جستجوی استاد راهنمای دانشگاهی

• Rich Web-based Applications Engineering
• Software Design
• Software Architectures
• +۴ مورد دیگر

Nalaka R. Dissanayake serves as a Senior Lecturer in the School of Computer Science and Engineering at the University of Westminster, where he contributes to the Software Systems Engineering Research Group. With 17 years of academic experience since 2007, he progressed from student instructor to his current senior position while completing his doctoral studies. His foundational education includes a B.Sc. in Information Technology from the Sri Lanka Institute of Information Technology (2007), an M.Phil. from the University of Colombo School of Computing (2017), and a Ph.D. from the University of Westminster (2024). Dr. Dissanayake's research centers on rich web-based applications engineering, where he has established significant contributions through architectural style development and design methodology formalization. His work introduces specialized concepts including RiWAArch Style, BAW-MVC, and delta communication principles that address core challenges in web application state management and communication efficiency. Current research frontiers actively explore prompt engineering techniques for integrating AI/ML capabilities into rich web applications, demonstrating adaptation to emerging technological paradigms while maintaining focus on architectural fundamentals. His publication portfolio reveals a clear research trajectory evolving from AJAX application foundations (2013-2014) through architectural pattern specification (2017-2020) toward comprehensive design methodology development (2023-2024). This progression demonstrates increasing theoretical sophistication while maintaining practical implementation relevance, with consistent output across journals like the Journal of Web Engineering and major conferences including FTC and ICTer. As an active academic contributor, Dr. Dissanayake serves as a reviewer for the Journal of Web Engineering and various conferences. His research group membership provides access to collaborative opportunities within the university's software engineering ecosystem, while his extensive teaching experience across multiple institutions offers valuable mentorship perspective for graduate students.

• Cloud Computing
• Machine Learning
• Computer Networks
• +۳ مورد دیگر

Hamed Hamzeh serves as Lecturer in Data Science at the School of Computer Science and Engineering, University of Westminster, and is affiliated with the Centre for Parallel Computing. His academic credentials include a Ph.D. in Cloud Computing from Bournemouth University and an MSc in Data Science from Istanbul Sehir University, Turkey. Dr. Hamzeh's research centers on cloud-native resource management, computer networks, and multi-agent systems, with groundbreaking work on fairness in cloud resource allocation. He developed novel algorithms including H-FFMRA and MRFS that address multi-resource scheduling in heterogeneous environments. His expertise spans AWS, Kubernetes, Python, and optimization techniques, bridging theoretical cloud computing with industrial applications in orchestration and resource management. Analysis of his 11 publications (2017-2023) reveals an evolving research trajectory: beginning with network bandwidth allocation (2017), advancing to cloud resource fairness (2018-2021), and culminating in cloud-to-things continuum orchestration (2023). This progression demonstrates increasing system complexity while maintaining core focus on fairness metrics across distributed environments. Dr. Hamzeh actively contributes to the academic community as technical committee member for IEEE ICCCS and Distributed AI conferences, and as reviewer for Springer's Journal of Grid Computing and Journal of Supercomputing. His service reflects recognition within cloud computing research circles. Prospective students receive supervision in Cloud Computing, Artificial Intelligence, Machine Learning, Software Engineering, and Computer Networks. Current research opportunities emphasize practical implementation of resource allocation algorithms in cloud-native environments through the Centre for Parallel Computing.

Thorsten Riemke-Gurzkiمشاهده پروفایل

استاد

• Web Technology
• Web Development
• User Experience
• +۶ مورد دیگر

Thorsten Riemke-Gurzki is a fulltime Professor for Web Technology, Corporate Portals & Usability at Stuttgart Media University in Stuttgart, Germany, where he has been working since 2008. Prior to his academic career, he worked as a freelance and employed consultant for various companies including SAP Germany. He describes himself as having 'grown up with hardware, CP/M, mailbox systems' and being 'web addicted since 1991,' positioning himself as both a frontend and backend developer and a 'web technology evangelist.' Professor Riemke-Gurzki's research interests span a comprehensive range of web technologies: Frontend and backend web development HTML, CSS, JavaScript and modern frameworks (React, React Native, Vue.js) Server-side technologies including Node.js, Go, Docker, and database systems Microservices architectures and single page applications Web 3D/AR, AI applications for the web, and Internet of Things User experience, usability, and content management systems His recent publications demonstrate a strong focus on practical web development techniques, covering topics from CSS fundamentals to advanced server implementation in Go and exploration of emerging technologies like JavaScript-based microcontrollers. His work bridges academic research with practical industry applications through his website riemke.dev where he shares technical articles and hosts a podcast on web development topics. Professor Riemke-Gurzki maintains an active presence in both academic and developer communities, with educational materials on GitHub and engagement with industry technologies like the Daimler UXLab truck. His approach emphasizes hands-on learning and practical application of web technologies.

