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Suren ChilingaryanView profile

Researcher

• High Performance Computing
• Parallel Computing Architectures
• Data Acquisition Systems
• +5 more

Dr. Suren Chilingaryan is a Data Processing and Software Performance Expert at the Institute for Process Data Processing and Electronics (IPE) at Karlsruhe Institute of Technology (KIT), where he has been working since 2007. His expertise lies in developing high-performance computing solutions for scientific experiments, with a focus on data acquisition systems, parallel computing architectures, and optimization of image processing software for scientific applications. His educational background includes: PhD in Computer Science (2006) from the Institute for Informatics and Automation Problems of the National Academy of Science of the Republic of Armenia Diploma in Mechanics and Mathematics (2001) from Moscow State University, with thesis on neural networks Dr. Chilingaryan's research spans high-performance computing, parallel hardware architectures, and scientific data management. He specializes in developing efficient algorithms for data acquisition and control systems, particularly for particle physics experiments. His current activities include high-speed data acquisition and control, management of large data, and optimization of image processing software for parallel computing architectures. He has pioneered techniques for GPU-accelerated data processing that enable real-time analysis of massive datasets from experiments like KATRIN (neutrino mass measurement), PANDA, and ASEC (Aragats Space Environmental Center). His recent publications demonstrate a strong focus on neutrino physics, cosmic ray phenomena, and high-speed data acquisition systems. The integration of FPGA and GPU technologies in his research has enabled breakthroughs in real-time data processing for scientific experiments, particularly in synchrotron imaging and particle detection applications. His work bridges computer science and experimental physics, with particular emphasis on developing scalable infrastructure for managing and analyzing large scientific datasets. Dr. Chilingaryan leads development of key software infrastructure including: UFO (Ultra fast X-Ray imaging platform) for smart experiments using GPU computing ADEI (Advanced Data Extraction Infrastructure) for time-series data management High-throughput DAQ Platform for FPGA-based scientific experiments His technical leadership supports major international collaborations including KATRIN, PANDA, and ASEC, providing critical data management solutions that enable new types of scientific investigations across particle physics, astrophysics, and atmospheric science disciplines.

Hesham AlmataryView profile

Researcher

• Computer Security
• Operating Systems
• Computer Architecture
• +3 more

Hesham Almatary is a Researcher at the Department of Computer Science and Technology, University of Cambridge. He holds a PhD from the University of Cambridge on CHERI compartmentalisation for embedded systems. His research focuses on computer security, operating systems, systems software, and computer architecture, with contributions to open-source projects including Linux, seL4, RTEMS, and FreeRTOS. Education: PhD in Computer Science, University of Cambridge (CHERI Compartmentalisation for Embedded Systems) Research Interests: Secure embedded systems and operating systems CHERI architecture and compartmentalization techniques RISC-V processors and hardware validation Memory management and capability-based security Publications: Recent work includes studies on CHERI implementation in MMU-less Linux, embedded system security frameworks (CompartOS), and testing RISC-V processors. His contributions span both theoretical advancements in computer architecture and practical implementations in open-source software. Awards: No scientific awards explicitly mentioned. Advising/Grants: No advisees or grants listed in the provided information.

Minxuan ZhouView profile

Assistant Professor

• Systems
• High Performance Computing
• Parallel Computing
• +5 more

Minxuan Zhou is an Assistant Professor of Computer Science at Illinois Institute of Technology. His research focuses on systems, high-performance computing, and parallel computing with an emphasis on processing-in-memory (PIM) acceleration, fully homomorphic encryption (FHE), and hyperdimensional computing. He holds a Ph.D. and M.S. in Computer Science from the University of California San Diego, and a B.S. in Computer Science and Technology from Beihang University. His work spans hardware-software co-design, cryptographic acceleration, and energy-efficient computing systems. Research Interests Processing-in-Memory (PIM) architectures for accelerating machine learning and graph processing Hardware acceleration for secure computation (FHE) Hyperdimensional computing frameworks for edge AI Thermal-aware design and memory optimization in 3D systems Recent Research Trends Recent publications emphasize co-optimization techniques for PIM architectures, FHE hardware acceleration, and privacy-preserving machine learning. His work often bridges theoretical algorithm design with practical hardware implementations using novel memory technologies like ReRAM and 3D-stacked memory. Grants & Labs Engaged in cutting-edge research through collaborations on projects involving PIM accelerators and encrypted computation systems. His lab focuses on developing full-stack solutions for next-generation computing architectures.

