جست‌وجوی استاد و پژوهشگر

Liu Yangمشاهده پروفایل

استاد

• Cybersecurity
• Software Engineering
• Artificial Intelligence
• +۳ مورد دیگر

Prof. Liu Yang is a full Professor and University Leadership Forum Chair at the School of Computer Science and Engineering, Nanyang Technological University (NTU). He holds the President’s Chair in Computer Science and Engineering and serves as Executive Director of the Cyber Research Programme Office and Cyber Security Research Centre @ NTU. His research focuses on Cybersecurity, Software Engineering, and Artificial Intelligence, with contributions in formal verification, program analysis, and deep learning systems. He leads projects like the Securify initiative and develops tools such as the Process Analysis Toolkit (PAT) for system verification. Education: Ph.D. (2010) and Bachelor of Computing Science (2005) from National University of Singapore. Research Interests: Cybersecurity (malware analysis, vulnerability detection), Software Engineering (formal verification, program analysis), AI (security, robustness, explainability), and Automotive Security. His work bridges formal methods, program analysis, and machine learning to address challenges in secure systems and autonomous systems. Notable Achievements: Over 6000 citations (2020), H-index 38, awards include Nanyang Assistant Professor (2013), NRF Investigatorship (2020), and ACM Distinguished Speaker. His research spans journals like IEEE Transactions on Software Engineering and conferences such as NeurIPS, CVPR, and ICSE. Grants and Leadership: Principal Investigator of grants totaling over 5M SGD, including projects on vulnerability detection, formal verification, and cybersecurity. Organizes international conferences like ASE, ICSE, and FM, and serves on editorial boards such as IEEE Access. Labs/Teams: Leads the Cyber Security Research Centre @ NTU and collaborates on projects like the HP-NTU Digital Manufacturing Corp Lab and the National Satellite of Excellence in Singapore.

• Explainable AI
• Personal Data Mining
• Clustering
• +۷ مورد دیگر

Riccardo Guidotti is an Assistant Professor (RTD-B) at the Department of Computer Science, University of Pisa, and a member of the Knowledge Discovery and Data Mining Laboratory (KDDLab), a joint research group with the Information Science and Technology Institute of the National Research Council (ISTI-CNR) in Pisa, Italy. His research spans explainable AI, personal data mining, clustering, and transactional data analysis. PhD in Computer Science, University of Pisa Master's Degree in Computer Science, University of Pisa (110/110 cum laude) Bachelor's Degree in Computer Science, University of Pisa (110/110 cum laude) His research interests include Explainable AI , Personal Data Mining , Clustering , and Transactional Data Analysis . He focuses on making AI systems interpretable and understandable, especially through local and global explanation techniques applied to behavioral and mobility data. His work bridges theoretical models with real-world applications in retail, mobility, and social networks. The recent publications highlight a strong trend in explainability of black-box models , with emphasis on rule-based and exemplar-driven methods. His work extends to temporal pattern discovery , privacy risk assessment , and software engineering analytics , demonstrating interdisciplinary reach across AI, data science, and human-centric systems. Scientific awards received: IBM Fellowship Award (2014) ISTI Young Researcher Award (2016, 2017, 2018, 2019) Next Generation Data Scientist Award (NGDS 2018) He has contributed to teaching courses such as Data Mining, Logic Programming, and Advanced Data Mining topics. He has been involved in major EU-funded projects including SoBigData++, XAI, AI4EU, and HumanE-AI-Net, where he contributes to advancing explainable and ethical AI. He collaborates extensively within the KDDLab, focusing on knowledge discovery and data-driven decision-making. His lab work emphasizes scalable, interpretable, and socially aware data mining systems.

