جست‌وجوی استاد و پژوهشگر

Zhi Jinمشاهده پروفایل

استاد

• Requirements Engineering
• Self-Adaptive Systems
• Learning from Source Code
• +۵ مورد دیگر

Zhi Jin is a Professor in the Department of Computer Science and Technology at Peking University, where he has been employed since 2009. Previously, he served as a professor at the Academy of Mathematics and System Sciences, Chinese Academy of Sciences from 1994-2009. He received his BS from Zhejiang University in 1984 and MS/PhD from National University of Defense Technology in 1984 and 1992 respectively. He progressed from assistant professor (1992) to associate professor (1995) to full professor (2001). His research focuses on knowledge engineering and software engineering, with special interests in knowledge graphs, self-adaptive systems, and deep learning applications. Current research directions include Self-Adaptive Software in Human-Cyber-Physical Systems, Crowd-based Requirements Engineering, and Learning from both Natural Language and Programming Language. His work bridges theoretical knowledge engineering with practical software development challenges. His recent publications demonstrate a strong trend toward applying large language models and AI techniques to traditional software engineering problems, particularly in requirements engineering, code generation, and vulnerability detection. The articles span multiple high-impact venues including ASE, ICSE, and RE, with significant focus on aerospace applications and multi-agent collaboration approaches. Scientific honors include: Winner of National Science Fund for Distinguished Young Scholars (2006) Project 973 project lead scientist (2014) Member of Discipline Appraisal Group of the Academic Degree Commission (2015) Multiple ACM Distinguished Paper Awards He serves in numerous editorial roles including Associate Editor for IEEE Transactions on Software Engineering (2018-present) and IEEE Transactions on Reliability (2019-present). He is also an Editorial Board Member for Empirical Software Engineering and Requirements Engineering Journal, and holds leadership positions in the China Computer Federation. His extensive conference service includes PC membership for ICSE, FSE, RE, and other major software engineering venues.

• Service-oriented Computing
• Web Services
• Workflow Technology
• +۷ مورد دیگر

Jun Yan is a Professor at the University of Wollongong's School of Computing and Information Technology within the Faculty of Engineering and Information Sciences. His roles include academic leadership and research supervision, with active involvement in committees like the Student Academic Experience Sub-Committee and Quality Assurance Review Group. Current research focuses on service-oriented computing, workflow technology, adaptive process management, and AI-driven systems. His work intersects with IoT, UAV systems, federated learning, and multi-agent reinforcement learning. Research interests span service-oriented software engineering, decentralized workflow management, and cybersecurity challenges in autonomous systems. Notable projects include an ARC-funded initiative on robust defenses against adversarial ML for UAV systems (2025–2027). He supervises Masters/PhD projects on topics like diffusion model-based MRI, graph prompt learning, and industrial defect detection. His publications from 2023–2025 emphasize scalable multi-agent systems, federated learning with non-IID data, and UAV applications in intelligent transportation. Key areas of contribution include trust models for e-commerce, privacy-preserving cloud computing, and fault-tolerant service architectures.

Michael S. Branickyمشاهده پروفایل

استاد

• Cyber-Physical Systems
• Internet of Things
• Artificial Intelligence
• +۵ مورد دیگر

Michael S. Branicky is Professor of Electrical Engineering & Computer Science (EECS) at the University of Kansas School of Engineering. Previously serving as KU's Dean of Engineering from July 2013 to June 2018, he maintains active research leadership following his tenure as professor and chair at Case Western Reserve University (CWRU) since 1996. His research spans Cyber-Physical Systems/IoT , Artificial Intelligence , Machine Learning , and Robotics , with particular expertise in switched and hybrid control systems. His work bridges theoretical control theory with practical applications in rehabilitation engineering and autonomous systems, evidenced by his h-index of 40 and nearly 17,000 citations. Recent publications demonstrate strong focus on reinforcement learning for human-robot interaction and cyber-physical systems. His editorial leadership in IEEE Transactions reflects his standing in the CPS community. Notable funding includes NSF awards for Active Sensing in Deformable Environments and CPS Week support. Fellow, IEEE (2016) NSF Director's Award for Collaborative Integration (2012) NSF Director's Superior Accomplishment Award (2010) Walter R. Fried Memorial Best Paper Award (2010) Multiple teaching awards at CWRU and Case School of Engineering Branicky leads significant research initiatives including the IUCRC Center for High-Assurance Secure Systems and IoT (CHASSI) and NRT-HDR: Internet of Catalysis. His laboratory work focuses on image-guided interventions and deformable environment mapping, supported by over $1 million in NSF funding.

