جست‌وجوی استاد و پژوهشگر

• Database systems
• Large scale analytics
• Distributed systems

Beng Chin Ooi is a Lee Kong Chian Centennial Professor at the National University of Singapore (NUS), School of Computing. He has been with NUS since 1991, progressing through the ranks from Lecturer to his current distinguished position. He previously served as Dean of the School of Computing from 2007 to 2013 and as Director of the Smart Systems Institute from 2011 to 2021. His educational background includes: 1985: B.Sc. (1st Class Honors) from Monash University, Melbourne, Australia 1989: Ph.D. in Computer Science from Monash University, Melbourne, Australia Beng Chin Ooi's research focuses on database systems, large scale analytics, and distributed systems. His work has been instrumental in advancing the field of data management technology, particularly in the context of "big data" in large-scale parallel and distributed systems. He has made significant contributions to spatio-temporal and distributed data management, as well as pioneering research in distributed database management and peer-to-peer based enterprise quality management. His recent publications demonstrate a strong focus on blockchain technology, machine learning systems, and healthcare informatics. There's a clear progression from foundational database research to applications in emerging technologies like blockchain and AI. His work bridges theoretical advances with practical system implementations, as evidenced by multiple open-source projects associated with his publications. His notable awards include: 2021: NUS Research Recognition Award 2020: ACM SIGMOD E.F. Codd Innovations Award 2020: ACM SIGMOD Research Highlight Award 2019: VLDB Best Paper Award 2016: Fellow of Singapore National Academy of Science 2016: China Computer Federation Overseas Outstanding Contributions Award 2014: VLDB Best Paper Award 2014: IEEE TCDE CSEE Impact Award 2013: Singapore National Day's Public Administration Medal (Silver) 2013: NUS Outstanding Researcher Award 2012: IEEE Computer Society Kanai Award 2011: ACM Fellow 2011: Singapore President's Science Award 2009: IEEE Fellow 2009: ACM SIGMOD Contributions Award Throughout his career, Professor Ooi has demonstrated exceptional leadership in the database community, promoting high standards of database research at both international and regional levels. His BLOCKBENCH framework became the world's first benchmarking tool for private blockchains, and his work on data provenance on blockchain systems earned both the VLDB Best Paper Award and the ACM Research Highlight Award. He has led several major research initiatives, including the Smart Systems Institute at NUS. Professor Ooi has established multiple open-source projects including FabricSharp for blockchain data provenance and Cool for cohort online analytical processing. His research group has consistently produced high-impact work that bridges theoretical advances with practical system implementations.

• Software Engineering
• Probabilistic Programming
• Security and Penetration Testing
• +۵ مورد دیگر

Jürgen Cito is an Associate Professor in the Department of Software Engineering at the Faculty of Informatics, TU Wien, where he leads research in probabilistic programming, security, and configuration management. His work is supported by major grants from the Austrian Science Fund (FWF), European Commission, and Meta Platforms, Inc., with active projects spanning 2022-2027. His research focuses on the intersection of software engineering and machine learning, particularly in static analysis of probabilistic programs, AI-driven penetration testing, and infrastructure security. Key contributions include identifying secret exposure in configuration files, grammar inference for ad hoc parsers, and performance prediction from source code, often combining empirical studies with tool development. Analysis of his 15 most recent publications (2020-2024) reveals three dominant trends: (1) Security vulnerabilities in configuration management systems, especially secret leakage in dotfiles; (2) Application of large language models to offensive security testing; and (3) Machine learning techniques for performance prediction and AutoML optimization in software contexts. Cito has supervised 22 Master's students on cutting-edge topics including AI security, infrastructure as code, and program analysis. His current research portfolio includes: Types4Strings (FWF, 2024-2027): Type systems for string processing Cloud Open Source Research Mobility Network (EU, 2023-2026): Open-source cloud infrastructure Software Assistants for Probabilistic Programming (Meta, 2022-2026): AI tools for probabilistic code He is embedded in TU Wien's Institute of Software Technology and Interactive Systems (E194), collaborating on cross-institutional projects focused on software security and developer tooling, with particular emphasis on empirical validation of security practices and configuration management systems.

