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Feng ChengView profile

Researcher

• Data-Driven Security Engineering
• Security Analytics
• Network Security
• +19 more

Dr. Feng Cheng is a Senior Researcher and head of the IT Security Engineering (Sec-Eng) Team at the Hasso Plattner Institute (HPI), Germany, and serves as the Representative of the Chair "Internet Technologies and Systems" at the Digital Engineering Faculty, University of Potsdam. He holds a PhD from the University of Potsdam, an MEng from Beijing University of Technology, and a BEng from Beijing University of Aeronautics and Astronautics. His research is centered on data-driven security engineering, security analytics, network security, cloud security, firewall, IDS/IPS, attack modeling, and penetration testing. Dr. Cheng's research interests span a broad range of cybersecurity domains, with a strong emphasis on security analytics , data-driven threat detection , cloud and network security , and authentication . His work integrates advanced data engineering and machine learning techniques to enhance security operations, including intrusion detection, threat intelligence, and security information and event management (SIEM). Key application areas include IoT security, mobile security, and web security. The recent publications of Dr. Cheng and his team reflect a significant trend towards leveraging big data analytics , machine learning , and graph-based methods for cybersecurity. Their work focuses on developing frameworks for blockchain-based public key infrastructures, advanced SIEM analytics, malware detection using NLP, and chaos engineering for cloud security. This demonstrates a strong commitment to creating practical, scalable, and intelligent solutions for modern security challenges in complex, distributed environments. Dr. Cheng is actively involved in the academic community as a member of ACM and IEEE, a program committee member for numerous international conferences, and an organizer of workshops on cloud computing and cybersecurity. He has also served on editorial boards for international journals. He supervises a large group of PhD, master's, and bachelor's students, guiding research on topics such as security analytics, cyber threat intelligence, digital credentials, and behavioral authentication. He leads the Sec-Eng team, which conducts research on a diverse portfolio including the Security Analytics Lab, HPI-VDB (vulnerability database), Lock-Keeper (physical separation technology), and the Security Lab Generator for scenario-driven security training.

Tobias FiebigView profile

Assistant Professor

• Network Security
• Human Factors in Security
• Internet Measurement
• +5 more

Dr.-Ing. Tobias Fiebig is a Senior Researcher at the Max Planck Institute for Informatics (Internet Architecture department) since April 2022. He previously served as a permanent Assistant Professor at TU Delft's Faculty of Technology, Policy and Management from 2017-2022. His work combines network measurement with human factors to improve security practices, focusing on email protocols, DNS infrastructure, and academic cloud migration. PhD in Computer Science from TU Berlin (2017) MSc in System and Network Engineering from University of Amsterdam (2013) BSc in Cognitive Science from University of Osnabrück (2012) His research spans Internet security (DNS, IPv6, email), academic freedom in cloud contexts, and human-centered security challenges. Recent work analyzes MTA-STS deployment, DNS sampling biases, and university cloud migration privacy implications. He has presented at top conferences including USENIX Security , IEEE Symposium on Security and Privacy , and ACM CCS . Teaching roles include lecturing Data Networks (2023-24) and On the Practice of System and Network Engineering (2024), with prior module management in cyber security analytics and sustainable system operations.

• Computer Architecture
• Non-Volatile Memory Systems
• Deep Learning Systems
• +3 more

Jishen Zhao is an Assistant Professor in the Department of Computer Science and Engineering at the University of California, San Diego (Jacobs School of Engineering). His research focuses on computer architecture, non-volatile memory systems, and deep learning acceleration. Dr. Zhao has published extensively in top venues including ISCA, MICRO, ASPLOS, and IEEE Transactions. He collaborates with researchers at UCSD and beyond to advance systems for emerging applications in AI and autonomous vehicles. Dr. Zhao's primary research areas include persistent memory systems, hardware/software co-design for deep learning, and safety-critical computing. He develops techniques for crash consistency, memory disaggregation, and efficient neural network deployment. His work on autonomous vehicles addresses scenario generation and perception-aware system design. Recent projects explore LLM applications for software engineering and hardware verification. Analysis of Dr. Zhao's 2024-2025 publications reveals a strong shift toward AI-integrated systems research. He applies large language models to tasks like RTL verification and software issue localization while continuing to innovate in memory systems for serverless computing. There is growing emphasis on safety-critical systems for autonomous vehicles and energy-efficient neural network training using novel hardware architectures. Information about Dr. Zhao's scientific awards, advising activities, grants, and laboratory facilities was not available in the provided documentation.

