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Jean-Yves Le BoudecView profile

Professor

• Network Calculus
• Time-Sensitive Networking
• Deterministic Networking
• +7 more

Jean-Yves Le Boudec is a Professor at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Computer and Communication Sciences and the Institute of Electrical Engineering. He has been a key figure in advancing the theory and application of network calculus and deterministic networking, contributing significantly to standards such as IEEE Time-Sensitive Networking (TSN) and IETF DetNet. His research focuses on network calculus , time-sensitive and deterministic networking , traffic regulation , worst-case delay analysis , and cyber-physical systems , with cross-cutting applications in smart grids , real-time communication , and network security . He has co-authored foundational texts on network calculus and developed theoretical frameworks for traffic regulators, service curves, and delay bounds in complex networked systems. The recent publications highlight a strong trend in analyzing and improving performance guarantees in deterministic networks, including scheduling mechanisms like Deficit Round-Robin and Cyclic Queuing and Forwarding, traffic shaping via interleaved regulators, and security against time-synchronization attacks in power systems. The work spans theoretical modeling using stochastic and min-plus/max-plus algebra, practical algorithm design, and application to critical infrastructure. IEEE Fellow Le Boudec has advised numerous researchers and PhD students, including Ehsan Mohammadpour, Ludovic Thomas, and Seyed Mohammadhossein Tabatabaee. His collaborative projects often involve grants related to European and Swiss research initiatives in networking and smart grid technologies. He leads a research group focused on networked systems at EPFL, contributing to both theoretical advances and real-world implementations in industrial and energy-critical networks. His lab work centers on modeling and verification of time-sensitive network behaviors, integrating formal methods with practical experimentation. The team investigates regulators, shapers, and synchronization mechanisms, aiming to ensure robustness, predictability, and security in next-generation communication infrastructures. Future work continues to explore the interplay between communication, control, and energy systems in highly reliable environments.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +5 more

Bo Wang is an active academic researcher primarily affiliated with multiple Chinese institutions, with strong connections to Tsinghua University, Beijing Jiaotong University, and other leading Chinese universities. His research spans artificial intelligence, machine learning, computer vision, medical image analysis, and intelligent control systems, demonstrating significant interdisciplinary work across computer science, engineering, and biomedical applications. Primary institutional affiliation: School of Computer Science and Technology at multiple Chinese universities Active research areas: AI/ML applications in healthcare, computer vision, federated learning, and intelligent control systems Extensive publication record across top-tier venues in multiple disciplines Wang's research interests focus on the intersection of artificial intelligence and practical applications. His work demonstrates strong expertise in developing novel machine learning architectures for medical image analysis, including applications in CT imaging, MRI, and sperm tracking. He has made significant contributions to federated learning approaches for large language models, sliding mode control systems, and molecular optimization frameworks. His research consistently bridges theoretical advances with practical implementations across healthcare, manufacturing, and environmental monitoring domains. Analysis of Wang's recent publications reveals a strong trend toward interdisciplinary AI applications, particularly in medical imaging and bioinformatics. His work on VAE-GANMDA for microbe-drug association prediction, ACE-QSM for accelerating MRI acquisition, and text-guided molecular optimization demonstrates innovative approaches at the intersection of AI and life sciences. Wang also maintains active research in industrial applications including digital twin technology for energy systems and robust scheduling approaches for multi-factory production. Notable research contributions include: FLFT: A Large-Scale Pre-Training Model Distributed Fine-Tuning Method with Federated Learning VAE-GANMDA: Microbe-drug association prediction model ACE-QSM: Accelerating quantitative susceptibility mapping using diffusion models Digital twin-empowered power consumption prediction systems Wang actively collaborates with researchers across China and internationally, with publications spanning computer science, engineering, medical imaging, and environmental science journals. His work demonstrates strong technical depth across multiple AI methodologies while maintaining focus on practical applications that address real-world challenges in healthcare, manufacturing, and environmental monitoring.

