Find Academic Supervisors

• Production Planning
• Logistics Management
• Operations Research
• +3 more

Hubert Missbauer is a Full Professor for Production and Logistics Management at the University of Innsbruck. He holds a Diploma (1982) and Doctorate (1986) in Business Administration from the University of Linz, with a Habilitation in Business Administration (1994) focusing on manufacturing planning systems. His research emphasizes production planning concepts, workload control, and optimization in manufacturing systems, particularly in steel production. He co-organizes the International Working Seminar on Production Economics and serves on the editorial board of the International Journal of Production Economics . Research interests include order release optimization, production scheduling in steel industries, and quantitative methods in operations management. Recent work focuses on Lagrangian decomposition algorithms, behavioral perspectives in workload control, and integrated scheduling in steel production processes. He has published extensively in top-tier journals like International Journal of Production Research and European Journal of Operational Research . Education: University of Linz (Diploma 1982, PhD 1986, Habilitation 1994) Affiliations: Institute for Information Systems, Production and Logistics Management at University of Innsbruck Key Projects: Steel production scheduling, iterative LP-simulation algorithms, behavioral studies in manufacturing control Editorial Roles: Guest Editor for International Journal of Production Economics since 2020 His work bridges theoretical models (e.g., clearing functions, transient analysis) with practical applications in industries like steelmaking and construction. Recent presentations include discussions on Lagrangian approaches at the INFORMS Annual Meeting (2024) and EURO conferences.

• Harmonic Analysis
• Sampling Theory
• Gabor Frames
• +4 more

Jose Luis Romero is an Associate Professor at the Department of Mathematics, Faculty of Mathematics, University of Vienna. His research spans harmonic analysis, sampling theory, Gabor frames, and signal processing, with a focus on mathematical physics and approximation theory. He has collaborated extensively on projects related to time-frequency analysis, Gaussian processes, and lattice structures. His work includes 15 most recent publications (2024–2019) on topics such as: Gabor frames and their stability under deformations Sampling theory in shift-invariant spaces and phase-space covers Time-frequency concentration problems and spectral estimates Determinantal point processes and Coulomb gas equidistribution His articles often integrate applied mathematics , signal processing , and mathematical physics , with keywords like Gaussian distribution, asymptotics, and Fourier transforms. While specific grants and advised students are not listed here, his collaborative projects highlight interdisciplinary applications in imaging sciences and partial differential equations.

Thede KahlView profile

Professor

• Endangered dialects and non-standard language varieties
• Ethnolinguistic field research
• Minorities in Southeast Europe and Asia Minor
• +3 more

Thede Kahl is a Professor of South Slavic Studies at the Friedrich Schiller University Jena , holding the Bessarion Research Center professorship. His academic career includes habilitation at the University of Vienna and a doctorate at the University of Münster, where he studied Slavic Studies, Geography, and Byzantine Studies. He focuses on endangered dialects, ethnographic field research, and cultural relations in Southeast Europe and Asia Minor. Education: Habilitation: University of Vienna (2007), thesis on linguistic and cultural change among nomadic shepherds Doctorate: University of Münster (1999), dissertation on Aromanian ethnic distribution Undergraduate: Münster, Cologne, and Hamburg universities Research Trends from his 15 most recent publications show sustained focus on: Linguistic endangerment in Balkan and Italian enclaves Ethnic and religious coexistence patterns Cultural memory through oral narratives Historical migration and diaspora studies Minority language documentation Transnational ethnographic comparisons Scientific Projects include leadership roles in initiatives like the Bessarion Group (2023-2026), LaZAR data archiving project (since 2015), and EU-funded Interphraseology for Study and Professional Mobility (2013-2014). He also directs studies on the Non-Aligned Movement's museum impact and co-edits key journals like Hellenika and VLACH . Academic Influence spans multiple institutions as editorial board member (e.g., Hiperboreea , Verbum – Analecta Neolatina ), commission leadership (International Congress of Slavists, Austrian Academy of Sciences), and supervision of 12+ doctoral projects examining topics from Pontic Greek identity to Bosnian youth assimilation.