• Edge Computing
• Fog Computing
• Distributed Systems
• +5 more

Umakishore Ramachandran is a Professor in the School of Computer Science within the College of Computing at Georgia Institute of Technology. His research spans edge computing, distributed systems, and real-time video analytics, with significant contributions to fog computing infrastructure, mobile systems, and sensor networks. Over a prolific 38-year career, he has authored 142 publications with major contributions in 2022-2025. His research interests focus on bridging the gap between cloud and edge computing, with pioneering work in video analytics systems like EVA and MicroEdge. He investigates resource optimization for latency-sensitive applications, developing novel approaches for load shedding, data management, and container runtime efficiency at the network edge. His work addresses fundamental challenges in distributed camera networks, autonomous vehicle systems, and real-time stream processing. Ramachandran's recent publications reveal a strong emphasis on practical edge computing solutions, with 75% of his 2021-2025 work focusing on video analytics and infrastructure optimization. His research shows increasing collaboration with industry partners while maintaining academic rigor, with publications appearing in top venues like SIGMOD, Middleware, and DEBS. The work consistently addresses real-world constraints of resource-constrained edge environments. Ramachandran has mentored numerous researchers who have become principal investigators on edge computing projects, with notable collaborators including Harshit Gupta, Enrique Saurez, and Zhuangdi Xu appearing as first authors on multiple papers. His work has received significant grant support for projects related to mobile fog computing and distributed video analytics. He leads research in the Edge Computing Laboratory at Georgia Tech, focusing on the development of practical frameworks for real-world deployment of edge infrastructure. Current projects include eCAV for connected autonomous vehicles and MicroEdge for multi-tenant camera processing systems.

• Database Systems
• Graph Databases
• Data Management
• +7 more

Amol Deshpande is a Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley, College of Engineering. With over 160 publications spanning from 2000 to 2025, his research has significantly impacted the database systems community. His work bridges theoretical foundations with practical systems, evidenced by numerous publications in top-tier venues including SIGMOD, VLDB, and ICDE. Professor Deshpande's research focuses on database systems, with particular expertise in graph databases, data management, probabilistic databases, query optimization, and data provenance. His work addresses fundamental challenges in managing complex data, including efficient graph analytics, dataset versioning, streaming data processing, and privacy-preserving data management. Recent research directions include entity-relationship abstractions beyond traditional relations, standalone catalog engines for large data systems, and graph theoretical approaches to dataset versioning. His publication trends show a consistent focus on evolving database technologies, with early work on probabilistic databases and query optimization, transitioning to graph analytics and data provenance, and more recently addressing modern challenges in data cataloging, privacy-first data management, and serverless stream processing. His research spans both theoretical contributions (e.g., approximation algorithms for stochastic optimization) and practical systems building (e.g., RStore, TreeCat). Professor Deshpande has mentored numerous PhD students who have become active researchers in the database community, including Hui Miao, Souvik Bhattacherjee, and Konstantinos Xirogiannopoulos. His collaborative work spans across institutions, with frequent collaborations with researchers from MIT, University of Maryland, and other leading institutions. His research has been supported by major funding agencies and has influenced both academic research and industry practices in data management. The evolution of his work reflects the changing landscape of data management, from traditional relational systems to modern graph and streaming data challenges.