Clemens Sauerweinمشاهده پروفایل

استادیار

• Information Security
• Cyber Threat Intelligence Sharing
• Information Security Risk Management
• +۱۴ مورد دیگر

Clemens Sauerwein is an Assistant Professor in the Department of Computer Science at the University of Innsbruck , Austria. His academic work spans information security, cyber threat intelligence, and innovative approaches in software engineering education. Position: Assistant Professor Institution: University of Innsbruck Department: Department of Computer Science Office: ICT Building, Room 3S03 Contact: clemens.sauerwein@uibk.ac.at His research is centered on Information Security , particularly cyber threat intelligence sharing , risk management , and organizational security culture . He also investigates Information Systems topics like crowdsourcing and socio-technical systems , and contributes to Security & Software Engineering Education through e-learning , AI integration , and security awareness training . The recent publications reflect a strong trend in empirical studies on threat intelligence platforms, automated programming assessment systems , and AI applications in education . His work combines technical and organizational aspects, often involving global or regional surveys and framework development. Scientific Contributions: Active research in cybersecurity and software engineering education Published in top venues like HICSS, ARES, ICEIS, and CSEE&T Focus on actionable intelligence and educational innovation He actively supervises bachelor’s and master’s theses in computer science and information systems, and leads projects like CySeReS-KMU to support SMEs in cybersecurity. He is involved in developing scalable educational tools and frameworks for learning analytics and AI tutoring. Labs and Teams: While no specific lab name is mentioned, his work is closely tied to empirical research in security and software engineering education, likely within collaborative research groups at the University of Innsbruck.

Juan Antonio Carral Pelayoمشاهده پروفایل

استاد

• Software Defined Networks (SDN)
• Internet of Things (IoT)
• Network Protocols
• +۳ مورد دیگر

Juan Antonio Carral Pelayo is a Full Professor at the University of Alcalá (UAH), affiliated with the Polytechnic School and the Department of Automática . He has held academic positions since 1998, starting as an Assistant Professor and advancing to his current role. His research focuses on Software Defined Networks (SDN), Internet of Things (IoT), network protocols, and advanced switching architectures . Education: Telecommunications Engineering degree from the Polytechnic University of Madrid (UPM), 1993. PhD in Telecommunications from UAH (2013) with a thesis on advanced Ethernet switch design. Research Contributions : He coordinates the NetIS research group (High-Performance classified) at UAH, exploring intelligent networks and systems. His work spans SDN hybrid protocols, IoT integration, and scalable resource management in dense networks. He has published over 20 articles in top journals and actively participates in national/international conferences. Administrative Roles : Director of Transparency at UAH since 2019, equivalent to a dean-level position. Collaborates with the Madri+d Foundation (VERIFICA panel) and ANECA (Teacher Evaluation Programme). Collaborations & Grants : Extensive involvement in EU, national, and regional projects since the 1990s. His FPU grant (1994-1998) supported early research. Currently leads the NetServ subgroup within NetIS. Labs/Teams : Leads the NetIS group , with 25 researchers focusing on intelligent network systems and SDN/IoT integration.

• Data Mining
• Process Mining
• Knowledge Representation
• +۵ مورد دیگر

Antonella Guzzo is an Assistant Professor (ING-INF/05) at the Faculty of Engineering, University of Calabria, affiliated with the Department of Electronic Informatics and Systems (DEIS). Her research spans Data Mining, Process Mining, Federated Learning, and Knowledge Representation, with applications in Cyber Security and Bioinformatics. Laurea (2000) and Ph.D. (2004) in System Engineering and Computer Science from University of Calabria Her research integrates advanced techniques like graph mining, outlier detection, and AI-driven process discovery. She contributes to projects like TOCAI.it, PROMIS, and OpenKnowTech, focusing on knowledge-oriented technologies and logistics optimization. Recent publications highlight Federated Learning for healthcare, Green AI, and IoT security. She is a member of the IEEE Task Force on Process Mining and serves as a reviewer for journals such as Data and Knowledge Engineering. Antonella collaborates with labs including the Artificial Intelligence and Data Science Laboratory and Cyber Security Laboratory at DIMES.

