جست‌وجوی استاد و پژوهشگر

Chetan Aroraمشاهده پروفایل

مدرس ارشد

• Software Engineering
• Requirements Engineering
• Natural Language Processing
• +۴ مورد دیگر

Dr. Chetan Arora is a Senior Lecturer in Software Engineering and Director of Education for Software Systems and Cybersecurity at Monash University's Faculty of Information Technology in Australia. With a PhD in Computer Science from the University of Luxembourg and a Master's degree from Technische Universität Kaiserslautern in Germany, he brings both academic rigor and extensive industry experience to his research and teaching. Current Position: Senior Lecturer in Software Engineering at Monash University Leadership Role: Director of Education for Software Systems and Cybersecurity Previous Positions: Academic Director at Deakin University, Innovation Programs at SES Satellites Education: PhD (University of Luxembourg), MSc (TU Kaiserslautern), B.Tech (Thapar University) Dr. Arora's research focuses on the intersection of Software Engineering, Requirements Engineering, and Applied Artificial Intelligence. His work investigates how Natural Language Processing and Machine Learning can improve software reliability and trustworthiness. He has published extensively in top-tier venues including ICSE, FSE, RE, and ASE, with recent publications examining the application of large language models in software engineering practices. His research also extends to satellite communications (particularly dynamic resource allocation) and Internet of Things applications. His work demonstrates clear trends toward integrating AI technologies with traditional software engineering practices, particularly in requirements analysis, quality assurance, and testing. He has secured significant research funding from Australian Defence organizations for projects related to satellite communications and AI-based decision making. His recent publications show growing emphasis on ethical considerations of AI in software development and human-centered aspects of software engineering. 2022 Deakin School of Information Technology International and Partnership Award for Excellence in Advancing International Teaching Partnership 2022 Best Paper Award for Automated Question Answering for Improved Understanding of Compliance Multiple publications in top-tier software engineering conferences and journals As an educator and academic leader, Dr. Arora supervises numerous PhD students working on cutting-edge topics at the AI-software engineering intersection. His industry experience across multiple countries (Australia, Luxembourg, Germany, and India) provides valuable practical perspective to his academic work, particularly in satellite communications, IoT, and AI applications in critical systems. He serves on program committees for major conferences including ICSE, FSE, and RE, contributing to the broader software engineering research community.

Peng Diمشاهده پروفایل

دانشیار مدعو

• Program Analysis
• Compiler
• Parallel Computing
• +۵ مورد دیگر

Peng Di serves as an Adjunct Associate Professor at the University of New South Wales, Sydney while working as a Senior Staff Engineer and leading the Intelligent Platform Engineering team at Ant Group in Hangzhou, China. They also serve as an Industrial PhD Mentor at Zhejiang University. PhD from UNSW under supervision of Scientia Professor Jingling Xue Former Research Associate at Compiler Research Group Former Technical Expert of AI compiler at Huawei Peng Di's research spans the intersection of programming languages, artificial intelligence, and software engineering. Their work focuses on leveraging large language models for software development, program analysis techniques, compiler technologies, and security applications. They have pioneered approaches to integrate code large language models with traditional program analysis, creating hybrid systems that enhance software development efficiency and security. Their research in parallel computing and optimizations for heterogeneous systems has resulted in practical industrial applications, particularly in the domain of AI compilers and microservices architecture. Recent publications demonstrate a clear trend toward integrating graph-based representations with large language models for code understanding, advancing static analysis techniques using datalog and neural networks, and applying these innovations to practical industrial settings. Their work bridges theoretical program analysis with real-world software engineering challenges, particularly in microservices architectures and security-critical applications. T-Star Award (Ant Group's highest technological award, 2024) ACM SIGSOFT Distinguished Paper Award (2025) Distinguished Dissertation Award by the CCF Systems Software (2024) distinguished paper award of ASE'24 (2024) Peng Di leads the Intelligent Platform Engineering team at Ant Group, which has developed several significant open-source projects including CodeFuse (a pretrained code large language model), Ling-Coder-Lite (a MoE code LLM), and OpenDeRisk (an AI-native risk intelligence system). Their team collaborates with academic institutions including UNSW, East China Normal University, ETH Zurich, Chinese Academy of Sciences, and Nanjing University on cutting-edge research. Peng Di actively mentors PhD students as an Industrial PhD Mentor at Zhejiang University and recruits technical experts and interns for research in machine learning, program analysis, and AI software engineering. The Ant Intelligent Platform Engineering team, led by Peng Di, is committed to building a safe and trusted framework for intelligent, automated software engineering. They manage the CodeFuse AI Coding and DeRisk AIOps communities, which encompass pre-trained Code LLMs, agents, frameworks, toolchains, and databases. Their team has also contributed to agent development platforms Tbox and WeaveFox, with the latter being an AI frontend development platform launched by Ant Group.