• Machine Learning
• Optimization Algorithm
• Dynamic Optimization Algorithm
• +۳ مورد دیگر

Philipp Neuhauser is a researcher at the University of Applied Sciences Upper Austria , focusing on Production and Operations Management . His work involves integrating Machine Learning and Dynamic Optimization Algorithms into industrial systems. Active in Adaptive Optimization projects since 2019 Collaborates with institutions like Springer and Elsevier Research emphasizes: Smart Production and Industrial Automation AutoML and Software Solutions for dynamic environments Real-time Decision Systems in logistics Recent publications address evolutionary algorithms and dynamic tree-search applications. Collaborations span Austria, Italy, and Spain, with presentations at EUROCAST and EMSS conferences.

Thomas Gremslمشاهده پروفایل

استاد

• AI Ethics
• Digital Transformation
• Neuroscience Ethics
• +۲ مورد دیگر

Univ.-Prof. Dr. Thomas Gremsl specializes in technology ethics at the University of Graz. His research addresses AI governance, mental privacy in neuroscience, and socio-ethical implications of digitalization. Current EU projects include neuroprivacy protection and AutoML regulation. Key Publications: Examine emotional AI ethics, digital life, and fair AI in healthcare. Teaches doctoral courses in theology and ethics.

• Machine Learning
• Data Mining
• Recommendation Systems
• +۵ مورد دیگر

Univ.-Prof. Dr. Günther Specht is a faculty member in the Department of Computer Science at Universität Innsbruck. His research focuses on machine learning, data mining, and their applications in recommendation systems, music information retrieval, and authorship attribution. He has contributed to projects such as HPT4Rec (a hyperparameter optimization framework for recommenders) and Cloudgene (a cloud computing tool for biomedical pipelines). His work spans database systems, text analysis, and social media analytics. Notable tools include HaploGrep 2 (for mitochondrial DNA analysis) and StyleExplorer (for textual style visualization). Key research areas include: (1) Developing algorithms for music popularity prediction and playlist analysis; (2) Enhancing authorship attribution techniques using grammar profiling and syntax tree analysis; (3) Designing efficient database index structures like Height Optimized Tries; and (4) Investigating ethical aspects of recommendation systems and plagiarism detection. Publications from 2020-2024 highlight advancements in cross-domain text classification, social media behavior analysis, and automated music genre recognition. His work bridges traditional statistical methods with modern machine learning, emphasizing practical applications in both academic and industry contexts.

• Human-Centered Artificial Intelligence
• Explainable Artificial Intelligence
• Interactive Machine Learning
• +۷ مورد دیگر

Andreas Holzinger is a University Professor for Digital Transformation for Smart Agriculture and Forestry at the Institute of Forest Engineering, Department of Agricultural Sciences, University of Natural Resources and Life Sciences Vienna (BOKU). He pioneered interactive machine learning with human-in-the-loop frameworks to advance trustworthy AI, focusing on explainability, robustness, and ethical alignment with human values. His work bridges artificial intelligence with forestry technology, ensuring safety, privacy, and sustainability. 2022: University Professor at BOKU 2021: IFIP Fellow 2020: ELLIS Member 2019: Visiting Professor at University of Alberta 2019: Academia Europaea Full Member His research interests span Human-Centered AI , Explainable AI , Digital Transformation in Forestry , and Trustworthy AI . He develops frameworks where humans collaborate with AI systems to enhance decision-making in forest operations, focusing on transparency and controllability. His recent publications emphasize Interactive Machine Learning and Explainable AI in forestry contexts, including cable corridor planning, danger zone detection, and sensor-based data interpretation. Articles explore 3D point cloud analysis , human-robot collaboration , and contextual counterfactuals to improve AI systems' reliability. Scientific Awards: Most influential Paper Award (2025) Fellow of IFIP (2021) Member of ELLIS (2020) Full Member, Academia Europaea (2019) He supervises theses on Forestry Technology , Plant Development , and Stress Resilience , mentoring students working on smart sensors, genetic analysis, and AI applications in agricultural systems. His collaborations span institutions in Austria, Canada, Finland, Italy, New Zealand, and the USA.