• Computer Security
• Network Security
• Systems Security
• +4 more

Nuno Santos is an Associate Professor in the Department of Computer Science and Engineering at Instituto Superior Técnico (IST), University of Lisbon, and a senior researcher at INESC-ID Lisbon. He leads the SysSec team, focusing on systems security and privacy. His research spans secure enclaves, network security, and censorship-resistant systems. Education: Ph.D. in Computer Science (2013) from Max Planck Institute for Software Systems (MPI-SWS) in affiliation with Saarland University. Visiting research stints at Vrije Universiteit Amsterdam (2018) and Technical University of Munich (2024). Research Interests: Systems security, privacy, trusted execution environments (TEEs), network security, censorship resistance, AI security, and secure cloud computing. He has pioneered work on mitigating vulnerabilities in TrustZone-based TEEs and enhancing confidential computing. Publications: Over 30+ peer-reviewed articles in top-tier venues like S&P, USENIX Security, CCS, and NDSS. Recent trends focus on AI-driven security (e.g., automated exploit generation, prompt-to-SQL injections) and confidential computing (e.g., AMD SEV-SNP analysis). Awards: IST Outstanding Teaching Award (2019/2020), 2024 Prémio Científico Universidade de Lisboa/Caixa Geral de Depósitos. Advising & Grants: Supervised MSc theses in areas like AI-powered vishing attacks and confidential VMs. Active in conference organization (e.g., USENIX Security’25 Vice Chair, IEEE EuroSP’26 Co-Chair). Labs/Teams: Leads the SysSec team at INESC-ID Lisbon, collaborating on projects like AnyTEE (TEE framework) and FlowLens (network security tool).

• Databases
• Spatial Data Management
• Geospatial Systems
• +3 more

Mohamed Sarwat is an Associate Professor at Arizona State University specializing in databases , spatial data management , and recommender systems . His research focuses on GeoSpark —a cluster computing framework for spatial data—and its extensions like GeoSparkViz for visualization and GeoSparkSim for traffic simulation. Key Contributions: LARS* (Location-Aware Recommender System), Horton* (Graph Reachability), Sindbad (GeoSocial Platform), and Riso-Tree (Graph Database Indexing) Research Themes: Integration of spatial/temporal data with machine learning, efficient indexing for big geospatial datasets, and scalable frameworks for mobility data science His work spans collaborations with 23+ co-authors across institutions like University of Minnesota, University of Melbourne, and University of Salzburg. Current projects emphasize GeoTorchAI —a spatiotemporal deep learning system—and mobility data science infrastructure.

• Database Systems
• Big Data Analytics
• Cloud Database Systems
• +5 more

Michael J. Franklin is a Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley's College of Engineering. He has a prolific publication record spanning over three decades with more than 300 publications in top-tier database and systems conferences and journals, demonstrating his continued active research and leadership in the field. Franklin's research spans multiple areas within data management, with a recent focus on time-series analysis, AI-integrated database systems, cloud-native databases, and data quality. His work has evolved from traditional database systems to address modern challenges in big data, machine learning integration, and distributed systems. He has made significant contributions to data cleaning, crowdsourced data management, and stream processing systems. Analysis of his recent publications (2022-2025) reveals a strong trend toward integrating AI/ML capabilities with database systems, particularly in time-series anomaly detection, LLM applications for data management, and resource-adaptive query processing for cloud environments. His work increasingly focuses on practical systems that address real-world data challenges, often involving collaborations with industry partners and other leading academic researchers. Throughout his career, Franklin has mentored numerous PhD students who have become prominent researchers in their own right, including Sanjay Krishnan, Aaron Elmore, and Jiannan Wang. His collaborative research has frequently involved significant funding from NSF and industry partnerships, enabling large-scale systems research with real-world impact. Franklin leads research efforts that bridge theoretical database principles with practical system implementations. His work on projects like Data Station demonstrates his commitment to building trustworthy infrastructure for data sharing and analysis, addressing critical challenges in data privacy, security, and usability in collaborative environments.

Michael FeldererView profile

Professor

• Software Testing
• Software Quality Assurance
• Software Architectures
• +5 more

Prof. Michael Felderer is the Director of the Institute for Software Technology at the German Aerospace Center (DLR) and a full professor at the University of Cologne. His expertise spans software testing, security, architectures, empirical software engineering, and emerging technologies like AI and quantum computing. Previously, he held roles as associate professor at the University of Innsbruck, guest professor at Blekinge Institute of Technology, and CEO of QE LaB Business Services. His research focuses on developing methods to enhance software quality and trustworthiness through collaborations with academia and industry. Education details are not explicitly provided, but his career trajectory indicates advanced academic training in software engineering. Research interests include AI-driven software systems, data trustworthiness in IoT, and agile methodologies. He has co-authored over 200 publications and received 14 best paper awards, with notable recognition from the Journal of Systems and Software. Labs/Teams: Leads the DLR Institute for Software Technology, focusing on open-source software solutions for aerospace, energy, and security domains. Collaborates with global researchers and companies on advanced engineering applications like quantum computing and digital twins.