Amin Milani Fardمشاهده پروفایل

دانشیار

• Software Engineering
• Cybersecurity
• Software Testing
• +۵ مورد دیگر

Amin Milani Fard is an Associate Professor of Computer Science at New York Institute of Technology - Vancouver Campus, and a visiting faculty member in Management Information Systems at Simon Fraser University's Beedie School of Business in Vancouver, Canada. Previously, he served as an Assistant Professor at NYIT Vancouver from 2018 to 2023. Dr. Milani Fard received his Ph.D. in Computer Software Engineering from the University of British Columbia (2012-2017), his M.Sc. in Computer Science from Simon Fraser University (2009-2010), and his B.Sc. in Computer Software Engineering from Ferdowsi University of Mashhad. His academic journey began with notable achievements including the 1st Rank Khwarizmi Award (awarded by Iran's President Mohammad Khatami) and an Exceptional Talents Admission Award. His research spans multiple disciplines, primarily focusing on Security, Privacy, and Assurance of Software and Information , with significant contributions to software testing, blockchain security, financial market prediction, and machine learning applications. His work on JavaScript security code smells, Ethereum smart contract vulnerabilities, and financial market prediction using multimodal data has been widely recognized. His publications demonstrate a consistent pattern of high-quality research with numerous papers in top conferences including ASE, ICST, and IEEE journals. Dr. Milani Fard's research portfolio shows a clear evolution from foundational work in software testing and JavaScript analysis toward more complex applications in financial technology and AI-driven security solutions. His most recent work focuses on integrating LLM technologies with security applications and advancing financial prediction models using sophisticated time series analysis. Scientific Awards and Recognition: Most Influential Paper Award at IEEE SCAM 2023 Presidential Excellence Award Finalist for Teaching at NYIT (2023) Best Paper Award at ECIR 2019 Multiple research grants including NSERC Alexander Graham Bell Canada Graduate Scholarship IEEE Best Paper Award Nominee at ICST 2017 As an active researcher, Dr. Milani Fard serves on program committees for major conferences including ASE and has contributed significantly to the software engineering community through his publications and research leadership. His work bridges theoretical computer science with practical applications in cybersecurity and financial technology, demonstrating both academic rigor and real-world impact.

Yuan Tianمشاهده پروفایل

استادیار

• Software Engineering
• Applied Machine Learning
• AI for Software Engineering
• +۷ مورد دیگر

Dr. Yuan Tian is an Assistant Professor in the School of Computing at Queen's University, Canada. Her research focuses on applying artificial intelligence and machine learning techniques to solve software engineering challenges, particularly in the areas of code analysis, technical debt management, and developer productivity enhancement. Dr. Tian received her Ph.D. in Information Systems from Singapore Management University in May 2017 under the supervision of Prof. David Lo (IEEE/ACM fellow). Prior to joining Queen's University, she worked as a data scientist at the Living Analytics Research Centre (LARC) in Singapore. She has also conducted research visits at Carnegie Mellon University in 2015 and Inria Paris in 2013. Dr. Tian's research spans several key areas in software engineering with AI: Automatic technical debt, bug, and code change management LLM applications for code transformation and generation Human-AI collaboration in software development Analysis of developer interactions with AI tools like ChatGPT Mining software repositories for insights into development practices Her recent work has increasingly focused on leveraging Large Language Models to address software engineering challenges, with publications examining code translation, technical debt identification, and the dynamics of developer-AI interactions. Her research demonstrates a strong empirical approach, often analyzing large datasets from GitHub and other software development platforms. Dr. Tian has received recognition for her work, including the Best Research Paper Award at AI Foundation Models and Software Engineering (Forge), 2024 for her paper "Exploring the Impact of the Output Format on the Evaluation of Large Language Models for Code Translation." Dr. Tian leads the RISE research lab at Queen's University, which currently includes 5 PhD students, 2 MSc students, and 2 undergraduate research assistants. She has successfully supervised several graduate students to completion, with alumni now working at institutions including Duke University and Veeva Systems. Her research is supported by funding including an NSERC Alliance-Mitacs project on "Pragmatic Automated Code Transformation Leveraging Large Language Models" in collaboration with industry partner Ross Video. The RISE lab (Goodwin 621) is dedicated to developing reliable and intelligent support for software engineering. The lab's current research focuses on three main thrusts: automatic technical debt/bug/code change management, LLM for code transformation, and human-AI collaboration in software development.

