جست‌وجوی استاد و پژوهشگر

Matthew B. Dwyerمشاهده پروفایل

استاد

• Software Verification and Validation
• Software Engineering
• Program Analysis
• +۴ مورد دیگر

Matthew B. Dwyer is the Robert Thomson Distinguished Professor of Computer Science at the University of Virginia, leading research in software verification, program analysis, and autonomous systems. His work focuses on formal methods for ensuring dependable software, particularly in safety-critical domains like autonomous vehicles. He has advised numerous PhD and Master’s students, many of whom hold academic and industry positions globally. Education: Ph.D. in Computer Science from the University of Massachusetts Amherst, M.S. from UMass Boston, and BSEE from the University of Rochester. Research Interests: Software Verification & Validation, Program Analysis, Formal Methods, Neural Network Testing, Safety-Critical Systems. His lab, the Laboratory for Engineering Safe Software, develops tools like DNNV for verifying deep neural networks. Awards: ACM Fellow (2019), IEEE Fellow (2013), multiple test-of-time and impact paper awards from ISSTA, ICSE, and SIGSOFT conferences. Service: Program chair for ICSE (2022), FSE (2004), and other top venues; editorial roles at IEEE TSE, ACM TOPLAS, and Springer STTT. Co-developed property specification patterns for model checking, widely used in industry and academia.

• Energy-Efficient Computing
• Hardware Acceleration
• Machine Learning
• +۳ مورد دیگر

Dr. David Boland is a Senior Lecturer at the School of Electrical and Computer Engineering, University of Sydney. He holds an MEng and PhD from Imperial College London. His research focuses on energy-efficient hardware acceleration, particularly using FPGAs and application-specific integrated circuits (ASICs), to optimize computational efficiency in domains like machine learning and optical communications. He has contributed to projects involving custom hardware accelerators, federated learning for edge computing, and real-time signal processing. Education: MEng, Imperial College London, 2007 PhD, Imperial College London, 2012 Research Interests: Dr. Boland’s work emphasizes reducing computational overhead through customized hardware solutions. He explores techniques for minimizing unnecessary computations while maintaining accuracy, leveraging FPGA-based designs for parallelism and energy efficiency. Key areas include: Hardware acceleration for machine learning FPGA optimization for neural networks Energy-efficient algorithms for edge computing Online arithmetic and latency-accuracy trade-offs Grants & Collaborations: 2022: On-Board Federated Learning in Orbital Edge Computing (NSW Department of Industry) 2017: Fast Automated Anomaly Detection in Communication Networks (Defence Science & Technology Group) Affiliations: Member of the Net Zero Institute, collaborating on sustainable computing solutions.

• Human-Computer Interaction (HCI)
• Tangible User Interfaces (TUIs)
• Smart Home Systems
• +۳ مورد دیگر

Michael B. Chamunorwa is a Research and Design Engineer and PhD candidate at the University of Oldenburg's Department of Computing Science (School II), specializing in Media Informatics and Multimedia Systems. His research focuses on designing user interfaces embedded in everyday objects for smart home control, exploring embodied interaction and repurposing household items as smart home interfaces. He holds a Bachelor of Technology in Software Engineering and a Master's in Computer Science from the Namibia University of Science and Technology. Michael has taught as a tutor and project supervisor in courses such as 'Experiments and Studies' and 'Makers’ Lab - Things that Think', supervising projects like 'Rich Interactive Materials for Everyday Objects in the Home'. His work bridges theoretical research with practical applications, including developing tools for cultural preservation with marginalized communities in Namibia. Key research areas include tangible user interfaces, embodied interaction, and smart home systems. His projects often involve collaborations with industry-standard frameworks and have been published in venues like ACM International Conference on Mobile and Ubiquitous Multimedia (MUM) and Interactive Surfaces and Spaces. Michael's current PhD project investigates secondary affordances of everyday objects to enhance smart home user experiences. He has contributed to open-source tools like the 'Popup Observation Kit' for remote usability testing and the 'Sweet Spots' AR platform for interaction area visualization. He has been actively involved in the Virtual and Augmented Reality Lab (inf174) and the Makers’ Lab, emphasizing hands-on innovation in technology design. His work reflects a commitment to both academic rigor and real-world societal impact through technology.