Shuhai XiaoView profile

Professor

• Paleobiology
• Geobiology
• Ediacaran-Cambrian Transition
• +6 more

Shuhai Xiao is a Professor of Geobiology in the Department of Geosciences, College of Science, at Virginia Tech. His research integrates paleobiology, sedimentology, and geochemistry to investigate Earth-life co-evolution during critical transitions, particularly the Ediacaran-Cambrian period. He directs the Geobiology Lab, advising PhD students on projects ranging from Siberian Ediacaran fossils to Tonian eukaryote evolution. Education: Ph.D., Organismic and Evolutionary Biology, Harvard University, 1998 M.A., Organismic and Evolutionary Biology, Harvard University, 1996 M.Sc., Geology, Peking University, 1991 B.Sc., Geology, Peking University, 1988 Research Interests: Dr. Xiao explores the environmental drivers of evolutionary innovations—such as the rise of eukaryotes, multicellularity, and animals—and their feedback effects on Earth's systems. His work combines field geology, geochemical analysis, and taphonomic experiments to decode ancient biosphere-environment interactions, with emphasis on the Ediacaran-Cambrian transition. Publications: Recent articles (2024–2025) focus on Ediacaran-Cambrian paleobiology, geochemical proxies (δ 13 C, Li, Ba isotopes), and global-scale events (Shuram Excursion, Marinoan glaciation). Key themes include microfossil preservation, biomineralization origins, redox fluctuations, and geomagnetic influences on early animal radiation. Awards: National Academy of Sciences Member (2023) Mary Clark Thompson Medal (2021) Guggenheim Fellowship (2010) Charles Schuchert Award (2006) AAAS Fellow (2019) Lab & Outreach: Leads a team studying Ediacaran fossils, taphonomy, and geochemistry. Actively contributes to the Virginia Tech Museum of Geosciences and conducts workshops for K–12 educators internationally.

Sai Manoj Pudukotai DinakarraoView profile

Assistant Professor

• Hardware security
• Adversarial machine learning
• Internet of Things networks
• +9 more

Sai Manoj Pudukotai Dinakarrao is an Assistant Professor in the Department of Electrical and Computer Engineering at George Mason University's College of Engineering and Computing. He leads the HArt (Hardware and AI Research) Group, focusing on cutting-edge research at the intersection of hardware security and artificial intelligence. His educational journey includes a BTech in Electronics and Communication Engineering from Jawaharlal Nehru Technological University (2010), an MTech in Information Technology from International Institute of Information Technology Bangalore (2012), and a PhD in Electrical Engineering from Nanyang Technological University, Singapore (2015). Following his doctoral studies, he completed post-doctoral research at TU Wien, Vienna (2015-2017) and George Mason University (2017-2018). Dr. Dinakarrao's research spans hardware security, adversarial machine learning, IoT networks, and deep learning in resource-constrained environments. His work integrates hardware design with AI techniques to address security challenges in computing systems, with particular focus on side-channel attack detection, malware detection in IoT networks, on-chip security, and hardware accelerator design for machine learning applications. His research has resulted in numerous publications in top-tier conferences and journals including IEEE Transactions, ACM conferences, and Design Automation Conference. Analysis of his recent publications reveals a strong trend toward hardware security solutions using machine learning techniques. His work increasingly focuses on Processing-in-Memory architectures, energy-efficient security solutions for IoT devices, and innovative approaches to hardware Trojan detection. Many publications demonstrate interdisciplinary collaboration across electrical engineering, computer science, and cybersecurity domains. Young Research Fellow Award at Design Automation Conference (DAC) 2013 Best paper award at International Conference on Data Mining (ICDM) 2019 Best paper award at International Conference on Consumer Electronics (ICCE) 2020 Best paper nomination at International Conference on Computer-Aided Design (ICCAD) 2019 Best paper nomination at Design Automation and Test in Europe (DATE) 2018 Dr. Dinakarrao has successfully mentored numerous PhD and MS students, with alumni securing positions at AMD-Xilinx, US Government agencies, and academic institutions. His research has been supported by significant grants from NSF, DARPA, and Virginia Commonwealth Cyber Initiative. Current projects include securing supply chains with UVA, developing novel architectures for machine learning acceleration, and creating energy-preserving cryptography protocols. The HArt Group maintains active collaborations with industry partners including AMD-Xilinx and government agencies. The lab focuses on practical implementations of theoretical security concepts, with particular emphasis on creating deployable security solutions for real-world hardware systems. Current research directions include intermittent computing with energy harvesting, hardware fuzzing techniques, and robust machine learning models resistant to adversarial attacks.

