جست‌وجوی استاد و پژوهشگر

Umakishore Ramachandranمشاهده پروفایل

استاد

• Distributed Systems
• Edge Computing
• Sensor Networks
• +۵ مورد دیگر

Umakishore Ramachandran is a Professor in the School of Computer Science at Georgia Institute of Technology's College of Computing, where he directs the Embedded Pervasive Lab. He received his Ph.D. from the University of Wisconsin-Madison in 1986 and has led transformative initiatives including the Online MS in Computer Science (OMSCS) program. His research spans distributed systems, edge computing, and real-time sensor networks, with applications in smart surveillance and connected vehicles. His research interests include architectural design of parallel/distributed systems, large-scale situation awareness using camera networks, cloud-edge continuum optimization, and latency-sensitive applications for geo-distributed infrastructures. Recent work focuses on elevating edge computing to parity with cloud resources. His publications show strong emphasis on edge computing innovations (MicroEdge, FogStore), real-time video analytics (EVA, ClairvoyantEdge), and adaptive mobile systems (Foresight). Trends include multi-tier architectures, quality-of-experience optimization, and scalable processing for IoT workloads. Major Awards: IEEE Fellow (2014) NSF Presidential Young Investigator (1990) ACM/IFIP Middleware Best Paper (2022) 3x College of Computing Dean's Awards He has advised 40+ PhD students, with recent graduates at Google, Microsoft, and academia. Current NSF/CPS grants support his work on geo-distributed latency-sensitive applications. He co-leads the STAR Center and Samsung-funded embedded software programs. His Embedded Pervasive Lab develops systems like Stampede (stream processing) and DFuse (sensor fusion), with testbeds in the Aware Home and transportation networks. Teams collaborate with Intel, Microsoft, and Bosch on edge-AI deployments.

Arjun Guhaمشاهده پروفایل

دانشیار

• Programming Languages
• Program Synthesis
• Low-Resource Programming Languages
• +۶ مورد دیگر

Arjun Guha is an Associate Professor at Northeastern University's Khoury College of Computer Sciences, where he also serves as the Area Chair for Software. He conducts research in programming languages with a focus on program synthesis for low-resource programming languages and understanding how computer science education is impacted by large language models. His work spans multiple domains including WebAssembly, software-defined networking, and serverless computing. Guha's research interests center around programming language design, implementation, and application. He has made significant contributions to understanding JavaScript through formal semantics (LambdaJS), developing functional reactive programming for web applications (Flapjax), and creating tools for software-defined networks (NetKAT, Frenetic). His recent work focuses on leveraging large language models for code generation in specialized programming languages and understanding their impact on programming education. His publications reveal a strong trend toward applying AI and machine learning techniques to programming language problems, particularly in code generation and understanding. The research spans from foundational programming language theory to practical applications in education and software development tools, with increasing focus on the intersection of programming languages and large language models. Guha has received several prestigious awards including the OOPSLA Most Influential Paper Award in 2019 for his work on Flapjax, an ACM SIGPLAN Research Highlight for his work on NetKAT, and a Best Student Paper Award. His research has been recognized for its foundational contributions to programming language theory and practical impact on software development. OOPSLA Most Influential Paper Award (2019) for Flapjax ACM SIGPLAN Research Highlight for A Fast Compiler for NetKAT Best Student Paper Award for Flapjax paper Guha advises numerous PhD, MS, and undergraduate students, with several alumni now at leading tech companies and academic institutions. His research has been supported by the National Science Foundation, the Department of Energy, the Office of Naval Research, and industry partners including Google, JPMorgan Chase, MathWorks, Meta, Oracle, and Roblox. He leads the Programming Research Laboratory at Northeastern and is actively involved in major research collaborations like the BigCode Project. Guha is a member of the Programming Research Laboratory at Northeastern and leads several major research initiatives including the BigCode Project's evaluation working group. His lab develops practical software systems like MultiPL-E (a polyglot benchmark for Code LLMs) and WasmFX (bringing effect handlers to WebAssembly), with applications in education, software development, and high-performance computing.

