جستجوی استاد راهنمای دانشگاهی

Gregory Gutinمشاهده پروفایل

استاد

• Combinatorial Optimization
• Algorithms
• Graph Theory
• +۲ مورد دیگر

Gregory Gutin is a Professor of Computer Science at Royal Holloway, University of London, UK. He has held academic positions at Brunel University (Lecturer in Mathematics, 1996), Odense University (Visiting Lecturer in Computer Science, 1995; Postdoctoral Researcher, 1993), and was a PhD student at Tel Aviv University's School of Mathematics (1991). His career spans roles as a School Teacher in Gomel (Byelorussia, 1979), Researcher in Byelorussian institutions (1982-1987), and academic staff in the UK, Denmark, and Israel. He earned a PhD in Mathematics from Tel Aviv University, with prior research roles in Byelorussia (geology, oil, mathematics). His work bridges theoretical and applied computer science, focusing on combinatorial optimization, parameterized algorithms, and information security. Dr. Gutin's research centers on combinatorial optimization and parameterized algorithms , with applications in graph theory , constraint satisfaction , and access control in information security. His publications address arc routing problems, workflow satisfiability, and probabilistic methods for parameterized complexity, contributing both to foundational theory and practical implementations. His selected publications highlight a focus on fixed-parameter tractable algorithms for constraint satisfaction, arc routing in operations research, and access control mechanisms. These works solved open problems in algorithm design and influenced subsequent research in parameterized complexity and security systems. Best Paper Award at ACM SACMAT 2016 Best Paper Award at ACM SACMAT 2015 Royal Society Wolfson Research Merit Award 2014 Kirkman Medal 1996 Wolf Prize for PhD Students 1992 Dr. Gutin has collaborated extensively with researchers like Magnus Wahlstrom, Anders Yeo, and David Karapetyan. His work on workflow satisfiability introduced novel constraint classes used in access control systems, and he co-authored the influential textbook Digraphs: Theory, Algorithms and Applications (2009). The Royal Society award in 2014 recognized his sustained contributions to algorithmic research.

• High-Performance Computing
• Big Data Processing
• Performance Portability
• +۳ مورد دیگر

Enes Bajrovic is a researcher affiliated with the Faculty of Computer Science, focusing on high-performance computing (HPC), big data processing, and performance portability. His work spans task-based parallelism, runtime systems, and optimization frameworks for heterogeneous architectures. He has contributed to major European projects like PEPPHER and AutoTune, which aim to advance HPC software tools and autotuning methodologies. His research emphasizes practical applications of parallel computing in domains such as mobile networks and scientific simulations. Education: Dipl.-Ing. Dr.techn., BSc in Computer Science His research interests include developing frameworks for compute- and data-intensive applications, leveraging technologies like Kubernetes, OpenCL, and Intel Xeon Phi coprocessors. He has authored numerous peer-reviewed publications on topics such as pipeline patterns, autotuning algorithms, and hybrid execution models. His work bridges theoretical advancements in parallel computing with real-world software engineering challenges. Bajrovic has collaborated on projects funded by the European Commission’s FP7 program, contributing to deliverables like runtime systems, tuning frameworks, and benchmarking tools. His research also addresses the integration of big data processing with HPC, particularly in telecommunications and distributed computing environments.

Stefan Huberمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Industrial Automation
• +۷ مورد دیگر