• Memory Management
• Virtualization Techniques
• Operating Systems Architecture
• +3 more

David Hildenbrand is a Researcher at the Department of Informatics , Technical University of Munich , specializing in Memory Management and Virtualization Techniques for operating systems. His work focuses on hypervisors , virtual machine memory dynamics , and hardware virtualization . His research interests include: Dynamic Resizing of VM Memory Paravirtualization Interfaces Operating System Support for Emerging Architectures Binary Optimization His recent publications address memory allocation inefficiencies in virtualized environments. He holds a Master of Science in Computer Science from TUM (2019) and is pursuing his PhD externally at the Chair of Computer Architecture and Parallel Systems.

• Cybersecurity
• Cloud Security
• Biometric Authentication
• +5 more

Patrice CLEMENTE serves as a Lecturer at INSA Centre Val de Loire, France, affiliated with the LIFO research laboratory (Laboratoire d'Informatique Fondamentale d'Orléans). His academic work focuses on cybersecurity with emphasis on cloud infrastructure security, biometric authentication systems, and attribute-based access control models. His research trajectory spans two decades, evolving from foundational SELinux policy analysis and honeypot forensics (2004-2012) toward contemporary medical security applications. Recent publications demonstrate specialization in photoplethysmography-based biometric authentication and Internet of Medical Things security architectures, reflecting adaptation to emerging healthcare technology challenges. Methodologically, his work combines systematic literature reviews with practical security framework development. Analysis of his 25 HAL publications reveals consistent contributions to security policy specification, virtualization security, and intrusion detection systems. The 2023-2024 publications indicate a strategic shift toward healthcare security domains while maintaining core expertise in access control and threat modeling. Scientific awards: No specific awards or fellowships were documented in the source material. Advising and grants: The provided information contains no records of supervised students, doctoral committees, or secured research funding. Labs and teams: CLEMENTE operates within LIFO, a joint research unit of the University of Orléans and INSA Centre Val de Loire under CNRS supervision, focusing on fundamental computer science research with security as a primary pillar.

• Operating System Verification
• Formal Methods
• Software Security
• +7 more

Alexander Konstantinovich Petrenko is a Professor at the Faculty of Computer Science of the National Research University Higher School of Economics (HSE), where he has been working since 2009. He serves as the Academic Director of the educational program 'System Programming' and is affiliated with the Basic Department 'System Programming' of the Institute for System Programming named after V.P. Ivannikov of the Russian Academy of Sciences (ISP RAS). With 42 years of scientific and teaching experience, he holds a Doctor of Physical and Mathematical Sciences degree (2004) and was awarded the academic title of Professor in 2011. His research focuses on verification and validation technologies, formalization of interface standards, and free software/open standards. His work primarily centers on operating system security, formal methods in software engineering, and system programming. He has made significant contributions to runtime verification of operating systems, access control models, and security policy integration. Professor Petrenko's publication record demonstrates a consistent focus on formal methods for operating system verification, with recent work emphasizing security by design principles, dynamic verification techniques, and integration of multiple security models (RBAC, MIC, MLS). His research bridges theoretical computer science with practical applications in system security and reliability. Letter of thanks from the Vice-Rector of HSE (November 2021) Gratitude from the Faculty of Computer Science of HSE (August 2017) With extensive experience across multiple institutions including the Keldysh Institute of Applied Mathematics (1974-1994), the Institute of System Programming of the Russian Academy of Sciences (since 1994, as head of the Department of Programming Technologies since 2004), and Moscow State University (since 1999, as professor since 2004), Professor Petrenko has established himself as a leading expert in system programming and formal verification. He has contributed significantly to educational programs in software engineering that recently achieved professional accreditation. His work spans both theoretical foundations and practical applications in system security, with strong connections to industry needs through his involvement with the Institute for System Programming and collaborations on real-world security challenges.

• Machine Learning
• Natural Language Processing (NLP)
• Political Science
• +4 more

Victor Kristof is a researcher at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the INDY2 laboratory. His work spans interdisciplinary domains, combining Natural Language Processing (NLP) , Machine Learning , and Social Process Modeling to analyze legislative dynamics, vote prediction, and environmental perception. Research Focus: Kristof develops interpretable models for democratic transparency, including aligning interest group positions with parliamentary speeches. He pioneered methods for predicting legislative edit acceptance using matrix factorization and NLP. His work on Swiss referendum prediction integrates historical data with real-time analysis via the Predikon platform . Methodological Contributions: He applies Bayesian statistics , time-dynamic pairwise comparison models , and active learning algorithms to diverse problems, from carbon footprint perception to sports analytics. His War of Words framework reveals ideological patterns in EU law-making, while his Player Kernel model improves football match prediction. Labs & Collaborations: Based at EPFL's Laboratory of Dynamic Information and Networks (INDY2) , he collaborates with researchers like Matthias Grossglauser and Patrick Thiran. His datasets on legislative edits and carbon perception have advanced transparency studies.