• Blockchain Technology
• Distributed Systems
• Permissioned Blockchains
• +3 more

Artem Barger is a researcher specializing in blockchain technology, distributed systems, and database optimization. With affiliations primarily in blockchain development and academic research, he has contributed extensively to Hyperledger Fabric enhancements and decentralized information systems. Research Interests Optimizing state databases for blockchain platforms Byzantine Fault Tolerance in distributed networks Permissioned blockchain architectures Tokenization of real-world assets AI applications in soft skills evaluation Recent Publications Barger's work focuses on improving blockchain scalability and security through techniques like certification blocks, Patricia Merkle tries, and verifiable randomness. He has also explored tokenization applications in charity and energy sectors.

David YauView profile

Professor

• Network Protocol Design
• Cyber-Physical System Security
• Network Security and Privacy
• +3 more

David Yau is a Professor in the Information Systems Technology and Design (ISTD) pillar at the Singapore University of Technology and Design (SUTD). He holds a B.Sc. from the Chinese University of Hong Kong and M.S./Ph.D. from the University of Texas at Austin, all in Computer Science. Previously, he was Distinguished Scientist at the Advanced Digital Sciences Center (ADSC) in Singapore and Qiushi Chaired Professor at Zhejiang University, China. His research focuses on network security, cyber-physical systems, and smart grid resilience. He has led over 15 research projects funded by agencies like Singapore's NRF, A*Star, and the U.S. NSF. Notable awards include the IBM Fellowship, NSF CAREER Award, and Best Paper awards at IPSN 2017 and IEEE MFI 2010. Yau has advised numerous PhD/Master’s students, many now in academia and industry. His work spans secure protocols, intrusion detection in critical infrastructures, and privacy-preserving smart grid technologies. He has authored over 100 peer-reviewed publications and served on editorial boards of ACM Transactions on Sensor Networks and IEEE journals.

Oliver HöftbergerView profile

Researcher

• Cyber-Physical Systems
• Real-Time Systems
• Embedded Systems
• +5 more

Oliver Höftberger is affiliated with TU Wien's Institut für Technische Informatik , part of the Faculty of Informatics. His research focuses on Cyber-Physical Systems , Real-Time Systems , Embedded Systems , and Resilient Systems-of-Systems . He has contributed to frameworks for self-healing cyber-physical systems and resilient master clocks, emphasizing fault tolerance and dynamic reconfiguration. Publications highlight work on emergence phenomena in systems-of-systems, ontology-based runtime adaptation, and multi-core processor architectures like the ACROSS MPSoC for safety-critical applications. He has advised multiple PhD and Master’s students, including Denise Ratasich, Nazir Alakhras, and Julian Grahsl. Key projects include the universAAL platform for Ambient Assisted Living and sensor fusion packages for robotics (ROS). His research bridges theoretical systems modeling with practical implementations in complex, safety-critical domains.

