جستجوی استاد راهنمای دانشگاهی

Qing Huangمشاهده پروفایل

دانشیار

• Software Engineering
• Knowledge Graphs
• Human-Computer Interaction
• +۵ مورد دیگر

Qing Huang is an Associate Professor in the School of Computer and Information Engineering at Jiangxi Normal University in Nanchang, China. His academic career focuses on bridging software engineering with artificial intelligence, particularly through the application of large language models to enhance software development processes. His research interests span multiple interconnected domains: Software Engineering Knowledge Graphs Human-Computer Interaction Programming Languages Artificial Intelligence applications in software development Dr. Huang's recent work demonstrates a strong focus on leveraging large language models (LLMs) to address fundamental challenges in software engineering. His research explores how AI can enhance code generation, API understanding, software testing, and knowledge representation in programming contexts. A significant portion of his work investigates the intersection of knowledge graphs and LLMs to create more intelligent software development tools. His publications reveal a consistent pattern of innovation in applying cutting-edge AI techniques to practical software engineering problems, with particular emphasis on improving developer productivity through better tooling and knowledge management. Dr. Huang has made notable contributions to the field of prompt engineering for software development tasks, exploring how natural language interfaces can serve as "APIs" for human-AI interaction. His work on AI Chains represents a novel approach to connecting human developers with LLM capabilities through structured knowledge representations. Additionally, he has conducted significant research on smart contract analysis, code reuse, and type inference in partial code contexts. His scientific contributions have been recognized through publications at major software engineering conferences including ASE and ICSE, with multiple papers accepted across different tracks (Research Papers, Journal-First, Tool Demonstrations). Dr. Huang serves as a Program Committee member for ASE 2025 in the Research Papers track, demonstrating his standing in the software engineering research community. Dr. Huang collaborates extensively with researchers from various institutions, including Data61 (Australia), Nanyang Technological University, and other Chinese universities. His work demonstrates strong interdisciplinary connections between traditional software engineering and emerging AI technologies.

Song Wangمشاهده پروفایل

دانشیار

• Software Engineering
• Artificial Intelligence
• Software Reliability
• +۵ مورد دیگر

Song Wang is an Associate Professor in the Department of Electrical Engineering and Computer Science at York University's Lassonde School of Engineering since May 2024. Previously, he served as an Assistant Professor at the same institution from July 2019 to May 2024. He earned his Ph.D. in Computer Engineering from the University of Waterloo in December 2018 under Prof. Lin Tan, an MS degree from the Chinese Academy of Sciences in June 2014 under Profs. Ye Yang and Wen Zhang, and BE and BHRM degrees from Sichuan University in June 2011. Dr. Wang's research focuses on the intersection of Software Engineering and Artificial Intelligence, with two main thrusts: 1) leveraging AI technologies to address software reliability challenges (AI for SE), and 2) developing software reliability techniques to improve AI infrastructure systems (SE for AI). His specific interests include software testing, program analysis, software reliability, and machine learning applications in software engineering. His research has led to tools that have detected hundreds of true bugs across various open-source projects. His recent publications reveal a strong focus on applying large language models to software engineering tasks, analyzing vulnerabilities in deep learning libraries, and developing techniques for software testing and reliability. The research spans multiple subfields including API testing, vulnerability detection, bias analysis in generated code, and automated assurance case generation. TOSEM Distinguished Reviewer Award 2023 APSEC'23 Distinguished Paper Award ACM SIGSOFT Distinguished Paper Award (ICPC 2022) ACM SIGSOFT Distinguished Paper Award (ICSE 2020) Best Paper Award at PROMISE 2019 Dr. Wang actively mentors students at all levels, currently supervising multiple PhD and MASc students. His research group has produced numerous publications in top-tier software engineering venues, including ICSE, FSE, ASE, and TOSEM. He also serves on the editorial board of ACM TOSEM and has been involved in organizing major conferences like ASE and CASCON.

Gregor Wiedemannمشاهده پروفایل

پژوهشگر

• Natural Language Processing
• Text Mining
• Computational Social Science
• +۵ مورد دیگر

Dr. Gregor Wiedemann serves as a Senior Researcher in Computational Social Science at the Leibniz Institute for Media Research (Hans Bredow Institute) since September 2020, co-heading the Media Research Methods Lab (MRML) with Sascha Hölig. His work bridges computer science and social sciences through methodological innovation in empirical media research. Wiedemann holds a doctorate in computer science from Leipzig University (2016), where his dissertation focused on automating discourse analysis using text mining and machine learning. His educational background combines political science and computer science studies at Leipzig University and the University of Miami, followed by postdoctoral work in Language Technology at the University of Hamburg under Prof. Chris Biemann. His research centers on natural language processing and text mining applications for social and media analysis, with significant contributions in hate speech detection, argument mining, and cross-platform misinformation tracking. Recent work demonstrates a strategic shift toward building research infrastructures for sensitive data handling, including the Community Data Trust model for extremism research and the Social Media Observatory open-science platform. His methodology development specifically targets unsupervised information extraction from large document corpora to support investigative journalism and social science inquiry. Wiedemann's publication trends reveal deepening specialization in computational infrastructure development, with 7 of his 11 most recent works (2024-2025) focusing on data trust frameworks, cross-platform methodologies, and AI-driven analysis systems. His projects consistently intersect computational linguistics with pressing social issues including election integrity, climate discourse, and child safety in digital spaces. He has secured major funding through the German Research Foundation (DFG) for the FAME project on argument mining and evaluation, and leads collaborative initiatives including NOTORIOUS (mis- and disinformation tracking) and ComAI (communicative AI impact studies). His work with state media authorities on family influencing content demonstrates applied policy relevance. As co-director of the Media Research Methods Lab, Wiedemann oversees a dynamic team developing cutting-edge computational approaches for media analysis. The lab functions as an interdisciplinary hub connecting computer scientists with social researchers, with current projects spanning TikTok political campaigning analysis, right-wing extremism data infrastructure, and ethical AI applications in public discourse monitoring.

• Cybersecurity
• Artificial Intelligence
• Machine Learning
• +۳ مورد دیگر

Prof. Dr. Dieter Landes serves as a Professor in the Faculty of Electrical Engineering and Computer Science at Coburg University of Applied Sciences, Germany. His research bridges theoretical AI advancements with practical cybersecurity and energy solutions, particularly targeting small and medium enterprises (SMEs) through federally funded initiatives. Landes' core research interests include: Cybersecurity applications of Large Language Models for Blue/Red Teaming operations AI-driven predictive maintenance in photovoltaic energy systems Realistic data generation for security system training and testing Intelligent educational technologies for personalized learning navigation High Performance Computing infrastructure for applied AI workloads His current grant portfolio demonstrates sustained research leadership: Blue-and Red-Agent-based Cyber Security (2025-2028): Federal Office for Information Security project developing LLM-powered defenses against SME-targeted cyberattacks Kick-PV (2023-2026): AI characterization methods for remote photovoltaic system diagnostics GENESIS (2022-2025): Framework for generating realistic data flows to train insider threat detection systems VoLL-AI (2021-2025): Multi-university collaboration (Erlangen/Bamberg) on AI-driven learning navigation HPC4AAI (2021-2024): Infrastructure development for applied artificial intelligence computing Landes operates within interdisciplinary research collectives at Coburg University, frequently partnering with Friedrich-Alexander University of Erlangen-Nuremberg and Otto-Friedrich University of Bamberg. His project pipeline extending to 2028 confirms active research trajectory in applied computer science.