Find Academic Supervisors

• Cloud Computing
• Serverless Computing
• Distributed Systems
• +5 more

Ass.-Prof. Dr. Sashko Ristov is an Assistant Professor at the Department of Computer Science, University of Innsbruck. His research focuses on serverless computing, distributed systems, and workflow orchestration, with applications in cloud computing, healthcare informatics, and high-performance computing. He leads research on federated serverless infrastructures, resilient function choreographies, and digital twins in construction engineering. His work addresses challenges in cross-cloud resource management, workflow scheduling, and anomaly detection in microservices. Recent contributions include frameworks like StoreLess for federated storage orchestration and CODE for cross-platform serverless deployment. Research trends in his publications emphasize bi-objective optimization for batch workflows, GPU-accelerated document processing, and disaster-resilient cloud systems. He actively participates in workflows community summits to advance scientific workflow standards and interoperability. Dr. Ristov has collaborated on projects like the Montage workflow analysis and ECG monitoring systems using serverless architectures. His work bridges theoretical computing models with practical implementations in healthcare and civil engineering domains.

Paul HofmarcherView profile

Associate Professor

• Bayesian Methods
• Big Data
• Text Mining
• +3 more

Paul Hofmarcher is Associate Professor of Economics at the Department of Economics , Paris Lodron University of Salzburg , and affiliated with the Salzburg Centre of European Union Studies (SCEUS) . He holds a doctoral degree from WU Vienna and studied economics/mathematics at the University of Vienna. Research Interests : Focus on Bayesian methods , text mining , economic growth , and statistical/machine learning applications . His work bridges econometrics and computational methods to analyze complex socio-economic phenomena like central bank communication and media politicization. Recent Research Trends : Recent publications address central bank communication dynamics , EU policy discourse , and modeling techniques for high-dimensional data . He develops innovative methods like seeded Poisson factorization and temporal topic modeling to decode textual data in economics and politics. Affiliations & Roles : Active in interdisciplinary research at SCEUS and maintains close ties with Vienna-based institutions. Supervises research projects on macroeconomic forecasting, credit risk assessment, and geriatric health analysis. Professional Activities : Regularly contributes to academic journals like Journal of European Public Policy and Scandinavian Journal of Statistics . Co-developed the bms R package for Bayesian model averaging.

Alexander OstermannView profile

Professor

• Numerical Analysis
• Scientific Computing
• Partial Differential Equations
• +3 more

Prof. Alexander Ostermann is a Full Professor of Numerical Analysis and Scientific Computing at the University of Innsbruck, leading the Department of Mathematics and the Research Area Scientific Computing. He holds roles such as Dean of the Faculty of Mathematics, Computer Science, and Physics, and editorial board memberships in journals like GAMM Mitteilungen and Advances in Applied Mathematics and Mechanics . His research focuses on numerical methods for PDEs, exponential integrators, splitting techniques, and high-performance computing. He has supervised numerous PhD/Master’s students and collaborates internationally on projects like EuroCC Austria and Vienna Scientific Cluster. Education : Dr. rer. nat. (1988) and Mag. rer. nat. (1984) from Universität Innsbruck. Professional milestones include roles at the University of Geneva and extensive administrative leadership in academic governance. Research Interests : Development and analysis of numerical methods for time-dependent PDEs, including exponential integrators, splitting methods, and error estimation under low-regularity conditions. Applications span computational physics, fluid dynamics, and quantum mechanics. Publications & Impact : Over 100 peer-reviewed articles, including work on Fourier integrators for the nonlinear Schrödinger equation and dynamical low-rank approaches. His methods are applied in high-performance computing for complex systems like Vlasov-Maxwell equations. Advising & Teams : Advisor to PhD students in numerical analysis and computational mathematics. Collaborates with teams on projects involving scientific computing frameworks and software development (e.g., avaflow for mass flow simulations).

Robert LegensteinView profile

Professor

• Computational Neuroscience
• Neuromorphic Computing
• Spiking Neural Networks
• +3 more

Dr. Robert Legenstein is a Full Professor and Institute Head at the Institute of Machine Learning and Neural Computation , Graz University of Technology. He serves as Speaker of the Graz Center for Machine Learning and Action Editor for Transactions on Machine Learning Research (TMLR) . His research bridges computational neuroscience and machine learning, focusing on neuromorphic computing systems that mimic biological neural networks. Research Leadership: Leads EU-funded projects like Adaptive Optical Dendrites (FET-Open) , SYNCH (FET-Proactive) , and Stochastic Assemblies in SNNs (FWF) . Scientific Contributions: Develops learning algorithms for spiking neural networks (SNNs), with applications to memristive architectures, neuroprosthetics, and energy-efficient AI systems. Key Publications: 15+ recent works on topics including dendritic computing, hardware-aware training, and context-dependent neural processing. Teaching Roles: Offers courses like Deep Learning , Principles of Brain Computation , and Data Structures & Algorithms . Contact: robert.legenstein@tugraz.at | +43 316 873 5824 | Inffeldgasse 16b/I, 8010 Graz, Austria.

Harald NiederreiterView profile

Researcher

• Numerical Analysis
• Pseudorandom Numbers
• Quasi-Monte Carlo Methods
• +6 more

Harald Niederreiter is a Senior Scientist at the Johann Radon Institute for Computational and Applied Mathematics (RICAM), part of the Austrian Academy of Sciences. He has held prominent roles including Director of the Institute of Discrete Mathematics and Institute of Information Processing (Austrian Academy of Sciences), and Professorships at the University of Illinois at Urbana-Champaign and National University of Singapore. His research spans numerical analysis, cryptology, finite fields, and coding theory. Former positions: Directorships at multiple institutes, Professorships at UIUC and NUS Visiting scholarships at institutions including IAS Princeton, UC San Diego, and ETH Zurich Research interests focus on computational methods for PDEs, pseudorandom number generation, quasi-Monte Carlo techniques, and applied algebra. He is a Fellow of the American Mathematical Society and has received the Singapore National Science Award and the Cardinal Innitzer Prize. No specific grants or advising records are listed in the provided text. His work is affiliated with RICAM's research groups in mathematical data science and optimization.

