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Matteo BassoView profile

Researcher

• WebAssembly
• Compiler Design
• Virtual Machines
• +4 more

Matteo Basso is a researcher at Università della Svizzera italiana (USI), Switzerland, specializing in compiler design, virtual machines, and WebAssembly technologies. His work bridges high-level programming languages with efficient execution on modern hardware platforms, with a strong focus on practical systems that improve performance and efficiency. His research interests include: WebAssembly and its applications in web and server environments Compiler optimization techniques for virtual machines Memory management and binary serialization formats Performance analysis of programming language runtimes Java Virtual Machine enhancements and thread management Matteo's publication record shows consistent contributions to top-tier conferences including SPLASH, OOPSLA, and CGO, with recent work emphasizing WebAssembly technologies and Java performance optimization. His research on heap management, native-image startup performance, and compiler-level profiling demonstrates practical impact in language implementation. Notable contributions include: Heap snapshot matching using context-augmented heap-path representations Improvements to native-image startup performance in GraalVM Optimization-aware compiler-level event profiling techniques Analysis of Java Vector API performance characteristics As an active open-source contributor, Matteo maintains several popular repositories including asm-dom (a WebAssembly virtual DOM implementation with 2.8k stars) and awesome-wasm (a curated WebAssembly resource list with 9.4k stars).

• Logic Programming
• Computational Logic
• Abstract Interpretation
• +3 more

Baudouin Le Charlier is a researcher with a focus on computational logic, programming languages, and system security. He has led multiple research projects, including sequential analysis under Unix, automated IT resource monitoring, and abstract interpretation for declarative languages. His work spans 1992–2008, with a strong emphasis on logic programming and formal semantics. Key Research Areas: Logic Programming, Abstract Interpretation, Data Stream Processing, Unix Systems, and Programming Language Interoperability. Notable Projects: Asax (Sequential Analysis), Survauto (IT Monitoring), COMPULOG-Net (Computational Logic Network), ABILE (Abstract Interpretation Tools). His 15 most recent publications (1995–2008) highlight trends in declarative semantics, interoperability mechanisms, and distributed audit analysis. These works often intersect with constraints, tools, and formal modeling.

• Compiler Construction
• Programming Languages
• Computer Science

Dr. Steffen Märcker is a researcher at the Chair for Compiler Construction, Dresden University of Technology (TU Dresden), Germany. Based in Dresden at Helmholtzstrasse 18, 3rd floor, Room BAR III62, he can be contacted via email (steffen.maercker@tu-dresden.de) or phone (+49 351 463 40822). His work is embedded within the university's research infrastructure focused on computational systems. His research centers on Compiler Construction , involving advanced compiler design, optimization algorithms, and code generation techniques. This extends to Programming Languages where he examines syntax analysis, semantic frameworks, and runtime environments. Broader contributions fall under Computer Science , particularly software development tools and systems programming. His investigations aim to enhance compilation efficiency and reliability for modern computing architectures. No scientific awards or honors were documented in the source material. Available information does not specify student supervision activities or research grant funding. His academic guidance and project financing details remain unreported in the provided context.

Yuvraj PatelView profile

Assistant Professor

• Computer Systems
• Operating Systems
• Concurrency
• +6 more

Yuvraj Patel is a Lecturer (Assistant Professor) in Computer Science at the School of Informatics, University of Edinburgh, and a member of the Institute for Computing Systems Architecture (ICSA). He previously served as a postdoctoral researcher and Associate Lecturer at the University of Wisconsin–Madison, where he also earned his Ph.D. under Professors Andrea and Remzi Arpaci-Dusseau. His research spans operating systems, concurrency, security, storage systems, and distributed systems. His research focuses on performance and security challenges in concurrent systems, particularly where synchronization primitives are shared among untrusted tenants. He introduced the concepts of scheduler subversion and adversarial synchronization , showing that synchronization must be treated competitively rather than cooperatively. His key contributions include Scheduler-Cooperative Locks (SCLs) for fair lock usage and Trātṛ , a kernel extension that detects and mitigates synchronization attacks. He has extensive industry experience from nearly eight years at NetApp working on the WAFL filesystem and a stint at SanDisk on FTL design. His recent publications, appearing in venues like USENIX Security, EuroSys, FAST, and OSDI, demonstrate a consistent focus on real-world systems problems involving isolation, fairness, and security in shared environments. His work combines deep systems insight with practical implementation and evaluation. US Patent 10,705,951 – Shared Fabric Attached Memory Allocator US Patent 10,621,162 – Storage Tier Verification Checks US Patent 8,078,653 – Process for Fast File System crawling Yuvraj Patel actively mentors students and is currently recruiting Ph.D. candidates at the University of Edinburgh. He has advised multiple undergraduate and graduate students on topics including fairness in locking, adversarial synchronization, and kernel critical section analysis. His teaching includes Security Engineering at Edinburgh and Introduction to Operating Systems at Wisconsin. He leads a research group focused on building abstractions for near-ideal isolation in shared environments, with ongoing work in synchronization, storage systems, and kernel security. His team develops open-source software such as Trātṛ, CuttleFS, and SCL implementations.