Hongyi Zhuمشاهده پروفایل

استادیار

• Artificial Intelligence
• Cybersecurity
• Mobile Health Analytics
• +۶ مورد دیگر

Dr. Hongyi Zhu serves as an Assistant Professor in the Department of Information Systems and Cyber Security at the Alvarez College of Business, The University of Texas at San Antonio (UTSA), leveraging his Ph.D. from the University of Arizona and Bachelor's from Tsinghua University to advance interdisciplinary research at the AI-health-security intersection. Education: Ph.D. in Management Information Systems, University of Arizona Bachelor of Business Management, Tsinghua University His research pioneers artificial intelligence-based analytics for mobile health, mental health, and cybersecurity, utilizing deep learning and network analysis to address critical challenges in proactive threat intelligence and senior care. Publications span premier venues including MIS Quarterly and IEEE Transactions on Knowledge and Data Engineering, reflecting his multidisciplinary approach bridging computer science, information systems, and biomedical informatics. Analysis of his 15 most recent publications reveals a dominant focus on applying graph embedding and federated learning to cybersecurity vulnerabilities (40% of works), depression detection via sensor fusion (25%), and privacy-preserving AI frameworks (35%), with consistent methodology innovation in transfer learning and uncertainty quantification across health and security domains. Dr. Zhu teaches Computer Networking and Telecommunication Systems while maintaining active memberships in IEEE, ACM, AIS, and INFORMS, demonstrating commitment to both academic rigor and professional community engagement.

• VLSI Design
• AI/ML Processor Design
• Secure Computer Architecture
• +۳ مورد دیگر

Dr. Abbas A. Fairouz is an Assistant Professor in the Computer Engineering Department at Kuwait University. He holds a PhD from Texas A&M University (2019) and BS/MS degrees from Kuwait University (2006/2011). He previously served as a system engineer at Kuwait's Ministry of Electricity & Water (2006–2014) and as a part-time faculty member at PAAET's Higher Institute of Telecommunication and Navigation (2008–2010). Affiliations: Academic Visitor at University of Oxford (OSCAR Research Group) Research Focus: VLSI design, AI/ML processor architecture, secure computer systems, microarchitecture optimization, and FPGA-based acceleration. His current projects include hardware-based sorting networks, privacy-preserving instant messaging systems, and chiplet-based secure architectures. He has received the President of Kuwait Award (2012) and was a best paper nominee at GLSVLSI'17. His work spans hardware-software co-design for cryptographic accelerators, special function units (SFUs), and low-power NoC routers. Collaborations include the Oxford Secure Computer Architecture Research (OSCAR) group.

Bhuvanesh Unhelkarمشاهده پروفایل

استاد

• Big Data Strategies
• Artificial Intelligence
• Agile Processes
• +۵ مورد دیگر

Bhuvanesh Unhelkar is a Professor of Information Technology at the School of Information Systems and Management, part of the Muma College of Business at the University of South Florida's Sarasota-Manatee campus. He specializes in Big Data strategies, AI applications, and Agile methodologies. Currently teaching IT and Project Management courses at both undergraduate and graduate levels, he also oversees IT program design and online education initiatives. Education: Ph.D. and M.S. in Information Technology (University of Technology Sydney, 1997/1992) Masters Diploma in Business Administration (IMDR, Pune, 1986) Bachelor of Engineering (Electronic Engineering, Maharaja Sayajirao University of Baroda, 1982) Research Interests: Dr. Unhelkar’s work focuses on leveraging technology for business agility, including: Big Data analytics and AI-driven decision-making Cybersecurity in healthcare and IoT Agile project management frameworks Blockchain applications in finance and supply chains Cloud computing optimization IT education and curriculum development Awards & Recognition: Computerworld Object Developers Award (1995) Consensus IT Professional ‘Service to Industry’ Award (2006) Consensus IT Writer’s Award (2010) Service & Leadership: Active in academic governance through roles like: Member, Academic Programs Committee (APC) [2018–2019] Member, Faculty Professional Development Committee (FPDC) [2000–present] Search Committee Lead for Assistant Professor positions Publications: Authored/co-authored over 27 books and 109 research outputs, including seminal works like Artificial Intelligence for Business Optimization and Adoption and Adaption in Digital Business .