Yue LiView profile

Associate Professor

• Program Analysis
• Programming Languages
• Static Program Analysis
• +3 more

Yue Li is an Associate Professor at the School of Computer Science, Nanjing University, where they co-run the PASCAL Research Group with Tian Tan. Their work focuses on static program analysis techniques and tools for programming languages, software engineering, security, and hardware verification. PhD in Computer Science from UNSW Sydney (2016) Postdoctoral research at Aarhus University (Denmark) and UNSW Sydney B.Eng and M.Eng from Northwestern Polytechnical University (2010, 2012) Research interests center on Program Analysis and Programming Languages , with a focus on: Pointer analysis for database-backed applications Context sensitivity optimization Reflection analysis in Java/Android Operational semantics for hardware languages Distributed dataflow analysis frameworks Developer-friendly static analysis tools Key publication trends (2016-2025) span static analysis , pointer precision , reflection handling , and tool frameworks across conferences like OOPSLA, PLDI, ICSE, ISSTA, and journals including TOPLAS and IEEE TSE. Notable artifacts include Tai-e and Chianina systems. 2025: ICSE Best Artifact & Distinguished Paper Awards 2024: IEEE TSE Publication on Generic Sensitivity 2023: OOPSLA Distinguished Artifact, SPLASH/ECOOP committees 2021: National Youth Talent Support Program, ZiJin Scholar 2016: ECOOP Distinguished Paper, CGO Best Paper As co-PI of PASCAL Research Group, they lead projects on precision-guided analysis, microservice systems, and cloud-based dataflow frameworks, with teaching awards for SICP and Software Analysis courses.

• Blockchain Technology
• Distributed Systems
• Consensus Protocols
• +6 more

Mohammad Sadoghi is a Professor at the University of California, Davis, with former affiliations at Purdue University, IBM T.J. Watson Research Center, and the University of Toronto. His research focuses on distributed systems, blockchain technologies, consensus protocols, and fault-tolerant computing. He has contributed extensively to transaction processing, stream processing architectures, and the integration of edge-cloud systems with blockchain frameworks. Current Affiliation: University of California, Davis Former Affiliations: Purdue University, IBM, University of Toronto Research Interests include consensus algorithms, Byzantine fault tolerance, distributed ledger technologies, and scalable data processing. He has pioneered systems like ResilientDB and ByShard, addressing challenges in global-scale distributed systems and blockchain fabrics. His work bridges theoretical foundations with practical implementations, emphasizing real-world applications in edge computing and hybrid cloud-edge environments. Key publications highlight advancements in consensus protocols, blockchain scalability, and fault-tolerant architectures. Recent trends in his work focus on concurrent consensus mechanisms, DAG-based systems, and secure geo-replication. Contributions span both academic publications and industry-oriented solutions, such as the Bedrock platform for BFT protocol analysis. Grants and advising roles are implied through his extensive research output, though specific grants are not detailed in the provided text. His collaborations include projects on self-curating databases (e.g., L-Store) and systems like SplitJoin for stream processing.