Mike ThivView profile

Researcher

• Plant Systematics
• Biogeography
• Floristic Mapping
• +3 more

Mike Thiv is a Researcher and Head of the Botany Department at the State Museum of Natural History Stuttgart . His work focuses on plant systematics, biogeography , and floristic mapping , with a particular emphasis on the Eritrean-Arabian region, the Mediterranean, and Macaronesia. He has contributed to understanding evolutionary patterns in laurel forests, island endemism, and glacial refugia dynamics. His academic journey includes a Habilitation at Heidelberg University (2015), a PhD on Gentianaceae phylogeny (2000), and a Diploma in Biology (1995). He has led third-party funded projects with grants totaling ~€800,000 and served as an associate editor for journals like Plant Systematics and Evolution . Research projects span molecular phytogeography of the Socotra Archipelago, decline of clubmoss in Baden-Württemberg, and floristic dynamics under global change. His publications address adaptive radiation, vicariant speciation, and taxonomic revisions across diverse plant families. Notable collaborations include work with M. Koch on Macaronesian Gesnouinia and Mediterranean Soleirolia woodiness evolution (2019), and with R. Linder on molecular phytogeography of Socotra plants (2001–2003). He has mentored no explicit advisees but contributes to interdisciplinary teams. Key trends in his publications include integrating molecular and morphological data for taxonomy, exploring biogeographic disjunctions, and analyzing conservation genetics. His work bridges historical botany with modern phylogenetic methods, emphasizing biodiversity in Mediterranean and island ecosystems.

• VLSI Design
• Computer-Aided Design
• Electronic Design Automation
• +4 more

Matthew R. Guthaus is a Professor in the Electrical and Computer Engineering Department at the University of California Santa Cruz's Baskin School of Engineering. With an active research career spanning over two decades, his work focuses on VLSI design, Electronic Design Automation, and memory systems. His research interests include VLSI Design, Computer-Aided Design, Electronic Design Automation, Memory Design, Clock Distribution, Resonant Clocking, and Machine Learning for EDA. Guthaus has made significant contributions to open-source EDA tools, most notably OpenRAM, which has become an important resource for memory compiler development in academia and industry. Analysis of his recent publications (2021-2025) reveals a strategic evolution in his research, incorporating machine learning techniques into traditional EDA problems while maintaining strong foundations in circuit design. His work spans from fundamental circuit design to high-level system considerations, with particular emphasis on open-source frameworks that enable broader collaboration in the semiconductor community. Guthaus has been actively involved in national initiatives addressing semiconductor research and workforce development, as evidenced by his contributions to NSF workshops on integrated circuits. His recent publications show continued innovation in memory design, clock distribution, and the application of AI to traditional EDA challenges. His research has practical applications in low-power circuit design, high-performance computing systems, and the growing field of neuromorphic computing. The consistent publication record through 2025 demonstrates ongoing active research and leadership in the EDA community.

• Microelectronics
• Circuit Design
• Analog-to-Digital Converters
• +3 more

Zhangming Zhu is a Professor at Xidian University in the School of Microelectronics . He specializes in Microelectronics and Circuit Design , with a focus on Analog-to-Digital Converters (ADCs) , CMOS Technology , and Low-Power Electronics . His work addresses challenges in high-speed, high-precision, and energy-efficient circuit design. Research Interests: His publications highlight expertise in ADCs, PLLs, energy harvesting, biomedical sensors, and RF systems. Recent Publications: 2025 papers include a 12-bit 1.5-GS/s ADC , a 5-18-GHz Quadrature Receiver , and 20-bit SAR ADC with thermal error suppression. Collaborations: Frequently co-authors with Shubin Liu, Yi Shen, Ruixue Ding, and others. Applications: Work spans consumer electronics, IoT, biomedical devices, and energy-efficient systems.