Stefan Huber is Professor and Head of Research at the Department for Information Technologies and Digitalisation at Salzburg University of Applied Sciences. He also serves as Research Group Leader and Head of the Josef Ressel Center for Intelligent and Secure Industrial Automation, a €2.5M research center established in 2022 focusing on digital assistants for industrial machines with research fields in system architectures, artificial intelligence, and cybersecurity for operational technology. His research spans multiple domains including Machine Learning , Industrial Automation , Cyber Security , Computational Geometry , and Algorithm Theory . Huber's work has evolved chronologically from theoretical computational geometry to practical applications in industrial automation systems. His current research focuses on AI applications in industrial settings, particularly in Industry 4.0 contexts, with significant contributions to OPC UA security, reinforcement learning for control systems, and time series forecasting for industrial processes. Analysis of his recent publications (2023-2025) reveals a strong focus on practical AI applications in industrial automation, with particular emphasis on security aspects of operational technology, predictive modeling for manufacturing processes, and integration of machine learning techniques into industrial control systems. His work bridges theoretical computer science with real-world industrial applications. Excellence rating for Josef Ressel Centre evaluation (2024) Recognition for successful high-tech research (2024) Huber leads multiple significant research projects including AI4GREEN (Data Science for Sustainability, 2024-2027), JRZ ISIA (Josef Ressel Centre for Intelligent Industry Automation, 2022-2027), and IAI (Industrial Artificial Intelligence, 2023-2024). His research group at the Josef Ressel Centre comprises numerous researchers working on system architectures, AI, and cybersecurity for industrial automation. The center has received positive evaluations and media coverage for its high-tech research contributions to the field.

• Applied Mathematics
• Computational Physics
• Scientific Machine Learning
• +۱۰ مورد دیگر

Lukas Exl is a Senior Lecturer at the University of Vienna and a Research Director at the Wolfgang Pauli Institute (WPI), where he leads the Mathematical AI/ML Research Division. He holds a habilitation (venia docendi) in Computational Science from the University of Vienna, the first in this interdisciplinary field. Research Platform MMM Mathematics-Magnetism-Materials Wolfgang Pauli Institute (WPI), Vienna His research integrates Applied Mathematics, Computational Physics, and Scientific Machine Learning, focusing on numerical methods for PDE-based simulations, data-driven modeling, and reduced-order approaches. Key applications include computational micromagnetism for green energy materials and developing physics-informed neural networks (PINNs) with interpretable architectures. Recent publications emphasize machine learning techniques for magnetic material optimization, stray field computation, and trustworthy AI (TAI/XAI). He supervises students in Computational Science, Applied Mathematics, and Physics, with a focus on Extreme Learning Machines (ELMs), PINNs, and tensor decomposition methods. Data-driven Reduced Order Approaches for Micromagnetism (FWF Project, €484k, 2024-2028) Design of Nanocomposite Magnets by Machine Learning (FWF Project, €254k, 2022-2027) Reduced Order Approaches for Micromagnetics (FWF Project, €402k, 2018-2024) His team includes researchers like Dr. Sebastian Schaffer (PhD graduate), Kein Gjordeni, and Caroline Maitz. Collaborations span Danube University Krems and Technical University of Denmark's Energy Conversion and Storage department.

Nils Morten Kriegeمشاهده پروفایل

دانشیار

• Data Mining
• Machine Learning with Graphs
• Combinatorial Optimization
• +۵ مورد دیگر

