جستجوی استاد راهنمای دانشگاهی

• Knowledge Graph
• Semantic Web
• Natural Language Processing
• +۱ مورد دیگر

Dr. Sven Hertling serves as Substitute Professor at the University of Mannheim while maintaining a reduced research role at FIZ Karlsruhe's Information Service Engineering group. His career spans institutional affiliations including DFKI GmbH and the University of Mannheim's Data and Web Science Group. His educational background includes: PhD from University of Mannheim (2017-2023) Master of Science in Computer Science from TU Darmstadt (2012-2015) Bachelor of Science in Computer Science from TU Darmstadt (2009-2012) Dr. Hertling's research integrates Knowledge Graphs, Semantic Web, and Machine Learning with Natural Language Processing. His work transitions from foundational contributions like SPARQL endpoint discoverability (ISWC 2013 Best Poster) to contemporary applications including LLM-based climate data extraction and ontology alignment systems, consistently targeting practical implementations in GUI search and knowledge engineering. His 2024-2025 publications reveal a strategic shift toward leveraging large language models for standardizing scientific repositories while advancing core Semantic Web techniques through machine learning datasets. This evolution demonstrates both technical depth in graph algorithms and responsiveness to emerging AI paradigms. Major recognitions include: ESWC 2021 Best Demo (kgextension package) AICA 2017 Best Paper (GUI search engine) ESWC 2016 Top-K Shortest Paths Challenge Winner ISWC 2013 Best Poster (SPARQL endpoints) As an emerging supervisor, Dr. Hertling leverages his Software Campus leadership training and active conference participation (serving on 12+ ESWC/ISWC program committees) to foster student development. His current roles at Mannheim and FIZ Karlsruhe indicate robust institutional support for his research trajectory, though specific grant details remain undisclosed in the source material. He operates within FIZ Karlsruhe's Information Service Engineering framework and maintains ties to Mannheim's Data and Web Science ecosystem, facilitating interdisciplinary work at the intersection of knowledge representation and AI-driven data analysis.

• Natural Language Processing
• Machine Learning
• Deep Learning
• +۵ مورد دیگر

Mary Ann Tan is a PhD student and Junior Researcher at Karlsruhe Institute of Technology (KIT) and FIZ Karlsruhe – Leibniz Institute for Information Infrastructure, working within the Information Service Engineering group and the Institute of Applied Informatics and Formal Description Methods (AIFB). Her academic background includes: PhD candidate at KIT/FIZ Karlsruhe (2020–present) MSc in Computational Linguistics, Ludwig-Maximilians University, Munich (2018–2020) MSc in Computer Science (NLP specialization), De La Salle University, Manila (2002–2004) BSc in Computer Science, De La Salle University, Manila (1997–2001) Tan's research integrates Natural Language Processing, Knowledge Graphs, and Deep Learning to solve challenges in Cultural Heritage digitization. She develops methods for cross-lingual embeddings, knowledge graph refinement, multimodal search, and transformer-based workflows – transforming legacy cultural data into structured, AI-processable formats. Her work bridges technical AI innovation with practical heritage preservation needs, emphasizing under-resourced languages and multimodal cultural artifacts. Her 11 publications (2021-2025) reveal a cohesive trajectory: starting with bibliographic knowledge graphs (2021), advancing to multimodal art search and audio ontologies (2022-2023), and recently focusing on LLM integration for cultural data and mathematical semantics (2024-2025). This progression demonstrates increasing technical sophistication while maintaining consistent application to cultural heritage challenges. As an active collaborator in large international projects (e.g., the 50+ author Semantic Web and Creative AI report), Tan contributes to team-based research while developing her independent expertise. Her industry experience in software engineering informs her practical approach to research implementation within the Information Service Engineering group.

Shing-Chi Cheungمشاهده پروفایل

استاد

• Software Engineering
• Program Analysis
• Software Testing
• +۷ مورد دیگر

Shing-Chi Cheung is a Professor of Computer Science and Engineering at the Hong Kong University of Science and Technology (HKUST), School of Engineering. He founded the CASTLE research group and co-founded the International Workshop on Automation of Software Testing (AST) in 2006. His leadership includes serving as General Chair of FSE 2014 and chairing multiple APSEC conferences. His research focuses on software quality enhancement through program analysis, testing, debugging, and AI techniques, targeting Android apps, open-source software, deep learning systems, smart contracts, and spreadsheets. Current projects include metamorphic testing frameworks, binary analysis tools, and vulnerability detection systems for emerging technologies. His publication portfolio demonstrates consistent contributions to software engineering since 2016, with recent work emphasizing AI-integrated testing methodologies, smart contract security, and deep learning system reliability. Key trends show increasing focus on cross-language analysis, data visualization quality, and compiler-level verification for modern software stacks. Distinguished Member of the ACM Fellow of the British Computer Society Editorial board member: Science of Computer Programming (SCP), Journal of Computer Science and Technology (JCST) Former editorial board member: IEEE Transactions on Software Engineering (2006-2009), Information and Software Technology (2012-2015) Four patents in China and the United States Cheung actively mentors through the CASTLE research group and serves on program committees for major conferences including ICSE, ESEC/FSE, and ISSTA. His work bridges academic research with practical applications through industry collaborations and tool development. He has contributed to numerous workshops and symposia as steering committee member and program chair.

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.

• Artificial Intelligence
• Machine Learning
• Data Science
• +۵ مورد دیگر

Professor Kristina Schädler is a faculty member at the West Coast University of Applied Sciences (FH Westküste), where she serves as Professor of Data Processing within the School of Technology. She has been with the university since 2005 and also served as Dean of the Department of Technology. Her academic background includes a PhD in machine learning from TU Berlin, where she was awarded the Chorafas Research Prize for young scientists. West Coast University of Applied Sciences (since 2005) TU Berlin, Institute of Computer Science (1994-1999) Martin Luther University Halle/Wittenberg (1990-1994) Professor Schädler's research focuses on artificial intelligence and machine learning applications, particularly in image processing and data analysis. Her work spans multiple domains including industrial automation, agricultural technology, renewable energy, and animal husbandry. She has led numerous research projects that bridge academic theory with practical industrial applications, with particular emphasis on developing robust image processing systems that can be deployed in real-world settings. Her research portfolio demonstrates a consistent pattern of applying advanced machine learning techniques to solve practical problems across diverse industries. The ANIMET project, which developed facial recognition for horses, and the MaviSeg system for multichannel image segmentation represent her innovative approach to adapting computer vision technologies for specialized applications. Her work often involves close collaboration with industry partners to ensure practical relevance and implementation. Chorafas Research Prize for young scientists Innovationspreis at Equitana (2013) for the ANIMET project Professor Schädler has supervised numerous student theses that have resulted in practical applications across various domains. Her research group has secured funding from multiple sources including the European Commission, BMBF, and regional development agencies. She has established the CICAD project as a sustainable competence center for industrial image processing, which has trained multiple doctoral students through cooperative programs with the University of Lübeck. Her work demonstrates strong industry connections with companies like HIT Hinrichs Innovation + Technik, MBJ Solutions, and Fischer und Tausche Kondensatoren. Her research laboratory focuses on industrial image processing applications, with specialized equipment for 2D/3D imaging, spectral analysis, and machine learning implementation. The CICAD project established a dedicated competence center that continues to develop new applications of image processing technology across multiple industries.