• Machine Learning for Visual Computing
• Big Data Visualization and Analytics
• Data Science and High-Performance Computing (HPC)
• +4 more

Soumya Dutta is an Assistant Professor in the Department of Computer Science and Engineering (CSE) at the Indian Institute of Technology Kanpur (IITK) since 2022. He previously held positions at Los Alamos National Laboratory as a Postdoctoral Researcher (2018-2019) and Scientist II (2019-2022). Dr. Dutta earned his Ph.D. and M.S. in Computer Science from The Ohio State University (2011-2018) and a B.Tech in Electronics and Communication Engineering from the West Bengal University of Technology (2005-2009). His research lies at the intersection of Machine Learning , Visual Computing , Big Data Analytics , and High-Performance Computing (HPC) . He focuses on developing scalable solutions for extreme-scale data, such as exascale simulations, social media, IoT, and healthcare. His work emphasizes uncertainty quantification in AI models and interactive visualization techniques. Dr. Dutta’s recent publications highlight his expertise in in situ visualization for climate modeling, implicit neural representations for uncertainty-aware rendering, and statistical sampling for exascale systems. His funded projects include AI-driven data analytics frameworks and deepfake defense mechanisms supported by ISRO, SERB, and C3iHub. Scientific Awards include Best Reviewer (TVCG), Best Paper (ISAV, TopoInVis), and LAAP Award (LANL).

Heiko Andreas SchmidtView profile

Researcher

• Phylogenetics
• Phylogenomics
• Evolutionary Biology
• +5 more

Dr. Heiko Andreas Schmidt is a computational biologist affiliated with the University of Vienna , where he works at the Department of Structural and Computational Biology as part of the Center for Integrative Bioinformatics Vienna (CIBIV) and Max Perutz Labs. His research focuses on phylogenetics, phylogenomics, and the development of computational tools for evolutionary tree reconstruction, including contributions to software like IQ-TREE and TREE-PUZZLE . His work spans parallel computing applications in phylogenetics, scientific workflows, and quality assessments of alignments. He has co-authored numerous high-impact studies in molecular biology, developmental biology, and bioinformatics, often collaborating with Prof. Arndt von Haeseler and other researchers. Dr. Schmidt has contributed to methodological advancements in phylogenetic inference, particularly for large genomic datasets. He has taught courses such as Bioinformatics for Phylogeny and Evolutionary Tree Reconstruction and Algorithmic Bioinformatics at the University of Vienna.

Thomas MagauerView profile

Professor

• Synthetic Organic Chemistry
• Molecular Design
• Organic Synthesis
• +1 more

Thomas Magauer is a University Professor at the Institute of Organic Chemistry, University of Innsbruck, where he has held a faculty position since 2017. He specializes in synthetic organic chemistry, describing his research as molecular-scale architecture where entirely new molecules with unknown properties are designed and constructed from scratch. His academic journey began at the University of Vienna after initially considering mathematics, followed by formative research stays in Cambridge, Munich, and Wisconsin. Magauer overcame early academic struggles in chemistry during his school years to develop expertise through persistent effort and hands-on laboratory work. Magauer's research focuses on creating novel molecular structures through synthetic organic chemistry, drawing explicit parallels to architectural design principles. He emphasizes geometric precision and structural complexity in molecule construction, inspired by M.C. Escher's interwoven symmetrical art. His work explores unknown molecular properties and synthetic pathways, often generating breakthrough ideas during mountain hiking excursions in the Tyrolean Alps where cellular distractions are absent. As an educator, Magauer demonstrates deep commitment to mentoring students through their academic evolution from first-year undergraduates to independent researchers. He finds profound satisfaction in knowledge transmission and observes student development as a dynamic, rewarding process integral to his professional identity. Scientific Awards: No specific awards documented in source material Magauer leads an active research group centered on innovative synthetic methodologies within the Institute of Organic Chemistry. His laboratory environment combines rigorous molecular design with inspiration drawn from natural landscapes, maintaining strong connections to Austria's research ecosystem. Future objectives include advancing the institute's research capabilities while pursuing his personal mountaineering goals across the Tyrolean region.