Konstantinos Kallasمشاهده پروفایل

استادیار

• Computer Systems
• Compilers
• Programming Languages
• +۱ مورد دیگر

Konstantinos Kallas serves as Assistant Professor of Computer Science at the University of California, Los Angeles (UCLA), commencing his appointment in January 2025. Previously affiliated with the University of Pennsylvania as evidenced by his 2020 PLDI contribution, his research bridges theoretical formal methods with practical systems engineering across multiple high-impact conferences including PLDI, POPL, and SPLASH. His research program centers on enhancing computational efficiency and correctness in systems software, with three flagship projects defining his trajectory: PaSh for automatic shell script parallelization, Durable Functions for stateful serverless computing semantics, and DiffStream for differential testing of stream processing. These efforts consistently target the intersection of programming language theory and real-world systems constraints, particularly in parallelism, concurrency, and cloud-native environments where correctness guarantees are challenging to implement. Analysis of his publication history since 2020 reveals a methodological pattern: developing formal semantic models to enable practical optimizations in distributed systems. His work increasingly focuses on serverless architectures and data-intensive pipelines, with recent contributions emphasizing automated verification techniques. The evolution from shell script optimization (2020-2021) to serverless state management (2021-2022) demonstrates strategic expansion into cloud computing's hardest problems. Dr. Kallas actively contributes to the academic community through program committee service for PLDI (2022, 2025), POPL (2021, 2022, 2023), and SPLASH (2020-2023), including leadership roles as Publicity Co-Chair for PLDI 2025 and 2026. His June 2024 announcement confirms recruitment for Fall 2025 students at UCLA, targeting researchers interested in systems, compilers, and programming languages who can advance his work on correctness-preserving parallelization and serverless computing.

Mustafa ULAŞمشاهده پروفایل

استادیار

• Data Mining
• Big Data
• Machine Learning
• +۷ مورد دیگر

Mustafa ULAŞ is an Assistant Professor in the Software Engineering Department at Fırat University, Turkey. He also serves as a University Advisor and Coordinator of the Digital Transformation and Software Office at Fırat University since October 2020. With academic roots entirely at Fırat University, he has established himself as a prominent researcher in data science and software engineering. Born in November 1981 in Elazığ, Turkey PhD in Electrical-Electronics Engineering (2011) Master's in Computer Engineering (2006) Bachelor's in Electrical-Electronics Engineering (2003) Dr. ULAŞ's research spans multiple domains of computer science and engineering with particular emphasis on practical applications. His work bridges theoretical computer science with real-world problems in healthcare, finance, and industrial systems. Recent publications reveal a strong focus on machine learning applications, especially in medical diagnostics and explainable AI, while maintaining his longstanding interest in VLF signal analysis for earthquake prediction. His publication record shows a clear evolution from foundational work in database systems and medical imaging to cutting-edge research in deep learning and explainable AI. The most recent articles (2024-2025) predominantly focus on healthcare applications of machine learning, particularly diabetes and cancer diagnosis, while maintaining parallel research streams in industrial applications, financial analytics, and drone network optimization. This multidisciplinary approach demonstrates his ability to adapt core computational techniques to diverse problem domains. Dr. ULAŞ has been actively involved in numerous research projects, including TÜBİTAK-funded initiatives such as the 'Enriched Virtual Laboratory' and 'A New Approach in Teacher Education: Effective Blended Learning.' His project portfolio spans infrastructure development, educational technology, and advanced research applications. As an educator, he teaches courses including C Programming and Algorithms, Internet-Based Programming, Server Operating Systems, and Web Project Management. His administrative roles include serving as University Advisor and Coordinator of the Digital Transformation and Software Office at Fırat University since 2020.

• Blockchain
• Decentralized Systems
• Federated Learning
• +۳ مورد دیگر

J.A. Pouwelse is a Professor at the Data-Intensive Systems department within the Electrical Engineering, Mathematics and Computer Science school at Delft University of Technology . With 125 research outputs and 4 supervised works, his work focuses on Blockchain , Decentralized Systems , Federated Learning , and Peer-to-Peer Networks , particularly for Web3 applications. His recent publications analyze topics such as: Decentralized adaptive ranking Serverless federated learning Green smart contracts Search index optimization Zero-trust frameworks Notable recognition includes the LCN Best Paper Award 2021 . Research spans decentralized infrastructure design, privacy preservation, and scalable system implementation.