Eduard-Cristian POPOVICIمشاهده پروفایل

دانشیار

• AI-Driven Systems
• Machine Learning
• Cybersecurity
• +۱۱ مورد دیگر

Eduard-Cristian POPOVICI serves as an Associate Professor and Head of the SAIM Lab at the Faculty of Electronics, Telecommunications and Information Technology, Politehnica University of Bucharest. He earned his Ph.D. in Telecommunications in 2001 and previously directed the Telecom Department at the ETTI Faculty. His expertise lies in software architecture and telecommunications. His educational attainment includes a Ph.D. in Telecommunications (2001). Dr. Popovici's research interests are highly diverse and interdisciplinary, covering AI-Driven Systems, Machine Learning, Cybersecurity, Decision Support Systems, Edge and Cloud Computing, Smart Environments and Cities, Space Technologies, Affective Computing, Healthcare, Digital Twins, Omniverse, Microservices, Blockchain, and Quantum Computing. He has made significant contributions to autonomous cybersecurity systems and modern communication networks including 4G/5G, IoT, and blockchain-based solutions. With over 40 research projects to his credit, including more than 20 international collaborations (e.g., BRAVIS, RIWCOS/802.21, BIOMETRIC, FF-RIWER, DEFRAUDIFY4ALL), he has designed frameworks for biometric security, environmental monitoring, distributed videoconferencing, and fraud protection. He founded the SMICES Cybersecurity Master program and currently partners with NVIDIA to build Romania's national AI community. He directs the SAIM Lab (Solutions based on Agility, Innovation & Multidisciplinarity Laboratory), which evolved from the former Services and Applications for Mobile Internet Laboratory (previously supported by NOKIA and SAMSUNG).

Pinjia Heمشاهده پروفایل

استادیار

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

Pinjia He is an Assistant Professor and Presidential Young Fellow at The Chinese University of Hong Kong, Shenzhen's School of Data Science. He is also recognized as a national-level young talent in China. His academic journey includes a postdoctoral position at ETH Zurich's Department of Computer Science under Prof. Zhendong Su, a Ph.D. in Computer Science and Engineering from The Chinese University of Hong Kong supervised by Prof. Michael R. Lyu, and a B.E. in Computer Science and Technology from South China University of Technology. Ph.D. in Computer Science and Engineering, The Chinese University of Hong Kong Postdoctoral Scholar, ETH Zurich B.E. in Computer Science and Technology, South China University of Technology Dr. He's research spans software engineering, natural language processing, and systems, with particular focus on (1) AI for SE (e.g., LLM for code, AIOps), (2) SE for AI (e.g., LLM safety), and (3) software testing. He is renowned for his work on robust NLP systems and software log analysis. His research has been published at top venues including ICSE, FSE, ASE, ISSTA, ICLR, and OSDI. His publication trends show a consistent focus on log analysis systems, with recent work shifting toward LLM applications in software engineering and safety evaluation of conversational AI systems. His research demonstrates strong industry impact with tools downloaded over 60,000 times by more than 450 organizations. Most Influential Paper Award (ISSRE) IEEE Open Software Services Award Dr. He actively contributes to the academic community as Social Media Co-Chair for FSE 2025, Associate Editor of TOSEM, and serves on program committees for major conferences including FSE 2025, ICSE 2025, ISSTA 2025, and ASE 2024. His GitHub repositories (logparser, loglizer, loghub) have garnered over 5,000 stars and significant industry recognition including from IBM. While specific grant information isn't detailed in the provided text, his extensive publication record and tool development suggest substantial research funding. His work has been cited over 5,000 times according to Google Scholar, and his open-source tools have been widely adopted in both academia and industry. His current research focuses on advancing the intersection of software engineering and artificial intelligence, particularly in leveraging LLMs for software development tasks while ensuring their safety and reliability.