Gabriel Gomezمشاهده پروفایل

استاد

• Information Science
• Library and Information Science
• Big Data and Society
• +۷ مورد دیگر

Gabriel Gomez is a Professor in the Department of Information Studies within the College of Arts & Sciences at Chicago State University. His work bridges library science, social justice, and digital culture, with a strong emphasis on LGBTQ history and the societal implications of big data. He holds a Ph.D. and MA from Northwestern University and a BFA from the School of the Art Institute of Chicago. Ph.D., Northwestern University MA, Northwestern University BFA, School of the Art Institute of Chicago His research centers on the cultural impact of data, information literacy, and LGBTQ education and history. He critically examines how big data shapes human behavior, challenges empowerment through information literacy, and promotes inclusive narratives through public history projects like The Legacy Project . His interdisciplinary approach connects library science with critical theory, museum studies, and social justice advocacy. The recent publications reveal a consistent focus on big data ethics, information behavior, and LGBTQ representation. His work interrogates the instrumentalist views of data, advocates for ethical information practices, and documents marginalized histories. He frequently explores how digital tools can either reinforce or challenge power structures in education and society. Scientific awards and recognitions include: 2024 Spring CHICAGO STATE RESEARCH CUES AWARD 2018–2020 Fulbright Specialist Award (New Delhi, India) 2017 Faculty Excellence Award 2014 Professional Advancement Increase (PAI) Award 2013 Center for Teaching and Research Excellence Grant 2007 Dean’s Professional Development Award 2005 Fulbright Lecturer Award (Aligarh Muslim University, India) Gomez has secured multiple grants supporting research in information literacy, LGBTQ history, and data mining in education. He has collaborated with institutions internationally, including the Council of Scientific and Industrial Research (CSIR)-NISCAIR in India, and has contributed to accreditation frameworks in library education. His advising may involve students in public history, digital curation, and social justice projects, though specific advisees are not listed. He is a key figure in The Legacy Project , an initiative creating permanent outdoor exhibits of LGBTQ history, demonstrating his commitment to community-based scholarship and public engagement. This project connects museum advocacy with education and role model visibility, serving as a living archive of LGBTQ contributions.

Chung-Wei Linمشاهده پروفایل

دانشیار

• Cyber-physical systems
• Connected and Autonomous Vehicles
• System Security
• +۵ مورد دیگر

