Find Academic Supervisors

• Software Engineering
• Artificial Intelligence
• Software Quality
• +7 more

Eman AlOmar is an Assistant Professor in the Department of Systems and Enterprises at Stevens Institute of Technology, affiliated with the Charles V. Schaefer, Jr. School of Engineering and Science. She holds a PhD in Computing and Information Sciences from Rochester Institute of Technology (2021). Her research focuses on the intersection of Software Engineering (SE) and Artificial Intelligence (AI), emphasizing software quality, refactoring, maintenance, technical debt, empirical studies, and mining software repositories. Education: PhD in Computing and Information Sciences (Rochester Institute of Technology, 2021). Research Interests: Software Maintenance & Evolution Empirical Software Engineering Mining Software Repositories Code Refactoring and Documentation Technical Debt Management AI Applications in Software Development Awards: 2024 Jess H. Davis Memorial Award for Research Excellence 2024 Best Mining Challenge Paper Award at MSR 2023 Distinguished Doctoral Research Award at MSR 2022 Best Reviewer Award at Journal of Systems and Software Grants: Co-PI on NSF DUE grant (2022-2025) to cultivate performance-aware software engineers, totaling $299,754. Teaching: Courses include Artificial Intelligence for Software Engineering, Software Testing & Quality Assurance, Object-oriented Software Engineering, and Informatics.

Lingyu WangView profile

Adjunct Professor

• Cloud Security
• Network Security
• Software Security
• +3 more

Lingyu Wang is an Affiliate Professor at the Concordia Institute for Information Systems Engineering, Concordia University. He holds a Ph.D. in Information Technology from George Mason University (2006). His research focuses on cloud security, SDN/NFV security, security metrics, network security, software security, and privacy preservation. Dr. Wang has published extensively on topics including differential privacy systems, proactive security policy enforcement in containers, and cross-cluster security models for edge-core environments. His work also addresses challenges in securing industrial systems like IEC 61850 substations and 5G networks, leveraging techniques such as state auditing and event monitoring. His recent articles emphasize balancing security with performance efficiency, such as optimizing anomaly detection while preserving privacy and developing tools for real-time inconsistency detection in NFV environments. He has contributed to frameworks like ProSAS for proactive security auditing and VinciDecoder for automated forensic reporting in cloud environments. Dr. Wang’s research bridges theoretical cybersecurity principles with practical applications in cloud infrastructure, emphasizing resilience against zero-day attacks and supply chain vulnerabilities. His work frequently integrates machine learning and deep learning for enhanced security analysis and tool development.

Jolan PHILIPPEView profile

Lecturer

• Distributed Systems
• Infrastructure as Code (IaC)
• Model Driven Engineering
• +5 more

Jolan PHILIPPE is a Lecturer at University of Orléans affiliated with LMV research laboratory, and currently holds a postdoctoral position at Université de Rennes in the DiverSE team under the supervision of Olivier Barais. His research focuses on Infrastructure as Code (IaC) and distributed systems reconfiguration, with particular emphasis on formal methods and model-driven approaches. His primary research interests include: Distributed Systems and Cloud Computing Infrastructure as Code (IaC) and DevOps Model Driven Engineering Formal Methods and Verification Parallel and Distributed Programming Algorithmic Skeletons Dr. PHILIPPE's work demonstrates a strong trajectory from foundational research in parallel programming models (particularly through his work on PySke) to applied research in distributed system reconfiguration. His recent publications show increasing focus on formal approaches to infrastructure management, with significant contributions to languages like Concerto-D and frameworks like Ballet for cross-DevOps reconfiguration. His research bridges theoretical computer science (formal methods, verification) with practical cloud infrastructure concerns. He is actively supervising two PhD candidates: Haitam El Hayani (Taranis project, 2024-2027) - Extending Infrastructure as Code (IaC) languages, focusing on enhancing capabilities and integration with modern cloud deployment practices Olivia Proust (For-Coala project, 2025-2028) - Towards formally verified configuration management languages Dr. PHILIPPE is deeply involved in research coordination: Coordinator of the "Défi GDR GPL" (challenge) ADDYCT (ADaptation DYnamique et ConTinue) for 2026-2030 One of the leaders of the GDR GPL's GT SyLA (Groupe de Travail - Système Logiciel Adaptable) since February 2025 Helped organize the VELVET (Verification and Software Engineering for DevOps and Reconfiguration) days in Nantes, December 2023 His service to the academic community includes: Program Committee Member for DebConf 2025, ICCS 2025, LowCode@MODELS 2025 Publicity Chair and Program Committee Member for UCC 2025 and BDCAT 2025 Reviewer for journals (COLA, SoSyM) and multiple international conferences

