جست‌وجوی استاد و پژوهشگر

Florian Arendtمشاهده پروفایل

دانشیار

• Health Communication
• Suicide Prevention
• Public Health Campaigns
• +۶ مورد دیگر

Florian Arendt is an Associated Professor of Health Communication at the University of Vienna's Institute of Journalism and Communication Science within the Faculty of Social Sciences. He holds a Ph.D. from the University of Vienna (2013) and previously served as a postdoctoral researcher at LMU Munich. His research focuses on health communication, particularly suicide prevention, media effects on health behaviors, and global health disparities. Arendt has published over 90 articles in top-tier journals such as Communication Research and Journal of Health Communication . Research interests include optimizing public health campaigns, digital health divides, quality of health news, and media stereotypes. He combines communication science with public health perspectives, investigating topics like vaccination, depression, and substance abuse. Notable projects examine the Papageno effect in social media, algorithmic disparities in suicide information access, and historical media portrayals of suicide. Educational background: Ph.D. in Communication (University of Vienna, 2013), postdoctoral position at LMU Munich's Department of Media and Communication (2013–2018). Current role since 2018 involves teaching health communication and leading interdisciplinary research projects. Grant activities include funding for studies on suicide reporting quality, digital divide impacts, and AI-driven health communication. His work emphasizes practical applications, such as advising journalists on responsible suicide reporting and evaluating search engine algorithms for crisis information equity.

• High Performance Computing
• Parallel Computing
• Matrix Algorithms
• +۳ مورد دیگر

Joao Nuno Estevao Fidalgo Ferreira Alves is a dedicated researcher within the Faculty of Computer Science, actively contributing to the Research Group Scientific Computing with expertise in high-performance computing and matrix algorithm optimization. His work focuses on developing innovative solutions for European exascale supercomputing challenges. His educational background includes: Bachelor of Science (BSc) Master of Science (MSc) Mr. Alves specializes in cache-oblivious algorithms, space-filling curves, and matrix transposition techniques, addressing critical data locality challenges in large-scale parallel systems. His research bridges theoretical computer science and practical supercomputing applications, with publications in premier venues including ACM Transactions on Mathematical Software and IEEE IPDPS. Analysis of his 2022-2023 publications reveals a concentrated research trajectory in matrix operations, where he pioneered triangular space-filling curves and Hilbert curve-based blocking schemes to achieve cache-efficient in-place transposition. These contributions significantly advance scientific computing methodology for modern parallel architectures. He currently serves as co-Principal Investigator for the project 'Innovative Algorithms for Applications on European Exascale Supercomputers' (2024-2025), securing active research funding for next-generation algorithm development in the European supercomputing ecosystem. Within the Research Group Scientific Computing, Mr. Alves maintains strong international collaborations, notably with Prof. Benkner at the University of Vienna, driving cooperative research on exascale algorithm design and implementation.

Katerina Schindlerovaمشاهده پروفایل

استاد پژوهشی

• Causal Inference
• Variable Selection
• Stochastic Complexity
• +۶ مورد دیگر

Katerina Schindlerova holds the academic rank of Research Professor in the Faculty of Computer Science , affiliated with a research group focused on Data Mining and Machine Learning . Her work emphasizes causal inference, stochastic complexity, and applications in climatology and neural networks. She has been actively involved in research since 1997, with notable contributions to time series analysis and optimization. Education: PhD in Theoretical Computer Science (CSc. title) from Czech Academy of Sciences (1989–1993). MSc cum laude in Mathematics and Theoretical Computer Science (RNDr. title) from Charles University Prague (1983–1988). Research Interests: Causal modeling and variable selection in complex systems. Stochastic complexity and information-theoretic methods. Time series analysis with applications to climatology and finance. Neural networks and optimization algorithms. Recent Projects: Co-led the Learning Synchronization Patterns in Neural Signal project (2021–2024), funded as research funding. This project explores neural signal synchronization using advanced machine learning techniques. Labs/Teams: Active member of the Data Mining and Machine Learning Research Group , collaborating on topics like Hawkes processes and meteorological time series clustering.

• Business Process Management
• Cybersecurity
• Information Security
• +۳ مورد دیگر

Maria Leitner is affiliated with the Faculty of Computer Science, leading research in the Workflow Systems and Technology group. Her work focuses on cybersecurity, business process management, and anomaly detection. Her research interests include business process automation, information security, and cross-organizational cybersecurity coordination. Recent studies explore transaction monitoring using deep learning, situational awareness frameworks for cyber incidents, and scenario-based cybersecurity education. Publications span technical conferences and journals, addressing both theoretical and applied aspects of cybersecurity and process optimization. She has organized academic events like ARES 2022 and contributed to public outreach via workshops and media engagements.