Antonio Filieriمشاهده پروفایل

دانشیار مهمان

• Static analysis
• Privacy
• Automated test generation
• +۵ مورد دیگر

Antonio Filieri is a Senior Applied Scientist at Amazon Web Services (AWS) and holds a Visiting Associate Professor position at the Department of Computing, Imperial College London. Previously, he was a tenured Associate Professor at Imperial College London (2022-2024) and Assistant Professor (2016-2022), and served as Assistant Professor at the University of Stuttgart between 2013 and 2015. His academic career spans over a decade with significant contributions to software engineering research. Dr. Filieri's research focuses on formal mathematical methods for software design, verification, self-adaptation, and security. His primary research areas include static analysis, privacy, and automated test generation for security; exact and approximate methods for probabilistic program analysis; control theory for adaptive software; quantitative verification and model checking; and runtime-efficient and incremental verification. His work bridges theoretical foundations with practical applications in industry settings, particularly in cloud computing and security domains. His recent publications demonstrate a strong focus on probabilistic methods for software analysis, security testing, and performance modeling. The research trends show increasing integration of formal methods with machine learning techniques, particularly in test oracle generation and neural network analysis. There's also a clear emphasis on scalability and practical applicability of verification techniques to real-world systems like serverless computing and microservices architectures. Dr. Filieri has received numerous prestigious awards for his contributions: Best Student Paper Award (2025) for 'Robust Probabilistic Model Checking with Continuous Reward Domains' ACM Distinguished Paper Award (2023) for 'Sibyl: Improving Software Engineering Tools with SMT Selection' Best Paper Award (2022) for 'Enhancing Performance Modeling of Serverless Functions via Static Analysis' Best Artifact Award (2017) for 'Self-adaptive video encoder: comparison of multiple adaptation strategies made simple' Most Influential Paper Award (awarded at SEAMS 2025) for 'Software Engineering Meets Control Theory' ACM SigSoft Distinguished Paper Award (2011) for 'Run-time Efficient Probabilistic Model Checking' Dr. Filieri has advised several PhD students including Donato Clun (2024), Runan Wang (2024), and Xiaotong Ji (expected 2025). His advising focuses on probabilistic program analysis, automated testing, and security verification. His research has been supported by significant grants from both academic and industry sources, enabling collaborations across multiple institutions and contributing to advancements in software engineering practices. While specific lab information isn't prominently featured in the provided materials, Dr. Filieri's work suggests strong connections with research groups focused on formal methods, software verification, and adaptive systems at both Imperial College London and AWS. His research often involves interdisciplinary collaboration between theoretical computer science and practical software engineering challenges.

Mohammad Hamdaqaمشاهده پروفایل

دانشیار

• Model-Based Software Engineering
• Blockchain
• Smart Contracts
• +۶ مورد دیگر

Mohammad Hamdaqa is an Associate Professor at the Department of Computer Engineering and Software Engineering at Polytechnique Montréal (Canada), where he leads the Software and Emerging Technologies Lab. He received his PhD in Software Engineering from the University of Waterloo in Canada in 2016 and holds multiple advanced degrees including a Master of Applied Science in Software Engineering and an MBA with a minor in Management Information Systems. His educational background includes: Ph.D. in Electrical and Computer Engineering, University of Waterloo, Canada Master in Electrical and Computer Engineering, Concordia University, Canada Master in Business Administration, New York Institute of Technology, USA Bachelor in Computer Engineering, Jordan University of Science and Technology, Jordan Dr. Hamdaqa's research focuses on the intersection of software engineering and emerging technologies. His work explores how software engineering approaches can be tailored to address the complexities of architecting, building, and deploying applications for new platforms like Cloud Computing and Blockchain. He is particularly interested in how emerging technologies can advance software creation, evolution, and management practices. His research spans model-driven software engineering, cloud computing, blockchain, and the application of AI in software development processes. His recent publications demonstrate a strong focus on smart contract security, infrastructure as code, model-driven engineering, and the application of large language models in software engineering tasks. His work bridges theoretical software engineering concepts with practical applications in cutting-edge technology domains, with particular emphasis on addressing security, sustainability, and maintainability challenges in next-generation software systems. Dr. Hamdaqa has received recognition through service on program committees for major software engineering conferences including ASE, ICSE, MODELS, and SANER. He serves on the editorial board of Service Transactions on Internet of Things and is a Member of the IEEE Computer Society and the Association for Computing Machinery. He has supervised multiple Master's students to completion, with recent theses focusing on OCL generation, smart contract auditing, epidemiological modeling, and infrastructure as code security. His current research group continues to explore innovative approaches at the intersection of software engineering and emerging technologies. Dr. Hamdaqa leads the Software and Emerging Technologies Lab at Polytechnique Montréal, which brings together researchers and students to investigate cutting-edge challenges in software engineering for new technology platforms. The lab focuses on practical solutions that balance theoretical rigor with real-world applicability.

