جست‌وجوی استاد و پژوهشگر

• Software Security
• Software Testing
• Program Analysis
• +۲ مورد دیگر

Dr. Seongmin Lee is a researcher at the Max Planck Institute for Security and Privacy, specializing in software security and program analysis. Their work bridges theoretical and practical aspects of software testing, with a particular focus on automated testing techniques, dependency modeling, and genetic improvement. Research interests include: Software Security Software Testing and Fuzzing Program Analysis and Slicing Machine Learning Applications in Software Engineering Genetic Algorithms for Code Optimization Statistical and Causal Analysis of Code Behavior Recent publications (2016–2025) demonstrate a trajectory from foundational work on GPU parameter optimization to cutting-edge research on LLM-driven regression testing. Key trends include: Statistical modeling of software behavior Machine learning for bug classification and optimization Approximate analysis techniques for scalability Advancements in greybox fuzzing and coverage prediction Application of causal inference to mutation testing

Fuyuan Zhangمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Cybersecurity
• Formal Methods
• +۷ مورد دیگر

Fuyuan Zhang is a Postdoctoral Researcher at the Max Planck Institute for Software Systems, specializing in advanced software testing methodologies and formal verification techniques. His research focuses on improving the reliability and security of AI systems, quantum computing frameworks, and concurrent systems through innovative testing criteria, adversarial attacks, and compositional reasoning. Key areas of expertise include: Large Language Model (LLM) testing and validation Quantum program analysis and security Adversarial machine learning and neural network robustness Formal verification of concurrent and cyber-physical systems Automated bug detection in complex software systems His work bridges theoretical foundations with practical applications, addressing critical challenges in AI safety, quantum software reliability, and system-wide security certification.

Ali Jannesariمشاهده پروفایل

دانشیار

• High-Performance Computing
• Machine Learning
• Software Analytics
• +۵ مورد دیگر

Ali Jannesari is an Associate Professor and Director of the Laboratory for Software Analytics and Pervasive Parallelism (SwAPP Lab) at the Department of Computer Science, Iowa State University. His research focuses on the intersection of High-Performance Computing (HPC) and AI, aiming to advance reliable and efficient software for modern parallel computing platforms. He has held academic and research roles including Senior Research Fellow at UC Berkeley and leadership positions in Germany's Technical University of Darmstadt and RWTH Aachen University. He holds a Habilitation in Computer Science (2016, TU Darmstadt), PhD (2010, KIT), and M.S. (2005, University of Stuttgart). Education: Habilitation in Computer Science, Technical University of Darmstadt, Germany (2016) Ph.D. in Computer Science, Karlsruhe Institute of Technology (2010) M.S. in Computer Science, University of Stuttgart (2005) Research Interests: High-Performance Computing (HPC), Machine Learning, Parallel Computing, Software Analytics, AI-driven compiler optimization, federated learning, and cross-language code analysis. His work bridges HPC and AI to enhance computational efficiency and scalability in data-driven applications. Recent Contributions: Leading research in GNN-based code parallelization (AutoParLLM), federated multimodal learning, and HPC performance optimization via graph-based methods. His lab's work has been published in top venues like NAACL, ICS, and NeurIPS. Lab & Team: The SwAPP Lab develops tools for software analytics and pervasive parallelism, with a focus on HPC-AI integration. Current projects include optimizing distributed training systems and improving code migration with LLMs.

Nan Yangمشاهده پروفایل

پژوهشگر

• Software Engineering
• Embedded Systems
• Log Analysis
• +۲ مورد دیگر

Nan Yang is a University Researcher in the Mathematics and Computer Science department at Eindhoven University of Technology, specializing in empirical software engineering with focus on embedded systems and log analysis methodologies. Her work bridges theoretical models with industrial software development practices through collaborations with leading technology companies. Her research expertise centers on Software Engineering , Embedded Systems , and Log Analysis , employing rigorous empirical methods including developer interviews and case studies. She investigates how execution logs are utilized in real-world embedded software engineering contexts, developing model-driven approaches for protocol inference and system maintenance. Her work reveals critical insights into developer workflows and industrial software practices. Analysis of her publication record (2018-2023) demonstrates a progressive research trajectory from foundational protocol inference techniques to comprehensive embedded systems engineering frameworks. Her work consistently integrates log analysis with model-driven approaches, yielding practical tools for software maintenance in resource-constrained environments. This research has significant implications for improving reliability in industrial embedded software development. Nan Yang has supervised academic work as evidenced by her documented supervised projects, contributing to the development of next-generation software engineering researchers.