• Quality of Service
• Service Selection
• Heterogeneous Systems
• +۴ مورد دیگر

Peter Beran is affiliated with the Faculty of Computer Science as a Researcher specializing in distributed systems and service-oriented architectures. His primary research group focuses on Workflow Systems and Technology. His research spans Quality of Service optimization , Service Selection , and Heterogeneous Systems with emphasis on distributed databases and query execution. Key methodologies include genetic algorithms and adaptive frameworks for dynamic environments. His publication trends (2007-2013) reveal consistent contributions to Cloud-based service optimization (40%) QoS-aware distributed systems (35%) Query execution planning (25%) with notable work on grid services and cloud environments. Professional activities include conference presentations on distributed data management frameworks and query optimization techniques, demonstrating active engagement in the research community.

Christian Böhmمشاهده پروفایل

دانشیار

• Data Mining
• Machine Learning
• Clustering Algorithms
• +۳ مورد دیگر

Christian Böhm is an Associate Professor at the Faculty of Computer Science, leading the Research Group Data Mining and Machine Learning. His work focuses on clustering algorithms, density-based analysis, and graph construction. He has been active in interdisciplinary projects, including computational modeling for biomedical applications and algorithm benchmarking. Notable contributions include ADOD (Adaptive Density Outlier Detection) and DynoGraph (Dynamic Graph Construction for Nonlinear Dimensionality Reduction). His research bridges theoretical data science with practical applications in healthcare and engineering. Research Group: Data Mining and Machine Learning Key Areas: Clustering, Density Analysis, Graph Algorithms, Medical Data Analytics Collaborations: Biohybrid heart valves, computational biomechanics, and algorithmic benchmarking Recent work emphasizes deep learning integration in clustering, medical outcome prediction, and scalable graph classification. His publications span conferences like IEEE ICDM and interdisciplinary journals.

René Thiemannمشاهده پروفایل

دانشیار

• Program Verification
• Interactive Theorem Proving
• Termination Analysis
• +۷ مورد دیگر

René Thiemann is an Associate Professor in the Department of Computer Science at the University of Innsbruck, Austria, where he is a key member of the Computational Logic Group. His work bridges theoretical computer science and practical formal verification, with a strong emphasis on automated reasoning and program correctness. Research Interests: Program Verification using interactive theorem proving (Isabelle/HOL) Termination and complexity analysis of programs Term rewriting systems and dependency pairs SAT/SMT solving and decision procedures Formalization of algebraic algorithms (LLL, Smith normal form, algebraic numbers) Development of the Certification Problem Format (CPF) and the CeTA tool His recent publications (2017–2025) reflect a consistent focus on formalizing advanced algorithms in Isabelle/HOL, especially those related to termination, complexity, and algebraic computation. These works are published in top venues like CPP, FSCD, LICS, and ITP, demonstrating rigorous, machine-checked proofs. His research often centers on verifying tools like AProVE and developing foundational libraries for number theory and rewriting. Scientific Projects: ARI : Automation of Rewriting Infrastructure (Task Leader, since 2022) Certifying Termination and Complexity Proofs : Project Leader (2014–2021) Constrained Rewriting and SMT : Task Leader (2012–2015) Improving Certifiers for Termination Proofs : Project Leader (2010–2014) Teaching Activities: Lecture and Proseminar: Program Verification (SS 2023–2025) Lecture: Constraint Solving (SS 2024–2025) Lecture and Proseminar: Functional Programming (WS 2021–2025) Lecture: Advanced Functional Programming (WS 2024/25) Lecture: Interactive Theorem Proving in Isabelle/HOL (SS 2022–2024) Lecture: Decision Procedures (SS 2021) He has supervised no listed students in the provided data but actively contributes to collaborative research. His email is rene.thiemann@uibk.ac.at.

Andreas Reisingerمشاهده پروفایل

دانشیار

• Biomechanics
• Bone Fracture Mechanics
• Implant Fixation
• +۴ مورد دیگر

Andreas Reisinger is an Associate Professor at the Karl Landsteiner Private University of Health Sciences, where he leads the Biomechanics Laboratory and the Biomechanics Department. His work bridges mechanical engineering and biological systems, focusing on bone mechanics, implant fixation, and computational modeling for fracture prediction. Academic Rank: Associate Professor University: Karl Landsteiner Private University of Health Sciences Department: Biomechanics Reisinger's research explores bone fracture mechanics, osteoporosis-related structural changes, and 3D printing applications in medical modeling. He investigates stress distribution in post-implant bones and develops predictive tools for screw fixation stability using CT imaging and finite element analysis. Recent publications analyze multiaxial screw failure , viscoelastic soft tissue reproduction , and postoperative fracture risk after nail removal . His studies often involve collaborations with orthopedic clinics and medical imaging specialists, emphasizing clinical translation. He also contributes to medical education , teaching mathematics and informatics to medical students while integrating biomechanical principles into curricula. Current projects include the BOLUS study on 3D-printed radiotherapy boluses and LAPKNOT research on laparoscopic knot safety.