Adrian ALEXANDRESCUView profile

Associate Professor

• Internet technologies
• Web services
• Distributed systems
• +5 more

Adrian ALEXANDRESCU is an Associate Professor at the Department of Computer Science and Engineering within the Faculty of Automatic Control and Computer Engineering at “Gheorghe Asachi” Technical University of Iași. He is a member of the Open Infrastructure Research Center and specializes in interdisciplinary research areas such as blockchain technology, distributed systems, artificial intelligence (genetic algorithms, neural networks), and IoT applications. His work bridges theoretical computer science with practical implementations in education, healthcare, and smart technologies. His research interests focus on leveraging blockchain for secure transactions, optimizing distributed systems, and enhancing e-learning through gamification. He has contributed to projects involving sensor networks for health monitoring, real-time driver sobriety tracking, and decentralized identity management systems. His academic contributions span over 20 years, with notable work on genetic algorithms for task mapping in heterogeneous systems and cloud-based solutions for ambient assisted living. Dr. ALEXANDRESCU’s publications emphasize blockchain’s role in trustless systems, IoT-driven healthcare environments, and AI-driven solutions for education and logistics. His work on decentralized article retrieval systems and plagiarism detection frameworks underscores his commitment to ethical and efficient digital ecosystems. Despite no explicitly listed awards, his prolific publication record reflects sustained academic excellence. He advises on projects at the intersection of cloud computing, distributed architectures, and smart technologies. His labs and collaborations focus on developing scalable solutions for real-world challenges such as secure real estate transactions and community-driven academic publishing systems.

Muhammad Usman IlyasView profile

Assistant Professor

• Computer Science
• Machine Learning
• Artificial Intelligence
• +7 more

Dr. Muhammad Usman Ilyas is an Assistant Professor in the School of Computer Science at the University of Birmingham, Dubai, UAE. He previously held academic positions at the University of Jeddah and the National University of Sciences and Technology (NUST), Islamabad. His expertise lies in computer science, networking, and artificial intelligence, with a focus on practical and societal applications. Education: Ph.D. in Electrical Engineering, Michigan State University, USA (2009) M.S. in Electrical Engineering, Michigan State University, USA (2007) M.S. in Computer Engineering (Dean’s List), Lahore University of Management Sciences (LUMS), Pakistan (2005) B.E. (Honors) in Electrical Engineering, National University of Sciences & Technology (NUST), Pakistan (1999) His research interests span Machine Learning, Artificial Intelligence, Internet of Things (IoT), Network Security, Wireless Networks, Data Science, and Smart Cities . He integrates these areas into applications in healthcare, urban systems, and education. His recent publications demonstrate a strong trend in applying deep learning to medical diagnostics and enhancing cybersecurity in modern networks. His work also explores educational robotics and data-driven public health analysis. Scientific Awards: No awards explicitly mentioned. Dr. Ilyas has served as a Postgraduate Program Director and has advised students through research publications, though formal student names are not listed. He has contributed to research grants and projects, particularly in IoT infrastructure and network modeling, as reflected in his publications. He has also taught modules in AI, data science, security, and mathematical foundations of computing. Labs and Teams: While specific lab names are not mentioned, his research collaborations and publication topics suggest involvement in teams focused on AI, networking, and IoT systems, likely within the School of Computer Science at the University of Birmingham Dubai.

