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Bowen XuView profile

Assistant Professor

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

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

Mira MeziniView profile

Professor

• Software development paradigms and tools
• Programming systems for reliable distributed software
• Automated software analysis
• +3 more

Mira Mezini is a Professor of Computer Science at the Technical University of Darmstadt, Germany, leading the Software Technology Lab. She serves on steering committees, program committees, and editorial boards of top-tier conferences and journals. Her affiliations include the National Research Center for Applied Cybersecurity ATHENE and co-directorship of hessian.AI, the Hessian Center for Artificial Intelligence. Her research focuses on programming systems for reliable distributed software and AI, automated software analysis, and foundational code models. She has developed large-scale module concepts for adaptability, extensible programming languages, and intelligent software development environments that leverage web-based resources to generate programming rules and patterns. Specializes in software engineering Expert in programming language design Contribution to cybersecurity and AI Her recent work emphasizes static analysis, distributed systems, and programming language design, with trends in API misuse detection, decentralized applications, and secure local-first programming. The focus spans from foundational type systems to practical tools enhancing software quality and developer productivity. Scientific Awards Two-time IBM Eclipse Innovation Award recipient (2005, 2006) Google Research Award (2017) Horst Görtz IT Security Award (2014) ERC Advanced Grant (2012) ACM Fellow Dahl-Nygaard Prize (2025) She has held leadership roles including Dean of Computer Science at TU Darmstadt (2013-2014) and Vice President positions (2014-2019). Her service extends to international juries, funding panels, and editorial boards. Labs & Teams Leads the Software Technology Lab at TU Darmstadt, driving research in programming paradigms and tools. Co-directs hessian.AI and contributes to the Excellence Cluster 'Reasonable Artificial Intelligence.'

Juan Pablo GaleottiView profile

Professor

• Automatic Test Generation
• Program Verification
• Specification Inference
• +5 more

Juan Pablo Galeotti is a Professor at the University of Buenos Aires (UBA) in the Department of Computer Science within the Faculty of Exact and Natural Sciences . He is also a researcher at CONICET and a member of the LaFHIS and LIA-INFINIS laboratories. His research focuses on automatic test generation , program verification , specification inference , and decision procedures like SMT and SAT. Education & Background: While specific degrees are not mentioned, his PhD thesis was titled "Software Verification using Alloy" , advised by Marcelo Fabian Frias . Research Interests: His work spans: Search-based software testing REST API fuzzing and testing Smart contract verification Dynamic and static analysis Testability transformations Loop invariant inference Publications & Trends: Galeotti has published extensively in top-tier venues like ICSE, FSE, ASE, ISSTA, IEEE TSE , and ACM TOSEM . His recent work ( 2020–2025 ) focuses on tools like EvoMaster for REST API testing, EvoSuite for Java unit testing, and TACO for SAT-based verification. Awards & Service: He has served as PC Co-Chair for SBST@ICSE 2017–2018 , PC Member for ICSE, ISSTA, ICST , and reviewer for journals like TOSEM, TSE . He co-organized ISSTA 2016 and ICSE 2017 . Students & Projects: He currently supervises 3 PhD and 4 Master’s students. He has co-directed CONICET and UBA-funded projects on bounded verification and memory analysis. Labs & Collaborations: He leads research at LaFHIS and collaborates with LIA-INFINIS , working on tools like EvoMaster , DynaMate , and TACO .

Diego GarbervetskyView profile

Associate Professor

• Static Analysis
• Program Verification
• Java-like Programs
• +4 more

Dr. Diego Garbervetsky is an Associate Professor at the Computer Science Department , School of Sciences , University of Buenos Aires . He serves as Director of the Institute of Research in Computer Sciences (ICC/CONICET) . Research Focus: Static analysis techniques for Java-like programs and Smart Contracts Specializations: Automated program verification, resource analysis (gas/memory/energy), API validation Research Interests His work centers on: Compositional heap memory analysis for Java/.NET programs Symbolic resource analysis and contract-based verification Real-time system verification tools Scientific Awards Selected for PIP 2022 grant by CONICET (Argentinian science agency). Academic Service Active program committee member in 2018-2021 for conferences like ICSE , FSE , and SEFM . Currently hiring PhD and postdoc researchers. Tools Development Created analysis tools including Contractor.NET , JConsume2 , and VInTime suite.