Chung-Wei Lin is an Associate Professor and Deputy Director at the Department of Computer Science and Information Engineering and the Graduate Institute of Networking and Multimedia at National Taiwan University. His research focuses on cyber-physical systems, particularly in the domains of connected and autonomous vehicles, system security, and design methodologies. He maintains active collaborations with industry partners including Toyota and has established himself as a leading researcher in intelligent transportation systems in Taiwan. Education: Ph.D. (2015) from Department of Electrical Engineering and Computer Sciences, University of California, Berkeley (Advisor: Alberto L. Sangiovanni-Vincentelli) M.S. (2007) from Graduate Institute of Electronics Engineering, National Taiwan University (Advisor: Yao-Wen Chang) B.S. (2005) from Department of Computer Science and Information Engineering, National Taiwan University Dr. Lin's research interests center on cyber-physical systems with specific focus on connected and autonomous vehicles, security mechanisms, and system design methodology. Before returning to NTU in 2018, he worked at Toyota InfoTechnology Center, USA, Inc. His recent projects cover diverse topics including systems engineering, formal verification for robustness and compatibility, runtime monitoring, and intelligent intersection management. His work bridges theoretical foundations with practical applications, addressing real-world challenges in transportation systems through innovative technical solutions. Analysis of Dr. Lin's recent publications (2023-2025) reveals a strong emphasis on intelligent transportation systems with particular focus on security challenges for connected vehicles, formal verification techniques for safety-critical systems, and novel control algorithms for vehicle coordination. His research demonstrates increasing integration of machine learning approaches, especially reinforcement learning, to address complex decision-making problems in transportation. The work spans multiple technical domains including control theory, networking, cybersecurity, and formal methods, reflecting the inherently interdisciplinary nature of cyber-physical transportation systems research. Selected Awards: 2016 Best Paper Award, ACM Transactions on Design Automation of Electronic Systems 2015 Most Accessed ESL Paper Best Paper Award, IEEE ISSREW 2016 workshop Best Paper Award, ICCD 2010 Best Paper Nominee, ASP-DAC 2015 Dr. Lin currently advises multiple Ph.D. and M.S. students, with research focusing on various aspects of cyber-physical systems for transportation. His group includes Ph.D. students Pintusorn Suttiponpisarn and I-Ching Tseng, as well as several M.S. students. His extensive publication record and numerous patents (over 20 granted) indicate significant research impact and likely substantial research funding from both government and industry sources. His research program demonstrates strong translational potential, with many concepts moving from theoretical foundations to practical implementations. Dr. Lin leads the Cyber-Physical Systems Laboratory at NTU, which focuses on research related to intelligent transportation systems. The lab conducts research in areas including vehicle control, intersection management, security mechanisms, and formal verification for cyber-physical systems. His team collaborates with researchers from various institutions globally, as evidenced by his extensive publication record with international co-authors from universities and research institutions in the United States, Japan, and Europe.

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.

Youcheng Sunمشاهده پروفایل

پژوهشگر

• AI Safety
• Security
• Automated Reasoning
• +۴ مورد دیگر

Youcheng Sun is a researcher at The University of Manchester, affiliated with the Systems and Software Security group, Centre for Digital Trust and Society, Centre for Robotics and AI, and Autonomy and Verification Network. Previously, he held a Lecturer position at Queen's University Belfast and conducted postdoctoral research at the University of Oxford. He earned his PhD from Scuola Superiore Sant'Anna. His research focuses on AI Safety , Security , and Automated Reasoning , with contributions to trustworthy AI, formal verification, embedded systems, and robotics. His work has been funded by Google and the Ethereum Foundation. He has led projects such as SECT-AIR and AUTOSAC, and contributed to the EU H2020 SAFURE project on safety/security assurance in mixed-critical systems. His publications span top-tier venues like IEEE S&P, ICSE, NeurIPS, and IROS. Awards: Fellow of the Higher Education Academy (FHEA) Advising: Supervising PhD students in AI/Security domains (e.g., funded projects on LLMs in social recommendations) Labs/Teams: Member of interdisciplinary networks advancing robotics, AI ethics, and digital trust.

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).

• Artificial Intelligence
• Machine Learning
• Internet of Things
• +۱۳ مورد دیگر

Edoardo Patti is an Associate Professor in the Department of Control and Computer Engineering (DAUIN) at Politecnico di Torino, Italy. He is actively involved in research and teaching, focusing on artificial intelligence, machine learning, IoT, smart grids, energy communities, co-simulation, and digital twin technologies. He is a member of the EDA (Electronic Design Automation) research group and the Interdepartmental Center IAM@PoliTo for Integrated Additive Manufacturing. His research interests span Artificial Intelligence, Machine Learning, Internet of Things, Smart Grids, Energy Communities, Co-simulation, Distributed Software Infrastructure, Digital Twin, Physics-Informed Neural Networks, and Reinforcement Learning . His work is closely aligned with Sustainable Development Goals such as Affordable and Clean Energy (Goal 7), Sustainable Cities (Goal 11), and Climate Action (Goal 13). The latest publications reveal a strong trend in AI-driven modeling for energy systems, including digital twins for wave energy converters, urban electric mobility simulation, real-time battery health estimation, and co-simulation platforms for integrated energy systems. His work combines machine learning with physical models and large-scale distributed systems to address sustainability challenges. He supervises several PhD students, including Maria Adelaide Loffa, Matia Torlini, Rafael Natalio Fontana Crespo, Pietro Rando Mazzarino, Claudia De Vizia, and Marco Massano. He has led numerous research projects funded by EU (H2020), PNRR, regional initiatives, and corporate contracts, particularly in additive manufacturing, smart grids, and IoT platforms. He is involved in multiple research groups and centers: EDA - Electronic Design Automation (DAUIN) Interdepartmental Center IAM@PoliTo - Integrated Additive Manufacturing

