Prof. Klaus Weidenhaupt serves as a Professor and Computer Science Library Officer at the Niederrhein University of Applied Sciences within the Department of Electrical Engineering and Computer Science. He is a key member of the Gemeinschaftslabor Informatik (GLI), which provides shared computing facilities for computer science education and supports a wide range of software-oriented courses including Database Systems, Data Science, and Natural Language Processing. His academic interests are centered on data-driven technologies, with significant contributions in the areas of Information Retrieval , Web Mining , and Machine Learning . He teaches advanced courses such as Reinforcement Learning, Natural Language Processing, and IR and LLM-based web search, demonstrating expertise at the intersection of artificial intelligence and information systems. The GLI laboratory, where Prof. Weidenhaupt is based, operates multiple computer rooms equipped for practical instruction in software development, networking, and data processing. The laboratory also offers IT support services and manages virtual machine resources for academic projects, fostering a collaborative environment for students and faculty.
Masud Rahman is an Associate Professor in the Faculty of Computer Science at Dalhousie University, Canada, where he leads the RAISE Lab. Previously a tenure-track Assistant Professor, he completed his Ph.D. in Computer Science/Software Engineering from the University of Saskatchewan and a postdoctoral fellowship at Polytechnique Montreal. His academic career demonstrates strong progression with significant research impact in software engineering. Faculty of Computer Science, Dalhousie University (Current) University of Saskatchewan (Ph.D. studies) Polytechnique Montreal (Postdoctoral research) Dr. Rahman's research focuses on the intelligent automation of software maintenance and evolution, particularly targeting software debugging, code search, and code reviews. His work strategically combines Software Engineering with Artificial Intelligence techniques including Machine/Deep Learning, Information Retrieval, Mining Software Repositories, and Natural Language Processing. His industry experience as a professional developer for three years significantly shaped his research direction toward solving practical software maintenance challenges that cost the global economy billions annually. His research program addresses critical problems in software bug detection, diagnosis, explanation, and reproduction, with increasing focus on AI-powered and simulation modeling software. His publications demonstrate consistent output in top-tier venues including 7 papers at ICSE (A*), 3 at FSE (A*), 3 at ASE (A*), 8 at EMSE (A), 6 at ICSME (A), and 9 at MSR (A). The research trends show increasing focus on deep learning applications for software engineering problems, with particular attention to code search, bug localization, and debugging automation. His work has evolved from traditional information retrieval approaches to incorporate advanced neural network techniques and generative AI. Governor General's Gold Medal 2019 U of S Doctoral Thesis Award 2019 CS Best PhD Thesis Award 2019 TCSE Distinguished Paper Award Most Influential Paper Award Dr Keith Geddes Award Dalhousie Belong Research Fellowship President's Gold Medal (Bangladesh) Dr. Rahman has secured $500K+ in competitive research funding as Principal Investigator, including an NSERC Discovery Grant, Mitacs Accelerate International, and Climate Action and Awareness Fund. He actively collaborates with industry partners including Metabob Inc., Mozilla Firefox, and Vendasta Technologies. His service to the community includes extensive program committee work for major conferences and journal reviewing. He leads the RAISE Lab, which focuses on developing AI-powered solutions for software maintenance challenges, with current projects emphasizing sustainable software innovation and sustainable AI as part of Dalhousie's strategic goals.
Lian Li is a Professor in the Institute of Computing Technology at the Chinese Academy of Sciences, where he leads the program analysis research group. He holds a PhD from the University of New South Wales, Australia, and a Bachelor's degree from Tsinghua University in Engineering Physics. His research focuses on developing innovative program analysis techniques and tools to enhance software reliability and security. His educational background includes a PhD in Computer Science from the University of New South Wales (2003-2007) with a thesis on "ScratchPad Management for Static Data Aggregates" under Professor Jinling Xue, and a Bachelor's degree in Engineering Physics from Tsinghua University (1993-1998). Lian Li's research primarily centers on program analysis techniques, particularly static analysis methods for software security and reliability. His group developed Wukong, a static analysis and detection system capable of identifying deep security vulnerabilities across functions, components, and complex dependencies in C/C++, Java, and Android applications. This tool has discovered thousands of errors in popular open-source software including Google Chromium, Bash, sed, and Hadoop, with hundreds confirmed by developers and over 50 CVEs assigned. His publication record shows a strong focus on program analysis, particularly context-sensitive pointer analysis, taint analysis, and vulnerability detection. His recent work (2021-2024) demonstrates continued innovation in context-free language reachability, efficient IFDS algorithms, and specialized analysis for generics and authorization vulnerabilities. His research spans cybersecurity, programming languages, and software engineering domains, with emphasis on practical applications for real-world software systems. ASE 2019 Distinguished Paper Award CCS 2022 Best Paper Honorable Mention Lian Li has guided numerous PhD and Master's students in computer system architecture and software theory. His research group maintains active collaborations across various software analysis domains, with funding supporting their work on tools like Wukong. They have developed significant intellectual property including multiple patents related to program analysis techniques. The program analysis research group he leads focuses on developing practical tools for software reliability and security. Their work bridges theoretical program analysis with real-world applications, particularly through the Wukong analysis system which has been successfully applied to major open-source projects.
