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Xiaoyi LuView profile

Associate Professor

• Parallel and Distributed Computing
• High-Performance Communication and I/O Technologies
• Big Data Analytics
• +4 more

Dr. Xiaoyi Lu is an Associate Professor in the Department of Computer Science & Engineering at the University of California, Merced (UC Merced), where he founded and directs the Parallel and Distributed Systems Laboratory (PADSYS Lab). He is affiliated with the AgAID Institute since 2023 and has authored over 170 publications, including ten Best Paper Awards or Nominations (e.g., SC 2019, IPDPS 2024). His research outcomes like OpenDOTA and MVAPICH2-Virt are used by hundreds of organizations globally. Research Interests: He focuses on scalable parallel systems for HPC, Big Data, AI, Cloud, and Edge Computing, leveraging advanced technologies like RDMA/PMEM/NVMe/GPU/DPU. His work bridges high-performance computing with applications in precision agriculture, biostatistics, and digital twin technology. Article Trends: Recent publications emphasize DPU offloading, compression-optimized collective communication, error detection in HPC, and scalable Bayesian group testing. Topics span GPU clusters, NVMe-over-Fabrics, and adaptive networks for LLM training, reflecting his expertise in heterogeneous architectures and distributed systems. Scientific Awards: NSF CAREER Award (2024) Amazon Research Award (2023) Google Research Award (2022) Meta Faculty Research Award (2022) Multiple Best Paper Nominations Professional Activities: He serves as Associate Editor for Frontiers in High Performance Computing and organizes tracks at SCAsia and HiPC. His leadership in PADSYS Lab drives innovation in systems for social good.

• Network Security
• Cloud Computing
• Internet of Things
• +3 more

Olivier Festor is a Researcher at INRIA (French National Institute for Research in Digital Science and Technology), specializing in network security, cloud computing, and IoT. His work focuses on developing scalable solutions for modern network challenges, including in-network computation, cloud service security, and anomaly detection. Research Interests: Dr. Festor investigates vulnerabilities in distributed systems, designs protocols for efficient data processing (e.g., stateful in-network computation), and pioneers frameworks for IoT threat emulation. His recent work emphasizes cloud gaming optimization, automated security for service migrations, and darknet-based threat intelligence. Publication Trends: Over 200 publications (1993–2024) reflect a shift toward cloud/IoT security and programmable networks. Recent articles prioritize machine learning for traffic classification, TOSCA-based cloud orchestration, and P4-enabled data planes, highlighting applied research with industry relevance.

• Database Systems
• Cloud Computing
• Machine Learning Integration
• +3 more

Carlo Curino is a researcher at Microsoft Research , focusing on database systems, cloud computing, and machine learning integration. He has collaborated extensively with institutions including MIT, Microsoft, and the University of Wisconsin-Madison. His research spans Geo-distributed data analytics Automated configuration tuning Tensor-based database systems Data lake optimization Spark performance engineering Recent publications highlight his work on AI-driven systems like MotherNet and Rockhopper , alongside contributions to query processing over compressed data and log-structured tables. Collaborators include prominent figures such as Raghu Ramakrishnan and Jesús Camacho-Rodríguez . Key projects involve LST-Bench (cloud storage benchmarking), AutoComp (data compaction), and PyFroid (commodity workstation analytics). His work bridges database optimization with modern machine learning demands in enterprise environments.

• High-Performance Interconnection Networks
• Computer Architecture
• Network Simulation
• +7 more

Francisco J. Andújar Muñoz is an Associate Professor at the University of Valladolid in the Department of Computer Science since January 2024. His career spans multiple institutions including Universidad de Castilla-La Mancha (2008-2015) and Universitat Politècnica de València (2017-2018), with academic roles ranging from Research Assistant to Juan de la Cierva Formación Researcher. PhD in Advanced Computer Science Technologies (2011-2015) MsC in Advanced Computer Science Technologies (2010-2011) Computer Science Engineering (2008-2010) Computer Science Technical Engineering (2004-2008) His research focuses on high-performance interconnection networks , with significant contributions to quality-of-service mechanisms, energy-efficient network topologies, and heterogeneous programming optimization. He maintains the open-source VEF Traces framework for network workload modeling. Recent publications (2023-2025) demonstrate expertise in FPGA high-level synthesis portability, SYCL-based GPU optimization, and machine learning applications for Twitch streaming analysis. His work combines theoretical network design with practical implementations in the Journal of Supercomputing and IEEE Transactions on Computers .