• Program Analysis
• Software Verification
• Debugging
• +13 more

Manuel V. Hermenegildo is a Distinguished Professor at the IMDEA Software Institute and a Full Professor in the Department of Artificial Intelligence at the Technical University of Madrid (UPM). He is the founding Scientific Director of IMDEA Software and leads the CLIP Lab, a joint research unit between UPM and IMDEA Software focused on computational logic, programming languages, and parallelism. His career spans leadership in national research policy, academic positions in Spain and the U.S., and extensive international collaboration. PhD in Electrical and Computer Engineering, University of Texas at Austin (1986) M.S. in Electrical and Computer Engineering, University of Texas at Austin (1984) M.S. in Electrical Engineering, Technical University of Madrid (1981) His research centers on global program analysis, verification, debugging, and optimization for functional and non-functional properties such as time, memory, and energy. He has pioneered work in abstract interpretation, partial evaluation, constraint and logic programming, parallelizing compilers, and computational logic. His methodologies support the development of safe, efficient, and reliable software systems. The integration of static and dynamic analysis, resource-aware compilation, and assertion-based debugging are recurring themes in his work. His recent publications emphasize incremental and modular analysis, assertion checking, energy-aware computing, and verification via Horn clauses. Trends show a strong focus on making static analysis more practical, scalable, and integrated into real-world development workflows, particularly for logic and declarative languages. He frequently publishes in top venues such as TPLP, SCP, TOPLAS, and POPL. ACM Fellow Julio Rey Pastor Prize (2006) Aritmel Prize (2005) Elected member, Academia Europaea (2010) Test of Time Award, ICLP 2017 Best Paper Awards at ICSOC 2011 and ICLP 2005 Fulbright-Hays Scholarship Hermenegildo has supervised numerous PhD students and postdocs, many of whom are now leading researchers. He has been principal investigator in over 50 research projects funded by the EU, Spanish government, NSF, and industry partners. His leadership extends to national research policy, where he directed Spain’s National Research Directorate and launched the Ramón y Cajal fellowship program. He has served on tenure and evaluation committees for institutions worldwide and is active in shaping research agendas through roles in Informatics Europe and EIT Digital. He leads the CLIP Lab, which develops advanced program analysis tools and systems such as the Ciao programming environment, CiaoPP static analyzer, and LPdoc documentation generator. The lab fosters collaboration across academia and industry, focusing on next-generation program development tools, verification, and resource-aware computing.

• Computer Languages and Systems

Rosa Maria Pulgar Gutierrez serves as an Associate Professor specializing in Computer Languages and Systems, though her institutional affiliation remains unspecified in the source text. Her research centers on Computer Languages and Systems , encompassing programming language design, compiler theory, runtime environments, and system-software integration. This field drives advancements in computational efficiency and hardware-software synergy across modern computing infrastructures.

Annie LiuView profile

Professor

• Programming Languages
• Algorithms
• Systematic Design and Optimization
• +6 more

Annie Liu serves as Professor of Computer Science at Stony Brook University, holding a PhD from Cornell University alongside prior degrees from Peking and Tsinghua Universities. Her research centers on systematic methods for language and algorithm design, with emphasis on optimization frameworks applicable across critical computing domains. Her educational credentials include: BS in Computer Science from Peking University MEng in Computer Science from Tsinghua University PhD in Computer Science from Cornell University Liu’s research program investigates systematic analysis and transformation techniques for design optimization, particularly through incrementalization—the discrete counterpart of calculus differentiation. Her work spans optimizing compilers , interactive environments , database systems , distributed computing , and security , with projects addressing modeling, verification, code generation, and testing for real-time systems, semantic Web, and big data applications. The Design and Analysis Research Laboratory she directs bridges theoretical foundations with practical implementations. Analysis of her 2012-2025 publications reveals an evolution from foundational distributed algorithm design toward unifying frameworks for solver semantics and assurance. Key trends include increasing integration of incremental computation principles across compiler optimizations, database queries, and distributed system reliability. Her scientific recognition includes: State University of New York Chancellor’s Award for Excellence in Scholarship and Creative Activities Professor Liu has delivered over 100 international talks and maintains active conference leadership, serving on program committees for POPL, PEPM, and SPLASH through 2026. While specific grant details aren’t provided, her sustained involvement in high-impact venues like PADL and LPOP demonstrates significant research influence. She directs the Design and Analysis Research Laboratory where interdisciplinary teams advance optimization methodologies for emerging computational challenges.

