جستجوی استاد راهنمای دانشگاهی

• Dermatology
• Allergology
• Dermatoscopy
• +۴ مورد دیگر

Verena Ahlgrimm-Siess serves as a Clinical Associate Professor and Senior physician in the Department of Dermatology and Allergology at Salzburger Landeskliniken (SALK). With Privatdozent status indicating her post-doctoral academic qualification, she maintains an active clinical and research career with 150 research outputs spanning from 2007 to 2024. Her research interests focus on dermatoscopy as a non-invasive diagnostic tool with broad applications, particularly in facial lesion analysis, nevus examination, and melanoma detection. She has pioneered work in multiplex assays for allergy diagnosis, comparing technologies like Allergy Explorer 2 versus ImmunoCAP ISAC. Her fingerprint reveals strong expertise in allergen research (100%), sensitization (44%), immunoglobulin E (33%), and nevus analysis (33%). Dr. Ahlgrimm-Siess has demonstrated significant research productivity with publication peaks in 2012 (25 outputs) and consistent output through 2024. Her recent work integrates artificial intelligence with dermatology, enhancing diagnostic confidence in melanoma detection. She actively contributes to academic discourse through 98 recorded activities including 91 oral presentations, 5 publication peer-reviews, and conference organization. Her scientific engagement includes serving as a reviewer for Archives of Dermatological Research and delivering keynote presentations on dermatoscopy techniques, trichoscopy, and inflammatory skin condition visualization. Her research has gained notable attention with coverage by 10 news outlets, blog posts, and substantial readership on academic platforms.

• Epidermolysis Bullosa
• Genodermatoses
• Neurofibromatosis
• +۳ مورد دیگر

Tobias Welponer is a researcher at the Department of Dermatology and Allergology, Paracelsus Medical University, Salzburg, Austria, where he leads the Research Program of Molecular Therapy of Genodermatoses at the Salzburger Landeskliniken (SALK). His work bridges clinical dermatology and genetic research, focusing on improving therapeutic strategies and patient outcomes for rare skin disorders through translational science and collaborative networks. His primary research interests center on Epidermolysis Bullosa (100% of his fingerprint), with significant contributions to Neurofibromatosis Type I, pruritus management, and quality-of-life assessments in genodermatoses. Recent work includes developing standardized screening protocols for pediatric neurofibromatosis in Austria and pioneering explainable AI applications for melanoma diagnosis, reflecting a multidisciplinary approach that integrates clinical practice with computational innovation to address unmet needs in rare disease care. Analysis of his 2024–2025 publications reveals a strong emphasis on harmonizing clinical trial outcomes for Epidermolysis Bullosa, evidenced by systematic reviews and consensus guidelines. His expansion into AI-driven diagnostics marks an emerging trajectory, combining dermatological expertise with machine learning to enhance diagnostic accuracy and clinician trust—highlighting a strategic shift toward technology-integrated solutions for complex skin conditions. Scientific Awards: LEO Pharma Young Researcher Award (2023) Dr. Welponer actively collaborates with the Austrian NF network and international consortia, as demonstrated by co-authorship on multi-center studies in high-impact journals. His leadership in the Molecular Therapy of Genodermatoses program drives translational research from bench to bedside, with ongoing projects focused on outcome standardization and novel therapeutic interventions for epidermolysis bullosa. While specific grant details are not provided, his prolific publication record and industry recognition (e.g., LEO Pharma award) indicate robust funding support and growing influence in dermatological research.

Timothée Schmudeمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Interdisciplinary Approach
• Information Needs
• +۷ مورد دیگر

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.

• Artificial Intelligence
• Artificial Neural Networks
• Information Systems
• +۳ مورد دیگر

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

• Machine Learning
• Data Mining
• Artificial Intelligence
• +۳ مورد دیگر

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.

• Machine Learning
• Reinforcement Learning
• Plasma Physics Simulations
• +۱ مورد دیگر

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.

• Computer Vision
• Explainable Machine Learning
• Statistical Models in AI
• +۳ مورد دیگر

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.

Olga Mula Hernandezمشاهده پروفایل

استاد

• Numerical Analysis of Partial Differential Equations
• Mathematical Foundations of Scientific Machine Learning
• High-Dimensional and Nonlinear Approximation
• +۲ مورد دیگر

Olga Mula Hernandez is a Professor of Mathematics at the University of Vienna since September 2025, holding the Chair of Computational Partial Differential Equations. Previously, she served as an Associate Professor at Eindhoven University of Technology (2022-2025) and Assistant Professor at University Paris-Dauphine (2015-2022). Her educational background includes a double master's degree in Applied Mathematics and Nuclear Engineering from École Polytechnique (France) and Escuela Politécnica (Madrid) (2005-2011), followed by a PhD in Applied Mathematics from Sorbonne University (2011-2014). She also completed a postdoctoral fellowship at RWTH Aachen (2014-2015). Professor Mula's research centers on numerical analysis of partial differential equations with data-driven integration. Her work spans mathematical foundations of scientific machine learning (including PINNs), high-dimensional nonlinear approximation for PDE solvers, data assimilation and inverse problems, numerical optimal transport, and structure-preserving numerical schemes. She develops explainable, data-efficient computational methods that leverage physical insights rather than relying on black-box machine learning approaches. Her methodologies address critical applications across 3D-printing, haemodynamics, environmental pollution modeling, epidemiology, and nuclear engineering, where accurate physics-based simulations are essential for real-world problem solving.