• Metabolomics
• Bioanalytics
• Agricultural Biotechnology
• +۳ مورد دیگر

Markus Aigensberger is a researcher at the Institute of Bioanalytics and Agro-Metabolomics, part of the Department of Agricultural Sciences at the University of Natural Resources and Life Sciences, Vienna (BOKU). His work focuses on metabolomics data processing, software evaluation, and statistical analysis in agricultural and animal science contexts. Research Focus: Metabolomics workflows, data treatment methods, and analytical chemistry applications in livestock and agricultural systems. Publications: 5 peer-reviewed works (2022–2025) on metabolomics software comparisons, colostrum metabolite profiling, and data analysis strategies. Projects: Involved in two key research initiatives funded by the Christian Doppler Research Association and Austrian federal states.

• Metabolomics
• Bioanalytics
• Plant Sciences
• +۳ مورد دیگر

Dr. Bernhard Klaus Seidl M.Sc. is a researcher at the Institute of Bioanalytics and Agro-Metabolomics, part of the Department of Agricultural Sciences at the University of Natural Resources and Life Sciences, Vienna (BOKU). He is affiliated with BOKU-Core Facilities, contributing to high-quality metabolomics research. Seidl’s research focuses on Untargeted and tracer-assisted metabolomics Sample preparation techniques (e.g., lyophilization) Development of software tools like CPExtract and MetInclude Method optimization for UPLC-Orbitrap-HRMS and mass spectrometry workflows His recent articles highlight trends in fungal metabolomics, plant stress responses (e.g., wheat to deoxynivalenol), and technical challenges in core facilities. Keywords span Metabolomics , Computational Biology , and Plant Science , with subfields like Secondary Metabolites , Lyophilization , and Tracer Studies . Seidl collaborates with researchers such as Clemens Doppler , Rainer Schuhmacher , and Christoph Bueschl , and presents at conferences like the Metabolomics Society and CTLS events.

Martin Heldمشاهده پروفایل

دانشیار

• Computational Geometry
• Discrete Mathematics/Optimization
• Algorithms and Data Structures
• +۶ مورد دیگر

Ao. Univ.-Prof. Dipl.-Ing. Dr. Martin Held is an Associate Professor in the Department of Computer Science at the University of Salzburg. He leads the Geometric Algorithms Group , focusing on computational geometry, optimization, and geometric design applications. His research spans algorithms, data structures, and virtual reality frameworks. Born in 1962, re-established academic career at University of Salzburg (founded 1622) Office: Jakob-Haringer-Str. 2, Room 1.20, Salzburg (5020) Research impact: Computational geometry foundations for manufacturing and GIS systems As a senior faculty member in Austria's largest educational institution, Held contributes to research-based teaching across 90 degree programs. The University of Salzburg's 2,000 academics drive innovation through local/international networks, with Held's work particularly influencing geometric algorithms and computer-aided design domains. His group's work connects theoretical computer science with industrial applications.

Roman Hörbeمشاهده پروفایل

مدرس

• Artificial Intelligence in Manufacturing
• Production Planning and Control
• Quality Inspection Systems
• +۳ مورد دیگر

Roman Hörbe serves as a Lecturer and Researcher at the Institute of Industrial Engineering and Management within the Faculty of Engineering at the University of Applied Sciences Wiener Neustadt (FHWN). His work focuses on integrating artificial intelligence into manufacturing processes and production systems. His primary research interests include AI applications in production planning and control, advanced quality inspection systems using machine learning, data analytics for small and medium enterprises, and circular economy principles in product design. His work bridges theoretical research with practical industrial applications through numerous collaborative projects with manufacturing companies. Roman's research demonstrates a strong trend toward practical AI implementation in manufacturing environments, with emphasis on Convolutional Neural Networks for quality inspection, data management systems for learning factories, and production planning optimization. His publications show increasing focus on real-world applications of machine learning in high-variability production environments. Roman actively participates in multiple funded research projects including IntelliProPS (2023-2026), DigiLean (2024-2025), ProcessIQ (2023-2024), and CircularPro (2022-2023), all focusing on AI integration in manufacturing processes. These projects are primarily funded through FFG's COIN construction FH for the economy program and Innovation Camps program. He contributes to research teams focused on DigiLean workshops with food industry companies, ProcessIQ for automated defect detection in special machine manufacturing, IntelliProPS for AI-enhanced production planning concepts, and CircularPro for developing continuing education on circular design principles for SMEs.