Ding Liمشاهده پروفایل

استادیار

• Program Analysis
• Energy Optimization
• Mobile Applications
• +۲ مورد دیگر

Ding Li is an Assistant Professor in the School of Computer Science at Peking University. He holds a Ph.D. in Computer Science from the University of Southern California (USC) and a B.S. from Peking University. His research focuses on program analysis, energy optimization for mobile applications, and security, with publications in top conferences including ICSE, FSE, and ASE. His research interests span: Program Analysis : Techniques to optimize mobile application energy consumption. System Security : Identifying vulnerabilities in Android apps and WebAssembly binaries. Cloud/Edge Computing : Enhancing serverless computing efficiency and federated learning security. Dr. Li's recent work explores the integration of large language models into pointer analysis and automated optimization of resource inefficiencies. His publications demonstrate a consistent focus on practical system optimizations and security enhancements across mobile, cloud, and machine learning domains. Awards: Viterbi Undergraduate Research Mentoring Award (2014)

Harry Hochheiserمشاهده پروفایل

استادیار

• Human-Computer Interaction
• Information Visualization
• Bioinformatics
• +۷ مورد دیگر

Harry Hochheiser is an Assistant Professor in the Department of Biomedical Informatics at the University of Pittsburgh School of Medicine, where he also serves as Associate Director of the Biomedical Informatics Training Program. His academic career spans multiple disciplines at the intersection of computing and healthcare, with a strong focus on human-computer interaction, bioinformatics, and medical informatics. Dr. Hochheiser's research interests include human-computer interaction, information visualization, bioinformatics, universal usability, security, privacy, and public policy implications of computing systems. His work particularly focuses on NIH-funded projects related to bioinformatics research portals, visualization for review of chart records, and tools for aiding the discovery of animal models of human diseases. His research demonstrates a commitment to developing technology that addresses real clinical needs while considering user experience and ethical implications. His publication record shows consistent scholarly output across multiple domains, with recent work emphasizing biomedical informatics, machine learning applications in healthcare, epidemiological modeling, and academic publishing practices. His research demonstrates a strong interdisciplinary approach that bridges computer science with clinical medicine and public health, with particular attention to human-centered design principles in healthcare technology. Editorial Board Member, PeerJ - the Journal of Life & Environmental Sciences Editorial Board Member, PeerJ Computer Science Over 2,900 points on PeerJ representing 2,800 editorial contributions and 105 reviews Dr. Hochheiser has made significant contributions across numerous subject areas including Science and Medical Education, Science Policy, Statistics, Human-Computer Interaction, Computational Science, Bioinformatics, and many others. His work has practical implications for healthcare technology development, particularly in creating more effective, user-friendly systems that address real clinical needs while considering privacy and security concerns.

• Concurrent computation
• Distributed systems
• Actor model
• +۴ مورد دیگر

Gul Agha is a distinguished Professor of Computer Science at the University of Illinois at Urbana-Champaign and Director of the Open Systems Laboratory. His career spans over three decades of pioneering research in concurrent and distributed computing systems. Professor Agha's research focuses on the actor model of computation, formal methods, and statistical model checking. His work bridges theoretical foundations with practical applications in distributed systems, programming languages, and verification techniques. He has made seminal contributions to understanding concurrent computation through his development of the actor model framework. His publication record shows consistent contributions from the 1980s through the present, with recent work focusing on verification of safety-critical systems, serverless computing optimization, and advanced techniques for distributed actor systems. The trend in his research demonstrates a progression from theoretical foundations to practical applications in modern computing environments including cloud infrastructure and IoT systems. His scientific achievements have been recognized with numerous prestigious awards: Fellow of the IEEE (2002) IEEE Computer Society Meritorious Service Award Golden Core Member of the IEEE Computer Society International Lecturer for the ACM (1992-1997) ACM Fellow (2018) for research in concurrent programming and formal methods Professor Agha has supervised numerous graduate students and postdoctoral researchers through the Open Systems Laboratory. His laboratory has secured substantial research funding for projects in distributed systems, formal verification, and wireless sensor networks. His work on the actor model has influenced both academic research and industry practices in concurrent and distributed computing. As Director of the Open Systems Laboratory, Professor Agha leads a research group focused on advancing the state of the art in distributed and concurrent systems. The laboratory serves as an intellectual hub for theoretical and practical research in open distributed systems, with projects spanning from foundational theory to real-world implementations in areas like structural health monitoring and cloud computing.