Rishabh IyerView profile

Assistant Professor

• Computer Systems
• Operating Systems
• Networking
• +5 more

Rishabh Iyer is an Assistant Professor in the Electrical Engineering and Computer Sciences department at the University of California, Berkeley. His research focuses on developing techniques that enable engineers to build systems with well-understood performance and functionality. Iyer is affiliated with the Networked Systems Lab (NETSYS) at Berkeley and teaches courses such as CS 294-262: Performance Analysis and Optimization of Computer Systems. Dr. Iyer completed his PhD at EPFL in Switzerland and his bachelor's degree at IIT Bombay. His educational background includes: 2023, PhD, Computer Science, EPFL, Switzerland 2017, BTech, Electrical Engineering, IIT Bombay Iyer's research spans multiple areas in computer systems, with a particular focus on performance analysis and optimization. His work draws insights from operating systems, networking, computer architecture, and formal methods. He has developed techniques for creating performance interfaces that allow engineers to reason about system performance before deployment, focusing on areas such as packet processing applications, OS system calls, cryptographic libraries, and specialized hardware accelerators for deep learning. His research also includes work on kernel extensibility through frameworks like KFlex, which improves the flexibility of eBPF extensions in Linux while maintaining negligible performance overheads. Additionally, he has contributed to building reliable and provably correct systems, particularly in the domain of software-defined wide-area networks and network packet-processing applications. Dr. Iyer's publication record demonstrates a consistent focus on performance analysis, system reliability, and network function optimization. His recent work has centered on performance interfaces for various system components, kernel extensions, and validation techniques for software-defined networks. His research has practical applications, with several systems he has helped design being deployed in production at companies such as Meta and Alibaba. Dr. Iyer's contributions to the field have been recognized with several prestigious awards: ACM SIGOPS Dennis M. Ritchie Doctoral Dissertation Award (2023) Eurosys Roger Needham PhD Award Dimitris N. Chorafas Award As an educator, Dr. Iyer teaches courses related to computer systems and performance analysis. He has previously served as a teaching assistant for various computer science courses at EPFL and IIT Bombay. He actively seeks talented and ambitious students to join his research group, encouraging Berkeley students to contact him directly via email and prospective graduate students to mention his name on their Berkeley EECS Graduate Admissions applications.

Andrii ShalaginovView profile

Professor

• Internet of things (IoT)
• Machine learning
• Smart cities
• +4 more

Andrii Shalaginov is a Professor in Cyber Security at Kristiania University of Applied Sciences, School of Economics, Innovation and Technology. He leads the Smart Security Lab with a research focus on applying artificial intelligence for cybersecurity, detection of computer viruses, network attacks and protection of Internet of Things devices. Dr. Shalaginov holds a PhD in Information Security from the Norwegian University of Science and Technology (NTNU), where he worked with digital forensics at the Center for Cyber and Information Security (CCIS). His research spans multiple critical areas of modern cybersecurity with a particular emphasis on Internet of Things security , machine learning applications for threat detection, and digital forensics . He has developed expertise in intelligent malware detection systems, network security for smart environments, and forensic analysis of IoT devices. His work bridges theoretical computer science with practical security implementations for real-world systems. Analysis of his recent publication trends (2022-2025) reveals a strong focus on AI-enabled security solutions for IoT ecosystems, vulnerability detection frameworks, and resource-efficient security mechanisms for constrained devices. His research increasingly addresses the intersection of machine learning and practical security implementations, with growing attention to energy efficiency in security protocols and international data space security frameworks. Dr. Shalaginov has received professional recognition through: Nomination as Norwegian representative in EU COST Action CA17124 "DigForAsp" Nomination as Norwegian representative in EU COST Action CA22104 "BEiNG-WISE" Membership in the "Impact of Technology" Expert Group at the European Union Intellectual Property Office Observatory As head of the Smart Security Lab, Dr. Shalaginov oversees research projects focused on developing practical security solutions for emerging technologies. His GitHub activity demonstrates active development of open-source tools for malware analysis across multiple platforms (Linux, Windows, IoT) and implementation of machine learning approaches for security applications. His research group appears to focus on bridging the gap between theoretical security models and practical implementations for real-world systems.