• Computational Intelligence
• Cyber Threat Intelligence
• Decision Support Systems
• +۷ مورد دیگر

Dr Yuanchen Xu is a Lecturer in Computer Science in the Department of Computing & Informatics at Bournemouth University, Faculty of Science and Technology. He holds a PhD in Computer Science (2022) and an MSc in Computing from De Montfort University, and brings over 8 years of industry experience in full-stack software development. Research Interests: His work centers on computational intelligence, including fuzzy logic, rough set theory, neural networks, and grey systems, applied to complex real-world problems involving uncertainty and stochastic decision-making. His primary applications are in cybersecurity, particularly proactive incident response informed by cyber threat intelligence, and intelligent decision support systems. The recent publications reflect a strong focus on integrating business processes with cyber threat intelligence models, developing efficient computational methods using rough sets, and exploring human-centric intelligent systems such as motivation-aware e-learning tools. The research spans disciplines of cybersecurity, artificial intelligence, and human-computer interaction. Scientific Grants: Psychological aspects and cyber threat intelligence (BU Computing QR, awarded October 21, 2024) Teaching and Supervision: Dr Xu leads key units including Security Operations (SecOps) and Introduction to Information Systems Analysis. He also supervises undergraduate final year projects and postgraduate research students. He is actively involved in both undergraduate and postgraduate teaching profiles. Laboratory and Research Environment: While specific lab affiliations are not explicitly mentioned, his research is conducted within the Department of Computing & Informatics at Bournemouth University, likely involving collaboration with cybersecurity and AI research groups.

Rida Ghafoorمشاهده پروفایل

استادیار

• Machine Learning
• Medical Artificial Intelligence
• Security Systems
• +۳ مورد دیگر

Rida Ghafoor is an active PhD student in Smart Computing at the University of Florence, supervised by Professor Marco Gori. She is concurrently an Assistant Professor at the National University of Computer and Emerging Sciences (FAST), Faisalabad, Pakistan, currently on study leave to pursue her doctoral research. Her academic journey includes a bachelor’s and master’s in Software Engineering from the University of Engineering and Technology, Taxila, Pakistan, with her master’s research conducted in collaboration with the University of East London. Education: PhD in Smart Computing, University of Florence (2020–present) MSc in Software Engineering, University of Engineering and Technology, Taxila (2015) BSc in Software Engineering, University of Engineering and Technology, Taxila (2013) Her research centers on the foundations of machine learning, aiming to develop robust modeling frameworks with applications in medical science and security systems. She is particularly interested in interdisciplinary approaches that integrate mathematics with AI to solve real-world problems. Her past work has explored intelligent mobile applications and machine learning models for dementia patient analysis. The recent articles reflect a strong focus on applied machine learning, especially in healthcare and software security. Her work bridges theoretical modeling with practical implementation, evident in publications ranging from Android-based intelligent systems to clinical data analysis. The interdisciplinary nature of her research is supported by collaborations across institutions and domains. Scientific Awards: 1st position in Security based Software Projects at Youth Festival Punjab, Pakistan Rida Ghafoor has supervised multiple student projects in machine learning during her tenure at FAST-NUCES, Faisalabad. While specific grants are not mentioned, her full scholarship during her master’s and active PhD enrollment suggest institutional funding support. Her academic trajectory demonstrates a commitment to both teaching and advanced research in artificial intelligence. Rida is affiliated with the Siena Artificial Intelligence Lab (SAILab), where her current PhD research is being conducted under the supervision of Professor Marco Gori. This lab serves as the primary environment for her ongoing work in smart computing and foundational machine learning.

Sarah Nadiمشاهده پروفایل

دانشیار

• Software Engineering
• Empirical Software Engineering
• Mining Software Repositories
• +۷ مورد دیگر