Luigi De SimoneView profile

Assistant Professor

• Dependability
• Fault Injection Testing
• Operating Systems Reliability
• +7 more

Luigi De Simone is an Assistant Professor (non-tenure track) in the Department of Information Technology and Electrical Engineering at the University of Naples Federico II. He is a member of the DESSERT (Dependable and Secure Software Engineering and Real-Time Systems) research group and collaborates with the CINI laboratory (Consorzio Interuniversitario Nazionale per l'Informatica). His academic journey at Federico II University spans from undergraduate studies through his PhD and into his current faculty position. His educational background includes: M.Eng. (2013): Master of Engineering in Computer Engineering (cum laude), University of Naples Federico II B.Eng. (2009): Bachelor of Engineering in Computer Engineering, University of Naples Federico II Luigi De Simone's research focuses on the dependability of computing systems, with particular emphasis on fault injection testing methodologies for operating systems and cloud environments. His work bridges theoretical foundations with practical applications in virtualization technologies, exploring how these technologies can enhance system reliability while addressing the challenges of mixed-criticality environments. He investigates the intersection of cloud computing, virtualization, and system dependability, with applications spanning from industrial systems to telecommunications infrastructure. His research often involves developing novel testing frameworks and analytical models to assess system behavior under fault conditions. His recent publications demonstrate a consistent focus on virtualization technologies and their application in critical systems. There's a clear trajectory from foundational work on fault injection in cloud systems to more specialized applications in 5G networks, railway systems, and Industry 4.0 environments. His research increasingly incorporates machine learning techniques for failure prediction and analysis, showing adaptation to emerging methodologies in the field. The work spans both theoretical modeling (queueing theory, performability analysis) and practical implementation (tools like ThorFI and IRIS). His notable scientific achievements include: Reusable and Available Badge (2019) for paper at ESEC/FSE PhD Thesis Award from Associazione GUFPI-ISMA (2019) Best Paper Award at NetSoft 2015 Best Student Presentation Award at ISSRE 2014 PhD fellowship from University of Naples Federico II (2014) As an educator, De Simone has taught a range of courses from fundamental computer science to advanced specialized topics, with a particular focus on virtualization technologies and advanced programming. His teaching reflects his research interests, creating a synergy between his scholarly work and educational contributions. He has been involved in numerous research grants related to dependable systems, cloud computing, and virtualization technologies, contributing to both national and international research projects. He is actively involved with the DESSERT research group, which focuses on dependable and secure software engineering and real-time systems. His work with the CINI laboratory extends his research impact into national collaborative efforts in computer science. These affiliations provide the infrastructure for his experimental work on virtualized systems and fault injection methodologies.

• Edge/Fog computing
• Distributed computing
• Cyber-physical systems
• +8 more

BOUZARKOUNA Imen is a Researcher at CESI Engineering School in the Department of Computer Science. She is affiliated with the Engineering and Numerical Tools research team, focusing on the intersection of Edge/Fog computing, distributed systems, and Industry 4.0/5.0 technologies. PhD in Computer Science (INSA Rouen, 2021) Master 2 in Complex Systems Engineering (University of Lorraine, 2016) Engineering Degree in Computer Science (ENIG, 2015) Her research spans Fog computing, Cyber-Physical Systems, IoT, and their applications in smart manufacturing and logistics. Recent work explores integration with Industry 5.0 concepts like the Metaverse and real-time processing. Publications highlight Docker-based deployment, fractal modeling, and optimization of fog architectures. She contributes to educational activities in System Administration, Networking, Programming, and Image Processing, supporting both preparatory and engineering cycles in computer science disciplines. The Engineering and Numerical Tools research team drives her collaborative projects, emphasizing practical implementation of emerging technologies in industrial contexts.