Dr. Nils Morten Kriege is an Associate Professor at the Faculty of Computer Science, University of Vienna, where he leads the Data Mining and Machine Learning research group. Previously, he served as Assistant Professor (2020-2023) at the same institution and held positions at TU Dortmund including Interim Professor (2019/2020) and Postdoctoral Researcher (2015-2020). His research focuses on graph-based machine learning methods with applications in cheminformatics and drug discovery. His educational background includes a Doctorate in Computer Science (2015) and Diploma in Computer Science (2009), both from TU Dortmund. He has also been a Visiting Researcher at the University of York, UK. Dr. Kriege's research centers on graph algorithms and machine learning with graphs, particularly focusing on graph neural networks, graph kernels, and their applications in cheminformatics and drug discovery. His work bridges theoretical computer science with practical applications, developing novel methods for graph similarity, graph classification, and network analysis. He has made significant contributions to understanding the expressivity and robustness of graph neural networks, as well as developing efficient algorithms for graph similarity search and molecular analysis. His recent publications (2023-2025) demonstrate a strong focus on graph neural networks, with particular attention to their expressivity, robustness against attacks, and practical applications in drug discovery. His work spans theoretical foundations (Weisfeiler-Leman hierarchy, graph isomorphism testing), practical implementations (efficient quantization, defense frameworks), and domain-specific applications (cheminformatics, drug discovery). Vienna Research Groups for Young Investigators (2019) - €1,466k funding for "Algorithmic Data Science for Computational Drug Discovery" Member of the Global Young Faculty V, Stiftung Mercator (2017) Dr. Kriege leads an independent research group funded through the Vienna Research Groups for Young Investigators program, focusing on computational drug discovery. He has served on program committees for major conferences including NeurIPS, ICML, IJCAI, AAAI, ICLR, and ICDM, and has reviewed for prestigious journals such as Transactions on Pattern Analysis and Machine Intelligence. His teaching portfolio includes courses on Data Mining, Graph Learning, and Introduction to Machine Learning. He leads the Machine Learning with Graphs work group within the Data Mining and Machine Learning Research Group at the University of Vienna, collaborating with researchers like Wilfried Gansterer and Petra Mutzel on graph-based methods for drug design and molecular analysis.

Sagiv Shmuelمشاهده پروفایل

استاد

• Software verification
• Shape analysis
• Smart contracts
• +۷ مورد دیگر

Prof. Sagiv Shmuel is a Full Professor at Tel-Aviv University's Department of Computer Science. He has held various academic roles, including Associate Professor (2004–2005), Senior Lecturer (2000–2004), and visiting positions at institutions such as the University of Chicago and University of Copenhagen. His research focuses on software verification, shape analysis, smart contracts, and programming languages. Education: Ph.D. in Computer Science, Technion (1986–1990) B.A. in Computer Science, Technion (1982–1985), cum laude Research Interests: His work addresses challenges in static analysis, formal verification, and program analysis. Notable areas include invariant inference, smart contract security, and parametric shape analysis. These contributions have led to practical tools like Ivy and the foundation of Certora, a company specializing in smart contract verification. Awards: ACM Fellow (2016) Friedrich Wilhelm Bessel Research Award (2002) Microsoft Outstanding Collaborator Award (2016) Grants & Leadership: Principal Investigator (PI) of a Senior ERC Grant (1.57M Euros) for software composition verification Editor of Foundations and Trends in Programming Languages Chair of program committees for POPL, SAS, and other leading conferences His research has been applied to real-world systems, such as improving the Java concurrent library and ensuring kernel extension security in Linux.

Michael Reiterمشاهده پروفایل

مدرس ارشد

• Machine Learning
• Pattern Recognition
• Computer Vision
• +۴ مورد دیگر

Michael Reiter is a Senior Lecturer at the Institute of Visual Computing & Human-Centered Technology, part of the Faculty of Informatics at TU Wien. His research focuses on Machine Learning, Pattern Recognition, and Computer Vision, particularly in medical applications such as flow cytometry data analysis for leukemia diagnosis and minimal residual disease (MRD) assessment. He leads the MyeFLOW project (2020–2025), which develops automated methods for immunophenotyping and MRD detection in pediatric leukemia. His work bridges machine learning and clinical diagnostics, emphasizing explainable AI in healthcare. Reiter collaborates with medical institutions to translate algorithms into clinical tools, such as the FlowMe decision support system. His research interests include transformer networks, UMAP visualization, and Gaussian mixture models for biomedical data. He has supervised diploma theses on topics like meta learning for blast cell classification and U-Net-based flow cytometry analysis. Reiter’s publications span over two decades, with recent emphasis on medical AI applications. His lab, the Computer Vision Lab, focuses on visual computing for healthcare, robotics, and cultural heritage digitization. Current projects address automated analysis of museum herbarium collections and empathic robotic systems for autism therapy.

