distributed Systems, TU Wien. His work focuses on cloud-edge continuum systems, serverless computing, and service-level objective (SLO) management. He contributes to projects like RapidREC, TEADAL, and the EU-funded RAINBOW initiative. His research spans distributed scheduling algorithms, edge-cloud resource allocation, and SLO-aware middleware frameworks like Polaris and HyperDrive. Education: Completed a Diploma in Computer Science at TU Wien with a thesis on Model-based design and architecture of fog computing applications (2019). Active in academic conferences, presenting at IEEE/ACM Edge Computing, Cloud, and Fog/Edge events. Research interests include edge computing, cloud-native systems, IoT applications, and optimizing SLO compliance through novel scheduling techniques. His work emphasizes practical frameworks for real-world distributed systems challenges. Projects include developing HyperDrive for serverless function scheduling in 3D edge-cloud spaces, Polaris for SLO-aware microservices orchestration, and SLO Script for complex elasticity-driven SLO implementations.

• Distributed Systems
• Edge Computing
• Serverless Computing
• +۴ مورد دیگر

Stefan Nastic is an Assistant Professor at the Technische Universität Wien (TU Wien), affiliated with the Faculty of Informatics and the Distributed Systems department. He serves as Curriculum Coordinator for the Master’s program in Distributed and Next Generation Computing, and is a Substitute Member of the Curriculum Commission for Informatics. His research focuses on distributed systems, edge computing, serverless computing, IoT, and smart cities. He holds a PhD in IoT cloud systems (2016) and a BSc (not explicitly stated). Key research contributions include frameworks for serverless edge-cloud continuum (e.g., HyperDrive, GoldFish), federated learning applications (e.g., adaptive human activity recognition), and IoT infrastructure governance (e.g., Polaris Scheduler). He leads projects like RapidREC (2023–2025) on supply chain optimization and participates in initiatives like TEADAL (2022–2025) for edge-cloud workflows. Publications span 30+ peer-reviewed articles in top venues like IEEE IoT, ACM, and IEEE Cloud. He supervises graduate students on stateful serverless functions, federated learning, and edge-cloud scheduling. His work addresses challenges in resource management, latency reduction, and scalable distributed systems.