• Contemporary Art
• Painting
• Art Education
• +4 more

Gunda Maria Gruber is a Senior Lecturer for Painting/Art Education at the Department of Fine Arts and Design, Institute for Open Arts, Mozarteum University in Salzburg. Born in 1971 in Salzburg, she studied painting at the Mozarteum University and painting and graphic arts at the Academy of Fine Arts in Vienna. She has been teaching at the Department of Fine Arts & Design since 2004. Gruber's artistic practice spans painting, multimedia installations, video, and spatial concepts. Her work explores the relationship between interior and exterior spaces, perception, and the fragility of social and spatial orders. She creates 'spatial situations' using collage principles and various media, often incorporating performative elements where her presence is suggested through shadows or personal items. Her installations deliberately fragment conventional spatial understanding, creating experiences where boundaries between reality and representation dissolve. Her recent work focuses on the intersection of technology and organic systems, examining how digital interfaces reshape our spatial perception. The 2023 exhibition 'THE GEOMETRY OF NON-ORDERS' at Museum der Moderne Salzburg continued her exploration of spatial fragmentation, while 'TECHNOGARDEN' (2022) earned her the Grand Art Prize of the State of Salzburg. Her practice consistently engages with theoretical concepts from Zygmunt Baumann's 'volatile modernity' and Gaston Bachelard's 'Poetics of Space,' translating philosophical ideas into tangible artistic explorations. Award: Grand Art Prize of the State of Salzburg (2022) Gruber's teaching and artistic practice are deeply interconnected, with her theoretical explorations of space and perception informing both domains. She creates what might be called 'experience spaces' where conventional relationships between inside and outside, up and down, or light and dark are suspended, challenging viewers to confront the limitations of their perception while offering alternative ways of experiencing spatial environments.

• Artificial Intelligence
• Knowledge Representation and Reasoning
• Logic Programming
• +5 more

Thomas Eiter is a Full Professor at the Vienna University of Technology (TU Wien) in the Department of Knowledge-Based Systems, Faculty of Informatics. His research focuses on artificial intelligence, knowledge representation and reasoning, logic programming, computational logic, and neurosymbolic AI integration. He leads projects in declarative problem-solving, intelligent agent systems, and stream reasoning frameworks like LARS. Eiter has contributed to foundational work in answer set programming (ASP), algebraic reasoning, and their applications in scheduling, robotics, and real-time data processing. He has extensive international collaborations, including EU-funded projects like HumanE-AI-Net and the Austrian Science Fund (FWF) initiatives. His work emphasizes bridging symbolic AI with modern machine learning techniques, particularly in visual question answering and neural-symbolic systems. Eiter has supervised numerous PhD students and maintains active roles in academic leadership, including editorial boards of journals like Theory and Practice of Logic Programming . Key contributions include development of the DLVHEX system for hybrid knowledge representation, optimization frameworks for ASP, and methodologies for stream reasoning in dynamic environments. His research also addresses ethical AI through projects like the TAIGER initiative, focusing on training AI agents with ethical rules.