• Parallel and Distributed Computing
• High Performance Computing
• Edge-Cloud Computing
• +3 more

Overview Dr. Jean-François DOLLINGER is a Researcher-Lecturer at CESI LINEACT (Strasbourg campus), affiliated with the Engineering and Numerical Tools research team. His academic roles include teaching Computer Science courses (undergraduate/graduate) and supervising student projects in algorithmics, programming, databases, and networks. Education: PhD in Computer Science (2011-2015), University of Strasbourg - ICube Lab MSc in Computer Science (2009-2011), University of Strasbourg (Highest Honors) BSc in Computer Science (2008-2009), University of Strasbourg (Honors) Research Interests: Focuses on edge-cloud computing, high-performance distributed systems, combinatorial optimization in IoT networks, and smart city infrastructure. Specializes in optimizing federated learning, WSN deployment strategies, and hybrid CPU/GPU execution frameworks. Advising & Collaboration: Supervises PhD/Master’s students (e.g., A. BAAHMED on federated learning, K. BOUHOUCH on OpenStack edge deployment) Collaborated with Indonesian universities (Mercu Buana) on RPL protocol extensions Research Team: Leads the Engineering and Numerical Tools group, developing frameworks for edge-cloud infrastructures and BIM-based WSN deployments in smart buildings.

David M JohnsonView profile

Researcher

• Computer Systems
• Security
• Networking
• +5 more

David M Johnson is a Researcher and Software Engineer at the School of Computing, University of Utah, specializing in cloud infrastructure, wireless systems, and network security. He designs and builds software for large-scale testbeds like CloudLab, CapNet, and POWDER, focusing on low-level system interactions and secure network architectures. Research Interests: His work spans computer systems virtualization security forensics software-defined networking (SDN) capability-based network design mobile sensor networks and emphasizes least-privilege principles and end-to-end containment in cloud and wireless environments. Publication Trends: Recent articles highlight his contributions to radio dynamic zones open RAN (Radio Access Network) slicing spectrum sharing secure network capabilities testbed automation kernel-level debugging with a strong focus on open-source tool development for wireless and cloud research. Scientific Recognition: He received the 2024 IEEE DySPAN Award Paper for his work on POWDER-RDZ. Collaborative Efforts: Johnson has worked extensively with teams on projects like A3 (attack diagnosis), Stackdb (stackable debugging), and SeaCat (secure application containment), often bridging systems software with security and network innovations.

Earl T. BarrView profile

Professor

• Software Engineering
• Program Analysis
• Automated Program Repair
• +5 more

Earl T. Barr is a Professor of Software Engineering at University College London (UCL), where he heads the System Software Engineering Group and is a member of the Centre for Research on Evolution, Search and Testing (CREST). He received his Ph.D. in Computer Science from the University of California, Davis in 2009. His educational background includes: Ph.D. in Computer Science, 2009, University of California, Davis Barr's research focuses on software engineering, particularly program analysis, automated program repair, and the application of machine learning to code (AI4Code). He is renowned for his work on dual channel analysis, which examines how code combines natural language elements (in identifiers, comments, and stylistic choices) with formal programming language. His research also explores game theory applications to software development processes and how dual channel constraints can improve type inference and code understanding. His recent publications demonstrate a strong trend toward leveraging large language models for code understanding and modification, with particular emphasis on dual channel constraints and natural type inference. His work bridges traditional software engineering techniques with modern AI approaches, showing how machine learning can enhance program analysis and repair while maintaining rigorous theoretical foundations. Notable awards include: ACM SIGSOFT Distinguished Paper Award for Automated Software Transplantation (2015) ACM SIGSOFT Distinguished Paper Award for Learning Natural Coding Conventions (2014) ACM SIGSOFT Distinguished Paper Award for Collecting a Heap of Shapes (2013) Best Paper Award for TrustDavis: A Non-Exploitable Online Reputation System (2005) Barr actively supervises numerous PhD students and postdocs, with current research focusing on AI for code, software security, and program analysis. He collaborates extensively with Santanu Dash of Royal Holloway on dual channel program analysis, and they jointly supervise PhD students through the EPSRC Centre for Doctoral Training in Cyber Security for the Everyday. His work often combines theoretical rigor with practical tool development, resulting in several publicly available research tools that influence both academic research and industry practice. He leads the System Software Engineering Group at UCL, which focuses on developing novel approaches to software engineering challenges at the intersection of human and machine aspects of programming. Current research directions include dual channel analysis for vulnerability detection, game-theoretic approaches to development processes, and AI-assisted software development.