Knut OmangView profile

Associate Professor

• Virtualization
• High speed interconnects
• Software engineering
• +7 more

Knut Omang serves as an Associate Professor in the Department of Informatics at the University of Oslo, Faculty of Mathematics and Natural Sciences. His academic position has been maintained since completing his Dr.Scient (Ph.D) from the same department in 1998, where he has held the position of 1.amanuensis-II continuously. His educational background includes a Doctor Scientiarum (Ph.D) from the University of Oslo's Department of Informatics (1998). Prior to his academic career, he gained extensive industry experience including positions at Oracle (2010-2020), Paradial (2006-2010), Fast Search and Transfer (2001-2006), and Scali (1998-2001). Professor Omang's research spans virtualization technologies, high-speed interconnects, distributed systems architecture, and Linux kernel development. His work bridges academic research and practical industry applications, with particular focus on solving real-world challenges in system performance, maintainability, and scalability. He has developed significant open-source contributions including the Kernel Test Framework (KTF) for Linux kernel testing and has been actively involved in QEMU development. His current research interests include challenges with Internet of Things networks, novel applications of commercial building sensor data, Rust programming language applications, and virtualization platform development. He actively supervises master's students on topics related to these research areas. Professor Omang currently serves as CTO for Properate, a startup developing cloud-based solutions for monitoring and managing energy usage in commercial buildings, where he addresses challenges ranging from sensor network management to cloud infrastructure using Kubernetes. Haakon Andersen: Thesis on challenges around operating multiple gateways to remote sensor networks Arild Lillegård: Thesis on Test Driven Development in the Linux Kernel using KTF Christian Resell: Thesis on Forward-Edge and Backward-Edge Control-Flow Integrity Performance in the Linux Kernel His teaching responsibilities include courses INF 3151/4151 and IN 5070, with a focus on supervising master's students in systems-level computing topics. His industry experience directly informs his academic work, creating a strong bridge between theoretical research and practical implementation.

Viet-Hoang TranView profile

Researcher

• Network Protocols
• Transport Layer Security
• Multipath Communication
• +4 more

Dr. Viet-Hoang Tran is a former member of the Cloud and Large Scale Computing group at UCLouvain, where he conducted research on network protocols and transport layer technologies. His work focuses on improving the extensibility and performance of transport protocols like TCP and QUIC, with applications in multipath communication, TLS integration, and network asymmetry handling. He holds a PhD from UCLouvain (2019), titled 'Measuring and extending Multipath TCP.' Key Research Contributions: Developed TCPLS protocol combining TCP and TLS for modern transport services Advanced Multipath QUIC implementations for asymmetric networks Extended Linux TCP stack using eBPF for enhanced flexibility Published extensively on MPTCP measurement and protocol optimization Recent Work: Contributions include Multipath Extension for QUIC, SRv6-FEC integration, and Plugin Distribution Systems. His research emphasizes practical implementations resistant to network intermediaries while maintaining performance. Collaborations: Worked closely with researchers like Olivier Bonaventure, Quentin De Coninck, and Maxime Piraux on transport layer innovations. Active contributor to IETF-related projects.

Andy WangView profile

Assistant Professor

• File Systems
• Storage Systems
• Optimistic Peer Replication
• +4 more

Andy Wang is an Assistant Professor in the Department of Computer Science at Florida State University (FSU), where he has been since 2003. He earned his Ph.D. and M.S. in Computer Science from UCLA (2003 and 1998, respectively) and a B.S. from UC Berkeley (1995). His research focuses on file systems, storage systems, optimistic replication, and distributed systems, with notable contributions including the Composite-File File System (CFFS), Cashtags, and TrueErase. He has received prestigious awards such as the NSF CAREER Award (2009), Excellence in Teaching (2019), and the Mainline Endowed Professorship (2024). Research interests span data flow management, secure deletion (TrueErase), power-aware storage (PARAID), and performance evaluation. His work also explores mobile security (Cashtags) and real-time storage systems. Key projects include CFFS (decoupling metadata and data for performance) and the ADAPT toolkit for storage path optimization. Teaching includes operating systems courses (COP 4610/5611), advanced performance analysis (CIS 5105), and kernel programming. He has administered NSF-funded S-STEM programs to broaden participation in CS. His publications span conferences like USENIX FAST, ACM SIGMETRICS, and journals like ACM Transactions on Storage. Notable grants include NSF S-STEM scholarships and work on energy-efficient storage systems. He advises students on projects involving file system consistency, fuzzing, and secure deletion. His lab's innovations aim to bridge theoretical insights with practical storage solutions.