Vahid Rafeمشاهده پروفایل

مدرس

• Search-Based Software Engineering
• Blockchain Technology
• Formal Verification
• +۳ مورد دیگر

Dr. Vahid Rafe is Lecturer and co-program lead for Computer Science at Goldsmiths, University of London. His research focuses on search-based software engineering and blockchain technology, with additional expertise in formal verification and model transformation. Research integrates artificial intelligence with software engineering practices including automated testing techniques, bug localization, and formal verification. Recent work applies machine learning to software quality assurance, combinatorial testing optimization, and blockchain security analysis. Publications demonstrate consistent innovation in AI-enhanced software engineering methods, particularly hybrid algorithms for test generation and deep learning approaches for software maintenance. Recent work addresses security vulnerabilities in cryptographic systems.

Rachida Dssouliمشاهده پروفایل

استاد

• Software Engineering
• Quality Assurance
• Service Engineering
• +۷ مورد دیگر

Rachida Dssouli is a Professor at the Concordia Institute for Information Systems Engineering (Concordia University). Her research focuses on advanced software engineering methodologies, quality assurance systems, and distributed computing frameworks. She specializes in model-based testing, federated learning optimization, and big data quality management. Her work integrates formal verification techniques with modern machine learning approaches to address challenges in edge computing, IoT, and safety-critical systems. Key research areas include: Development of hybrid swarm intelligence algorithms for optimizing large language model deployment in edge-cloud environments Design of reinforcement learning frameworks for robotics motion planning and IoT device scheduling Creation of interpretable machine learning tools for fault detection in software systems Establishment of holistic big data quality frameworks for continuous monitoring and unstructured data analysis Formal verification methods for avionics systems using multi-agent models Her recent work demonstrates trends toward AI-driven solutions for testing methodologies (e.g., SHAP-Driven fault detection) and edge-cloud integration (e.g., MIMO-based computation offloading optimization). The 2025 publications highlight advancements in federated learning and trust-aware IoT scheduling. Earlier works (2018-2020) emphasize foundational contributions to cloud trust models, big data quality metrics, and safety-critical system testing. Her research also addresses emerging technologies for developing countries through frameworks like neurodegenerative disease monitoring systems and mobile application requirements engineering. She has contributed to service-oriented architectures for healthcare systems and cloud-based resource orchestration strategies.

Prosanta Gopeمشاهده پروفایل

مدرس ارشد

• Cybersecurity
• IoT Security
• Authentication Protocols
• +۳ مورد دیگر

Dr. Prosanta Gope is Senior Lecturer in Cybersecurity at the University of Sheffield's School of Computer Science. He directs the MSc Cybersecurity & AI program and leads research in IoT security, authentication protocols, and privacy-preserving systems. Research encompasses: Lightweight authentication for IoT/5G networks Hardware security using PUFs Privacy-preserving machine learning Industrial control system security Recent publications focus on privacy-preserving authentication for 5G handovers, digital twins, and V2G communications using decentralized identity frameworks. His work demonstrates strong application focus in smart grids, healthcare, and industrial IoT. As associate editor for IEEE IoT Journal and IEEE Systems Journal, he shapes research direction in security fields. Secured funding from EPSRC, Innovate UK, and Royal Society for projects on hardware security and intrusion detection systems.

Riccardo Bettatiمشاهده پروفایل

استاد

• Distributed real-time systems
• Scheduling algorithms
• Communication protocols
• +۴ مورد دیگر

Riccardo Bettati is a Professor and Associate Department Head in the Department of Computer Science & Engineering at Texas A&M University, part of the College of Engineering. He holds a Ph.D. from the University of Illinois at Urbana-Champaign (1994) and a DipInf. from ETH Zurich. His research focuses on distributed real-time systems, scheduling algorithms, communication protocols, and security, particularly in privacy and anonymity. He directs the Texas A&M University Center for Information Assurance and Security. Education: Ph.D. in Computer Science (UIUC), DipInf. in Computer Science (ETH Zurich). Research Interests: Distributed real-time systems, scheduling, communication security, privacy in networks, and cyber-physical systems. Notable projects include NetCamo, RT-ARM, and VTech. He has received awards like the Chevron Faculty Fellowship (2008-2009) and Best Paper at EuroMicro (2006). Recent publications span cybersecurity, IoT security, and side-channel attack countermeasures. He teaches courses on operating systems, computer systems, and cryptocurrencies. His work emphasizes secure networked systems and resilience against modern threats.

