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Edouard BugnionView profile

Professor

• Computer Systems
• Data Center Systems
• Data Center Infrastructure and Networking
• +7 more

Prof. Edouard Bugnion is a Full Professor at the Data Center Systems Laboratory within the School of Computer Science and Communications at École Polytechnique Fédérale de Lausanne (EPFL). Since joining EPFL in 2012, he has led research in datacenter systems and virtualization, with prior experience as VMware's first CTO and Cisco's VP/CTO. Research Interests: Data Center Systems Network and Data Plane Efficiency Operating System Design Virtualization Technologies Trusted Execution Environments Low-Latency Computing His recent publications focus on datacenter efficiency for OLDI applications, system security through hardware-based trust, and network-compute co-design . These works span topics from microsecond-scale latency optimization to privacy-preserving proximity tracing systems. Scientific Recognition: Elected ACM Fellow (2017) ACM Software Systems Award (2009) Teaching: Currently teaching CS-212: Systems Programming Project , with past courses including CS-522: Principles of Computer Systems and CS-410: Technology Ventures .

Harry XuView profile

Professor

• Computer Systems
• Programming Languages
• Compilers
• +7 more

Harry Xu is a Professor in the Computer Science Department at the Samueli School of Engineering , University of California, Los Angeles . His research spans computer systems, programming languages, compilers, and AI infrastructure. He co-founded BreezeML for GenAI risk management and has held visiting roles at Microsoft Research and IBM Watson Research Center. Current research focuses on user-defined clouds and AI application infrastructures Founded BreezeML and contributed to Niijima (SOSP'19) and Yak GC (OSDI'16) Developed VQPy, integrated into Cisco's DeepVision Scientific Awards 2018 Dahl-Nygaard Junior Prize ACM Distinguished Scientist Advising and Collaborations Current students: Shan Yu, Zhenting Zhu, Shu Anzai, Yicheng Liu Alumni: Shi Liu (Databricks), Jiyuan Wang (AWS), Haoran Ma (ByteDance AI Infra), Yifan Qiao (UC Berkeley), Christian Navasca (BreezeML), Pengzhan Zhao (BreezeML co-founder)

Roua TOUIHRIView profile

Lecturer

• Software-Defined Networking (SDN)
• Data Center Networks
• Cloud Computing
• +4 more

Roua TOUIHRI is a Lecturer at Telecom SudParis and an associate member of the ACMES group. She is affiliated with the SAMOVAR research laboratory, which focuses on networking and distributed systems. She holds a PhD in Hardware Architecture from Paris-Est University (2021), where her thesis addressed optimizing routing and resource allocation in SDN-based server-only data center networks for private cloud architectures. Her research interests center on Software-Defined Networking (SDN), cloud computing, and service-level agreement (SLA) management. She explores topics such as autonomous multi-cloud SLA adaptation, process mining for SLA reporting, and optimizing routing protocols in data center networks. Her work often intersects with network protocol design, resource allocation strategies, and improving network throughput in distributed systems. Her publications span conferences like IEEE BigData, IEEE ICC, and GLOBECOM, focusing on SDN-based solutions for data center networks, multi-cloud SLA management, and protocol optimization. She has contributed to developing novel routing schemes like M-CRP and CRP for CamCube server-only architectures, addressing latency reduction and resource utilization challenges. Roua TOUIHRI’s academic contributions are anchored in practical applications of SDN and cloud technologies, with a focus on scalable and efficient network designs for modern distributed systems.

Steve BlackburnView profile

Professor

• Computer Science
• Programming Languages
• Memory Management
• +5 more

Steve Blackburn is a Professor of Computer Science at the Australian National University (ANU), affiliated with the ANU College of Systems & Society. His research focuses on programming language implementation, garbage collection algorithms, performance analysis, and high-performance runtime systems. He leads key projects like the DaCapo benchmark suite and the MMTk memory management framework. **Education & Background**: No specific education details provided, but his career spans decades with extensive contributions to computer science research. **Research Interests**: Blackburn prioritizes garbage collection innovation, memory management optimization, and empirical performance evaluation. His work bridges theory and practice, addressing challenges in managed runtimes, just-in-time compilation, and hardware-software co-design. He emphasizes reproducible research through shared infrastructure. **Recent Article Trends**: His 2020–2025 publications emphasize low-latency garbage collection, memory management in distributed systems (e.g., Mako collector for datacenters), and novel approaches to Java/JVM performance analysis. He also explores hardware features like transactional memory and their impact on runtime systems. **Awards**: ACM Fellow (2020+). **Grants & Leadership**: Served as Associate Dean for Diversity & Inclusion in ANU’s College of Engineering and Computer Science (2014–2019). Editorial roles include ACM TOPLAS Associate Editor (2017–2020) and program chairs for top conferences like PLDI (2015) and ISMM (2008). **Labs & Projects**: Leads the MMTk and DaCapo initiatives, foundational tools for memory management and benchmarking in academia and industry.