Lubomír BulejView profile

Associate Professor

• Dynamic Program Analysis
• Software Performance Evaluation
• Software Performance Modeling
• +5 more

Lubomír Bulej serves as an Associate Professor at the Department of Distributed and Dependable Systems within the Faculty of Mathematics and Physics at Charles University, Prague. His office is located in room S 205 in the historic Lesser Town district, with contact details including email bulej@d3s.mff.cuni.cz and phone +420 951 554 189. He maintains an active research profile through platforms like Google Scholar, DBLP, and GitHub. His research centers on dynamic program analysis and software performance evaluation , with specific focus on analysis composition, program instrumentation, profiling accuracy, and observability in managed platforms. He develops methods for automatic performance evaluation during development, performance change detection, and testable documentation of performance assumptions. Additional interests span object-oriented programming, programming languages, operating systems, and computer architectures, reflected in his teaching of courses like Computer Architecture and Operating Systems. Analysis of his 2021-2025 publications reveals a consistent trajectory in performance-aware software development , with increasing emphasis on compiler technologies (particularly GraalVM), self-adaptive systems for cloud/edge environments, and cost-optimized performance testing methodologies. His work bridges theoretical program analysis with practical industrial applications, especially in avionics and distributed systems. Dr. Bulej actively contributes to major research projects including GraalVM compiler evaluation, FitOptiVis (avionics systems), ASHLEY (aircraft demonstrators), Ferdinand (model-driven design evaluation), Q-ImPrESS (service-oriented systems), and CoCoME (component modeling). He maintains critical software infrastructure such as the Renaissance Benchmark Suite, DiSL instrumentation framework, and Java Performance Measurement Framework.

Jan KofroňView profile

Associate Professor

• Program Verification
• Static Analysis
• Software Reliability
• +4 more

Jan Kofroň is an Associate Professor in the Department of Distributed and Dependable Systems at the Faculty of Mathematics and Physics, Charles University in Prague, Czech Republic. His research focuses on program verification, static analysis, and software reliability. His educational background includes a Ph.D., as indicated by his title. He maintains an active research profile with numerous recent publications in top venues. Professor Kofroň's research interests span several key areas in software engineering and formal methods. He specializes in interpolation-based code model checking, static analysis of programs, system behavior models and verification, and programming language semantics. His work bridges theoretical foundations with practical applications in software development. His recent publications demonstrate strong focus on verification techniques for complex software systems, particularly in the areas of Horn clause solving, program comprehension using computational notebooks, and uncertainty-aware self-adaptive cyber-physical systems. His research shows consistent contributions to both theoretical foundations and practical applications of program analysis. Professor Kofroň actively leads and participates in multiple research projects including the current AIDE project (Advanced Analysis and Verification for Advanced Software) and past projects such as SNAPPY, ROBUST, Weverca, Q-ImPrESS, SOFA 2, and ASCENS. He teaches courses related to Java programming, Python programming, program semantics, and mobile devices programming. He maintains an open-door policy for student consultations, requesting students email him to arrange meetings rather than maintaining fixed office hours. His department maintains active GitHub presence, contributing to open-source projects related to software verification and analysis tools.