• Machine Learning
• Industrial Applications
• Smart Manufacturing
• +۳ مورد دیگر

Florian Bachinger is a researcher at the Research Center Hagenberg, University of Applied Sciences Upper Austria, affiliated with the Center of Excellence for Smart Production Digital Transformation and HEAL research group. His work focuses on bridging machine learning with industrial applications, particularly in smart manufacturing contexts. Active researcher with h-index of 26 Involved in the Josef Ressel Center for Symbolic Regression project (2018-2022) Regular participant in international conferences including EUROCAST and Industry 4.0 events His research interests center on Machine Learning systems for industrial applications, with specializations in symbolic regression, constraint evaluation methods, and composable evolutionary computation. His work addresses practical challenges in implementing AI solutions within manufacturing environments, focusing on domain knowledge integration and constraint handling. Analysis of his recent publications reveals a strong trend toward developing practical machine learning frameworks that incorporate physical constraints and domain knowledge for industrial settings. His 2024-2025 work shows increasing focus on modular, composable systems that can be adapted to specific manufacturing contexts while maintaining scientific rigor. Florian actively collaborates with researchers including Haider, Zenisek, and Affenzeller across multiple projects focused on smart production systems. His work demonstrates strong industry-academia connections through applied research in manufacturing contexts.

• Cyber-Physical Systems
• Smart Grids
• Swarm Intelligence
• +۵ مورد دیگر

Prof. Wilfried Elmenreich is a Professor at the Department of Networked and Embedded Systems at Alpen-Adria-Universität Klagenfurt. He serves as a Member of the Senate and Member of the Works Council for Academic Staff. His research focuses on Cyber-Physical Systems, Smart Grids, Swarm Intelligence, and Sustainability Technologies. He leads projects involving swarm robotics, decentralized energy management, and educational technology. Key research areas include: Design of swarm-based algorithms for production scheduling and robotics Development of frameworks like SwarmFabSim for bottom-up optimization Non-Intrusive Load Monitoring (NILM) using neural networks and edge computing Integration of federated learning into smart grid systems Sustainability education and pandemic-related behavioral studies His work combines theoretical contributions with practical implementations, such as the CPSwarm project for swarm system design and the Yomopie energy monitoring tool. Recent publications emphasize interdisciplinary approaches, blending robotics, machine learning, and electrical engineering to address challenges in energy systems and autonomous systems.

Ellen Richterمشاهده پروفایل

مربی

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

Ellen Richter is affiliated with Management Center Innsbruck (MCI), where she served as a Tutor for Algorithms & Data Structures from April to May 2021. Currently employed in Assistance & Project Management at MCI since October 2023, her academic background includes dual bachelor's degrees from the institution. Her educational qualifications: BA in Management, Communication & IT (2020-2022) B.Sc in Digital Business & Software Engineering (2019-2022) Office Management Assistant training (2016-2018) Business and Commerce diploma (2005-2009) Richter's research bridges artificial intelligence and business applications, with emphasis on practical AI implementation for non-technical domains. Her work explores how automated machine learning tools can democratize AI access for small enterprises and investigates psychological drivers of user interaction with conversational agents in media contexts. Her 2022 publications reveal a consistent focus on human-centered AI deployment, examining both technical feasibility (AutoML for SMEs) and behavioral aspects (chatbot engagement). This dual perspective highlights her interdisciplinary approach to solving real-world adoption barriers in digital transformation.