Leonardo Bonatiمشاهده پروفایل

پژوهشگر

• Wireless Communications
• Telecommunications
• Network Security
• +۴ مورد دیگر

Leonardo Bonati is an Associate Research Scientist in the Department of Electrical and Computer Engineering at Northeastern University. He specializes in cutting-edge wireless communication systems, particularly focusing on Open RAN, 5G/6G networks, and AI-driven network intelligence. His work emphasizes automated testing, network slicing, and security in software-defined cellular systems. He leads and contributes to high-profile projects such as AutoRAN and DigiRAN, funded under the CHIPS and Science Act, which aim to enhance open and disaggregated cellular network testing and digital twin frameworks. His research also involves experimental platforms like Colosseum and OpenAirInterface for large-scale wireless emulation and real-world testing. Key research interests include: AI-based network control (via dApps/xApps), digital twins for network validation, zero-touch deployment, and security in O-RAN interfaces. He holds multiple patents on topics ranging from network slicing to private 5G connectivity through steganography. Notably, Bonati was recognized among the top 2% most-cited scientists globally in 2024 by Stanford University. He collaborates actively with industry and academia on next-generation cellular technologies, contributing to both theoretical advancements and practical implementations. His advising and grant activities include co-PI roles on major initiatives like AutoRAN (testing automation) and DigiRAN (high-fidelity digital twins), totaling over $4M in funding. Bonati’s work bridges academic research and industry-ready solutions through platforms like Colosseum and the Open RAN Gym.

Martin Reedمشاهده پروفایل

استاد

• Computer Networking
• Network Security
• Internet of Things Security
• +۵ مورد دیگر

Professor Martin Reed is a Professor and Deputy Head of the School of Computer Science and Electronic Engineering at the University of Essex. His academic career spans over two decades, with significant contributions to computer networking and network security research. He has played a key leadership role in curriculum development for the school, merging the curricula of two former departments into the current structure. Martin Reed earned his PhD from the University of Essex in 1997 and completed his BEng (1st Class) with an Industrial Year at BBC Research Department from the University of Surrey in 1989. His educational background provided a strong foundation for his subsequent research career in networking technologies. Professor Reed's research focuses on computer networking and network security, with particular emphasis on Internet of Things security, Software Defined Networking (SDN) security, Information Centric Networking, and multimedia transmission over networks. His work bridges theoretical research with practical applications, addressing real-world challenges in network infrastructure. He has supervised numerous PhD students who have gone on to contribute to these fields, and his research has evolved to address emerging challenges in networked systems as technology has advanced. Analysis of Professor Reed's recent publications reveals a strong focus on applying artificial intelligence and machine learning techniques to network security challenges, particularly in the context of Software Defined Networking and Internet of Things environments. His work demonstrates a progression from traditional network security approaches to more sophisticated, AI-driven solutions that can adapt to evolving threats. There's also a clear trend toward addressing security challenges in distributed and edge computing environments, reflecting the changing landscape of networked systems. Professor Reed has successfully secured funding from multiple prestigious sources including the European Commission, Innovate UK, and British Telecommunications. His grants portfolio demonstrates his ability to translate theoretical research into practical applications with industry relevance. He has led projects focused on network security, information-centric networking, and IoT security, often collaborating with industry partners to ensure real-world applicability of research outcomes. As an academic supervisor, Professor Reed has guided numerous PhD students through their research journeys, with recent completions focusing on topics like IoT security, SDN security, and information-centric networking. His supervision record spans over a decade, showing consistent engagement with emerging research areas as the field has evolved. He also serves as an external examiner for undergraduate and postgraduate programs at multiple UK universities, contributing to academic quality assurance across the sector.