Maliheh Izadiمشاهده پروفایل

استادیار

• Software Engineering
• Machine Learning
• Large Language Models for Code
• +۷ مورد دیگر

Maliheh Izadi is a tenure-track assistant professor in the Faculty of Electrical Engineering, Mathematics, and Computer Science at Delft University of Technology (TU Delft), Netherlands. She leads the AISE (AI-enabled Software Engineering) research lab and serves as the scientific manager for the TU Delft/JetBrains Collaboration (AI4SE). She is also a member of the Software Engineering Research Group (SERG) at TU Delft and actively supervises PhD, MSc, and BSc students. Dr. Izadi's research focuses on enhancing software development tools through building smarter software and tailoring machine learning and NLP techniques to source code. Her primary interests include building and tailoring large language models (LLMs) and autonomous agents to source code, with specific focus areas including evaluation, benchmarking, model memorization, IDE integration, in-IDE Human-AI interaction, and extending models' capabilities to low-resource programming languages. Her work bridges the gap between deep learning and source code analysis, with applications in code understanding, generation, documentation, and developer productivity enhancement. Her recent publications reveal a strong trend toward evaluating and improving LLMs for code, with emphasis on safety, usability, and efficiency. She has made significant contributions to benchmark development, harmfulness assessment of LLMs in programming contexts, and improving the integration of AI tools within development environments. Her research consistently addresses real-world industrial challenges while advancing theoretical understanding of model behavior. Google Research Scholar Award (2025) for proposal on Tackling LLM Hallucinations Amazon Research Award (2024) for proposal on Addressing Memorization in Code LLMs ACM SIGSOFT Distinguished Paper Award (2025) for How Much Do Code Language Models Remember? ACM SIGSOFT Distinguished Paper Award (2024) for A Transformer-Based Approach for Smart Invocation of Automatic Code Completion Best Tool Award at SaTML'22 competition for STACC Best Tool Award at NLBSE'22 competition for Catiss Dr. Izadi actively collaborates with industry partners, particularly JetBrains Research, where she leads the AI4SE ICAI lab. She has supervised multiple PhD and master's students and has served on program committees for major software engineering conferences including ASE, ICSE, FSE, and MSR. Her research has been published in top venues such as IEEE/ACM ICSE, FSE, ASE, TOSEM, EMSE, MSR, ICSME, SANER, and JSS. She is also organizing the First International workshop on Autonomous Agents in Software Engineering (AgenticSE) co-located with ASE'25.

Tianyi Zhangمشاهده پروفایل

استادیار

• Software Engineering
• Human-Computer Interaction
• Artificial Intelligence
• +۵ مورد دیگر

Tianyi Zhang is a Tenure-Track Assistant Professor of Computer Science and Societal Impact Fellow at Purdue University's College of Science, where he leads the Human-Centered Software Systems Lab. His research focuses on building interactive intelligent systems that synergize human expertise with machine intelligence to improve programming productivity and software robustness. Dr. Zhang's research interests span Software Engineering, Human-Computer Interaction, and Artificial Intelligence. His work centers on developing systems that augment human intelligence with data-driven insights and augment machine intelligence with human guidance, primarily for programming domains including software developers, novice programmers, and computer end-users. His research on code mining and visualization helps programmers make more informed decisions through GitHub and Stack Overflow analysis, while his work on program synthesis assists novices with enriched feedback loops and interpretability. His recent publications (2024-2025) demonstrate a strong focus on leveraging large language models for code generation, program repair, and data wrangling, with particular emphasis on interactive systems that incorporate human feedback. This research direction shows consistent growth in understanding the intersection between human cognition and AI capabilities in programming contexts. Awards and Recognition: NSF Career Award for research on safe and reliable LLM-based code generation Amazon Research Award for human-in-the-loop deep learning optimization Best Paper Honorable Mention Award from SIGCHI for visualizing examples of deep neural networks Best Paper Honorable Mention Award from VAHC for interactive cohort analysis Dr. Zhang actively serves the research community as Program Committee member for major conferences including ICSE, ASE, FSE, CHI, and UIST. His service includes chairing workshops and student research competitions, demonstrating his commitment to mentoring the next generation of researchers. His lab develops systems that address real-world challenges in programming productivity and software safety, with applications spanning from autonomous driving systems testing to data science workflows.