Franz Wotawaمشاهده پروفایل

استاد

• Model-Based Reasoning
• Qualitative Reasoning
• Theorem Proving
• +۶ مورد دیگر

Franz Wotawa is a Professor of Software Engineering at Graz University of Technology. He holds a M.Sc. (1994) and PhD (1996) from Vienna University of Technology. He has served as head of the Institute for Software Technology from 2003–2009 and since 2020. His research focuses on model-based reasoning, software testing, autonomous systems, and diagnosis, with over 390 peer-reviewed publications. He founded Softnet Austria (2006) to bridge research and industry. He leads the Christian Doppler Laboratory for Quality Assurance Methodologies for Autonomous Cyber-Physical Systems since 2017 and has supervised 90+ master and 36+ PhD students. His awards include the 2016 Lifetime Achievement Award from the International Diagnosis Community. He is a member of Academia Europaea, IEEE, and AAAI. **Education**: M.Sc. in Computer Science, Vienna University of Technology, 1994 PhD, Vienna University of Technology, 1996 **Research Interests**: Model-based reasoning, qualitative reasoning, theorem proving, mobile robotics, verification/validation, software testing/debugging, AI, and autonomous systems. **Notable Projects**: A-IQ Ready (2022–2026): Quantum sensing for autonomous systems. ALFA (2024–2027): AI for smart diagnosis in building automation. Bilateral AI (2024–2029): Combining symbolic and sub-symbolic AI. VARCOS (2025–2028): Vehicle-road cooperative systems for autonomous driving. **Awards & Memberships**: Lifetime Achievement Award (2016, International Diagnosis Community) Senior Member, AAAI Member of Academia Europaea, IEEE, ACM, and Austrian Computer Society **Labs/Teams**: Christian Doppler Laboratory for Quality Assurance Methodologies (since 2017). Active in Cluster of Excellence “Bilateral AI” at TU Graz.

• Computer Science
• Cybersecurity
• WebAssembly Security
• +۳ مورد دیگر

Weihang Wang is a Volunteer Assistant Professor in the Department of Computer Science and Engineering at the University of Southern California (USC), part of the School of Engineering and Applied Sciences. His research focuses on WebAssembly security, program analysis, and static/dynamic analysis frameworks. He holds a PhD and has published extensively on topics like WebAssembly obfuscation, decompilation techniques, and flaky test mitigation. Research interests include cybersecurity, software engineering, and the application of AI in program analysis. His work addresses challenges in cross-compilation for WebAssembly, decompilation accuracy, and automated detection of security vulnerabilities in web applications. Notable projects include WaSCR (side channel repairer), WBSan (bug detection), and Wefix (flaky test automation). He has received NSF travel grants for IEEE Security Development (SecDev) conferences in 2022 and 2023. His articles span 2010–2025, showing sustained contributions to web security, compiler optimization, and program transformation. His work intersects with practical applications like ad-blocking systems (Adhere) and bird flu outbreak prediction using migration data (2010–2013). Grants and awards include NSF funding for travel and research, reflecting his active role in academic conferences. He is affiliated with USC's computer science department and maintains an active Google Scholar profile with over 30 publications listed.

• Artificial Intelligence
• Collaborative Robotics
• Human-Machine Interaction
• +۳ مورد دیگر

Madeleine EL ZAHER is a Researcher-Lecturer at CESI, affiliated with the Engineering and Numerical Tools research team. Her work focuses on Artificial Intelligence, Collaborative Robotics, Human-Machine Interaction, and Multi-Agent Systems. She holds a PhD in Computer Sciences from the University of Technology of Belfort-Montbéliard (2013) and a Master’s degree in Computer Sciences and Telecommunications from Paul Sabatier University (2010). Teaching responsibilities include Computer Sciences and Electronics at the Engineering program level, emphasizing project-based learning and training through research. She co-supervises PhD students in industrial robotics and cyber-physical systems, including Abdessalem ACHOUR (defending in 2024) and Badra Souhila GUENDOUZI (defending in 2025). Her research spans semantic mapping in mobile robotics, federated learning for industrial systems, and platooning algorithms for autonomous vehicles. Notable publications include work on semantic mapping with 3D models (2024), federated learning frameworks using genetic algorithms (2023), and verification of platooning systems (2012–2015). No scientific awards are explicitly listed, but her contributions reflect impactful work in autonomous systems and robotics. Grants and lab affiliations are not detailed in the provided text, though her team’s research aligns with CESI’s focus on engineering and numerical tools.