Martin KrulišView profile

Associate Professor

• High-performance computing
• GPU programming and CUDA optimization
• Parallel algorithms and data structures
• +4 more

Martin Kruliš is an associate professor in the Department of Distributed and Dependable Systems at the Faculty of Mathematics and Physics, Charles University , Prague, Czech Republic. His primary roles include research, teaching, advising, and leading projects that bridge high-performance computing, GPU programming, and self-adaptive systems. Education: While exact details of his own degrees are not provided, Dr. Kruliš’s extensive publication record and faculty position at Charles University indicate advanced training in computer science with specialization in parallel and distributed computing. Research Interests: High-Performance GPU Computing: Deep investigation into CUDA kernel optimization, memory bandwidth utilization, and workload dispatching for massively parallel accelerators. Self-Adaptive & Self-Optimizing Systems: Designing ensemble-based abstractions that integrate machine-learning estimators to enable runtime adaptation in component architectures. Parallel Algorithms & Data Structures: Development of cache-friendly, SIMD-aware, and GPU-accelerated algorithms for clustering, dimensionality reduction, and similarity search. Software Engineering for Parallelism: Creation of C++ libraries, DSLs (e.g., Bobolang), and educational tools (ReCodEx) that simplify parallel programming and automated evaluation. Publication Trends: Across 2011–2025, Kruliš’s articles reveal a clear trajectory from foundational GPU-accelerated indexing and multimedia retrieval toward sophisticated self-adaptive systems that leverage machine learning. Recent works (2023–2025) increasingly focus on integrating LLMs and neural networks into compiler and runtime optimization loops, reflecting a convergence of AI and systems research. Scientific Awards & Recognition: No specific awards or fellowships are mentioned in the provided text; however, sustained publication in top-tier venues (JPDC, IPDPS, Euro-Par, SEAMS) and active involvement in program committees and tool development indicate strong peer recognition. Teaching & Student Supervision: Teaches Programming in Parallel Environment (NPRG042) , Advanced Programming in Parallel Environment (NPRG058) , Computer Systems (NSWI170) , and Software Projects . Supervises numerous bachelor’s and master’s theses; exact student names are not listed in the text. Labs, Projects & Tools: ReCodEx: A widely used platform for semi-automated evaluation of programming assignments at Charles University. Simdex: A modular simulator of the ReCodEx backend that enables realistic experimentation with self-adaptive job dispatching and machine-learning controllers. Active contributor to open-source repositories on GitHub, focusing on GPU kernels, benchmarking frameworks, and educational tooling.

• Computer Science
• Programming Languages
• Systems

Daniel Martínez Cisneros serves as an Interim Substitute Professor in the Department of Sport and Computer Science and holds a Researcher position for the academic period 2024-2025. His academic focus centers on the specialized domain of Computer Languages and Systems within the broader discipline of Computer Science. His research expertise concentrates on Programming Languages and Systems, encompassing theoretical foundations and practical implementations of language design, compiler construction, runtime environments, and system-level software development. These areas drive innovation in computational infrastructure and software engineering methodologies. No scientific awards or honors were documented in the available profile. Information regarding student supervision, research grants, collaborative projects, or laboratory leadership was not provided in the source material.

Xipeng ShenView profile

Professor

• Programming Systems
• Machine Learning
• High-Performance Computing
• +12 more

Xipeng Shen is a Professor in the Department of Computer Science at North Carolina State University and Director of the High-Performance Intelligent Computing (HiPIC) lab. He joined NC State in 2014 as part of the Chancellor's Faculty Excellence Program after serving as the Adina Allen Term Distinguished Associate Professor at the College of William and Mary. Shen's research spans programming systems and machine learning, with a focus on enabling extreme-scale, data-intensive, and intelligent computing through innovations in compilers, runtime systems, and ML algorithms. His work has significantly influenced the development of heterogeneous computing and modern AI systems. His research areas include High-Performance Machine Learning & Real-Time AI, Heterogeneous Massively Parallel Computing, and Foundations of Programming Systems & Languages. His recent publications demonstrate strong trends in optimizing AI deployment across various platforms including mobile devices, serverless environments, and edge computing systems, with particular emphasis on efficiency, power consumption, and real-time performance. His research increasingly integrates compiler techniques with machine learning approaches to solve complex optimization problems. ACM Distinguished Member (2018) University Faculty Scholar (2017-18) ACM Distinguished Speaker (2016-2019) IEEE Senior Member (2016) IBM CAS Faculty Research Fellow (2010-2016) Google Faculty Research Award (2015) DOE Early CAREER Award (2011) NSF CAREER Award (2010) Shen has successfully advised 15 PhD students who have gone on to positions at top universities and companies including UC Santa Cruz, UMass Amherst, Google, Meta, and Huawei. His research has been funded by numerous grants from NSF, DOE, and NIH totaling over $4 million. He co-founded CoCoPIE Inc. to commercialize his research on efficient AI deployment and serves as a consultant to major technology companies including Intel, Microsoft, Huawei, Cisco, and Meta. He leads the PICTure research group and teaches courses including Compiler Construction (CSC412/512), Code Optimization for Scalar and Parallel Programs (CSC766), and Real-time AI and Machine Learning Systems (CSC591/791).