Qi Xinمشاهده پروفایل

دانشیار

• Software Engineering
• Automated Program Repair
• Software Debugging
• +۴ مورد دیگر

Qi Xin is an Associate Professor at the School of Computer Science, Wuhan University. He is a member of the Centre of Software Testing, Analysis and Reliability (CSTAR) and serves on program committees for major software engineering conferences including ASE, ISSTA, and ICSE. Dr. Xin received his Ph.D. from Brown University under the supervision of Dr. Steven Reiss and was previously a Postdoctoral Researcher at Georgia Institute of Technology working with Dr. Alex Orso. His research focuses on software engineering, particularly on developing automated and semi-automated approaches to improve software development processes and enhance software quality, security, and reliability. His recent work centers on automated program repair, software debugging, and program debloating. Dr. Xin has published extensively in top-tier software engineering venues, with research that frequently addresses practical challenges in real-world software systems. Dr. Xin's publication record shows a consistent focus on improving software reliability through automated techniques. His work spans from foundational program analysis to practical tool development, with several publications focusing on Android applications and addressing challenges specific to mobile software development. His recent work has begun exploring the application of large language models to software engineering problems, as evidenced by his 2025 paper on conversational LLM-based repair. ACM SIGSOFT Distinguished Paper Award for fault localization research Guest editor for Automated Software Engineering (AUSE) Special Issue Reviewer for ACM TOSEM, IEEE TSE, and EMSE journals Dr. Xin actively mentors students and serves the academic community through conference organization and journal reviewing. He teaches courses including Compiler Design and Software Testing and Practice at Wuhan University, bridging his research expertise with classroom instruction to train the next generation of software engineers.

Fiorella Zampettiمشاهده پروفایل

پژوهشگر

• Experimental Software Engineering
• Continuous Integration and Delivery
• Static Analysis Tools
• +۴ مورد دیگر

Fiorella Zampetti is a researcher at the University of Sannio, Italy, specializing in empirical software engineering with a focus on continuous integration and delivery, static analysis tools, mining software repositories, and software maintenance and evolution. Her work bridges theoretical research with practical applications in real-world software development environments. Her research interests span multiple critical areas of modern software engineering, with particular emphasis on Experimental methodologies for evaluating software engineering practices Technical debt identification and management, especially in emerging domains like deep learning systems Continuous integration and delivery pipelines, including common anti-patterns and solutions Mining software repositories to extract valuable insights about development processes Software quality assessment through static analysis and empirical studies Analysis of her recent publications reveals an evolving research trajectory that has increasingly incorporated artificial intelligence and large language models into traditional software engineering concerns. Her 2023-2025 work shows a significant focus on how AI-generated code impacts software development practices, licensing concerns, and educational contexts. She has also maintained a strong thread of research on technical debt, particularly examining self-admitted technical debt across different software domains. Dr. Zampetti has been actively involved in the software engineering research community as a program committee member for major conferences including ASE, ICSE, ESEC/FSE, and ICSME. Her service on these committees demonstrates recognition by her peers as a subject matter expert in her research domains. Her research has practical implications for software development teams looking to improve their CI/CD practices, manage technical debt effectively, and understand the emerging challenges posed by AI-assisted development tools. She has conducted numerous empirical studies that provide evidence-based insights into software engineering practices across both open-source and industrial contexts.