• High Performance Computing
• Distributed Systems
• Deep Learning
• +۴ مورد دیگر

Muhammad Mustafa Rafique is an Associate Professor in the Department of Computer Science at Rochester Institute of Technology's Golisano College of Computing and Information Sciences. His research specializes in optimizing large-scale computing systems, with focus areas including: High-performance computing (HPC) resource management Distributed deep learning acceleration Fault-tolerant cloud architectures GPU-accelerated checkpointing systems Serverless computing frameworks Dr. Rafique's work demonstrates consistent innovation in improving computational efficiency for containerized HPC workflows, multi-GPU scheduling, memory optimization, and distributed training pipelines. His publications frequently appear in premier IEEE/ACM conferences, reflecting contributions to systems performance engineering. While no awards or student advisees are mentioned in available materials, his research collaborations include co-authors from institutions worldwide, indicating active engagement in the high-performance computing research community. Current work explores emerging memory technologies like CXL and advanced containerization techniques for next-generation datacenters.

Jie Liuمشاهده پروفایل

استاد

• Big Data Intelligent Analysis
• Machine Learning Systems
• Intelligent Software Engineering
• +۳ مورد دیگر

Jie Liu is a Researcher at the Institute of Software, Chinese Academy of Sciences and a Professor and Doctoral Supervisor at University of Chinese Academy of Sciences. He is also a Member of the Youth Innovation Promotion Association of the Chinese Academy of Sciences and an Executive Committee Member of the System Software Committee of the CCF Computer Society. His research is conducted within the Software Engineering Technology R&D Center. Dr. Liu received his Ph.D. from the University of Science and Technology of China in 2011 and his B.A. from the same institution in 2004. He has progressed through the ranks at the Institute of Software, CAS, starting as an Assistant Research Fellow (2011-2014), then Associate Research Fellow (2014-2024), and currently as a Researcher (since 2024). His research spans Big Data Intelligent Analysis Models and Systems at the intersection of AI, Software Engineering, and System Software. Specifically, his work covers three main areas: Big Data and Machine Learning Systems (statistics and AI algorithm model libraries, data quantitative analysis tools, LLM reasoning optimization, Earth Big Data); Intelligent Software Engineering (code model constraint decoding, data science agents, system log analysis agents); and Knowledge-Enhanced Intelligent Model Construction (knowledge extraction, knowledge graphs, domain AI model design). His research has resulted in innovative approaches to handling complex data analysis challenges across multiple domains. Dr. Liu's research has produced significant outcomes including EarthDataMiner, which supports SDG indicator calculations and won the 2024 Beijing Municipal Science and Technology Progress First Prize. His work on RISC-V software migration technology has been integrated into the Ruiqian tool (https://rvpt.top/), demonstrating practical applications of his research in emerging computing architectures. Beijing Science and Technology Progress Award, First Prize, 2024 2023 Surveying and Mapping Science and Technology Award, Special Prize, 2023 DASFAA Best Paper Runner-up, Second Prize, 2013 Dr. Liu has successfully guided numerous graduate students who have secured positions at major technology companies including Alibaba, ByteDance, Southern Power Grid, and Agricultural Bank of China. He has secured funding through multiple National Natural Science Foundation projects, National Key R&D Program projects, and over ten other research initiatives. His research collaborations span industry leaders like Huawei, JD.com, and TravelSky, as well as academic institutions within the Chinese Academy of Sciences. He teaches graduate courses such as 'Machine Learning Systems' and 'Cloud Computing and Big Data Technology' at University of Chinese Academy of Sciences, and has established a research group focused on developing innovative solutions at the intersection of AI and software engineering with real-world applications in earth sciences, healthcare, and intelligent systems.

Wei Chenمشاهده پروفایل

استاد

• Software Engineering
• Intelligent Software Engineering
• Network Distributed Computing
• +۶ مورد دیگر