Frank Sommerمشاهده پروفایل

پژوهشگر ارشد

• Parameterized algorithms
• Algorithm engineering
• Graph algorithms
• +۲ مورد دیگر

Frank Sommer is a Humboldt fellow (Feodor Lynen scholarship for Postdocs) at Vienna University of Technology since June 2024. He is affiliated with the Institute of Logic and Computation, where he conducts research in theoretical computer science and algorithms. Previously, he held postdoc positions at Friedrich Schiller University Jena (May 2023-May 2024) and Philipps University Marburg (November 2022-May 2023). Dr. Sommer earned his PhD in Mathematics from Philipps-University Marburg (2017-2022), following a Master's degree (2015-2017) and Bachelor's degree (2012-2015), both in Mathematics from Friedrich Schiller University Jena. His research focuses on parameterized algorithms , algorithm engineering , and graph algorithms , with applications to hard problems in Data Science and Machine Learning. He has made significant contributions to the theoretical foundations of graph problems, particularly in network analysis, community detection, and subgraph optimization. His methodology combines theoretical analysis with practical implementation, emphasizing both computational complexity and real-world applicability. Dr. Sommer's publication record shows a strong emphasis on parameterized complexity, kernelization techniques, and practical algorithm implementations. His work spans from theoretical foundations to practical applications in network analysis, machine learning, and data science, with particular expertise in graph-theoretic problems and their computational aspects. Scientific Awards: Humboldt fellow (Feodor Lynen scholarship for Postdocs) Dr. Sommer collaborates extensively with researchers including Christian Komusiewicz, Niels Grüttemeier, and Tomohiro Koana. His work is supported by the Humboldt Foundation and has been published in top-tier computer science venues including Algorithmica, Journal of Graph Algorithms and Applications, and proceedings of major conferences like ESA and ISAAC. He is part of the Algorithms and Complexity Group within the Institute of Logic and Computation at TU Vienna, contributing to research on fundamental algorithmic problems with applications across various domains. His current research explores the intersection of theoretical computer science with practical challenges in data science and machine learning.

Jens Carsten Jantzenمشاهده پروفایل

استاد

• Algebraic Groups
• Lie Algebras
• Representation Theory

Jens Carsten Jantzen is a Professor of Mathematics at Aarhus Universitet , Denmark. Born in Störtewerkerkoog, Germany on October 18, 1948, he has held academic positions at prestigious institutions including the University of Oregon, Universität Hamburg, and Universität Bonn. His career spans over four decades with significant contributions to algebraic groups, Lie algebras, and representation theory. Current: Professor at Aarhus Universitet (1995–present) Previous: University of Oregon (1988–1995), Universität Hamburg (1985–1988), Universität Bonn (1978–1985) Jantzen’s research focuses on Algebraic Groups , Lie Algebras , and Representation Theory , with particular emphasis on modular representations, cohomology, and nilpotent orbits. His publications demonstrate a deep exploration of quantum groups, highest weight modules, and geometric methods in representation theory. He has served on editorial boards for leading journals such as Representation Theory and Acta mathematica . Notable awards include the Felix-Hausdorff-Gedächtnispreis (1975) , Fellow of the American Mathematical Society (2011) , and Member of the Academia Europaea (2012) .

Astola Jaakko Tapioمشاهده پروفایل

استاد

• Signal Processing
• Image Processing
• Nonlinear Signal Processing
• +۹ مورد دیگر