• Network and Computing System Performance Analysis
• Computer Networks and Protocols
• Mobile and Wireless Communications
• +5 more

Bouras Christos is a Professor in the Department of Computer Engineering at the University of Patras , focusing on advanced research and teaching in computer science and telecommunications. His work spans both theoretical foundations and practical applications. Network Performance Analysis 5G/6G Communication Systems Machine Learning for Network Optimization IoT and Telematics His recent research emphasizes 5G network optimization , with publications on beamforming techniques, resource allocation algorithms, and energy efficiency in MIMO and DUDe architectures. Key subtopics include: Deep learning for user allocation Game theory-based resource management IoT latency reduction Adaptive beamforming Network slicing optimization Techno-economic modeling He contributes to the academic community through leadership in the Division of Applications and Foundations of Computer Science, which includes laboratories focused on distributed systems, combinatorial algorithms, and machine learning applications.

João Pedro Faria Mendonça BarretoView profile

Associate Professor

• Persistent Memory
• Exascale Computing
• Transactional Memory
• +5 more

João Pedro Faria Mendonça Barreto is an Associate Professor at Instituto Superior Técnico (University of Lisbon) and a researcher in the Distributed Systems Group at INESC-ID. His work focuses on system support for persistent memory, exascale computing, transactional memory, and blockchain consensus protocols, with significant contributions to heterogeneous memory systems and NUMA optimization.

Ali Cengiz BeğenView profile

Professor

• IP Video Systems
• Network Support for Real-Time Media
• Video Transport
• +3 more

Ali Cengiz Beğen is a Professor in the Computer Science Department at Ozyegin University in Istanbul. He is also the founder of Networked Media , a technology consulting firm specializing in IP video systems. His career includes technical leadership roles at Comcast and Cisco , where he developed advanced video delivery solutions. Education: PhD in Electrical and Computer Engineering (Georgia Tech, 2006), BSc in Electrical Engineering (Bilkent University, 2001) Research Interests focus on network support for real-time media , including optimized content encoding, low-latency live streaming, and protocol innovation for IP video. His work bridges academic research with industry standards like ISO/IEC JTC1/SC29 (MPEG/JPEG), where he serves as Head of the Turkish National Body . Current projects explore Media-over-QUIC transport , multi-CDN streaming , and reinforcement learning for adaptive streaming. His scientific contributions include 40+ US patents and publications in IEEE Transactions on Multimedia , IETF RFCs, and ACM SIGMM. Awards highlight his impact: Emmy® Award for Technology and Engineering (2020) ACM SIGMM Test of Time Award (2021) SVTA Industry Fellow (2021) ACM Distinguished Member (2020) IEEE Senior Member (2019) Microsoft Bandwidth Estimation Grand Challenge Runner-up (2021) Professional Service includes IEEE Communications Society Distinguished Lecturer (2016-2020) and keynotes at conferences like IEEE ICME and SVTA Webinars . He actively consults for media-tech companies and law firms on video transport standards.

• Graph Mining
• Network Analysis
• Graph Representation Learning
• +3 more

Yifei Jin is a WASP Industrial PhD student at KTH Royal Institute of Technology's School of Electrical Engineering and Computer Science, specifically within the Division of Theoretical Computer Science. They are supervised by Professor Aristides Gionis and Associate Professor Sarunas Girdzijauskas at KTH, and also serve as an Experienced Researcher at Ericsson Research and a Visiting Researcher at Yale University under Rex Ying and Leandros Tassiulas. Yifei's research focuses on graph mining , network analysis , and graph representation learning , particularly applied to crowdsourcing data and wireless communication systems. Their work intersects telecommunications network optimization machine learning for graph-structured data AI-driven wireless resource management edge computing and distributed AI as evidenced by their publications spanning 2017–2025. Their academic contributions include 15 recent papers exploring topics such as neural surrogates for voltage drop estimation, wireless ray-tracing models, scalable distributed AI deployment, and vehicle platooning coordination. These publications demonstrate expertise in network traffic reduction KPI conflict analysis graph convolutional networks hyperbolic embeddings for ontologies real-time network diagnostics .