Rafał Gabriel LaskowskiView profile

Researcher

• Artificial Intelligence
• Machine Learning
• Posthumanism
• +8 more

Rafał Gabriel Laskowski is a sociobiologist and Head of the Metaverse & Gaming Team at SWPS University's HumanTech Center for Social and Technological Innovation. He combines academic research with entrepreneurial ventures through his startup TediBerry Little Metaverse, a gaming edtech platform. Doctoral studies in Sociology at Polish Academy of Sciences Master's in Political Science with National Security specialization at Collegium Civitas His research spans AI evolution, posthumanism, machine learning, and the intersection of technology with Classical European values. He pioneered the Precision Depth API for AR engines and developed the TRoAI project grounded in his PAAT and Rational Democracy theories. Collaborations include the Collegium Civitas Terrorism Research Center, Polish Parliamentary Defense Committee, and Euro-Atlantic Association, focusing on AI applications in national security and military innovation. He also explores Asian cultural systems (China, Japan, South Korea, Singapore). Co-investigator in technology-enabled national security frameworks Developer of battlefield robotics strategies Advocate for interdisciplinary AI education Additionally, Laskowski studied ballet at L'ecole-Atelier Rudra-Bejart (Lausanne) and Roman Turczynowicz Ballet School (Warsaw), integrating artistic perspectives into his work.

Emilio LeonardiView profile

Professor

• Communication Networks
• Complex Systems
• Epidemic Spreading
• +7 more

Emilio Leonardi is a Full Professor at the Department of Electronics and Telecommunications (DET) of Politecnico di Torino, Italy, since 2015. His research focuses on communication systems, complex networks, epidemic spreading, and online social networks. He has held visiting roles at INRIA (2016-2017), NEC Laboratories Europe (2012), and collaborated with institutions like UCLA, Bell Labs, and Stanford. Key research areas: Telecommunications and network engineering Stochastic processes in network modeling AI-driven caching and content delivery Social network temporal dynamics Epidemic propagation on graphs Recent publication trends highlight his expertise in similarity caching algorithms, federated learning, re-identification attacks, and generative AI applications in information retrieval. His work spans both theoretical modeling and practical implementation in real-world networks. Scientific recognition: Best Paper Award at IEEE Globecom (2002) Multiple IEEE/ACM conference awards (2006, 2012) Guest Editor for IEEE special issues Editorial board member of IEEE Transactions Teaching and mentoring: Main teacher for PhD courses in Electrical, Electronic and Communications Engineering, including Stochastic processes and queuing theory and Operational research . Supervised PhD student Franco Galante (2020-2024) on social interaction modeling. Labs and collaborations: Member of TNG research group at DET. Participated in European projects like NAPA-WINE (FP7), COOPERATION-ICT, and national PRIN initiatives. Industry collaborations with Lucent, IBM, Microsoft Research, and NEC.

• Software Reliability
• Bayesian Inference
• Safety Engineering
• +5 more

K.O. Salako is a researcher at City, University of London, specializing in software reliability, Bayesian inference, and safety-critical systems. His work spans autonomous vehicles, cybersecurity, and critical infrastructure resilience, with a focus on conservative statistical approaches and probabilistic modeling. Key research interests include: Software Reliability Assessment Bayesian Statistical Methods Autonomous Vehicle Safety Cybersecurity and Malware Detection Critical Infrastructure Risk Analysis His recent publications demonstrate trends in malware behavior analysis using machine learning, reinforcement learning generalization , and conservative Bayesian approaches for reliability claims. He has contributed to stochastic modeling of database protocols and interdependency analysis in infrastructure systems. Salako’s work appears in journals like Quality and Reliability Engineering International and IEEE Transactions on Software Engineering , with conference presentations at venues including European Dependable Computing Conference and IEEE/IFIP DSN . His collaborations with Zhao, Strigini, Popov, and others highlight interdisciplinary research in safety and security domains.

Salah ShariehView profile

Professor

• Digital Transformation
• Cybersecurity
• API Economy
• +3 more

Salah Sharieh is a Senior Technical Innovator with roles spanning industry and academia. Currently affiliated with Ryerson University's Yeates School of Graduate Studies, he supervises Ph.D. and Master’s students while teaching courses in security, algorithms, and networks. He has led transformative digital initiatives at RBC and BMO, including Canada’s first Developer Portal, mobile account opening system, and biometric touch ID solution. Research Focus: Digital Economy, CIO roles in digital transformation, cybersecurity Leadership: Spearheaded National Occupation Standards for Cyber Security Academic Affiliation: Ryerson University His work bridges academia and industry, with over 45 peer-reviewed publications and contributions to books. Salah actively presents at IEEE conferences and holds a Ph.D. from McMaster University. Scientific Awards Canada’s Tech Titan: Top 19 of 2019 Celent Model Bank Award (2015 & API Strategy) Financial Institutions of the Year (2018 Canadian FinTech & AI Awards) 2019 Digital Transformation Award (Large Private Sector Business Transformation) Excellence in Digital Banking Award