Nysret MusliuView profile

Associate Professor

• Artificial Intelligence
• Combinatorial Optimization
• Heuristic Optimization
• +5 more

Associate Professor Dr. Nysret Musliu is a faculty member at TU Wien's Faculty of Informatics, leading the Christian Doppler Laboratory for Artificial Intelligence and Optimization for Planning and Scheduling. He holds a PhD (2001) and habilitation (2007) from TU Wien. His research focuses on AI techniques, combinatorial optimization, scheduling, and heuristic methods applied to real-world problems like workforce scheduling and timetabling. Education: PhD in Computer Science (2001) TU Wien Habilitation in Applied Computer Science (2007) TU Wien Master's degree (1996) University of Prishtina Research Interests: Artificial Intelligence Techniques Combinatorial Optimization Heuristic Algorithms Machine Learning Integration Hybrid Techniques for Scheduling Problems Grants: Christian Doppler Laboratory (2017-2024), FWF Projects, Softnet Austria, and industry collaborations. Advises PhD students in optimization and scheduling domains. Labs/Teams: Christian Doppler Laboratory, Databases and Artificial Intelligence Group.

Stefan RotterView profile

Professor

• Non-Hermitian Physics
• Theoretical Quantum Optics
• Wave Propagation in Complex Media
• +3 more

Stefan Rotter is a Professor of Theoretical Physics at TU Wien’s Institute for Theoretical Physics, part of the Faculty of Physics. His research group focuses on non-Hermitian physics, theoretical quantum optics, and wave propagation through complex media. He obtained his PhD in 2004 and served as a postdoc at Yale University before establishing his group in 2011. Key collaborations include work with teams in France, Scotland, and Israel, exploring topics like quantum ping-pong, microwave focusing, and neural network-based signal processing. His group’s work has been featured in journals like Nature Photonics and Physical Review Letters , addressing fundamental limits in optics and quantum systems. They also engage in educational efforts, with courses like Quantum Theory I shortlisted for TU Wien’s Best Lecture Award. Research interests emphasize non-Hermitian degeneracies, wavefront shaping, and quantum information theory. Notable projects include designing optical cavities for perfect absorption, optimizing microwave focusing in disordered environments, and advancing quantum sensor technologies. Rotter’s team has pioneered methods to reach theoretical precision limits (Cramér–Rao bound) using neural networks. They also investigate applications in quantum communication, nanophotonics, and metamaterials. His group has produced over 30 PhD graduates and postdocs, including recipients of prestigious grants like the Erwin Schrödinger Fellowship. Supported by initiatives such as the Excellence Cluster ‘Quantum Science Austria’, their work bridges theory and experiment, with notable contributions to the 2025 International Year of Quantum Science. Rotter actively collaborates with institutions like IST Austria and the Hebrew University of Jerusalem, driving interdisciplinary advancements in quantum technologies and optics.

Wolfgang LechnerView profile

Assistant Professor

• Quantum Computing
• Quantum Algorithms
• Optimization Problems
• +4 more

Wolfgang Lechner is an Assistant Professor at the University of Innsbruck's Institute for Theoretical Physics. His research focuses on applying near-term quantum computers to solve optimization problems through quantum algorithms. His work encompasses both digital quantum computing and quantum simulation-based approaches, combining numerical and analytical methodologies. He is affiliated with the Erwin Schrödinger Center for Quantum Science & Technology (ESQ) as a member. The ESQ aims to advance global quantum computing development, offering opportunities for fellows to contribute to this cutting-edge field. No awards, grants, or specific publications are explicitly mentioned in the provided text. His research group actively explores interdisciplinary quantum technologies.

• Virtual Screening
• Cytochrome P450
• Small Molecule
• +4 more

Johannes Kirchmair is a Professor in the Department of Pharmaceutical Sciences at the Faculty of Life Sciences. His research focuses on computational methods in drug discovery, particularly virtual screening, machine learning applications, and understanding drug metabolism. He is actively involved in projects such as the Anti-Infectives Drug Discovery initiative and the AIDD: Advanced Machine Learning for Innovative Drug Discovery program. His work addresses challenges in toxicity prediction, enzyme inhibition, and assay interference detection. Key areas include cytochrome P450 metabolism, small molecule drug design, and natural product analysis. Research interests span virtual screening methodologies, metabolite identification, and computational toxicology. He has contributed to the development of tools like E-GuARD for detecting assay interference and aweSOM for predicting metabolic sites. His collaborations emphasize cross-disciplinary approaches to advance drug discovery and chemical safety. He has delivered talks on computational methods for early drug discovery and assay interference prediction, reflecting his commitment to both academic research and practical applications. Current projects aim to leverage machine learning to tackle antimicrobial resistance and enhance drug development efficiency.

Florian ArendtView profile

Associate Professor

• Health Communication
• Suicide Prevention
• Public Health Campaigns
• +6 more

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
• +3 more

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 SchindlerovaView profile

Research Professor

• Causal Inference
• Variable Selection
• Stochastic Complexity
• +6 more

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
• +3 more

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
• +4 more

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 BöhmView profile

Associate Professor

• Data Mining
• Machine Learning
• Clustering Algorithms
• +3 more

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.