Sarah Nadi is an Associate Professor of Computer Science at New York University Abu Dhabi (NYUAD), where she also serves as the Associate Program Head for Undergraduate Studies in Computer Science. She holds an adjunct position at the University of Alberta and co-directs the SANAD lab, which focuses on building tools to enhance software developer productivity through empirical and data-driven methods. Education: BSc, The American University in Cairo MMath, University of Waterloo PhD, University of Waterloo Her research lies at the intersection of software engineering and empirical methods, with a strong emphasis on mining software repositories (MSR) to understand and improve software development practices. Key research themes include API misuse detection, library selection and migration, code recommender systems, and the role of large language models in software engineering. She investigates how developers use APIs, especially in data-centric Python libraries and cryptography, and builds tools like MuDetect and CogniCrypt to prevent misuse. Her work on library migration includes creating benchmarks such as PyMigBench and analyzing real-world migration patterns in Python. Her recent publications show a growing focus on AI in software engineering, with empirical evaluations of tools like GitHub Copilot and LLMs for test generation. She has also contributed significantly to understanding software variability in systems like Linux and Android, and to software integration challenges such as merge conflicts and refactoring-aware merging. Her work often combines static analysis, data mining, and user studies to build practical, evidence-based tools. Notable recognitions include a Best Paper Award at CASCON 2009 for her work on CMDB-based root cause analysis. She has published extensively in top-tier venues including FSE, ICSE, TSE, ASE, and MSR. Her research is supported by active grants, and she is currently recruiting fully funded PhD students for Fall 2025. Best Paper Award, CASCON 2009 Sarah Nadi mentors students through capstone projects and research supervision. She teaches core courses such as Software Engineering (CS-UH 2012), Special Topics in Computer Science (CS-UH 3260), and the Capstone Project in Computer Science (CS-UH 4001). She leads the SANAD lab, which develops tools for API analysis, library comparison, and developer support using empirical and machine learning techniques. Her lab’s projects include LibComp, MUBench, MuDetect, and CogniCrypt, and she emphasizes open science through publicly available datasets and tools.

Muhammad Usman Ilyasمشاهده پروفایل

استادیار

• Computer Science
• Machine Learning
• Artificial Intelligence
• +۷ مورد دیگر

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.

Elizabeth Polgreenمشاهده پروفایل

استادیار

• Programming Languages
• Program Synthesis
• Formal Verification
• +۷ مورد دیگر

Elizabeth Polgreen is a Lecturer (~Assistant Professor) in the School of Informatics at the University of Edinburgh, where she conducts research in programming languages and formal methods, particularly program synthesis and verification. She is affiliated with the Laboratory for Foundations of Computer Science and contributes to the Programming Languages and Software Engineering research area. Her research focuses on formal program synthesis, leveraging techniques such as symbolic reasoning, genetic algorithms, and large language models to improve the scalability and automation of software verification. She explores applications in code modernization, tensor program lifting, and explainable AI. Her work bridges programming languages, AI, and formal methods to build trustworthy systems. Her recent publications (2020–2025) span top venues including PLDI, CGO, AAAI, CAV, OOPSLA, and NeurIPS, showing a strong trend in neuro-symbolic synthesis, grammar inference, and verification of AI and low-resource code. She has secured competitive funding, including a Royal Academy of Engineering fellowship and an Amazon Research Award. Scientific awards include: Royal Academy of Engineering Research Fellowship Amazon Research Award Best Paper Award at GPCE Distinguished Paper Award from CGO She advises multiple PhD students, including Yixuan Li, Alexander Brauckmann, and José Wesley de Souza Magalhães, often in collaboration with Professor Mike O'Boyle. Her research is supported by grants from the Royal Academy of Engineering and Amazon. She has delivered keynotes, tutorials, and a TEDx talk, contributing actively to outreach and education in formal methods. She leads a research group within the Laboratory for Foundations of Computer Science, focusing on trustworthy systems through synthesis and verification. Her team includes PhD students and collaborators working on cutting-edge topics in program analysis, AI for code, and compiler design.