Guo MinyiView profile

Professor

• Database systems
• Cloud computing
• Parallelizing compilers
• +4 more

Minyi Guo is a Chair Professor and Head of the Department of Computer Science and Engineering at Shanghai Jiao Tong University (SJTU), China. Previously, he served as Professor and Department Chair at the School of Computer Science and Engineering, University of Aizu, Japan. Dr. Guo received his BSc and ME degrees from Nanjing University, China in 1982 and 1986, and his PhD from University of Tsukuba, Japan in 1998. Dr. Guo's educational background includes: BSc in Computer Science, Nanjing University, China (1982) ME in Computer Science, Nanjing University, China (1986) PhD in Computer Science, University of Tsukuba, Japan (1998) Dr. Guo's research spans multiple areas in computer science, with a primary focus on parallel/distributed computing , compiler optimizations , cloud computing , database systems , and big data . He has published over 400 papers including approximately 150 in major journals and 250 in international conferences, with more than 60 papers in IEEE/ACM transactions and over 100 papers in prestigious conferences. Dr. Guo has also authored 7 books (4 in English, 3 in Chinese) and received 5 best/highlight paper awards from international conferences. Dr. Guo's publication record demonstrates strong contributions across multiple domains of computer systems research. His recent work shows particular emphasis on big data processing, edge computing, graph neural networks, and data center optimization. The publications reveal a consistent trajectory of impactful research in parallel and distributed systems, with increasing focus on AI/ML applications and blockchain technologies in more recent years. Dr. Guo has received numerous prestigious awards and honors: State Technological Invention Award of China (second class award, 2019) Shanghai Technological Invention Award (first class award, 2018) IEEE Technical Committee on Scalable Computing Award for Excellence in Scalable Computing (2018) Ministry of Education Natural Science Award (first class award, 2017) IEEE Fellow (2017) Chief Scientist of National Basic Research Project (973 Program, 2014) Recruitment Program of Global Experts (2010) Excellent Academic Leaders of Shanghai (2010) National Science Fund for Distinguished Young Scholars (2007) As an academic leader, Dr. Guo has served as Department Head for ten years, managing a department with over 100 faculty members and 1000+ students. Under his leadership, the department was promoted to the top tier in China and ranked among the top 40 in the world. He has secured significant research funding, including serving as Chief Scientist of the prestigious 973 Program in 2014 and receiving the National Science Fund for Distinguished Young Scholars in 2007. Dr. Guo has also been selected for the Recruitment Program of Global Experts in China (2010). Dr. Guo actively contributes to the academic community as an associate editor of IEEE Transactions on Parallel and Distributed Systems, IEEE Transactions on Cloud Computing, and Journal of Parallel and Distributed Computing. He has served as General/Program Chair for IEEE conferences and delivered keynote speeches at well-established conferences. His research group has developed practical technologies with industry impact, including 28 licensed patents, some of which have been transferred to companies like Alibaba.

Ulrich BauerView profile

Associate Professor

• Computational Topology
• Persistent Homology
• Discrete Morse Theory
• +3 more

Ulrich Bauer is an Associate Professor in the Department of Mathematics at Technical University of Munich (TUM), leading the Applied & Computational Topology group. His research focuses on topology and geometry, particularly persistent homology, discrete Morse theory, and geometric complexes, supported by DFG and MDSI grants. He developed Ripser, a leading software for computing Vietoris–Rips persistence barcodes, and contributed to PHAT, a persistent homology library. Bauer holds editorial roles at Foundations of Computational Mathematics , Journal of Applied and Computational Topology , and SIAM Journal on Applied Algebra and Geometry . He is a core member of the Munich Data Science Institute (MDSI) and a principal investigator at the Munich Center for Machine Learning (MCML). His academic journey includes a PhD from the University of Göttingen and postdoctoral work at IST Austria under Herbert Edelsbrunner. Research Interests: Bauer's work bridges computational topology and geometry, with applications in topological data analysis. His key areas include persistent homology algorithms, discrete Morse theory, and geometric complexes. Recent focus includes Reeb graph analysis, stability theorems, and topological machine learning. His software tools (e.g., Ripser) are widely used in academia and industry. Publications Trends: Bauer's recent work emphasizes theoretical foundations (e.g., Reeb graph stability, Morse theory) and practical software development. His articles often address computational efficiency, algorithmic innovation, and interdisciplinary applications in data science and machine learning. Scientific Awards: None explicitly listed. Grants & Funding: DFG Collaborative Research Center (Discretization in Geometry & Dynamics), Munich Data Science Institute (MDSI). Labs & Teams: Leads the Applied & Computational Topology group at TUM, collaborates with MDSI and MCML. His advisory roles include the DFG CRC board and EPSRC's Centre for Topological Data Analysis.