Jaakko Tapio Astola is a Professor of Signal Processing at Tampere University of Technology since 1993. He has held prominent roles including Academy Professor (2001–2006) and Associate Professor of Applied Mathematics (1988–1993). His academic journey includes positions at Lappeenranta and Eindhoven Universities of Technology. Astola is a member of the Finnish Academies of Science and Technology, and has received prestigious awards like the EURASIP Fellowship (2012), SPIE Fellowship (2007), and IEEE Fellowship (2000). His research spans signal processing, nonlinear filtering, bioinformatics, and algebraic coding theory. Notable contributions include foundational work on vector median filters and influential textbooks on nonlinear digital filtering. His interdisciplinary work bridges signal processing with systems biology and genetic network modeling. Education Highlights: 1984–1985: Research at Eindhoven University of Technology with Prof. J.H. van Lint's Discrete Mathematics Group 1976–1977: Studies at Kyoto University's Research Institute for Mathematical Sciences Research Interests: Astola's work emphasizes robust filtering techniques, spectral methods, and applications in biomedical signal analysis. His contributions to algebraic signal processing and bioinformatics include advancing Boolean network models for genetic systems and developing systematic bioinformatic analysis frameworks for large-scale genomic data. Awards: EURASIP Fellow (2012) Recipient of 2010 Nokia Award IEEE Fellow (2000) Grants & Advising: While specific grants are not detailed, his extensive academic roles and publications indicate sustained research funding. No student advisees are listed in available texts.

Mitra Sanjitمشاهده پروفایل

دانشگاهی

• Digital Signal Processing
• Analog Signal Processing
• Computer-Aided Design
• +۳ مورد دیگر

Sanjit K. Mitra is a Distinguished Professor Emeritus at the University of California, Santa Barbara (UCSB), and a Life Fellow of the IEEE. He holds memberships in prestigious academies including the U.S. National Academy of Engineering, European Academy of Arts & Sciences, and Bavarian Academy of Sciences and Humanities. His career includes roles as Distinguished Professor and Professor at UCSB, UC Davis, and Cornell University, alongside contributions at Bell Labs. Key Positions: Distinguished Professor Emeritus (UCSB), Member of Technical Staff (Bell Labs), 1962–2006. Education: Not explicitly detailed in texts, but his academic lineage spans decades of mentorship. Research focuses on digital and analog signal processing, filter design, and image/video compression. He authored 14 books and over 700 publications, cited 31k+ times (h-index 73). His work has been translated into multiple languages and influenced global engineering curricula. Awards include the IEEE Education Medal (2006), Golden Jubilee Medal (1999), and recognition as a 'Pioneer in Signal Processing' (1998 & 2017). His 48 Ph.D. students include 9 IEEE Fellows and a U.S. NAE member.

• Manifold Mathematics
• Symplectic Manifold
• Diffeomorphism
• +۷ مورد دیگر

Stefan Haller is a Senior Lecturer at the Faculty of Mathematics , affiliated with the Institute of Mathematics . His research spans differential geometry, symplectic structures, and topological invariants, with a focus on coadjoint orbits, sub-Riemannian geometry, and analytic torsion. He leads projects such as Analytical torsion of (2,3,5) distributions (2025–2029) and Analytical torsion of filtered manifolds (2019–2023). Stefan’s work explores manifolds , diffeomorphism groups , and coadjoint orbits , with key contributions to sub-Riemannian geometry and symplectic fiber bundles. His recent publications highlight the interplay between geometric structures and spectral invariants, particularly in filtered manifolds and (2,3,5) distributions. The 15 most recent articles emphasize differential geometry , symplectic geometry , and topology , analyzing topics like BGG sequences, nonlinear flag manifolds, and analytic eigenbranches. These works reflect his dedication to understanding geometric and topological properties of manifolds through algebraic and spectral methods. Stefan actively participates in academic activities, including invited lectures on spectral invariants and sub-Riemannian geometry at international conferences (2025). His collaborations, such as with C. Vizman, underscore his role in advancing geometric analysis.