Andreas PavlogiannisView profile

Associate Professor

• formal methods
• algorithmic verification
• automata theory
• +6 more

Andreas Pavlogiannis is an Associate Professor in the Department of Computer Science at Aarhus University. His research focuses on formal methods , algorithmic verification , automata theory , concurrency , static and dynamic program analysis , network diffusion , evolutionary graph theory , and evolutionary game theory . Teaching courses: Programming Languages (Bachelor) , Algorithmic Model Checking (Master) , and Program Analysis (Master) Service: Program committee member for POPL, ESOP, AAAI, IJCAI, CONCUR, OOPSLA, and organizer of CONFEST'25 His research has been supported by the Austrian Science Fund (FWF), VILLUM Foundation, Stibo Foundation, and Danish Council for Independent Research (DFF). He is actively recruiting PhD and PostDoc researchers. Recent publications span quantum computing , concurrent systems , evolutionary dynamics , and network science , with particular emphasis on symbolic algorithms , dynamic analysis , and graph-based models .

• Plant Systematics
• Biogeography
• Tropical Ecology
• +5 more

R. Toby Pennington is a Professor of Plant Systematics and Biogeography at the University of Exeter's College of Life and Environmental Sciences, Department of Geography, a position he has held since September 2017. He also maintains a long-standing research position at the Royal Botanic Garden Edinburgh, where he has been employed since January 1994. His work bridges academic research with practical botanical applications through this dual affiliation. Pennington's research focuses on the systematics, evolution, and biogeography of tropical plants, particularly legumes (Fabaceae family) in Neotropical regions. His work spans multiple dimensions of plant science: Evolutionary patterns and processes in Amazonian and Neotropical forest ecosystems Biogeographic history of dry forest species across South America Phylogenetic relationships within legume lineages, especially Inga and related genera Conservation implications of evolutionary diversity patterns in tropical forests Genomic approaches to understanding plant diversification in the tropics His recent research has increasingly incorporated genomic techniques to address fundamental questions about plant evolution and diversification. The analysis of Inga species genomes represents a cutting-edge approach to understanding the evolutionary history of one of the most diverse Neotropical plant genera. His work demonstrates how historical climate changes have shaped current biodiversity patterns, particularly in seasonally dry tropical forests. Pennington's research portfolio reveals a consistent focus on understanding the processes that generate and maintain tropical plant diversity. His publications demonstrate expertise in both traditional taxonomic approaches and modern genomic techniques, reflecting an evolving research program that has adapted to methodological advances in the field. Among his notable scientific contributions are insights into: The role of Quaternary climate fluctuations in shaping current species distributions The biogeographic barriers in the Andes that influence plant dispersal The relationship between evolutionary diversity and ecosystem function in tropical forests The taxonomic complexity within challenging plant groups like Ziziphus and Aldina Throughout his career, Pennington has maintained productive collaborations with researchers across multiple continents, reflecting the international nature of tropical botanical research. His work often involves large collaborative teams that combine field, laboratory, and analytical expertise to address complex questions about plant diversity.

Liang MinView profile

Researcher

• Electric grid digital transformation
• Clean energy systems
• AI for climate and energy
• +7 more

Dr. Liang Min serves as the Managing Director of the Bits & Watts Initiative at Stanford University's Precourt Institute for Energy and as the founding Managing Director of the Stanford Net-Zero Alliance at the Stanford Doerr School of Sustainability. He leads multidisciplinary programs advancing the digital transformation of the electric grid and promoting net-zero emissions solutions through research, education, and industry collaboration. Dr. Min's educational background includes: Ph.D. from Texas A&M University (2007) M.S. from Tianjin University (2004) B.S. from Tianjin University (2001) His research focuses on the digital transformation of electric grids, with pioneering work on 100% Clean Electric Grid, EV50, AI for Climate and Energy, and the Digital Grid platform for integrating distributed energy resources. He recently launched the Powering AI Sustainably program addressing AI's energy demands while accelerating clean power transition. His work bridges high-performance computing with power system operations to enhance grid reliability, security, and sustainability amid increasing renewable penetration and electrification trends. Analysis of Dr. Min's recent publications reveals a strong emphasis on grid modernization through digital technologies, with particular focus on integrating distributed energy resources, enhancing grid resilience, and applying advanced computational methods to power system challenges. His research spans from fundamental work in power system dynamics and control to practical applications addressing energy transition, electric vehicle integration, and AI's role in sustainable energy systems. Dr. Min has led significant research projects funded by the Department of Energy, Bonneville Power Administration, and other organizations, resulting in multiple U.S. patents related to grid technologies including voltage stability smart meters and synchronized electric meters with atomic clocks. As the founder of the Stanford Energy Executive Education Program, Dr. Min has created platforms to equip energy leaders with strategic insights for navigating the rapidly evolving energy landscape. He also established the Stanford Net-Zero Alliance to bring together industry leaders, faculty, and students for collaborative research and education toward net-zero emissions.