Guowei Yangمشاهده پروفایل

دانشیار

• Software Engineering
• Machine Learning Systems
• Programming Languages
• +۵ مورد دیگر

Guowei Yang is a Senior Lecturer (equivalent to U.S. Associate Professor) at the School of Electrical Engineering and Computer Science, Faculty of Engineering, Architecture and Information Technology, University of Queensland. His research bridges software engineering, machine learning, and programming languages, focusing on enhancing software and ML system reliability/security. Dr. Yang leads projects in symbolic execution, fuzzing techniques, Android app testing, and LLM-based tool development. Research Interests: Core interests include software testing automation, Android ecosystem analysis, ML system robustness, and symbolic execution optimization. His work emphasizes practical tools for API compatibility detection, security patch generation, and fuzzing efficiency. Recent projects leverage large language models for document-guided testing and dynamic vulnerability detection. Professional Service: Dr. Yang actively contributes to top-tier conferences as a program committee member (ICSE, FSE, ASE, ISSTA) and organizer (DSN 2024 Local Chair, TAIC PART Co-Chair). He mentors through Google Summer of Code and NSF Research Experiences for Undergraduates programs, and reviews for journals including IEEE TSE and ACM TOSEM.

• Tax Law
• Computational Law
• Legal Informatics

Sarah Lawsky is the Stanford Clinton Sr. and Zylpha Kilbride Clinton Research Professor of Law at Northwestern Pritzker School of Law. She leads research on computational approaches to tax law, notably co-developing Catala—a programming language for formalizing statutes—which was recognized as official software by the French government. Awarded the Inria International Chair (2024-2028), she collaborates with France's National Institute for Research in Digital Science on tax algorithm development. Her research explores tax law formalization, computational jurisprudence, and AI applications in legal reasoning. Recent publications focus on bridging statutory law with computational logic through tools like Catala, enabling transparent tax code implementation and government accountability. Publications demonstrate consistent focus on tax law computation, AI integration, and epistemic frameworks. Key themes include LLM capabilities in legal tasks, statute formalization methodologies, and interdisciplinary approaches to legal reasoning. Awards include the Inria International Chair for advancing computational tax law. Her work with Catala supports French government initiatives to modernize income tax and social benefit algorithms.

• Machine Learning
• Randomized Algorithms
• Artificial Intelligence
• +۳ مورد دیگر

Zhaozhuo Xu is an Assistant Professor of Computer Science at Stevens Institute of Technology, affiliated with the Charles V. Schaefer, Jr. School of Engineering and Science. He joined Stevens in 2024 after earning his Ph.D. in Computer Science from Rice University. His research focuses on machine learning, randomized algorithms, and efficient computing for large-scale AI systems. Xu has been recognized with the AAAI 2025 New Faculty Highlights award. His work emphasizes scalable and sustainable AI, including compression techniques for large language models (LLMs), efficient inference methods, and ethical considerations in AI deployment. Xu has served as an Area Chair for the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP) and is a member of IEEE and AAAI. Key research trends in Xu's articles include optimizing LLM efficiency through sparsity, quantization, and adaptive architectures; probing ethical boundaries in AI (e.g., copyright compliance); and developing multi-agent systems for financial decision-making. His contributions span theoretical foundations (e.g., randomized algorithms) and applied systems (e.g., on-device LLM execution). Awards: AAAI 2025 New Faculty Highlights Grants/Advising: No explicit grants listed; advising status unclear due to no student listings Labs/Teams: No dedicated lab/team named in provided texts

Yao Maمشاهده پروفایل

استادیار

• Machine Learning
• Graph Neural Networks
• Natural Language Processing
• +۳ مورد دیگر

Yao Ma is an Assistant Professor in the Department of Computer Science at Rensselaer Polytechnic Institute (RPI). His research bridges graph machine learning, natural language processing, and trustworthy AI, with a focus on scalable and robust computational frameworks. He directs the Graph Machine Learning Lab at RPI, advancing methodologies for graph condensation, neural architecture robustness, and foundation models. Research Interests: Dr. Ma's work spans graph neural networks (GNNs), adversarial robustness, language model efficiency, and data-centric AI. Key innovations include techniques for graph condensation, trustworthiness in LLMs, and multi-agent learning systems. His recent surveys systematize advancements in small language models and graph foundation models, highlighting scalability and transferability challenges. Publication Trends: His 2024–2025 articles emphasize graph-based NLP, efficient data valuation, and adversarial defenses. Dominant themes include graph condensation (6 papers), LLM/GNN integration (4 papers), and robustness benchmarking (3 papers), reflecting a cohesive agenda in scalable, reliable graph AI. Awards & Advising: No awards or students are noted in available sources. Lab & Team: Leads the Graph Machine Learning Lab at RPI, focusing on theoretical and applied graph AI. Collaborative projects include cross-departmental initiatives in quantum computing and NLP.