• Database Systems
• Operating Systems
• Distributed Systems
• +3 more

Athinagoras Skiadopoulos is a computer systems researcher at Stanford University's School of Engineering, Department of Computer Science, focusing on the intersection of database systems and operating systems. His work centers around the innovative DBOS (Database-oriented Operating System) project and large-scale machine learning infrastructure, collaborating with prominent researchers including Christos Kozyrakis and Michael Stonebraker. His primary research interests include: Database-oriented Operating Systems (DBOS) Distributed systems for large-scale machine learning Resource management and optimization in data-intensive systems Transaction processing and data governance High-performance networking for accelerated computing Fault tolerance in distributed training systems Skiadopoulos's research trajectory shows a clear evolution from foundational DBOS architecture toward applications in large-scale machine learning systems. His early publications established the DBOS framework for operating system design using database principles, while his recent work addresses critical challenges in distributed training of massive neural networks. Systems like ReCycle and SlipStream demonstrate innovative approaches to pipeline adaptation and failure recovery during distributed training. His most recent 2025 work on accelerating Mixture-of-Experts training represents the cutting edge of efficient large model training infrastructure. Through his research, Skiadopoulos has established himself in both the database and systems research communities, with publications in premier venues including SOSP, OSDI, VLDB, and CIDR. His work consistently bridges theoretical database concepts with practical systems implementations, demonstrating how database techniques can solve real-world systems challenges in modern computing environments.

• Computer Networking
• Software-Defined Networking (SDN)
• Distributed Systems
• +3 more

Mario Baldi is a researcher affiliated with the Polytechnic University of Turin, Italy , with significant contributions to computer networking , distributed systems , and software-defined networking . Key research themes: network function virtualization , programmable dataplanes , time-driven scheduling , and traffic analysis . Recent work focuses on RDMA-enabled compute offloading (2023) and DNN inference in network data planes (2023). Longstanding expertise in multicast routing , voice/data packet efficiency , and XML-based protocol parsing (2000–2006). Collaboration network includes Yoram Ofek , Fulvio Risso , and Han Hee Song , with 99+ publications spanning 1994–2023.

Benjamin SturmView profile

Research Fellow

• Distributed Ledger Technology
• Blockchain Systems
• Design Science Research
• +4 more

Benjamin Sturm is a postdoctoral researcher at the Chair of Information Infrastructures at the Technical University of Munich . His work focuses on Distributed Ledger Technology (DLT) , blockchain systems , and design science research . He has contributed to projects like COOLedger (DLT configuration analysis) and BISE Student (open access thesis platform using blockchain). Sturm’s research investigates security vulnerabilities in DLT , autonomous agent collaboration , and systematic information retrieval frameworks . His work on DLT design principles and configuration trade-offs has been published in top conferences like ICIS and HICSS . He has developed tools for transparent academic publishing using the bloxberg blockchain and IPFS for decentralized storage. Key publication trends include DLT security , blockchain configuration challenges , and methodological innovations in design science research . He collaborates with Prof. Ali Sunyaev and researchers like Niclas Kannengießer on DLT applications in cloud computing, autonomous driving, and academic infrastructure. Current projects focus on decentralized knowledge management and robustness in open multi-agent systems .

Alessio GambiView profile

Researcher

• Software Engineering
• Software Testing
• Self-Driving Cars
• +4 more

Alessio Gambi is a Researcher at the Austrian Institute of Technology (AIT) within the Security & Communication Technologies department, specializing in software engineering for autonomous systems. His current work focuses on testing methodologies for self-driving cars, self-adaptive systems, and cloud environments. His research interests center on Software Testing for Autonomous Vehicles , where he develops novel techniques for scenario generation, safety validation, and uncertainty management. Key areas include search-based procedural content generation, simulation-based testing, and the integration of large language models for test learning. His work bridges theoretical advances with practical tools like Flexcrash and TEASER for real-world validation. Analysis of his recent publications (2023-2025) reveals a strong trend toward autonomous vehicle testing with increasing incorporation of AI techniques. Approximately 60% of his work addresses self-driving car validation, 25% focuses on general software testing methodologies, and 15% explores AI/LLM applications in testing. His subfield specialization shows consistent emphasis on critical scenario generation, mixed-traffic simulation, and safety monitoring. Gambi actively contributes to the software engineering community through program committee roles at major conferences including ASE (2023-2025), ICSE (2024-2026), ISSTA (2021-2025), and ESEC/FSE. He has served as session chair, workshop organizer, and track committee member across these venues, demonstrating leadership in software testing research. His professional activities include developing open-source testing tools (visible on GitHub), teaching engagements like the Database Systems course at AIT (2024), and industry collaborations through AIT's research infrastructure. Current projects focus on predictive safety monitoring and uncertainty management for automated driving systems.