Dong WangView profile

Associate Professor

• Empirical Software Engineering
• Mining Software Repositories
• AI4SE

Dong Wang is an Associate Professor at Tianjin University, China, specializing in software engineering research with a focus on empirical methods and AI applications in software development. His primary research interests include: Empirical Software Engineering Mining Software Repositories AI for Software Engineering (AI4SE) Professor Wang has established himself as a significant contributor to the software engineering research community through numerous publications at top conferences. His work spans software testing, programming tools, developer productivity, and open source software development, with particular emphasis on applying AI techniques to software engineering problems. His recent publications demonstrate expertise in unit test generation, JVM fuzzing, equivalent mutant detection, and studies of developer tools like GitHub Copilot. His contributions have been recognized through publications at premier software engineering venues including ASE, ICSE, FSE, and ICSME across multiple years from 2023-2025. Professor Wang is actively engaged in the research community, serving on program committees for numerous conferences and participating in New Ideas and Emerging Results tracks, demonstrating his commitment to advancing the field and supporting emerging research directions.

Marco TaccaView profile

Senior Lecturer

• Optical Networks
• Network Optimization
• Wireless Sensor Networks
• +3 more

Marco Tacca is a Senior Lecturer III in the Department of Electrical Engineering at the Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas. He holds a Ph.D. in Electrical Engineering from UT Dallas (2002) and a Laurea Degree in Electrical Engineering from Politecnico di Torino, Italy (1998). His research focuses on optical network architectures, wavelength routing, network optimization, and performance analysis of computer networks. He has contributed to the development of optical network emulation tools and cross-layer monitoring platforms. Key research interests include SDN/NFV integration, high-speed optical networks, and wireless sensor networks. His work spans from theoretical modeling (e.g., blocking probability estimation) to practical implementations like the PROnet prototype and OpenNOP observability platform. He holds patents related to dynamic provisioning in optical networks and has authored over 80 peer-reviewed publications. Marcos professional preparation includes roles as a Post-Doctoral researcher and Research Assistant at UT Dallas. He advises on engineering ethics and has explored methodologies to enhance team-based ethical decision-making in academic projects. His recent work emphasizes edge computing, 5G network migration, and multi-layer network orchestration in OpenROADM environments.