Samuele Tosattoمشاهده پروفایل

استادیار

• Reinforcement Learning
• Robotics
• Machine Learning
• +۶ مورد دیگر

Samuele Tosatto is an Assistant Professor in the Computer Science Department at the University of Innsbruck and affiliated with the Digital Science Center. His research focuses on enabling robots to learn in real-world environments through reinforcement learning and abstraction techniques, addressing challenges in scalability and efficiency. Ph.D. in Computer Science from Technical University of Darmstadt (2020) M.Sc. and B.Sc. in Software Engineering from Polytechnic University of Milan His work emphasizes theoretical aspects of reinforcement learning, particularly policy gradient estimation and temporal-difference methods. Recent publications highlight innovations in data-driven teleoperation, movement primitives, and off-policy optimization, aligning with his goal of bridging simulation-to-reality gaps. He teaches courses including Introduction to Robotics, Optimization and Numerical Computation, Machine Learning, and Data Analysis II. His research is disseminated via platforms like Google Scholar and samueletosatto.com.

• Astroparticle Physics
• High-Energy Gamma-Ray Astronomy
• Very-High-Energy Gamma Rays
• +۱۰ مورد دیگر

Dr. Markus Holler is a Scientific Staff member at the Institute for Astro- and Particle Physics, Faculty of Mathematics, Computer Science and Physics, University of Innsbruck, Austria. He is a key contributor to the H.E.S.S. (High Energy Stereoscopic System) collaboration, focusing on very-high-energy gamma-ray astronomy and astroparticle physics. His research spans a wide range of high-energy astrophysical phenomena, including pulsars, supernova remnants, active galactic nuclei, gamma-ray binaries, and transient events such as gamma-ray bursts and neutron star mergers. His research interests are centered on high-energy gamma-ray astronomy , astroparticle physics , and multi-messenger astronomy . He specializes in the morphology and spectral analysis of extended gamma-ray sources, diffuse emission studies, and the development of advanced data analysis and simulation techniques for Imaging Atmospheric Cherenkov Telescopes. His work often involves large-scale international collaborations such as H.E.S.S., LIGO/Virgo, IceCube, and the Event Horizon Telescope (EHT). He has contributed to landmark discoveries, including the detection of 100 TeV gamma rays from the Crab Nebula, the study of particle acceleration in SS 433, and multi-messenger follow-ups of neutron star mergers. The trends in his recent publications highlight a strong focus on TeV emission from extreme astrophysical environments , morphological studies of nebulae and halos , multi-wavelength and multi-messenger campaigns , and methodological advancements in gamma-ray data analysis . His work frequently bridges observational results with theoretical models of particle acceleration, cosmic-ray propagation, and emission mechanisms. Dr. Holler actively participates in public outreach, delivering popular science lectures on topics such as Cherenkov telescopes and cosmic messengers. He has also held organizational roles within the H.E.S.S. collaboration, including chairing sessions and leading task forces related to diffuse emission and analysis frameworks. There are no indications of formal teaching roles or academic advising in the provided data.

• Inverse Problems
• Mathematical Imaging
• Computational Mathematics
• +۳ مورد دیگر

Prof. Otmar Scherzer is a distinguished academic at the Faculty of Mathematics, Department of Mathematics, University of Vienna . His work spans Inverse Problems, Mathematical Imaging, Computational Mathematics, and Applied Mathematics , with a focus on tomography, neural networks, and medical imaging. Over nearly two decades, he has published extensively in journals like Inverse Problems , SIAM Review , and IEEE Transactions . 2025 : Advanced data-driven regularization and neural network applications in inverse problems. 2024 : Explored diffraction tomography, Hilbert space approaches to scattering, and motion detection algorithms. 2023 : Addressed uncertainty quantification, visco-acoustic imaging, and Doppler ultrasound interpolation. His recent research emphasizes uncertainty-aware blob detection, strain reconstruction in elastography , and Fourier-based tomography . Publications frequently integrate numerical experiments and computational inverse methods , reflecting a blend of theoretical and applied mathematics. Prof. Scherzer collaborates with institutions like RICAM (Johann Radon Institute) and contributes to hybrid imaging modalities such as quantitative optical coherence elastography and photoacoustic tomography . He has supervised projects in 3D shape reconstruction and non-convex regularization , though specific student names are not listed in the provided data.