• Distributed Computing
• Self-stabilizing Algorithms
• Network Algorithms
• +1 more

Alain Cournier is a University Professor at the University of Picardy Jules Verne (UPJV), France, affiliated with the ALCO research unit (UR 4290) focusing on Algorithmic and Complexity. His office is located in room 401 with contact phone 5913. He maintains an active research profile with continuous publications from 1992 through 2024, primarily in distributed computing and theoretical computer science. Professor Cournier's research centers on fault-tolerant distributed algorithms, specializing in self-stabilizing systems for networked environments. His core contributions span leader election protocols, synchronous unison mechanisms, and message forwarding algorithms in directed and undirected topologies. He pioneered work in snap-stabilization and polynomial-time convergence guarantees, with recent focus on directed network challenges. Earlier career phases included modular graph decomposition algorithms (1990s), while 2018-2019 saw exploration of autoencoder neural networks for anomaly detection in connected buildings. Analysis of his 15 most recent publications reveals strong continuity in distributed algorithms research (12/15 papers), with 2023-2024 works exclusively addressing self-stabilization in directed networks using innovative metaphors like Roman infrastructure. The 2018-2019 neural network studies represent a brief interdisciplinary expansion before returning to core theoretical work. His publications consistently appear in premier venues including PODC, ICDCN, and Theoretical Computer Science, often collaborating with Altisen, Defalque, and Devismes. He operates within the ALCO research unit (UR 4290) at UPJV, which serves as the institutional home for his algorithmic complexity research. This unit facilitates collaboration across the French distributed computing community, evidenced by regular AlgoTel conference participation. While specific lab structures aren't documented, his publication patterns indicate sustained theoretical work with periodic applied explorations in cybersecurity contexts.

Anirudh SivaramanView profile

Assistant Professor

• Computer Networks
• Network Programmability
• Distributed Systems
• +3 more

Anirudh Sivaraman is an Assistant Professor of Computer Science at New York University's Courant Institute, specializing in computer networks and programmability. He holds a PhD from MIT (2017) and a BTech from IIT Madras (2010). His research focuses on enabling ubiquitous network programmability through hardware-software systems, including compilers, runtimes, and applications like distributed consensus and financial exchanges. Education: Ph.D. in Computer Science, MIT (2012–2017) S.M. in Computer Science, MIT (2010–2012) B.Tech in CSE, IIT Madras (2006–2010) Research Interests: Network programmability (e.g., P4, Barefoot Networks) High-speed packet processing and compiler optimization Distributed systems and consensus protocols Cybersecurity in programmable networks Cloud computing and microservice autoscaling Notable Articles Trends: Focus on programmable networks (P4, NIC design) Hardware-software co-design for performance Security and isolation in multi-tenant environments Compiler-based solutions for bug detection Awards: ACM SIGCOMM Doctoral Dissertation (2017), SIGCOMM Best Paper (2017), and multiple teaching awards. Advising & Grants: PI on grants totaling $900K+ (Google, Amazon, NSF). Advises 8+ PhD/Master’s students. Serves on committees for SIGCOMM, NSDI, and HotNets. Labs/Teams: Technical advisor at Clockwork. Co-organizes workshops on programmable networks and P4 standards.