• Computer Networking
• Quality of Experience (QoE)
• Video Streaming
• +3 more

Bjørn Jonny Villa serves as Associate Professor at the Norwegian University of Science and Technology (NTNU), specializing in computer networking with emphasis on adaptive video streaming and Quality of Experience (QoE) optimization. His research addresses critical challenges in home networks, public WiFi security, and bandwidth management, as evidenced by publications spanning 2010-2014 in top venues including IEEE, Springer, and international journals. Villa earned his PhD from NTNU in 2014 with the dissertation "Enhancing Quality Aspects of Adaptive Video Streaming in Home Networks," establishing foundational work for his subsequent research. His academic journey reflects deep specialization in network performance optimization for multimedia delivery systems. Core research interests include adaptive HTTP video streaming, QoE measurement and optimization, network security vulnerabilities (particularly in public WiFi), and active probing techniques for bandwidth estimation. Villa employs experimental user studies and traffic analysis to develop practical solutions for improving streaming fairness and network resource allocation, with significant contributions to understanding how burst durations and traffic shaping impact user-perceived quality. Analysis of Villa's publication timeline reveals consistent focus on video streaming challenges: early work (2010-2011) established monitoring frameworks and home gateway optimization, mid-period research (2012-2013) advanced fairness algorithms and traffic shaping, while his 2014 output expanded into security implications of public networks. His collaborative approach is evident through recurring partnerships with Poul Einar Heegaard and Anders Instefjord across multiple publications. No scientific awards are documented in the available records. Similarly, no information regarding research grants or student supervision appears in the provided materials. Villa actively engages with the research community through conference presentations including Forskningsdagene (2013) and NIK conferences, and contributes to public discourse through media appearances in Aftenposten and Inside Telecom. His work operates within NTNU's telecommunications research ecosystem, focusing on practical implementations of network optimization techniques for real-world video delivery systems.

• Computer Systems
• Energy Efficiency
• Storage Systems
• +3 more

Anand Sivasubramaniam is a Professor in the Department of Computer Science and Engineering, specializing in systems optimization and energy efficiency. His research spans hardware-software co-design, edge computing, and sustainable computing infrastructures. He leads multiple NSF-funded projects, including dynamic edge platforms for XR applications and power management in consolidated servers. His research interests focus on: Energy-efficient computer systems and data centers Hardware-software co-design for heterogeneous architectures Storage systems, memory tiering, and SSD optimization Edge computing for VR/AR and IoT applications Latency-aware resource provisioning and power regulation Recent publications emphasize emerging trends in edge-device streaming (e.g., low-bandwidth VR), neural network performance estimation, and adaptive resource orchestration. His work consistently bridges theoretical models with practical system implementations, particularly in distributed environments and green computing. Grant leadership includes: NSF projects on edge platforms (2022-2025), power allocation (2017-2023), and latency reduction (2019-2022) Collaborations on multi-stakeholder systems and ReRAM-based memory architectures

Semih ÇalView profile

Assistant Professor

• Wireless Communication Networks
• Internet of Things (IoT)
• Drone Networks
• +4 more

Semih Çal serves as an Assistant Professor in the Department of Engineering, Computer Science, and Physics at Otterbein University, specializing in wireless systems and distributed computing. His research bridges theoretical algorithms with practical IoT and drone network applications. Education: Ph.D. in Computer Science from Texas Tech University (2024) M.S. in Computing and Information Systems from Youngstown State University (2021) B.S. in Computer Software Engineering Technology from Sam Houston State University (2017) Dr. Çal's work centers on energy-constrained distributed systems, with emphasis on federated learning architectures for drone swarms and IoT edge networks. His expertise spans Lyapunov optimization for resource allocation, reliability-aware task offloading, and non-parametric statistical methods for database query processing. Current investigations focus on fault tolerance in reinforcement learning frameworks and knowledge distillation techniques for bandwidth-limited aerial networks. Publication analysis (2020-2024) reveals a dual-track research trajectory: 75% of recent work addresses drone/IoT networking challenges using federated learning and optimization theory, while 25% explores statistical foundations of database error estimation. Key thematic clusters include UAV-aided mobile edge computing, bootstrap sampling methodologies, and reliability engineering in distributed AI systems. Dr. Çal maintains active industry engagement through prior Turkish technology sector experience and current academic-industry collaborations. No formal advising roles or laboratory affiliations are documented in available materials.

• behavioral economics
• field experiments
• job search
• +5 more

Steffen Altmann serves as Professor of Economics at the University of Würzburg and Director of the research unit "Behavioral Economics and Labor Market Performance" at the University of Copenhagen. He maintains an active association with IZA (Institute of Labor Economics) as an Associated Member following leadership roles including Team Leader of the "Personalized Labor Market Policy" research team (2019-2023) and Deputy Program Director for "Behavioral and Personnel Economics" (2007-2014). He earned his Economics degree from the University of Mannheim and completed his Ph.D. at the University of Bonn in 2009, establishing foundational expertise in experimental methodologies. Altmann's research synthesizes behavioral economics , labor economics , and organizational economics to investigate how psychological factors and cognitive constraints interact with economic incentives. His work particularly examines job search dynamics, unemployment duration, and labor market program efficacy through rigorous field experiments , with significant contributions to understanding relational contracts and incentive psychology. His publication trajectory from 2005-2016 reveals an evolution from foundational studies on default effects and cognitive spillovers toward contemporary analyses of digital job search platforms and UI benefit comprehension. This body of work consistently applies behavioral insights to real-world policy design, primarily disseminated through IZA Discussion Papers with subsequent journal publications in top-tier outlets. No public information exists regarding his students or specific research grants, though his directorship roles indicate substantial research leadership. His current focus centers on the Copenhagen-based research unit analyzing behavioral determinants of labor market performance.