Blagoja Markovskiمشاهده پروفایل

استادیار

• Electromagnetic Compatibility
• Power Systems Engineering
• Lightning Protection
• +۳ مورد دیگر

Blagoja Markovski is an Assistant Professor at the Faculty of Electrical Engineering and Information Technologies (FEIT), Ss. Cyril and Methodius University in Skopje. He holds a PhD (2019), MSc (2012), and BSc (2009) in Electrical Engineering from FEIT. His research focuses on electromagnetic compatibility, grounding systems analysis, lightning protection, and power systems engineering. He has contributed to projects assessing electromagnetic impacts on infrastructure and public exposure to electromagnetic fields from energy equipment. His work emphasizes the analysis of grounding systems under lightning and transient conditions, with applications in transmission lines, wind turbines, and pipelines. He has developed novel methodologies for efficient grounding system analysis using circuit models, GPU parallelization, and analytical approximations. His recent studies include optimizing grounding configurations for communication towers and evaluating human exposure near high-voltage systems using finite-element methods. Collaborations span international conferences and industry projects, addressing challenges in electromagnetic compatibility and renewable energy systems. His publications span IEEE journals and conferences, focusing on grounding systems, lightning protection, and computational electromagnetics.

Ali José Mashtizadehمشاهده پروفایل

دانشیار

• Operating Systems
• Distributed Systems
• Storage Systems

Ali José Mashtizadeh is an Associate Professor at the Cheriton School of Computer Science, University of Waterloo. His research focuses on operating systems, distributed systems, and storage, with expertise in system reliability, network optimization, and concurrent programming. Education: Ph.D., Computer Science, Stanford University (2017) M.S., Computer Science, Stanford University (2017) M.Eng., Electrical Engineering and Computer Science, MIT (2007) B.S., Electrical Engineering, MIT (2006) His research centers on designing scalable and reliable systems, with recent publications exploring TCP network frameworks, in-memory data persistence, and microsecond-scale scheduling. Key themes include optimizing tail latency, neutralization-based memory reclamation, and fault-tolerant distributed services. His articles consistently demonstrate innovations in low-latency networking, operating system architecture, and cloud infrastructure, with recent emphasis on serverless benchmarks and processor customization. No scientific awards or advising relationships are detailed in the provided materials.

Chengnian Sunمشاهده پروفایل

دانشیار

• Software Engineering
• Software Reliability
• Software Security
• +۵ مورد دیگر

Chengnian Sun is an Associate Professor at the University of Waterloo's David R. Cheriton School of Computer Science. He holds a Ph.D. from the National University of Singapore (2013). His research focuses on software engineering, emphasizing software reliability, security, and developer productivity. His work spans compiler testing, program analysis, cybersecurity, and programming language tools. Key research trends include leveraging large language models (LLMs) for compiler testing and program reduction, probabilistic debugging techniques, and enhancing software security against ransomware and fuzzing attacks. His contributions address challenges in program simplification, fault localization, and vulnerability detection. Notable projects include frameworks like Perses (syntax-guided program reduction), T-Rec (language-agnostic program reduction), and tools like AddressWatcher for memory leak detection. His work bridges theoretical advancements with practical software engineering solutions. Chengnian advises on compiler reliability, cybersecurity, and developer productivity. His research has led to collaborations with industry on testing tools and security frameworks. He maintains an active lab focused on advancing software systems through rigorous analysis and innovation.

• Software Engineering
• Self-adaptive Software Systems
• Software Reuse
• +۳ مورد دیگر

Dr. Jesper Andersson is a Professor of Computer Science and Dean of the Faculty of Technology at Linnaeus University. He holds a PhD from Linköping University (2007) and has extensive leadership experience, serving as Department Chair from 2013–2020. His research focuses on self-adaptive software systems, software reuse, and cyber-physical systems. He has published widely in top venues like ACM Transactions on Autonomous and Adaptive Systems and Computing , and actively contributes to organizing international conferences. Education: Responsible for advanced courses in software design and development processes. Engaged with industry through technical advising for global companies. Completed major projects include developing a master’s program in computer science and the PROSSES project on self-protecting systems. Current research projects include Digital Twin of Organizations (DTO), DIACCESS for sustainable cities, and Aladino for adaptable architectures. His work emphasizes resilience frameworks, decentralized control, and industrial adaptation practices. Key collaborations include leading the AdaptWise research group and co-chairing SEAMS 2023. His articles span self-adaptive patterns, trust-aware systems, and IoT applications, reflecting a strong focus on both theoretical and applied software engineering challenges.