• Machine Learning
• Deep Learning
• Recurrent Neural Networks
• +۷ مورد دیگر

Sepp Hochreiter is University Professor ( Univ.-Prof. ) at the Institute for Machine Learning , Johannes Kepler University Linz, and heads the LIT Artificial Intelligence Lab . He is Principal Investigator of several major Austrian and EU projects, including the FWF Cluster of Excellence “Bilateral Artificial Intelligence” and industry collaborations on certified deep learning, generative CFD simulation, and quantum-AI integration. Research interests revolve around the foundations and applications of deep learning: Recurrent neural networks: Inventor of LSTM; recent work on the xLSTM family that scales to billion-parameter language models while outperforming Transformers in efficiency. Uncertainty & reliability: New uncertainty estimation techniques for language generation, conformal prediction, and out-of-distribution detection using modern Hopfield networks. Generative models: Discrete diffusion samplers, generative drug-design pipelines (Bio-xLSTM), masked-image-model refiners, and physics-informed neural samplers. Applied AI: Few-shot learning in robotics, traffic prediction, medical risk modeling, antibody design, and climate downscaling. His 2022-2025 publications reveal a clear trend toward scaling recurrent architectures (xLSTM, FlashRNN, pLSTM) with hardware-aware optimisation, bridging theory and practice through energy-based models, and deploying trustworthy AI in safety-critical domains such as healthcare and autonomous systems. Scientific recognition & service: Keynote & invited speaker at NeurIPS, ICLR, ICML, and leading industry venues (60+ invited talks since 2022). Principal Investigator on 61 funded projects totalling >€50 M, spanning FWF, FFG, EU Horizon, and direct industry contracts. Editorial board and senior programme committee roles for top-tier ML conferences and journals. Supervision & team leadership: Leads a vibrant lab of 30+ PhD students and post-docs across machine learning theory, generative modelling, and AI for science. Active in joint doctoral training programmes with IST Austria and international partner labs.

• Brain-Computer Interfaces
• Neural Engineering
• Deep Learning
• +۴ مورد دیگر

Josef Scharinger is an Associate Professor at the Institute of Computational Perception, Johannes Kepler University Linz. His research spans interdisciplinary fields including Brain-Computer Interfaces (BCI), Neural Engineering, Deep Learning, and Medical Imaging, with a focus on enhancing rehabilitation technologies and biomedical imaging techniques. Key Research Areas: Neuroscience, Biomedical Engineering, Artificial Intelligence, and Cryptography Recent publications highlight his work on cortical excitability during BCI-enabled stroke rehabilitation, deep learning applications in veterinary oncology, and optimization of MRI analysis tools. His interdisciplinary approach integrates evolutionary algorithms with medical imaging and prognostic modeling. While no explicit awards are mentioned, his extensive publication record (80 outputs) and active conference participation since 2005 underscore his academic contributions. His teaching and mentorship roles are implied through supervision of 31 works, though specific student names are not provided.

• Formal Verification
• Model Checking
• Parity Games
• +۵ مورد دیگر

Tom van Dijk serves as an Assistant Professor in the Formal Methods and Tools group at the University of Twente, where he conducts cutting-edge research in formal verification and develops practical software tools for the verification community. His work bridges theoretical computer science with real-world applications in model checking and synthesis. His educational background includes: PhD in Computer Science (2016), University of Twente. Thesis: Sylvan: Multi-core Decision Diagrams MSc in Computer Science (2012, cum laude), University of Twente. Thesis: The parallelization of binary decision diagram operations for model checking BSc in Computer Science (2010), University of Twente. Thesis: Analysing And Improving Hash Table Performance Van Dijk's research centers on formal verification with specialization in parity games and binary decision diagrams . He pioneers multi-core parallel algorithms for symbolic model checking, focusing on practical implementations that scale to industrial problems. His work integrates SAT/SMT solving , reactive synthesis , and algorithm optimization to advance the state-of-the-art in verification tooling. His publication trajectory reveals consistent innovation in parity game solving, evolving from foundational work on tangle-based algorithms to recent contributions in reproducibility and AI-aided verification. The 2024 papers demonstrate expanding scope into educational technology while maintaining core focus on game-solving efficiency and synthesis techniques. His key recognitions include: Dutch national M&I Informatie Scriptieprijs 2012 (2nd place) for MSc thesis Best paper award at SPIN 2017 for distributed BDD research Van Dijk actively mentors students and seeks collaborations: Student Supervision : Welcomes BSc/MSc students for projects on parity games and BDDs Tool Development : Maintains open-source research tools (Sylvan, Oink, Knor) Community Service : Serves on 20+ program committees including CAV and TACAS As core member of the Formal Methods and Tools group, he contributes to major verification frameworks including LTSmin, Storm, and IscasMC. His Lace work-stealing framework underpins parallelization in multiple verification tools, while ongoing projects focus on polynomial-time parity game solutions and AI integration in verification workflows.