Honggang ZhangView profile

Professor

• Edge computing
• deep reinforcement learning
• mobile computing
• +2 more

Honggang Zhang is a Professor in the Department of Engineering at the University of Massachusetts Boston. His educational background includes: PhD in Computer Science from the University of Massachusetts Amherst Professor Zhang's primary research interests are: Edge computing deep reinforcement learning mobile computing Internet of things computer networking His work focuses on solving complex resource allocation challenges in distributed computing environments through innovative algorithmic approaches. He specializes in developing optimization frameworks that integrate machine learning techniques with networking principles to enhance system efficiency and user experience in edge and cloud infrastructures. Analysis of his 2018-2019 publications reveals a concentrated research trajectory on edge computing systems, with particular emphasis on job scheduling optimization, computation offloading strategies, and economic modeling. His work consistently bridges theoretical reinforcement learning concepts with practical networking implementations, demonstrating strong interdisciplinary connections between computer science, operations research, and telecommunications engineering. No scientific awards are mentioned in the provided information. Information regarding Professor Zhang's student advising activities and research grant funding is not available in the current text. There is no mention of specific research laboratories or collaborative teams led by Professor Zhang in the provided excerpt.

• Computer Networks
• Optical Networking
• Software-Defined Networking (SDN)
• +3 more

Malathi Veeraraghavan is a Professor in the Department of Computer Science at the University of Virginia . Her research focuses on Computer Networks , Optical Networking , Software-Defined Networking (SDN) , and Data Transfer Protocols , with applications in Grid Computing and High-Performance Computing . Recent work includes the PORTFILER system for malware detection via network profiling, SnF scheduling for inter-datacenter optical networks, and reliable multicast protocols over multi-domain SDNs. She has contributed to congestion control frameworks and network function virtualization surveys. Key collaborations span institutions like the University of Virginia, Universiti Teknologi Malaysia, and Bell Labs. Her publications appear in journals such as IEEE Transactions on Cloud Computing and conferences like GLOBECOM and INFOCOM .

Cauligi RaghavendraView profile

Professor

• Computer Networks
• Wireless and Sensor Networks
• Parallel and Distributed Computing
• +1 more

Cauligi Raghavendra is a Professor of Electrical Engineering and Computer Science at the University of Southern California's Viterbi School of Engineering. He has held significant administrative roles including Vice Dean for Global Academic Initiatives (current), Senior Associate Dean for Strategic Initiatives (2006-2011), and Department Chair (2003-2005). His academic career spans multiple institutions including Washington State University (Boeing Chair Professor, 1992-1997) and The Aerospace Corporation (1997-2001). As a leading researcher in network systems, his work focuses on Computer Networks , Wireless and Sensor Networks , Parallel and Distributed Computing , and Machine Learning for Medical Applications . His research has produced groundbreaking contributions in inter-datacenter WAN optimization, sensor network energy efficiency, and medical imaging analytics through deep learning. His publications demonstrate consistent innovation across decades, with recent work (2023) developing interpretable frameworks for audio biomarker analysis in healthcare and (2021) creating machine learning pipelines for astronomical discovery. Earlier patents (1997-2000) established foundational techniques for ATM network routing, while his 2015 oilfield applications showcase cross-domain impact in industrial predictive maintenance. Dr. Raghavendra's recognition includes: Presidential Young Investigator Award (1985) IEEE Fellow designation His technical leadership appears in over 150 conference proceedings, patents, and journal papers spanning topics from fault-tolerant hypercube architectures (1980s-1990s) to modern cloud networking and medical AI applications.

