Find and connect with academic supervisors, professors, and scholars for your research journey.
Showing 136-140 of 140 supervisors
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Researcher
Timothée Schmude is a researcher affiliated with the Faculty of Computer Science and the Data Mining and Machine Learning Research Group . His work spans interdisciplinary approaches to artificial intelligence, focusing on ethical and human-centric aspects of algorithmic systems.
Associate Professor
Konstantin Schekotihin is an Associate Professor at the Department of Artificial Intelligence and Cybersecurity, Alpen-Adria University of Klagenfurt. His research focuses on artificial intelligence, machine learning, and semantic technologies with applications in industrial systems and semiconductor manufacturing. Reinforcement learning for industrial scheduling Answer Set Programming (ASP) and stream reasoning Failure analysis automation and ontology engineering Neuro-symbolic AI integration Knowledge-based systems in manufacturing Recent publications emphasize AI-driven optimization in semiconductor production, decomposition strategies for scheduling problems, and multi-agent systems for workflow management. His work combines symbolic reasoning with machine learning to address complex industrial challenges. Contact: Konstantin.Schekotihin@aau.at
Researcher
Van Quoc Huynh is a Researcher at the Institute for Application-oriented Knowledge Processing, Johannes Kepler University Linz (JKU), Austria. His work focuses on advancing machine learning and data mining techniques with emphasis on rule-based systems and symbolic artificial intelligence. His primary research interests include Machine Learning , Data Mining , and Symbolic AI , specializing in rule extraction algorithms, classification systems, and knowledge representation. He develops novel approaches for efficient pattern mining and interpretable model construction, bridging neural and symbolic paradigms. Recent publications demonstrate a clear trajectory toward hybrid neural-symbolic models for classification tasks and memory-efficient rule mining architectures. His work addresses critical challenges in scalability, parallel processing, and interpretability within knowledge discovery systems, with strong emphasis on practical implementations for complex datasets. Dr. Huynh actively contributes to FFG-funded research projects including the ongoing Automated Rule Extraction and Interpretation from Symbolic Regression Trees (2025-2026) and completed PreMoBAF (2021-2025). He engages with the academic community through conference organization (e.g., 20th International Conference on Foundations of Digital Games, 2025) and technical presentations on frequent itemsets mining. He operates within JKU's Institute for Application-oriented Knowledge Processing, which provides interdisciplinary infrastructure for applied AI research focusing on practical knowledge processing solutions.
Researcher
Fabian Paischer is a Researcher at the Institute for Machine Learning, Johannes Kepler University Linz (JKU), holding a Doctorate and Master of Science degree. His work bridges machine learning with high-impact scientific domains including plasma physics for fusion energy and reinforcement learning systems. Education: Doctorate (Dr.) Master of Science (MSc) His research focuses on developing neural surrogate models for plasma turbulence simulations and advancing reinforcement learning through pre-trained model modulation and human-readable memory architectures. This interdisciplinary work integrates deep learning with computational physics and autonomous decision-making, targeting applications in sustainable energy and intelligent systems where interpretability and data efficiency are critical. Analysis of his 2023-2025 publications reveals two dominant research thrusts: (1) neural operator applications for plasma edge simulations requiring long-term predictive accuracy in fusion environments, and (2) modular reinforcement learning frameworks enabling knowledge transfer between pre-trained models. These directions highlight his commitment to solving complex scientific computing challenges through novel AI methodologies. Dr. Paischer actively participates in academic communities, including the ELLIS Doctoral Symposium (2021), and maintains ongoing research output through preprints and conference publications at venues like NeurIPS and ICLR workshops.
Associate Professor
Siavash Arjomand Bigdeli serves as an Associate Professor of Computer Vision at the Technical University of Denmark, following prior employment as a scientist at the Swiss Center for Electronics and Microtechnologies (CSEM). His research focuses on: Ante-/Post-Hoc explainability of machine learning models Integration of statistical models in learning/inference processes Philosophical methodologies in artificial intelligence development Advanced computer vision techniques for visual understanding Recent publications reveal consistent specialization in image restoration and stereo vision, employing deep learning architectures and probabilistic graphical models to solve core challenges in visual data reconstruction and temporal coherence. His work demonstrates strong interdisciplinary connections between theoretical machine learning, practical computer vision applications, and epistemological considerations in AI systems.