Dávid SzabóView profile

Associate Professor

• Sociolinguistics
• French Language and Literature
• Lexicography
• +5 more

Dávid Szabó is a habilitated Associate Professor at the Institute of Romance Studies , Department of French Language and Literature , Eötvös Loránd University. His academic profile bridges sociolinguistics , focusing on argot and slang studies in French-Hungarian contexts, with computer graphics and C# software development . He has contributed to diverse fields, including AI in education , sustainable finance , and real-time graphics APIs . Research Highlights: His work explores the intersection of language evolution and technology, with recent publications on Green Finance , AI-driven educational tools , and parallel processing in graphics programming . He investigates linguistic taboos in Hungarian politics and translation challenges of urban French slang into Hungarian, while developing educational software like StudyHelper. Technical Contributions: Szabó has pioneered the integration of modern C# with graphics APIs (Vulkan, OpenGL) for real-time rendering , creating frameworks for multi-platform applications and shader program development . His technical papers emphasize code efficiency, API abstraction, and GPU optimization.

Nikhil JhaView profile

Research Fellow

• Privacy
• Cybersecurity
• Data Mining
• +3 more

Nikhil Jha is a Research Fellow at Politecnico di Torino's Department of Control and Computer Engineering (DAUIN), focusing on web privacy, data anonymization, and cybersecurity. He earned his PhD in 2024 with a dissertation titled Privacy on the Web: Algorithms, Tools and Measurements . Teaching collaborations in Data Protection, Privacy and Anonymity , Interdisciplinary Projects , and Internet Performance and Troubleshooting Lab courses Member of the DBDM - Database and Data Mining Group His research investigates privacy risks in web technologies, including re-identification attacks on browser APIs, anonymization techniques like z-anonymity, and user behavior with privacy banners. His work spans both theoretical and applied dimensions of web security. Recent publications analyze the robustness of Topics API, practical data stream anonymization, and the evolution of privacy policies. His interdisciplinary approach combines technical cybersecurity research with insights into user interactions and policy compliance. Co-authored publications at major conferences including The Web Conference, ACM Transactions on the Web, and Privacy Enhancing Technologies Symposium At Politecnico di Torino, Jha contributes to teaching in cybersecurity and interdisciplinary projects. He also pursues creative writing through a Master Daimon in Narrative Techniques (Scuola Holden, 2025) and holds Italian journalism credentials since 2018.

Ali DogruView profile

Professor

• Software Engineering
• Component-Based Systems
• Complex Event Processing
• +7 more

Ali Dogru is a Professor of Computer Science at the University of Texas at San Antonio (UTSA), affiliated with the College of Sciences. He holds a Ph.D. in Computer Science from Southern Methodist University, an M.S. in Electrical Engineering from the University of Texas at Arlington, and a B.S. in Electrical Engineering from the Technical University of Istanbul. His research focuses on software engineering methodologies, particularly component-based systems, compositional software development, complex event processing, and fault management. He explores interdisciplinary solutions through connector technologies to address IoT heterogeneity and variability modeling in microservices architecture. Dr. Dogru's work emphasizes axiomatic design theory applications in software systems, including service-oriented computing, model-driven engineering, and cloud-enabled development. His contributions span domain-specific languages for fault management, adaptive workflows in cloud environments, and synthetic medical data generation for healthcare analytics. He has published extensively on topics like UML connector enhancements, microservice interoperability, and EEG emotion recognition using wavelet transforms. His interdisciplinary research bridges theoretical concepts with practical industry applications, as evidenced by his work on legacy system migration and mashup strategies in service-oriented computing. While no advising or grant details are explicitly mentioned, his academic profile reflects a strong focus on innovation in software development practices. His involvement in conferences such as the Turkish National Software Architecture Conference highlights his role in advancing software engineering standards and methodologies. Dr. Dogru's research lab (if applicable) is not explicitly detailed in the provided texts, but his publications indicate sustained engagement with cutting-edge computational challenges in software systems.

Godmar BackView profile

Associate Professor

• Computer Systems
• Operating Systems
• Programming Languages
• +5 more

Godmar Back is an Associate Professor in the Department of Computer Science at Virginia Tech's College of Engineering. He holds a Ph.D. in Computer Science from the University of Utah (2002) and specializes in systems research, programming languages, and performance analysis. His work focuses on foundational aspects of operating systems, distributed systems, and cloud computing, with notable contributions to memory management, concurrency, and cybersecurity education. His research explores how hardware and software interact to optimize system performance, including studies on SPEC benchmarking, garbage collection, and undefined behavior in programming languages. He has developed frameworks like CloudBrowser 2.0 for cloud-based applications and the VT-ASOS system for many-core platforms. Back is also active in pedagogy, integrating DevOps practices into undergraduate research and incorporating cybersecurity modules into core curricula through spiral theory-based methods. Key projects include the Pintos instructional operating system kernel, the VirtuOS virtualized OS, and ReplayCache for predictive computations. His work frequently bridges theoretical systems research with practical applications, emphasizing reproducibility and real-world impact. His educational initiatives aim to broaden participation in systems research through high-impact practices and collaborative development environments.