Marcos Kalinowskiمشاهده پروفایل

استاد

• Software Engineering
• Machine Learning
• Empirical Software Engineering
• +۵ مورد دیگر

Professor Marcos Kalinowski is a faculty member in the Department of Informatics at Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Brazil. He serves as Professor of Software Engineering with an active research program focused on the intersection of software engineering and artificial intelligence. His research interests span: Software Engineering Machine Learning in Software Engineering Empirical Software Engineering Requirements Engineering for AI/ML Systems Trustworthy AI Systems Software Engineering Education Professor Kalinowski's recent work has concentrated on Large Language Models (LLMs) applications in software engineering processes, emphasizing empirical validation and practical relevance. His publications reveal a strong commitment to understanding how AI technologies can be effectively integrated into software development while maintaining quality standards and addressing human factors. He leads the international "Naming the Pain in Requirements Engineering" (NaPiRE) initiative, which investigates requirements engineering challenges across global organizations. His scientific contributions include: Framework development for trustworthy AI systems Empirical studies on LLMs in software engineering research Investigations of human factors in software development Analysis of machine learning code quality and technical debt Requirements engineering methodologies for AI-enabled systems Professor Kalinowski holds leadership positions in major software engineering conferences including General Co-Chair for ICSE 2026 and Program Co-Chair for ESEM 2021. His work appears consistently in top-tier venues such as ICSE, ESEC/FSE, EASE, and SANER, demonstrating significant recognition within the software engineering research community.

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.

Hongyu Zhangمشاهده پروفایل

استاد

• Intelligent software engineering
• Software analytics
• Data-driven software engineering
• +۴ مورد دیگر

Hongyu Zhang is a Professor and Dean of the School of Big Data and Software Engineering at Chongqing University, China, and an Honorary Professor at The University of Newcastle, Australia. Previously, he served as a Lead Researcher at Microsoft Research Asia and an Associate Professor at Tsinghua University, China. He received his PhD from the National University of Singapore in 2003. His academic journey spans prestigious institutions, combining industry research experience with academic leadership. Dr. Zhang's research interests focus on intelligent software engineering, software analytics, data-driven software engineering, software fault management, testing and debugging, and software maintenance and reuse. His work centers on improving software quality and productivity by mining and analyzing vast amounts of software data. Over the years, he has developed innovative methods that apply data mining, machine learning (including deep learning), and information retrieval techniques to extract knowledge from software data and solve complex software engineering problems. His research spans three major areas: intelligent programming (code search, code summarization, code generation), intelligent quality prediction (defect prediction, cloud failure prediction, performance prediction), and intelligent fault detection and diagnosis (log-based fault detection, crash-based fault localization, bug report analytics). His recent publications demonstrate a clear trend toward integrating large language models and deep learning techniques with traditional software engineering practices. The research spans intelligent programming assistance, code security, UI automation, distributed systems optimization, and performance analysis. His work increasingly focuses on practical applications of AI in software engineering, with emphasis on real-world impact in industrial settings, particularly in microservices, cloud systems, and large-scale software development environments. 8 ACM Distinguished Paper Awards Best Paper Award: How Long Will it Take to Mitigate this Incident for Online Service Systems? David Lorge Parnis Fellowship Senior Member of IEEE Distinguished Member of ACM Distinguished Member of CCF Fellow of Engineers Australia (FIEAust) Recognized in The Australian's Top Researchers special edition as leading researcher in Software Systems World's Top 2% Scientists (career-long) Dr. Zhang has successfully advised numerous PhD and Master's students who have gone on to prominent positions at leading technology companies and academic institutions worldwide. His research has been supported by significant grants including Australian Research Council Discovery Projects (as Lead CI) and multiple National Science Foundation of China projects. His work has made tangible impacts in industry, most notably through the Microsoft Developer Assistant project which received over 450K downloads in 2016. He leads research groups focused on intelligent software engineering and software analytics, with strong collaborations between Chongqing University, The University of Newcastle, and Microsoft Research. His teams develop practical tools for code intelligence, log analysis, and fault diagnosis that are deployed in real-world online service systems.