Richard Paigeمشاهده پروفایل

استاد

• Software Engineering
• Model-Driven Engineering
• Safety-Critical Systems
• +۱۰ مورد دیگر

Richard Paige is a Joseph Ip Distinguished Engineering Professor at McMaster University's Department of Computing and Software, Faculty of Engineering. He also holds a part-time position as Professor of Enterprise Systems at the University of York, UK. His primary research focuses on software engineering, particularly model-driven engineering, safety-critical systems, and low-code development. He leads the McMaster Centre for Software Certification (McSCert) and has extensive industry collaborations, including with Rolls-Royce, Leonardo, and NASA. Paige holds a PhD in Computer Science from the University of Toronto (1997), an MSc from the same institution (1994), and a BSc from McMaster University (1992). He has supervised over 22 PhD and 72 Master’s students, many of whom pursue careers in academia and industry. His funding exceeds $24M CAD from NSERC, Ontario Research Fund, and industry grants. His research emphasizes model management, assurance cases, and safety-critical systems. Key contributions include the Epsilon framework and advancements in model-driven tools. Paige chairs the STAF conferences and serves on editorial boards for journals like Software and Systems Modeling . He actively promotes open-source tooling and has won multiple best paper awards, including ACM Distinguished Paper at MoDELS (2010, 2017) and the Ten-Year Most Influential Paper Award (2016). Paige’s teaching focuses on problem-based learning and flipped classrooms, with awards for pedagogical innovation. He also leads interdisciplinary projects, such as SECT-AIR (reducing aerospace software costs) and CROSSMINER (developer-centric knowledge mining). His work bridges academia and industry, addressing challenges in automotive, healthcare, and aerospace sectors. Current projects include safety assurance for autonomous vehicles and model-driven sustainability evaluation.

Mark Lawfordمشاهده پروفایل

استاد

• Software engineering
• Safety critical systems
• Digital & Smart Systems
• +۳ مورد دیگر

Mark Lawford is a Professor in the Department of Computing and Software at McMaster University's Faculty of Engineering. His expertise spans software engineering, safety critical systems, and model-based development, with a focus on digital & smart systems and transportation research clusters. Contact: Email: lawford@mcmaster.ca Website: www.cas.mcmaster.ca/~lawford His research explores automotive systems , formal verification , and model-driven engineering , particularly addressing challenges in automotive electrification , centralized E/E architectures , and safety assurance cases . Recent publications emphasize test case generation , change impact analysis , and model transformation tools for Simulink, SysML, and AUTOSAR. Key trends in his work include autonomous vehicle safety , real-time control systems , and integration of formal methods with industrial practices across automotive, biomedical, and nuclear domains.

Diego Sevilla Ruizمشاهده پروفایل

دانشیار

• Software Engineering
• Database Systems
• NoSQL Technologies
• +۳ مورد دیگر

Diego Sevilla Ruiz is an Associate Professor at the University of Murcia's Faculty of Informatics, Department of Computer Engineering and Technology, where he leads initiatives in Software Engineering research. He completed his PhD in 2008 on distributed component models (CORBA-LC). His research focuses on database technologies, particularly schema evolution in NoSQL and relational databases, model-driven engineering approaches, and unified metamodeling. Recent work develops Skiql (a schema query language), Athena (schema definition language), and methodologies for referential integrity in graph databases. His publications advance database abstraction techniques, schema migration frameworks, and automated tools for database management and modernization.

Amro Al-Said Ahmadمشاهده پروفایل

مدرس

• Cloud Computing
• Software Engineering
• Software Testing
• +۴ مورد دیگر

Dr. Amro Al-Said Ahmad is a Lecturer in Computer Science at the School of Computer Science and Mathematics, Keele University, UK. He holds a Ph.D. in Cloud Computing from Keele University and has served as a teaching fellow and lecturer in Jordan prior to his current role. His research focuses on cloud computing scalability, software testing, and agile methodologies. He has published extensively in top-tier journals and conferences, including Information and Software Technology and Journal of Cloud Computing , and contributed to organizing events like BCS HCI and IEEE SSD. Education: BSc (Software Engineering, Philadelphia University, 2009), MSc (Computer Science, Amman Arab University, 2014), PhD (Cloud Computing, Keele University, 2019). Research interests include cloud scalability, chaos engineering, software testing, and healthcare informatics. His funded projects include AWS credits for scalability analysis and an Erasmus+ grant for international collaboration. He teaches courses like Cloud Computing and Software Engineering Project Management at Keele. Grants: AWS Cloud Credit ($5K), Keele New Starter Award (£2K), Erasmus+ MED2IaH (€58,325 share). Lab/Teams: Active in cloud resilience frameworks and chaos engineering research groups.