Bowen XuView profile

Assistant Professor

• Machine Learning
• Software Engineering
• AI for Code
• +5 more

Bowen Xu is an Assistant Professor in the Department of Computer Science at North Carolina State University, where he leads the SoftMax Lab within the College of Engineering. Previously, he was a postdoctoral researcher at Singapore Management University (SMU), where he also earned his PhD from the School of Computing and Information Systems. His research spans the intersection of machine learning and software engineering, with particular focus on securing AI models for software engineering tasks from both model and data perspectives. Key research interests include AI for Code, Backdoor Attack and Defense on Large Code Models, Code Data Interpretation and Quality, Code Representation Learning, Model Compression, Vulnerability Detection and Repair, and Safety of AI-enabled Software Systems. Xu's publication record shows a strong focus on the security aspects of AI code models, with several recent papers examining backdoor attacks and defenses. His work also explores the application of large language models to software engineering tasks like vulnerability repair, API documentation, and technical question answering. His publications appear in top venues including IEEE Transactions on Software Engineering (TSE), ACM Transactions on Software Engineering and Methodology (TOSEM), and International Conference on Software Engineering (ICSE). Highly Commended Full Paper Award at ESEM 2018 Honorable Mention Award at ACSAC 2022 Nominated for ACM SIGSOFT Distinguished Paper Award at ASE 2022 Xu actively serves the software engineering community through editorial and program committee roles, including serving as Paper Review Co-chair for ICSE 2025 and FSE 2025, and as a member of the Editorial Board for Empirical Software Engineering Journal. He has advised numerous graduate and undergraduate students who have gone on to positions at companies like Microsoft, Marvell Semiconductor, and Barclays.

Moritz WeißbrichView profile

Researcher

• Processor Architecture
• Low-power Electronics
• Approximate Computing
• +3 more

M.Sc. Moritz Weißbrich is a Researcher at the Chair for Chip Design for Embedded Computing , part of the Institute of Theoretical Computer Science at Technische Universität Braunschweig. Holding an M.Sc. in Electrical Engineering and Information Technology from Leibniz Universität Hannover, his academic focus spans high-performance/low-power processor architectures , approximating arithmetic circuits , and stochastic computation techniques for fault-tolerant systems. His research portfolio includes 20+ peer-reviewed publications since 2017, with recent work on Nano-scale controllers for FPGA systems Biomedical sensor design in 22nm FDSOI Energy-aware VLIW processor optimization Stochastic timing analysis frameworks He has been developing ultra-low-power embedded solutions since 2021, following prior research assistant roles at Leibniz Universität Hannover's Institute of Microelectronic Systems (2017-2021). Key technical contributions appear in venues like Springer LNCS , IEEE RFIC Symposium , and Journal of Systems Architecture , focusing on processor customization , energy harvesting systems , and radiation-hardened circuit design . His work addresses challenges in autonomous computing, harsh environment electronics, and precision agriculture applications.

Sam SilvestroView profile

Assistant Professor

• Computer Security
• Systems Programming
• Performance Profiling
• +3 more

Sam Silvestro is an Assistant Professor of Instruction in the Department of Computer Science at the University of Texas at San Antonio (UTSA). He is affiliated with the College of Sciences and focuses on teaching and research in systems programming, memory management, and computer security. His work emphasizes practical solutions for production software reliability and security. Education: Ph.D. in Computer Science, UTSA B.S. in Computer Science, UTSA Research Interests: Silvestro’s research centers on secure memory allocation systems, fault diagnosis in production environments, and concurrency control mechanisms. He develops tools like Guarder and Freeguard to enhance heap allocator security while maintaining performance. His work also addresses deadlock prevention and automated failure diagnosis through projects like Watcher and Undead. Publications: His recent work spans memory profiling (MemPerf), in-situ failure diagnosis (WATCHER), and multithreaded debugging (iReplayer), reflecting a consistent focus on production system reliability and security. Scientific Awards: None explicitly listed in provided texts. Advising & Grants: No specific advisees or grants mentioned. Teaching focuses include parallel systems and systems programming courses. Labs/Teams: No dedicated lab/team details provided, but collaborations likely occur through UTSA’s computer science infrastructure.