• Network Optimization
• Quantum Communication
• Machine Learning
• +6 more

Dr. Avishek Nag is an Assistant Professor in the School of Computer Science at University College Dublin, Ireland. His work focuses on network optimization, quantum communication, machine learning, and 6G technologies. He holds a PhD from the University of California, Davis, and has over 100 publications in journals, conferences, and book chapters. Education: B.E. (Honours) in Computer Science from Jadavpur University (2005) M.Tech. from Indian Institute of Technology Kharagpur (2007) Ph.D. in Computer Science from University of California, Davis (2012) Research Interests: Quantum Networks & Key Distribution (QKD) 6G Networks & Explainable AI IoT Security & Edge Computing Optical Network Optimization Machine Learning for Biometrics & Healthcare Recent Publications: Recent work includes hybrid noise modeling for quantum satellite communication, XAI-driven network slicing for 6G, and ECG-based biometric systems. His research bridges theoretical advancements with practical implementations in telecommunications and cybersecurity. Awards: 2008 IEEE Advanced Networks Best Paper Award Grants & Leadership: Outreach Lead for IEEE UK & Ireland Blockchain Group Coordinated Cross-Atlantic EU-US testbed experiments Teaching: Teaches Distributed Systems, Wireless Communications, and Software Engineering at UCD.

Monika HenzingerView profile

Professor

• Combinatorial Optimization
• Dynamic Optimization
• Parallel and Distributed Optimization
• +5 more

Monika Henzinger is a Full Professor of Computer Science at the Institute of Science and Technology Austria (IST Austria) and Deputy Speaker of the Vienna Graduate School on Computational Optimization. She holds a PhD from Princeton University and has held positions at Cornell University, Digital Equipment Corporation, Google, EPFL, and the University of Vienna. Her research focuses on combinatorial algorithms, dynamic optimization, and efficient graph algorithms. She leads a group at IST Austria exploring algorithm design for dynamic environments, privacy-preserving algorithms, and practical implementations of theoretical results. Research Interests: Combinatorial algorithms (especially graphs), dynamic algorithms, approximation algorithms, algorithmic game theory, and privacy-preserving computation. Her work includes breakthroughs in decremental graph algorithms, submodular optimization, and computational advertising. Collaborations: Works with Vladimir Kolmogorov, Nysret Musliu, Günther Raidl (Combinatorial Optimization), Birgit Rudloff (Dynamic Optimization), and Dan Alistarh (Parallel/Distributed Optimization). Awards: Wittgenstein Award (2021), ERC Advanced Grants (2021, 2014), ACM Fellow (2016), and numerous others listed in her CV. Grants & Labs: Principal investigator on an ERC Advanced Grant for graph algorithms. Her team includes PhD students (e.g., Bardiya Aryanfard, Antoine El-Hayek) and postdocs focused on algorithmic challenges in dynamic systems.

• Edge Computing
• Non-Orthogonal Multiple Access
• Energy Efficiency
• +7 more

Suzan Bayhan is an Associate Professor in the Design and Analysis of Communication Systems group and a member of the Digital Society Institute . Her research focuses on energy-efficient wireless networks, edge computing, and sustainable computing infrastructures. She has contributed to advancing technologies like Non-Orthogonal Multiple Access (NOMA), cellular network optimization, and federated learning in smart homes. Her work bridges theoretical advancements with practical applications, addressing challenges such as energy consumption in edge servers, security of critical infrastructure networks, and cross-technology communication between LTE-U and WiFi. Bayhan has received prestigious awards including the Best Demo Award at IEEE INFOCOM 2020 and Joint Best Paper Award at IEEE WoWMoM 2020 . Her research spans topics like network resilience, spectrum sensing, and distributed computing systems. She actively participates in academic activities, including organizing workshops like the 8th Networking Networking Women Workshop (N2Women 2019) and delivering invited talks on wireless network resilience. Key Research Themes: Edge Computing, Energy Efficiency, 5G/6G Networks, IoT Analytics Notable Contributions: LESS-ON energy-saving framework, MintEDGE simulator, and sustainable computing continuum proposals Collaborations: Cross-continental research partnerships in wireless systems and environmental computing

• Data center networking
• Optical circuit switching
• Distributed systems
• +3 more

Paolo Costa is a Senior Principal Research Manager at Microsoft Research Cambridge and Honorary Lecturer at Imperial College London. His research bridges distributed systems and networking with focus on optical technologies for next-generation data centers. Key innovations include Sirius nanosecond optical switching architecture, soliton microcomb-based circuit switching, and PANAMA in-network aggregation for machine learning clusters. Research explores programmable switches, hardware acceleration, and AI infrastructure optimization. Projects include 'CamCube' and 'Predictable Datacenters' enhancing application-network integration. Recent publications examine memory systems for AI, stateful in-network computing, and lite-GPU clusters for scalable AI infrastructure.