• Artificial Intelligence
• Human-Computer Interaction
• Machine Learning
• +۴ مورد دیگر

Jeffrey P. Bigham is a researcher at Carnegie Mellon University 's Human-Computer Interaction Institute . He specializes in Artificial Intelligence , Accessibility , and Human-Computer Interaction , with a focus on Inclusive AI systems Accessible web and mobile development Large language model applications User interface engineering His recent publications explore synthetic data generation for UI/Slide understanding, LLM-based code generation , and real-time notetaking systems . Key themes include Improving accessibility through AI Interactive ML model optimization Collaborative human-AI workflows Visual and speech interface analysis Bigham's work spans Computer Science , Machine Learning , and Natural Language Processing , often addressing disability inclusion and user-centered AI . He collaborates extensively with researchers like Jason Wu Yi-Hao Peng Amy Pavel Stephanie Valencia Henny Admoni across 300+ publications since 2006. Notable contributions include automated accessibility tools (like Screen Recognition ), stuttering accommodation in speech recognition , and generative AI literacy initiatives . His research bridges technical AI innovation with social impact , particularly for marginalized user groups.

Simone Paolo Ponzettoمشاهده پروفایل

استاد

• Natural Language Processing
• Information Retrieval
• Knowledge Graphs
• +۷ مورد دیگر

Prof. Simone Paolo Ponzetto is the Chair of Information Systems III (Enterprise Data Analysis) at the University of Mannheim, leading the Natural Language Processing and Information Retrieval (NLP and IR) group within the Data and Web Science Group since 2013. His research bridges computational methods with applications in Social Sciences and Humanities. Full Professor (W3) since February 2016 Based at School of Business Informatics and Mathematics Contact: simone@informatik.uni-mannheim.de | ponzetto@uni-mannheim.de His work spans knowledge acquisition , multimodal NLP , and LLM applications in: GUI prototyping, scientific text analysis, political discourse modeling, and social science data integration. Recent projects explore zero-shot synthesis , cross-lingual knowledge editing , and ethical analysis of language models . Key research trends in his publications include LLM-driven interface design , multimodal summarization , and robust cross-lingual methods for NLP tasks. His group's work combines symbolic approaches with deep learning , leveraging knowledge graphs and distributional semantics across disciplines. Research grants include DFG-funded projects JOIN-T 2 , SFB 884 , and UNCOVER , alongside MWFK Baden-Württemberg programs for junior professors and part-time master's education in Data Science. The Data and Web Science Group under his leadership focuses on empirical research in computational social science and digital humanities, with applications in political text analysis, survey data integration, and surgical language modeling through projects like SurgicBERTa and FrameASt .

Giovanni FABBROCINOمشاهده پروفایل

استاد

• Civil Engineering
• Structural Engineering
• Structural Health Monitoring (SHM)
• +۴ مورد دیگر

Prof. Giovanni FABBROCINO is a Full Professor at the Department of Biosciences and Territory of the University of Molise . His research focuses on structural engineering, heritage conservation, and structural health monitoring (SHM). He specializes in seismic analysis of historical buildings, vibration-based diagnostics, and the integration of digital technologies like BIM and virtual reality for infrastructure assessment. His work bridges civil engineering with cultural heritage preservation, emphasizing interdisciplinary methodologies. Key research areas include: Seismic retrofitting of historical structures Non-destructive testing of masonry and RC structures Operational modal analysis (OMA) for dynamic characterization Remote sensing applications in structural monitoring Digital twin development for heritage conservation He leads projects involving advanced materials testing (e.g., weathering steel analysis) and crowd-based technologies for cultural heritage documentation. His methodologies often combine experimental measurements with computational models to enhance infrastructure safety in seismic-prone regions. Current research trends include AI-driven permission models for Android systems and probabilistic frameworks for healthcare facility safety networks. Prof. FABBROCINO collaborates with institutions like the StreGa Laboratory , advancing multidisciplinary approaches to sustainable urban regeneration and disaster resilience. His work contributes to both academic discourse and practical solutions for safeguarding historical centers and strategic infrastructure.