Find Academic Supervisors

• Scientific Workflow Management
• Distributed Computing
• Numerical Linear Algebra
• +3 more

Wilfried Gansterer is a Professor at the Faculty of Computer Science, University of Vienna, leading the Theory and Applications of Algorithms research group. His work focuses on numerical algorithms, distributed computing, and machine learning, with notable contributions to graph neural networks and fault-tolerant systems. Active in projects such as Algorithmic Data Science for Computational Drug Discovery (2020–2028) and REPEAL (Resilience vs. Performance in Numerical Linear Algebra, 2016–2020). Research Interests: Dr. Gansterer’s expertise spans graph neural networks, matrix compression, adversarial defense mechanisms, and high-performance computing. His work addresses challenges in efficient computation, resilience against node failures, and optimizing distributed systems. Projects : Algorithmic Data Science for Computational Drug Discovery (2020–2028) REPEAL: Resilience vs. Performance in Numerical Linear Algebra (2016–2020) Verteiltes Rechnen (Distributed Computing, 2007–2014) Awards : 2023 Best Paper Award for work on Crossfire: An Elastic Defense Framework for Graph Neural Networks. Labs/Teams : Directs the Theory and Applications of Algorithms group, focusing on algorithmic innovation in distributed and high-performance computing environments.

Mher SafaryanView profile

Researcher

• Optimization theory and algorithms
• Machine learning
• Federated learning
• +7 more

Dr. Mher Safaryan is a postdoctoral researcher at the Institute of Science and Technology Austria (ISTA), affiliated with Prof. Dan Alistarh's research group since 2022. Previously, he held postdoctoral positions at King Abdullah University of Science and Technology (KAUST) from 2019-2022 and served as a research technician there from 2016-2019. He earned his Ph.D. in Mathematics from Yerevan State University in 2018 under Prof. Grigori Karagulyan's supervision. Education: Ph.D. in Mathematics (2018, YSU) Past Affiliations: KAUST (2016-2022), Neural Magic/Red Hat (industrial secondment) His research focuses on optimization theory and algorithms for machine learning , particularly in developing communication/computation/memory-efficient methods for large-scale training and federated learning. Key contributions include LDAdam (low-dimensional gradient statistics optimization), GradSkip (accelerated local gradient methods), and Unified Scaling Laws for compressed representations. He has published in top venues like NeurIPS, ICML, ICLR, TMLR, and The Journal of Geometric Analysis. Scientific Awards: Marie Skłodowska-Curie Fellowship (MSCA COFUND IST-BRIDGE) His work bridges machine learning optimization with mathematical foundations from his earlier research in real harmonic analysis. Current collaborations include Prof. Dan Alistarh (ISTA), Dr. Alexandre Marques (Neural Magic), and Prof. Peter Richtárik (KAUST).

• Algorithms
• Sparse Matrices
• Parallel Computing
• +3 more

Robert Ernstbrunner is a researcher affiliated with the Faculty of Computer Science , contributing to algorithm development and parallel computing. His work focuses on low-rank approximations and fault-tolerant strategies in linear algebra methods. Research Interests: His research spans computational efficiency, numerical linear algebra, and fault tolerance in high-performance computing environments. Key areas include sparse matrix operations and resilience mechanisms for iterative algorithms. Publications: He has published two peer-reviewed papers: (1) a 2022 study on precision-cost trade-offs in low-rank matrix approximations, and (2) a 2020 framework for node-failure resilience in iterative linear algebra methods. Academic Activities: Robert has presented at international conferences, including the 2022 IEEE IPDPS and the 2020 FTXS workshop.

• Mathematics
• Applied Mathematics
• Harmonic Analysis
• +3 more

Norbert Kaiblinger is an Associate Professor at the Institute of Mathematics, BOKU University, Vienna, Austria. His research focuses on applied mathematics, harmonic analysis, and numerical methods, with applications in fluid mechanics, statistics, and chemical engineering. He holds a habilitation in Mathematics from the University of Vienna, granting him teaching authorization in the field. His work bridges theoretical mathematics with practical computational problems, particularly in adsorption modeling and fluid dynamics. Key research interests include multicomponent batch adsorption models, dynamics of subaqueous systems, analysis of variance (ANOVA) methodologies, and special functions. Recent publications (2023–2025) highlight advancements in equilibrium composition calculations and statistical experimental design. His earlier work (2007–2019) explores Fourier analysis, operator theory, and algebraic structures like cyclotomic rings and circulant matrices. While no formal advising relationships or grants are listed, his collaborative research involves co-authors from diverse fields such as chemical engineering and fluid mechanics. Kaiblinger’s work is disseminated through journals like Adsorption, Journal of Fluid Mechanics, and Transactions of the American Mathematical Society.

AntonelaView profile

Adjunct Professor

• Recommender Systems
• Social Computing
• Machine Learning
• +5 more

Antonela is a researcher at CONICET and an Adjunct Professor at Universidad Nacional del Centro de la Provincia de Buenos Aires (UNICEN), affiliated with the ISISTAN Research Institute in Tandil, Argentina. She holds a PhD in Computer Sciences and is actively engaged in research at the intersection of social computing, recommender systems, and software engineering. Her research interests include: Social Computing and Machine Learning Applications Recommender Systems and Echo Chamber Mitigation Technical Debt and Architectural Smells in Software Systems Sensitivity Analysis for Software Quality Metrics Graph-based Text Representations (Graph-of-Words) Her recent work shows a strong trend in applying computational methods to both social systems and software engineering challenges, with publications and tools focusing on empirical analysis, reproducibility, and practical tooling for developers. Notable projects include Sen4Smells for technical debt prioritization and graphoW for NLP applications. Scientific contributions include: Paper on evolutionary analysis of technical debt (2019) RecSys 2021 paper on diversifying friend recommendations Development of open-source tools in Java, Python, and Kotlin She has contributed to software engineering and AI research through GitHub-hosted projects and reproducible research kits. While no formal advising or grant information is available, her role as a researcher and educator suggests involvement in student mentorship and collaborative research. She is part of dynamic research teams at ISISTAN, focusing on intelligent systems and software quality.

• Symbolic Computation
• High-Order Finite Elements
• Special Functions
• +3 more

Veronika Pillwein is an Associate Professor at the Research Institute for Symbolic Computation (RISC) within Johannes Kepler University in Linz, Austria. Her research focuses on symbolic computation, high-order finite elements, special functions, and algorithmic combinatorics. She contributes to advancing computational methods for sequence analysis, recurrence relations, and polynomial systems, with applications in numerical analysis and engineering. Pillwein has authored/co-authored numerous publications in top-tier journals and conference proceedings, including work on C²-finite sequences, hp-FEM element matrices, and positivity proofs for rational functions. She serves as an editor for academic volumes and actively participates in computational mathematics research. Her work integrates symbolic computation techniques with numerical methods, addressing challenges in high-order finite element analysis and algorithm design. Recent research emphasizes generalizing holonomic sequences, optimizing sparse shape functions for finite elements, and developing automated tools for proving mathematical properties. Pillwein collaborates internationally, contributing to interdisciplinary projects in computational mathematics and computer algebra systems. Affiliations: RISC Faculty, Johannes Kepler University (JKU) Key Research Areas: Symbolic computation, finite element methods, combinatorial algorithms, polynomial analysis Technical Contributions: Development of C²-finite sequence theory, hp-FEM element matrix evaluation, algorithmic proofs for positivity