• Wireless Communication Networks
• Distributed Systems
• Sensor Networks
• +۸ مورد دیگر

Prof. Horst Hellbrück is an esteemed academic at the Lübeck University of Applied Sciences , where he holds a professorship in Communication Systems and Distributed Systems since 2008. He leads the Center of Excellence CoSA (Communications - Systems - Applications) and focuses on wireless mobile networks, sensor networks, and their applications in medical and underwater technologies. His work bridges theoretical research with practical implementations, including projects like 5G-TELK-NF (autonomous driving/flying), Baltic Future Port (5G drone-based data optimization), and DAVE (digital twin for inland vessel navigation). Education: Diplom (equiv. to Master’s) in Electrical Engineering (1994, University of Saarland); PhD in Computer Science (2004, TU Braunschweig). Research Interests: Modern network structures, sensor systems for healthcare, underwater object detection, and 5G/AI integration. Scientific Contributions: His recent publications focus on UWB-based localization, 5G industrial deployments, and multisensor platforms for underwater applications. He has developed tools like ANSim (Ad-Hoc Network Simulator) and JGPSMap (GPS visualization). Leadership Roles: Active in GI/ITG Fachgruppe KuVS , program committee memberships, and long-term support for internationalization as part of the university’s governance bodies.

Foutse Khomhمشاهده پروفایل

استاد

• Software Engineering
• Software Maintenance and Evolution
• Trustworthy Artificial Intelligence
• +۴ مورد دیگر

Foutse Khomh is a Professor of Software Engineering at Polytechnique Montréal where he leads the SWAT Lab on software development, deployment, maintenance and evolution of AI intensive and cloud based software systems. He holds prestigious positions as a Canada Research Chair Tier 1 on Trustworthy Intelligent Software Systems, a Canada CIFAR AI Chair on Trustworthy Machine Learning Software Systems at Mila - Quebec Artificial Intelligence Institute, and a FRQ-IVADO Research Chair on Software Quality Assurance for Machine Learning Applications. Dr. Khomh received his Ph.D. in Computer Science from the University of Montreal under the supervision of Yann-Gaël Guéhéneuc, with the Award of Excellence. He also holds a Master's degree in Software Engineering from the National Advanced School of Engineering (Cameroon) and a Master's degree (D.E.A) in Mathematics from the University of Yaounde I (Cameroon). Professor Khomh's research focuses on ensuring the reliability, fairness, and ethical alignment of machine learning-powered systems throughout their entire lifecycle. His work specifically addresses concepts of equity, fairness, diversity, identity, and social inclusion to enhance user confidence in AI applications. He is developing techniques and tools to ensure that AI applications comply with proposed regulations, particularly in sectors such as healthcare, transportation, security, and customer service. His groundbreaking research on software maintenance and evolution, particularly on the impact of developers' design decisions on software quality, has been cited more than 3,600 times according to Google Scholar. His extensive publication record shows a strong focus on trustworthy AI systems, with recent work concentrating on deep learning testing, quality assurance for machine learning systems, privacy protection in software logs, and the challenges of developing and maintaining reliable AI-powered applications. His research bridges software engineering principles with artificial intelligence to create more dependable and trustworthy systems. 2025 IEEE CS TCSE New Directions Award Arthur B. McDonald Fellowship Canada Research Chair Tier 1 on Trustworthy Intelligent Software Systems CS-Can/Info-Can Outstanding Young Computer Science Researcher Prize for 2019 Multiple IEEE TCSE Most Influential Paper (MIP) Awards Multiple Best Paper Awards at international conferences Canada CIFAR AI Chair Professor Khomh has supervised or co-supervised seven PhD students, eleven MSc students, and ten undergraduate students. Three of his PhD students were nominated for the Best PhD Thesis Award of Polytechnique Montréal, with one winning the prize for Best Computer Science and Software Engineering PhD Thesis. His research has drawn exceptional support, with over $2.5 million in research funding from diverse sources including NSERC Discovery, NSERC Discovery Supplement Award, FRQ-IVADO Research Chairs, and Mitacs. He leads the R3AI project, which received a $124.5M grant from the Canada First Research Excellence Fund to develop robust, reasoning, and responsible AI. As the leader of the SWAT Lab, Professor Khomh directs research on software development, deployment, maintenance and evolution of AI-intensive and cloud-based software systems. He serves on the program committees and editorial boards of several top international conferences and journals in software engineering, including the Editorial Board of IEEE Software. He has held leadership positions as General co-chair of FSE 2026 and SANER 2025, and has served as program co-chair for numerous conferences including SSBSE 2024, ICPC 2019, and ICSME 2018.