Ben TitzerView profile

Research Professor

• Systems Programming Languages
• Compilers
• Virtual Machines
• +5 more

Ben Titzer is a Research Professor at Carnegie Mellon University 's Software and Societal Systems Department since 2022. His work spans systems programming languages , compilers , and WebAssembly virtual machines. Co-founder of the WebAssembly project (2014) Developed the Wizard Engine for Wasm research Former Member of Technical Staff at Sun Microsystems (2007-2010) Google employee (2010-2019) leading V8's TurboFan compiler Teaching/Research Fellow at Australian National University (2020) Private consultancy in 2021 His research interests focus on WebAssembly evolution for academic and industrial applications, including: Edge computing integration Dynamic instrumentation via Whamm! and Wasm-R3 Garbage collection and memory optimization Fast interpreters and execution models Linux-kernel interfaces for Wasm Security against Spectre-class vulnerabilities Key trends in recent publications (2025-2021) include: Scaling Wasm beyond web environments Improving debugging and analysis tooling Compiler design for lightweight systems Memory safety in embedded contexts LLM security as a new research direction

Alastair ReidView profile

Researcher

• formal verification
• security
• functional languages
• +1 more

Alastair Reid is a researcher at Intel (2021–present) focusing on formal instruction set architecture (ISA) specifications and model checking processor pipelines. His career spans roles at Google Research (2019–2021) developing Rust verification tools, Arm Ltd (2004–2019) working on formal ISA specifications and vectorizing compilers, University of Utah (1998–2004) on component-based operating systems, Yale University (1994–1998) contributing to Haskell functional reactive programming, and University of Glasgow (1988–1994) in formal specification and verification. Ph.D. in Defining interfaces between hardware and software: Quality and performance , Glasgow University M.Sc. in A precise semantics for Ultraloose Specifications , Glasgow University B.Sc., University of Strathclyde His research interests include formal verification, security analysis, functional programming languages (particularly Haskell and Rust), and computer architecture. He has contributed to the development of machine-readable ISA specifications for ARM, RISC-V, and Intel architectures, and worked on symbolic execution tools like KLEE for Rust verification. Key publications include work on RISC-V specification improvements (2024), formal methods for persistent programming (2023), PLARCH workshop contributions on ISA specification (2023), and foundational papers on ARM processor verification with ISA-Formal (2016). His technical blog posts cover topics in symbolic execution, SMT solvers, and practical verification challenges. Contributions to conferences include committee roles in PLDI (2024), PriSC (2024), and workshop organization in formal methods and programming language design.

Juliana Alves PereiraView profile

Professor

• Software Engineering
• Artificial Intelligence
• Machine Learning
• +8 more

Juliana Alves Pereira is a Professor at the Computer Science Department of the Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Brazil, where she leads the Artificial Intelligence for Software Engineering lab (AISE) as part of the Software Engineering lab (LES). Her academic journey includes a Ph.D. in Software Engineering with distinction (summa cum laude) from Otto-von-Guericke-Universität Magdeburg (OvGU) in Germany (2018), a Master's degree in Computer Science from the Federal University of Minas Gerais (Brazil), and postdoctoral research at the University of Rennes, Inria/IRISA (France). Her research focuses at the intersection of software engineering and artificial intelligence, particularly in automating software engineering through software analysis, machine learning, recommender systems, and meta-heuristic optimization. Dr. Pereira's work explores applications of Machine Learning, Explainability, Transfer Learning, Generative AI, Deep Learning, Natural Language Processing, and Recommender Systems in software quality and human aspects contexts. Her publications demonstrate a consistent focus on empirical software engineering, with significant contributions in code quality analysis, pull request characteristics, exception handling, and performance prediction in configurable systems. Her research shows increasing integration of advanced AI techniques with traditional software engineering practices. Best Dissertation Prize from the School of Computer Science in 2018 (Germany) Dissertationspreis award from OvGU (2018) ACM Best Paper Award from SPLC 2021 ACM Best Paper Award from ICPE 2020 CBSoft Most Influential Paper Award from Brazilian Computer Society (SBC) Dr. Pereira actively supervises master's and doctoral research in the Software Engineering concentration area at PUC-Rio. She has served on Program and Organizing Committees for major international conferences including ICSE, ASE, SANER, MSR, and ICSME. Her laboratory collaborations span national and international research groups, reflecting her significant impact in the software engineering community.