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.
Jan Gerken is a Professor of Data Science at the Faculty of Business, Flensburg University of Applied Sciences. He serves as Vice Dean of Faculty 4 (Business) and leads the Natural Language Processing & Text Analytics research group within FLAIR (Flensburg Artificial Intelligence Research). His contact information shows he is based in Room C 221 with telephone number 0461/805-1471, and his office hours are regularly announced on Stud.IP. Professor Gerken's research focuses on Text Analytics, Data Science, Machine Learning, Deep Learning, and Business Analytics. His work bridges theoretical computer science with practical business applications, particularly in public administration. He has established himself as a leading researcher in applying AI techniques to analyze textual data, with special emphasis on patent analysis and social media analytics. His publication record demonstrates a clear evolution from patent analysis and technology monitoring (2010-2015) to Business Intelligence applications in public administration (2018-2023) and most recently to Large Language Models and systematic literature review methods (2025). This progression reflects the broader field's movement from traditional text mining to contemporary AI-driven approaches. As Scientific Director of the AI Application Center (KIAZ) and leader of FLAIR, Professor Gerken plays a pivotal role in connecting academic research with practical implementation. His work with the Flensburg City Administration on Business Intelligence 2022 demonstrates his commitment to translating research into real-world solutions for municipal governance.
Prof. Dr. Jens Allmer is a full Professor of Medical Informatics and Bioinformatics at Ruhr West University of Applied Sciences (HRW) in Mülheim an der Ruhr, Germany. He previously held academic positions as Cluster Leader at Wageningen University and Research in the Netherlands (2017-2018) and as Assistant and Associate Professor at Izmir Institute of Technology in Turkey (2008-2016). His academic journey began with a PhD in Biology with distinction from the University of Münster in 2006. Prof. Allmer's research spans multiple omics disciplines with a primary focus on microRNA regulation and pathogen-host interactions. He employs machine learning techniques to explore these complex biological systems. His work has evolved from foundational bioinformatics methods to sophisticated integrative analyses, particularly in proteogenomics and computational miRNomics. He has made significant contributions to understanding microRNA detection algorithms, proteogenomic peptide mapping, and the development of comprehensive frameworks for omics data analysis. His recent publications demonstrate a strong trend toward integrative approaches that combine multiple data types, with increasing emphasis on machine learning applications in bioinformatics. The research spans from fundamental database development for noncoding RNAs to clinical applications in disease mechanisms, particularly in HIV infection and cancer pathways. His work shows consistent progression from method development to application in biological and medical contexts. Outstanding Young Scientist in Bioinformatics by the Turkish Academy of Sciences (2010) EMBO Short Term Fellowship Award (2013) DAAD Research Stays for University Academics and Scientists (2013, declined) Outstanding Young Researcher, Turkish Academy of Science (2010) Prof. Allmer has advised numerous doctoral and master's students throughout his career, primarily during his tenure at Izmir Institute of Technology. He has secured substantial research funding, with over 650,000 euros received for his projects. His teaching portfolio includes courses in medical informatics, bioinformatics, data mining, machine learning, and database systems across multiple institutions in Germany, Turkey, and through ERASMUS programs. He maintains active collaborations with researchers across Europe and continues to contribute significantly to both theoretical and applied aspects of bioinformatics.
Patrick Arni is a Senior Lecturer at the University of Bristol's School of Economics, specializing in empirical public policy analysis and applied microeconometrics with applications in digital transformation, labor markets, education, health, and social policy. Education: PhD in Economics from HEC Lausanne PhD program at Swiss National Bank Study Center Gerzensee Master's degree from University of Zurich Additional studies at University of Geneva Visiting scholar at UC Berkeley's Center of Labor Economics Visiting scholar at Tilburg University His research centers on labor economics and policy evaluation , with growing emphasis on digital transformation's impact on skill demand . Methodologically, he employs applied microeconometrics , duration models , and field experiments to analyze job search behavior, unemployment insurance design, and belief formation. A distinctive focus examines how overconfidence and social interactions influence economic outcomes like labor market participation and health behaviors. Recent work reveals two dominant trends: 1) Increasing integration of digital transformation with labor economics to study skill demand dynamics using big data from job vacancies, and 2) Continued refinement of labor market policy evaluation through natural experiments examining benefit sanctions, job search requirements, and counseling effectiveness. Research Grants: NRP 77 "Digital Transformation": The Swiss labour market in the digital transformation (SWISSLAB) [2020-2024] Field Experiments to Optimise Public Employment Service Counselling [2019-2024] Dr. Arni leads the SWISSLAB project constructing large-scale job vacancy databases using text mining and machine learning to analyze digital transformation's labor market effects. He designs field experiments for Switzerland's Public Employment Service and maintains an IZA Research Affiliate status (since 2010) with prior Research Associate role (2011-2016).