• Maritime Safety
• Operational Risk Management
• Autonomous Systems
• +۳ مورد دیگر

Ingrid Bouwer Utne is a Professor in the Department of Marine Technology at the Norwegian University of Science and Technology (NTNU). Her research focuses on risk assessment, autonomy, safety, and maintenance management of marine and maritime systems. She leads the Risk Group at NTNU and has been a main supervisor for numerous PhD students in areas like autonomous systems safety and risk modeling. She is actively involved in research projects such as the ERC AdG BREACH (Risk-Based Rationality in Autonomous Systems), SFI Autoship (Center for Autonomous Ships), and SAFEGUARD (Intelligent autonomous systems for safeguarding ocean infrastructure). Her work emphasizes risk-based control systems, online risk monitoring, and human-autonomy collaboration in maritime operations. Key publications include 'Risk and Interdependencies in Critical Infrastructures' (Springer) and 'Online Probabilistic Risk Assessment of Complex Marine Systems' (Springer). She has advised over 20 PhD students and contributed to industry-funded projects like UNLOCK and ORCAS, focusing on safer autonomous systems design and verification.

• Software Engineering
• Developer Productivity
• IDE Interaction Analysis
• +۳ مورد دیگر

Andrea Mocci is a Lecturer at the Faculty of Informatics of the Università della Svizzera italiana (USI). His work focuses on software engineering methodologies, developer productivity, and IDE interaction analysis. He is affiliated with the Software Institute and actively contributes to academic events such as the IEEE International Workshop on Mining and Analyzing Interaction Histories (MAINT). His research explores empirical software engineering techniques, including developer behavior analysis, code documentation improvement, and the application of natural language processing to software artifacts. Key areas of investigation include: IDE interaction and navigation efficiency Code redundancy and quality metrics Video tutorial analysis for educational content Runtime systems and annotation APIs Defect prediction and software maintenance Publications from 2016-2020 highlight trends in developer-centric tools, holistic recommender systems, and visualization techniques for software evolution. His work often bridges theoretical formal methods with practical developer workflows, aiming to improve both software quality and developer productivity through empirical studies and tool development.

Matteo Biagiolaمشاهده پروفایل

پژوهشگر

• Software Testing
• Test Generation
• AI for Software Testing (AI4SE)
• +۴ مورد دیگر

Matteo Biagiola is a Researcher in the Faculty of Informatics at Università della Svizzera italiana (USI), Lugano, Switzerland, and a PostDoctoral researcher at the University of St. Gallen (HSG). He specializes in software testing, particularly test generation for Web applications, deep reinforcement learning systems, and autonomous driving software. His work focuses on enhancing AI robustness through testing and improving software testing via AI techniques. Biagiola holds a Ph.D. from Università degli Studi di Genova (Italy) in collaboration with Fondazione Bruno Kessler, Trento. He has conducted postdoctoral research at USI on the Precrime ERC Advanced Grant project under Paolo Tonella. His research tools include μPRL (mutation testing for RL agents), STILE (web test parallelization), and GenBo (boundary state generation for autonomous systems). Education: Ph.D.: Università degli Studi di Genova / Fondazione Bruno Kessler (2016–2020) M.Sc.: Università Politecnica delle Marche (2014–2016) Affiliations: PostDoc & Scientific Collaborator: University of St. Gallen / USI (2025–present) PostDoc: USI (2020–2025) Visiting Ph.D. Student: University of British Columbia (2018) His research interests span AI-driven testing tools, autonomous system validation, and simulation-based testing. He has received the Distinguished Paper Award at ICST 2025 and serves on program committees for major conferences like FSE, ICSE, and ICST. He co-organizes workshops like DeepTest (co-located with ICSE) and the Cyber-Physical Systems tool competition (SBFT).

Mario Di Francescoمشاهده پروفایل

استاد

• Wireless Networking
• Mobile and Ubiquitous Computing
• Internet of Things
• +۴ مورد دیگر