Carlos Eduardo da SilvaView profile

Senior Lecturer

• Software Engineering
• Information Security
• Self-adaptive Systems
• +9 more

Dr. Carlos Eduardo da Silva is a Senior Lecturer in Software Engineering at the School of Computing and Digital Technologies, Sheffield Hallam University (UK), and leads the Applied Software Engineering Research Group (ASERG). His research focuses on Software Engineering's intersection with Information Security, emphasizing self-adaptive systems, AI planning, service-orientation, and secure-by-design methodologies. He has led projects in smart cities, IoT applications, and cloud computing, including consultancy for SMEs in the Sheffield City Region through the Digital Innovation for Growth (DIfG) programme. Education: PhD in Computer Science (University of Kent, 2011), MSc in Systems & Computing (Federal University of Rio Grande do Norte, 2007), BSc in Computer Science (Federal University of Rio Grande do Norte, 2005). Research Interests: Self-adaptive systems, business process engineering, digital identity management, insider threat detection, and secure software development. Current work includes AI-driven drug discovery, IoT-based fraud detection systems, and process mining for business process visualization. Teaching: Teaches modules like Secure Software Engineering, Software Architecture, and Professional Software Projects across undergraduate and postgraduate programs in Computer Science and Software Engineering. Supervises final-year projects and apprenticeship programs. Professional Activities: Member of RNP's Identity Management Technical Committee and Brazilian Computer Society's Security SIG. Former General Chair of SBSeg 2018 and reviewer for journals like Systems and Software and conferences like WBCI. Key Projects: Led ASERG's Knowledge Transfer Partnership (KTP) with Cafeology for coffee roasting process systems. Developed a data space infrastructure for the Landscape Laboratory initiative, supporting interdisciplinary environmental research.

• Software Engineering
• Artificial Intelligence
• Software Quality Assurance
• +5 more

Mohamed Wiem Mkaouer is an Associate Professor in the Department of Computer Science, Engineering, and Physics at the College of Innovation and Technology, University of Michigan-Flint. He serves as Graduate Director for the Master’s in Software Engineering and Artificial Intelligence programs. Research Focus: At the intersection of Software Engineering and Artificial Intelligence, with emphasis on software quality assurance, systems refactoring, infrastructure-as-code analysis, technical debt management, and accessibility in software design. His work employs multi-objective evolutionary search and machine learning techniques. Publications: With 140+ peer-reviewed papers in top venues (TSE, TOSEM, EMSE, CHI, ASE), recent studies include infrastructure-as-code analysis in OpenStack, ChatGPT-assisted refactoring, and accessibility challenges in mobile platforms. Collaborative work appears with Ali Ouni, Eman Abdullah AlOmar, and Marouane Kessentini. Awards: Recipient of 6 best-paper/presentation awards and the 2020 RIT GCCIS Best-Emerging Scholar Award. Grants: Leads $4.5M in federally funded projects including NSF SHF grant for DevOps configuration drift detection (2026-2029, $600k), DARPA TRACTOR project (2025-2029, $381k), and NSF SURGE model grant (2025-2029, $3.37M as Co-Investigator). Professional Activities: Regularly publishes in IEEE/ACM conferences like ICSE, SANER, and MSR. Key contributions include tools like AntiCopyPaster, SATDBailiff, and tsDetect for code quality analysis.

Reza RahaeimehrView profile

Assistant Professor

• Cybersecurity
• Side Channel Attacks
• Intrusion Detection
• +3 more

Reza Rahaeimehr serves as Assistant Professor in the Department of Computer & Cyber Sciences at Augusta University's School of Computer and Cyber Sciences, teaching courses including Operating Systems, Web Development, and specialized cybersecurity topics. His educational credentials feature: Ph.D. in Computer Science, University of Connecticut, 2019 Dr. Rahaeimehr's research centers on practical cybersecurity vulnerabilities , with pioneering work in acoustic side-channel attacks that exploit keyboard sounds to extract sensitive data. His investigations extend to machine learning security (particularly backdoor attacks in intrusion detection systems) and cryptographic protocols for cloud infrastructure and supply chain management, revealing critical weaknesses in real-world systems. Publication trends (2018-2023) demonstrate evolving focus from foundational cryptographic security toward AI-driven threat analysis, with recent work emphasizing transfer learning vulnerabilities and acoustic eavesdropping techniques. This trajectory highlights growing industry relevance in hardware security and adaptive defense mechanisms. No scientific awards are documented in current institutional records. Dr. Rahaeimehr actively contributes through departmental service as Faculty Advisor for the Undergraduate Courses & Curricula Committee (2023-2028) and professional service on technical program committees for major security conferences including the International Conference on Cryptology and Network Security (2023).