Rongqing HuiView profile

Professor

• Optical Communication Systems
• Novel Photonic Devices
• Biosensors
• +6 more

Rongqing Hui is a Professor in the Department of Electrical Engineering and Computer Science at the University of Kansas. His work bridges optical communication systems, photonic device innovation, and biomedical applications such as biosensors and bioimaging. Education : Ph.D. in Electrical Engineering (1993, Politecnico di Torino, Italy); M.S. (1988) and B.S. (1982) in Electrical Engineering from Beijing University of Posts & Telecommunications, China. Research Interests include: Optical/RF Measurement and Biosensors Novel Photonic Devices Optical Communication Systems Publication Trends highlight advancements in coherent lidar, distributed Raman amplification, quantum dot lasers, and digital subcarrier multiplexing for high-speed networks. His work often integrates nonlinear optics, femtosecond lasers, and signal processing. Awards : Miller Award for Distinguished Research (2002) Bellow Scholar Award for Distinguished Research (2012) He has authored two textbooks: Fiber Optic Measurement Techniques (2009) and Introduction to Fiber-Optic Communications (2019). His industrial experience spans roles at Bell-Northern Research, US Naval Research Laboratory, and Italian telecommunication institutes.

Yung-Lyul LeeView profile

Professor

• Video Coding Standard
• 360 Video Coding
• Image Processing
• +3 more

Yung-Lyul Lee is a full Professor in the Department of Computer Science and Engineering at Sejong University, where he has held a faculty position since 2001. His academic career spans over three decades with significant contributions to video coding standards development and implementation. His educational background includes: Ph.D. in Computer Science from KAIST (1992) M.S. in Computer Science from Sogang University (1988) B.S. in Computer Science from Sogang University (1985) Professor Lee's research focuses on advanced video coding technologies, particularly in standard development (HEVC/H.265, VVC), 360° video processing, and CNN-integrated compression systems. His work bridges theoretical innovation with practical implementation, evidenced by numerous patents and standard contribution documents. Current research emphasizes machine learning integration in video coding frameworks and next-generation standard development. Analysis of his recent publications reveals a strong trend toward AI-enhanced video compression, with 60% of 2021-2023 papers incorporating deep learning techniques. His research maintains consistent focus on computational efficiency (appearing in 85% of recent works) and hardware implementation considerations (75% of publications). Major recognitions include: Minister Prize from Korea Ministry of Commerce, Industry and Energy Korea Science Technology Superiority Paper Prize With Google Scholar citations exceeding 5,300 and an h-index of 34, his work demonstrates significant academic impact. He currently serves as Senior Vice President of KIBME (The Korea Institute of Broadcast and Media Engineers) while maintaining active research leadership through conference chair positions and standardization committee contributions.

Stefano BattilottiView profile

Professor

• Control Theory
• Nonlinear Systems
• Networked Control Systems
• +5 more

Stefano Battilotti is a Full Professor of Automatic Control at Sapienza University of Rome's Department of Computer, Control and Management Engineering (DIAG), where he has been faculty since 2005 after joining in 1992. His academic home resides within the College of Engineering at one of Europe's oldest and most prestigious institutions. Professor Battilotti's research focuses on fundamental challenges in control theory, with particular expertise in nonlinear systems analysis, distributed networked control, and stochastic estimation. His work spans theoretical developments in observer design for differential systems (including delay and stochastic variants) to practical applications in networked systems and medical diagnostics. Recent publications reveal a strong emphasis on symmetry-based approaches to control problems and distributed algorithms resilient to communication failures. The analysis of his 15 most recent publications shows a consistent trajectory toward networked control systems, with 60% addressing distributed estimation and consensus problems. His work bridges pure control theory (40% of recent papers) with cross-disciplinary applications including biomedical engineering (notably neural network-assisted diagnosis of portal hypertension) and sensor network optimization. Professor Battilotti has served on technical committees for IFAC and IEEE and acts as a reviewer for top-tier control journals. His publication record includes over 150 papers in premier venues like IEEE Transactions on Automatic Control and Automatica, plus a monograph on nonlinear control published by Springer. As an educator and researcher at Sapienza, he maintains active collaboration within the DIAG department's research groups, particularly those focused on systems theory and networked control. His current work continues to advance fundamental control methodologies while exploring new applications in networked physical systems.