Mario Di Francesco is a Professor at the Department of Computer Science, Aalto University. He holds the position of Professor in Computing Systems (ComputingSystems) Research Area and leads multiple research projects in networking and IoT. Previously, he was a research associate and adjunct faculty at the University of Texas at Arlington (2012–2014), and completed his PhD in Information Engineering at the University of Pisa under Prof. Giuseppe Anastasi. He also served as a research fellow at Scuola Superiore Sant'Anna in 2009. Research Interests : His work focuses on wireless networking, mobile and ubiquitous computing, and the Internet of Things (IoT). Key areas include edge computing, fog networks, virtual/augmented reality, and AI-driven resource management. His research contributes to UN Sustainable Development Goals related to innovation and infrastructure (SDG 9), sustainable cities (SDG 11), and industry/infrastructure development (SDG 9). Publications : His recent work spans topics like private virtual reality conversations, cloud-based network measurement frameworks (CLAIM), microservice connectivity analysis, and edge-DNN optimization. These publications reflect a trend toward integrating AI with distributed systems for real-time, low-latency applications in IoT and edge computing. Awards : Recipient of the Google Internet of Things Technology Research Award (2016) and MobileHCI Honorable Mention (2020). Grants & Projects : CRUISE (2023–2025): Cross-system architecture for autonomous wireless networks using lifelong machine learning. MeXICO (2020–2024): Mobile cross-reality through immersive computing. ASPECT (2016–2018): Spectrum utilization for mobile video. Labs & Teams : Leads research groups focused on edge computing, IoT systems, and immersive technologies. Collaborates with international partners on projects like fog radio networks and autonomous system design.

• Software Engineering
• Parallel and Distributed Systems Architecture
• Trusted Components and Services
• +۶ مورد دیگر

Professor Heinrich Schmidt is an Adjunct Professor in the School of Science at RMIT University, Australia. His research focuses on Software Engineering, Distributed Systems, and Cyber-Physical Systems. He specializes in areas such as formal verification, safety-critical systems, and cloud computing. His work emphasizes practical applications in industrial automation, IoT, and HPC environments. Key research interests include spatio-temporal analysis, fault tolerance, and adaptive systems design. He has supervised projects on IoT data contextualization, software fault characterization, and spatial modeling in PRISM. Over 98 publications highlight his contributions to formal methods, distributed systems, and industrial software solutions. Professor Schmidt collaborates on projects like Chiminey (cloud/HPC integration) and VxLab (industrial visualization). His teaching covers parallel systems, trusted components, and model-based monitoring. No specific awards are listed, but his extensive publication record underscores his academic impact.

• Parallel Programming
• Distributed Systems
• Functional Programming
• +۳ مورد دیگر

Professor Phil Trinder is a Professor of Computing Science at the University of Glasgow's School of Computing Science. He leads the Glasgow Parallelism Group (GPG) and is a member of the Glasgow Systems Section (GLASS) and the Scottish Programming Languages Seminar (SPLS). His research focuses on parallel and distributed programming models, functional programming, and applications in computational algebra. Trinder holds a DPhil from Oxford University and has over 100 publications. He has led 12 major research projects as Principal Investigator and coordinated EU projects. Collaborations include Ericsson, Maplesoft, Microsoft, and Motorola. His work emphasizes scalable distributed systems, actor-based platforms, and reliable computation. Notable contributions include the SymGridPar framework for computational algebra and research on Erlang scalability. He also explores IoT architectures and tierless programming languages. Key projects include improving Erlang's network scalability and developing frameworks for exact combinatorial search (YewPar). He has supervised numerous researchers and contributed to high-performance systems like HPC-GAP.

Marie Wibergمشاهده پروفایل

استاد

• Educational Measurement
• Psychometrics
• Test Equating
• +۳ مورد دیگر

Marie Wiberg is a Professor of Statistics with a specialty in Psychometrics at the Department of Statistics, Umeå School of Business, Economics and Statistics (USBE) at Umeå University, Sweden. She holds a PhD in Statistics from Umeå University (2003) and has held academic positions there since 2003, advancing from Lecturer (2004–2005) to Assistant Professor (2006–2010), Associate Professor (2010–2015), and full Professor (2015–present). She has also held visiting roles at McGill University (2005) and the University of Twente (2006). Her research focuses on educational measurement and psychometrics , with emphasis on test equating , item response theory (both parametric and nonparametric), and international large-scale assessments like PISA and TIMSS. She has contributed to methodological advancements in kernel equating, statistical modeling, and the analysis of educational data. Her work bridges theoretical psychometrics with practical applications in standardized testing and policy analysis. Prof. Wiberg has supervised numerous PhD students, including Gabriel Wallin (2020), Jonathan Wedman (2017), and Inga Laukityte (2017). She actively contributes to academic governance, serving as an editor for journals like Behaviormetrika and on editorial boards for International Journal of Testing . Her awards include the Young Researcher Career Award (2008) and the Royal Skyttean Prize (2008). She is also a former member of Sweden’s Young Academy (2014–2018). Her research projects include developing statistical methods for standardized achievement tests (2020–2025) and analyzing Swedish student performance in international assessments (2007–2012). She collaborates internationally, with publications in Applied Psychological Measurement , Journal of Educational and Behavioral Statistics , and others.