• High-Performance Computing
• Parallel Computing
• Scientific Computing
• +3 more

Siegfried Benkner is a full Professor at the Vienna University of Technology (TU Wien) within the Faculty of Computer Science and leads the Research Group for Scientific Computing. His work focuses on high-performance computing (HPC), parallel programming models, runtime systems, and performance optimization for heterogeneous architectures. He has actively contributed to EU-funded projects such as TROCI (2024–2027) and PEPPHER, addressing resilience in critical infrastructures and programmability for exascale systems. His research spans topics like task-based runtime systems (OCR-Vx), autotuning frameworks (Periscope PTF), and performance portability for GPUs/Xeon Phi architectures. Recent interests include accelerating graph neural networks via novel matrix compression formats and cloud-edge continuum systems for eHealth applications. Prof. Benkner has published over 270 articles, with a focus on runtime systems, parallel patterns, and HPC infrastructure. His work emphasizes practical applications, including semantic data management for medical research and cloud-based analytics frameworks for big data processing in cellular networks. He has led multiple EU projects (9 total), including the 2024 initiative on exascale computing and resilience, and frequently presents at conferences like Euro-Par and Supercomputing events. His activities include media engagement on topics like exascale hardware trends and HPC challenges.

Alexander RiederView profile

Research Fellow

• Numerical Analysis
• Partial Differential Equations
• Finite Element Methods (FEM)
• +6 more

Alexander Rieder is a Research Fellow and University Assistant in Computational Mathematics at TU Wien's Institute of Analysis and Scientific Computing. His work focuses on numerical methods for partial differential equations, particularly boundary element methods (BEM), finite element methods (FEM), and their applications in wave propagation and fractional calculus. He holds a PhD from TU Wien (2017) and has held postdoctoral positions at TU Wien and the University of Vienna. Notable achievements include the TU Best Paper Award 2020 for his contributions to numerical analysis. Education: Bachelor of Science (BSc), Mathematics, TU Wien (1989) Diploma in Mathematics, TU Wien (2011) Doctorate (Dr.techn.), TU Wien (2017) Research Interests: Alexander specializes in fractional differential operators , hp-adaptive FEM/BEM , and time-domain boundary integral equations . His projects include developing open-source libraries for advanced BEM simulations and studying wave propagation in composite media. Recent work emphasizes convolution quadrature for semigroups and nonlinear wave equations. Teaching: Current courses include Numerics of Partial Differential Equations and Numerical Computation . He oversees seminars on differential equations and computational mathematics, emphasizing practical implementation and theoretical rigor. Projects: Leading the Advanced BEM for Wave Propagation initiative, he designs algorithms to reduce computational effort while improving accuracy in wave simulations. The project also develops an open-source software library for broader academic use.

• 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.

Eduarda Sangiogo GilView profile

Researcher

• Nonadiabatic Dynamics
• Photochemistry
• Quantum Algorithms
• +3 more

Eduarda Sangiogo Gil is a researcher at the University of Vienna's Faculty of Chemistry, Department of Theoretical Chemistry. Her work focuses on nonadiabatic dynamics, quantum algorithms, and computational methods for simulating photochemical processes. She has developed software tools like EXASH for exciton-based surface hopping dynamics and contributed to packages such as Newton-X and MOPAC-PI. Her research integrates quantum-classical hybrid algorithms, semiempirical methods, and advanced trajectory-based simulations. Teaching: Courses on Chemical Dynamics Simulation, Computational Methods, and Journal Club at University of Vienna (2024). Collaborations: Active in international projects involving quantum computing for dynamics and exciton models in materials. Key research areas include extending exciton models beyond Frenkel theory, incorporating quantum algorithms into surface hopping approaches, and benchmarking molecular dynamics methods. Recent work explores azo-escitalopram photoisomerization and methanimine dynamics on quantum computers. Her software contributions enable efficient simulations of multichromophoric systems and nonadiabatic processes, with applications in drug design and material science.

• Machine Learning
• Deep Learning
• Recurrent Neural Networks
• +7 more

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.