Na MengView profile

Associate Professor

• Software Engineering
• Programming Languages

Na Meng is an Associate Professor in the Department of Computer Science at Virginia Tech, affiliated with the College of Engineering. His research focuses on software engineering and programming languages, with a particular emphasis on bug detection, secure coding practices, and software development tools. He holds a Ph.D. in Computer Science from the University of Texas at Austin, an M.S. from Peking University, and a B.E. from Northeastern University, China. His work spans areas such as automated bug localization, cryptographic API misuse detection, and blockchain-based software management. Recent contributions include studies on build issue resolution, StackOverflow code reuse practices, and benchmarking software merge tools. Na Meng's research addresses challenges in software quality assurance, developer productivity, and cybersecurity. He has published extensively in top venues, with a focus on practical solutions for real-world software development challenges. His articles often explore the intersection of empirical software engineering and theoretical computer science, aiming to improve developer workflows and system reliability.

Eli TilevichView profile

Professor

• Software Engineering
• Distributed Systems
• Mobile Applications
• +4 more

Eli Tilevich is a Full Professor in the Department of Computer Science at Virginia Tech and Associate Department Head for Graduate Studies. His research focuses on systems aspects of software engineering, distributed systems, mobile/IoT applications, and energy-efficient software. He leads the Software Innovations Lab and has over 130 refereed publications. His work has been supported by federal grants and industry awards like Microsoft Research and IBM Faculty Awards. Education: Ph.D., Computer Science, Georgia Tech (2005) M.S., Information Systems, New York University (1999) B.A., Computer Science and Math, Pace University (1997) Research Interests: Automated software transformation Middleware and distributed systems Security and privacy in mobile systems Music informatics Computing education innovations His recent work includes advancements in edge computing, secure communication protocols, and educational platforms for secure programming. Over 30 students have graduated under his mentorship, many now in academia and industry leadership roles. Awards: Microsoft Research Software Engineering Innovation Foundation Award IBM Faculty Award Multiple best paper awards at ICDCS, ICWE, and SIGCSE Teaching spans courses like Programming Languages , Software Engineering Capstone , and Research Methods . He has served as General Chair for GPCE 2021, MOBILESoft 2019, and program chair for ManLang 2018.

Martin GaedkeView profile

Professor

• Web Engineering
• Knowledge Engineering
• Artificial Intelligence (AI)
• +4 more

Prof. Dr.-Ing. Martin Gaedke is a Full Professor and Dean of the Department of Computer Science at Technische Universität Chemnitz (TU Chemnitz). He leads the research group Distributed and Self-organizing Systems (VSR) and holds roles such as President of the International Society of Web Engineering (ISWE), founder of the International AIQT Foundation, and member of the German Foundation for University Admissions' IT-advisory board. His expertise spans Web Engineering, AI-driven systems, and decentralized technologies. Education: Martin Gaedke earned a Master's degree (1997) and doctoral degree (2000) in Computer Science/Engineering from the University of Karlsruhe (now Karlsruhe Institute of Technology). Research Focus: Gaedke's work advances collaborative systems in hyper-connected societies, emphasizing Web Engineering, knowledge engineering, and AI-supported hypermedia systems. Key areas include chatbot design patterns, decentralized web trust, smart IoT systems, and end-user development tools. His contributions include pioneering work in Web Engineering since the 1990s, notably co-authoring the first Web Engineering paper at WWW6 (1997). Recent Trends in Publications: Recent research highlights dynamic code migration, GenAI-driven UIs, and ethical AI in chatbots. He emphasizes trust in decentralized systems (e.g., Solid-based social networks) and practical applications like autonomous smart home agents (WS3H). Awards: Ranked 39th globally in Knowledge Engineering (2020), honored with the AI 2000 Most Influential Scholars Honorable Mention and listed in the AI 2000 Annual List. His work is cited over 1,800 times in key areas like Web Engineering and IoT. Advising & Outreach: Gaedke has mentored numerous researchers and entrepreneurs through roles as a Certified High Performance Coach and business advisor. His lab, VSR, develops open-source tools like the Web-Based Network Simulator (WNSWE) for education and industry. Labs/Teams: The Distributed and Self-organizing Systems (VSR) group at TU Chemnitz focuses on innovative projects such as the Trusting Decentralized Web Data framework and the GenAI-Driven Multimodal UI Architecture .