Wei Chen is a Research Fellow at the Institute of Software, Chinese Academy of Sciences, where he serves as a PhD and Master's supervisor. He leads the Software Engineering Technology R&D Center and maintains affiliations with the University of Chinese Academy of Sciences and its Nanjing College. Dr. Chen has established himself as a leading figure in intelligent software engineering research within China's academic community. His primary research focuses on four interconnected areas: intelligent code maintenance and quality assurance (particularly Python ecosystem compatibility based on domain knowledge), reliability assurance of complex IoT systems in human-machine-object convergence scenarios, cloud-native system development with emphasis on Function-as-a-Service optimization, and quality assurance of deep learning frameworks in resource-constrained environments. Dr. Chen's work consistently bridges theoretical advances with practical applications, maintaining strong industry collaborations with major Chinese technology companies. Analysis of Dr. Chen's recent publications reveals a strategic integration of AI techniques with traditional software engineering challenges. His research shows increasing emphasis on leveraging large language models for IoT component synthesis, sophisticated dependency management solutions for Python ecosystems, and innovative approaches to testing autonomous systems. The work demonstrates both theoretical depth and practical utility, with many publications leading to implemented tools and systems. Second Prize of Science and Technology Progress Award of China Institute of Electronics (2022) First Prize of Science and Technology Progress Award of China Institute of Electronics (2021) ACM SIGSOFT Distinguished Paper Award (2023) Special Prize of the 4th China Software Open Source Innovation Competition (2021) First Prize in the 4th China Software Open Source Innovation Competition (2021) OW2 Programming Contest First Prize (2016) Dr. Chen has mentored over ten graduate students who have achieved notable success in academic competitions and industry placements. His laboratory (TCSE, http://tcse.cn/) currently manages multiple significant research projects including 'Complex IoT System Reliability Assurance Key Technology Research' (2025-2028), 'Intelligent Development, Testing, and Maintenance of Cloud-native Software Ecosystems' (2024-2027), and 'Traffic Infrastructure Digital Industrial Software Architecture and Core Technology Standard System' (2021-2024). The lab maintains active collaborations with Huawei, Alibaba, Tencent, and other leading technology enterprises.

Luciano Baresiمشاهده پروفایل

استاد

• Software Engineering
• Self-Adaptive Systems
• Edge Computing
• +۵ مورد دیگر

Luciano Baresi is a Full Professor at the Polytechnic University of Milan (Politecnico di Milano), Italy, affiliated with the Department of Electronics, Information and Bioengineering. He earned his laurea (MSc) and PhD in Computer Science from the same institution and has held visiting positions at the University of Oregon (USA), Tongji University (China), and the University of Paderborn (Germany). His research spans software engineering, with current focuses on self-adaptive systems, edge computing, and AI/ML-based software. His work integrates formal methods with practical applications, emphasizing autonomous systems, cloud-edge continuum, and federated learning. Recent publications highlight AI-driven advancements in software testing, resource optimization, and educational tools. Key research themes include: AI/ML for autonomous driving testing and data augmentation Serverless computing at the edge Federated learning system architectures Containerization and cloud resource management Awarded for impactful contributions: RE 2020 Most Influential Paper ICSOC 2020 Best Paper SEAMS 2022 Best Paper He advises 14+ PhD students and leads projects like Ketonet (health app), WHO's Essential Items Estimator, and dynaSpark. As Editor-in-Chief of Proceedings of the ACM on Software Engineering and senior editor for multiple journals, he shapes academic discourse in adaptive systems and software engineering.

Mohammad Adnan Hamdaqaمشاهده پروفایل

دانشیار

• Model-Based Software Engineering
• Cloud Computing
• Blockchain
• +۴ مورد دیگر

Mohammad Adnan Hamdaqa is an Associate Professor at the Department of Computer Engineering and Software Engineering at Polytechnique Montréal (Canada), where he leads the Software and Emerging Technologies Lab. He holds a Ph.D. in Software Engineering from the University of Waterloo (2016), along with a Master's in Electrical and Computer Engineering from Concordia University, an MBA from New York Institute of Technology, and a Bachelor's in Computer Engineering from Jordan University of Science and Technology. His research focuses on the intersection of emerging technologies and software engineering, particularly how software engineering approaches can be adapted for new platforms like Cloud Computing and Blockchain. His work spans model-driven software engineering, cloud applications, and blockchain technologies. Analysis of his recent publications reveals a strong emphasis on blockchain technology, especially smart contracts and Ethereum, with significant work on security, evolution, and visualization of these systems. His research also demonstrates growing integration of large language models in software engineering tasks, particularly for model specification and code generation. There's also a notable thread of work on sustainability in infrastructure as code and security practices across cloud platforms. Hamdaqa actively contributes to the academic community as a member of IEEE Computer Society and ACM, and has served on program committees for numerous conferences in software engineering and services communities. He has successfully supervised multiple Master's students, with recent theses focusing on smart contract auditing, epidemiological modeling using model-driven approaches, and security practices in infrastructure as code. His lab maintains active research in both theoretical and applied aspects of software engineering for emerging technologies.