Susan ZehraView profile

Senior Lecturer

• Cybersecurity
• Vehicular Networks
• Smart City Systems
• +3 more

Susan Zehra is a Senior Lecturer in the Department of Computer Science at Old Dominion University (ODU), affiliated with the College of Sciences. Her research focuses on vehicular networks, cybersecurity, smart city systems, and auction-based optimization in distributed environments. She holds a position in ODU's academic administration within the School of Data Science and Engineering divisions. Education: Not explicitly stated in available texts. Professional Affiliations: Active in ODU's Batten College of Engineering & Technology initiatives and the School of Data Science. Her work addresses challenges in securing vehicular communications (VANETs), developing auction-based algorithms for cloud resource management, and integrating IoT technologies into smart city infrastructure. Recent publications emphasize frameworks for privacy preservation in inter-vehicular systems, stochastic optimization in vehicular clouds, and decentralized deception detection. Publications span 2018-2025, with a focus on cybersecurity, vehicular systems, and distributed computing. Her articles propose novel solutions for datacenter scheduling, parking management in smart cities, and resilient communication protocols. Grants & Advising: No specific grants or advisees listed in provided data. Collaborates with ODU's research initiatives in cybersecurity and distributed systems. Labs/Teams: Involved with ODU's Cybersecurity and Smart City research groups, though specific lab affiliations are not detailed in the text.

• Network Virtualization
• Software-Defined Networking (SDN)
• Context-Aware Systems
• +4 more

Ahmed Karmouch is a Professor at the University of Ottawa, Canada, affiliated with the College of Engineering and Computer Science. His research focuses on Network Virtualization , Software-Defined Networking (SDN) , and Context-Aware Systems , with significant contributions to Cloud Computing Resource Allocation , Programmable Data-Planes , and Overlay Networks . He has co-authored over 161 publications since 1980, emphasizing scalable infrastructure management, QoS optimization, and autonomic network design. His recent work includes machine learning integration in P4-programmable networks (2024) and survivable IaaS management with SDN (2021). Publications span IEEE Transactions and ACM journals, with frequent collaborations with Nancy Samaan, Abdallah Jarray, and Hamid Harroud. Key research themes: Network Programmability Service Specific Overlay Networks Autonomic Network Management ML for Traffic Optimization Geographically Distributed Cloud

Anne C. ElsterView profile

Professor

• High-Performance Computing (HPC)
• Parallel Computing
• GPU Computing
• +6 more

Prof. Anne C. Elster is a Professor of Computer Science at NTNU's Department of Computer & Information Science (IDI), leading the HPC-Lab. She specializes in High-Performance Computing (HPC), GPU acceleration, and heterogeneous systems. Her work spans HPC applications in medical imaging, seismic processing, and oil & gas simulations, with collaborations at CERN, NVIDIA, and Schlumberger. Elster holds a PhD in Electrical Engineering from Cornell University (1994) and is an IEEE Senior Member since 2000. She has supervised over 70 master students and numerous PhD candidates, emphasizing GPU computing. Her teaching includes courses like Parallel Computing and Compilers , with a focus on programming and problem-based learning. She leads EU projects like CLOUDLIGHTNING (2015–2018) and has organized major conferences (e.g., ISC, SC, PARA). Her HPC-Lab is a CUDA Research and Teaching Center, and she advocates for HPC infrastructure investments in Norway through policy engagement.

Luigi PannocchiView profile

Researcher

• Computer Networking
• Real-Time Systems
• Autonomous Systems
• +3 more

Luigi Pannocchi is a Postdoctoral Researcher at Sant'Anna School of Advanced Studies since September 2021. His research focuses on optimizing network infrastructures, virtualized systems, and autonomous systems. He holds a PhD from 2015 to 2018, with a strong emphasis on real-time systems, embedded architectures, and control frameworks. Research interests include datacenter optimization, virtual machine (VM) placement in NFV environments, AI-driven network management, and the integration of real-time systems with autonomous robotics. His work bridges theoretical models with practical implementations in cloud computing and embedded systems. Recent publications highlight advancements in VM placement algorithms using logic programming, AI applications in network virtualization, and optimization techniques for NFV infrastructures. His 2023 IEEE TCCPS Early-Career Award recognizes contributions to softwarized network services and real-time systems. Key contributions include frameworks for autonomous systems prototyping, power consumption modeling in embedded systems, and HIL testing methodologies for multi-vehicle systems. His research spans interdisciplinary areas such as robotics, cloud engineering, and real-time computing.