• Blockchain Technology
• Business Process Management
• Process Mining
• +۳ مورد دیگر

Ingo Weber is a Professor affiliated with Technische Universität München (TU Munich) and Fraunhofer Gesellschaft. His research focuses on blockchain technology, business process management (BPM), and artificial intelligence (AI), with a particular emphasis on integrating these fields. He has held former positions at TU Berlin, CSIRO Data61, and other institutions. Current affiliations: TU Munich and Fraunhofer Gesellschaft Former affiliations: TU Berlin, CSIRO Sydney, University of New South Wales, SAP Research, and University of Massachusetts Amherst Research interests include blockchain applications in business processes, process mining, AI-driven systems, and sustainability-oriented process analysis. His work explores topics such as blockchain scalability, data confidentiality, and cost-efficient process execution on next-generation blockchains like Algorand. He has pioneered frameworks like SOPA for sustainability analysis and FhGenie for confidentiality-preserving AI. Key contributions include over 200 publications in journals like IEEE Access, Future Generation Computer Systems, and ACM Transactions on Management Information Systems. Recent work emphasizes AI-augmented BPM systems and the application of large language models (LLMs) in scientific contexts. Notable projects include blockchain-based process execution engines (e.g., Caterpillar), platform architectures for multi-tenant blockchain systems, and frameworks for evaluating payment channel networks. He has collaborated extensively with industry partners and academic institutions globally.

• 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.

Russ Cucinaمشاهده پروفایل

استاد

• Medical Informatics
• Genomics
• Healthcare Analytics
• +۴ مورد دیگر

Russ Cucina, MD, MS, is a Professor of Medicine in the Division of Hospital Medicine at the University of California, San Francisco (UCSF). He holds dual roles as Vice President and Chief Health Information Officer for UCSF Health System, overseeing analytics, software infrastructure, and genetic/genomic services. His academic and professional focus integrates clinical informatics, healthcare technology, and genomic medicine to advance patient care and institutional missions. Dr. Cucina's education includes an MD from UC Davis, an MS in Biomedical Informatics from Stanford, and a BA in Molecular and Cell Biology from UC Berkeley. He is board-certified in Internal Medicine. His research interests span electronic health record (EHR) interventions, clinical decision support systems, pharmacogenomics implementation, and healthcare operations optimization. Notable achievements include the 2010 Distinguished Paper Award from the American Medical Informatics Association and the AMDIS Award for clinical informatics excellence. His work emphasizes translating data-driven solutions into actionable clinical practices, such as reducing telemetry overuse and improving discharge processes. Dr. Cucina leads UCSF's genetics/genomics laboratories and strategic partnerships in health IT. He is a vocal advocate for leveraging technology to enhance healthcare quality and accessibility, with a focus on personalized medicine and system-wide informatics advancements.

• Robotics
• Artificial Intelligence
• Computer Vision
• +۳ مورد دیگر

Björn Jensen is a Professor and Co-Head of the AI Robotics Research Lab at Lucerne University of Applied Sciences and Arts (HSLU), specifically within the Lucerne School of Computer Science and Information Technology. He also teaches medical robotics at the University of Bern's Biomedical Engineering Program. His professional background includes roles at the Autonomous Systems Lab at EPFL, Switzerland, and founding the startup Singleton 3D focusing on 3D laser measurement technology. Educational background: MSc in Electrical Engineering (Automation & Image Processing) from TU Darmstadt (1998), followed by a Master's in Industrial Management from the same institution. PhD in human-robot interaction from EPFL (2005), with research stints at Tokyo University (2005) and involvement in projects like Robox@Expo.02 and Smarter-Elrob. Research interests span robotics, human-robot interaction, autonomous systems, medical robotics, and sensor-based navigation. Notable projects include the 'Smart Ennoblement Factory', 'NaviMow' autonomous lawnmower, and 'Bagger Assistenzsysteme'. His work emphasizes real-world applications of robotics in dynamic environments and human-centric systems. Lab leadership includes co-directing the AI Robotics Research Lab, focusing on advancing robotics technologies for practical scenarios. No scientific awards explicitly listed, but contributions to industry-academia collaborations are highlighted through startup ventures and applied research projects.