Find Academic Supervisors

Benediktsson Jon AtliView profile

Professor

• Remote Sensing
• Pattern Recognition
• Neural Networks
• +3 more

Jon Atli Benediktsson is the Rector and President of the University of Iceland (since 2015) and a Full Professor in Electrical Engineering (since 1996). He holds a Cand. Scient from the University of Iceland (1984), M.S.E.E. (1987), and Ph.D. (1990) from Purdue University. His research focuses on remote sensing, pattern recognition, neural networks, and data fusion. He has held key roles including Pro-Rector for Academic Affairs (2009–2015), Head of Graduate School (2009–present), and Visiting Professorships at the University of Trento and Kingston University. He has advised 9 PhD students and contributed to major editorial roles including Editor-in-Chief of IEEE GRSS journals and membership in prestigious committees. His honors include the IEEE GRSS David Landgrebe Award (2018), IEEE Fellow (2004), and SPIE Fellow (2013). His work spans hyperspectral data analysis, machine learning applications, and geospatial technologies. Key Positions: Rector, University of Iceland | Full Professor (1996–present) Education: Purdue University (Ph.D., 1990), University of Iceland (Cand. Scient, 1984) Research: Remote Sensing, Image Analysis, Biomedical Signal Processing Awards: Over 15 major international accolades including IEEE and SPIE fellowships

• Artificial Intelligence
• Machine Learning
• Industrial Automation
• +3 more

Stefan Paschek is a Senior Lecturer/Researcher at Technikum Wien, serving as Head of the Competence Center IoT and Electronics (ITE). His work focuses on applying artificial intelligence, machine learning, and IoT technologies to industrial problems, including parameter optimization, thermodynamic analysis, and automation. His research bridges theoretical advancements in neural networks and practical applications in SMEs. Research interests include AI-driven solutions for industrial challenges, such as lubrication system optimization, valve parameterization, and data clustering in thermodynamic systems. He actively contributes to conferences like KI4Industry, emphasizing AI's practical implementation in small and medium enterprises. His publications highlight interdisciplinary approaches, combining machine learning with mechanical engineering and data visualization. While no scientific awards are noted, his work reflects a commitment to advancing industrial automation through innovative computational methods. Advising and grants are not explicitly detailed, but his role as Competence Center Head suggests leadership in collaborative projects. The ITE Center serves as a hub for IoT and electronics research, fostering applied technological solutions.

• Discrete Optimization
• Algorithm Design
• Computer Vision
• +3 more

Prof. Vladimir Kolmogorov is a faculty member at the Institute of Science and Technology Austria (IST Austria), specializing in discrete optimization and algorithm design. He holds a Ph.D. in Computer Science from Cornell University and has held positions at Microsoft Research and University College London. His research focuses on combinatorial optimization, MAP inference in graphical models, and applications in computer vision. Educations: M.S. in Applied Mathematics and Physics, Moscow Institute of Physics and Technology Ph.D. in Computer Science, Cornell University Research Interests: Dr. Kolmogorov's work spans algorithmic optimization, including complexity analysis of constraint satisfaction problems, graph algorithms, and machine learning applications. His contributions include foundational work on graph cuts for computer vision and the development of efficient optimization methods for discrete problems. Publications: His recent work includes advancements in parallel algorithms for Gibbs distributions, semidefinite programming, and combinatorial optimization. These contributions highlight his expertise in bridging theoretical computer science with practical applications. Awards: Royal Academy of Engineering/EPSRC Research Fellowship (2006–2011) ERC Consolidator Grant (2014–2020) Best Paper Award at ECCV 2002 Outstanding Student Paper Award (NIPS 2007) Best Paper Honorable Mention (CVPR 2005) Advising and Grants: He has advised multiple PhD students and leads a research team at IST Austria. His grants include significant funding for exploring optimization in machine learning and discrete systems. Labs/Teams: His lab focuses on theoretical and applied discrete optimization, collaborating with institutions globally. Current projects include developing faster algorithms for graph problems and advancing Gibbs distribution analysis.

• Machine Learning
• Computer Vision
• Robust Learning
• +7 more

Christoph H. Lampert is a Professor at the Institute of Science and Technology Austria (ISTA), leading the Machine Learning and Computer Vision Group. His research focuses on creating robust, fair, and verifiable machine learning systems with strong theoretical foundations. Academic Rank: Professor (ISTA) Research Focus: Machine Learning, Computer Vision, Robustness, Fairness, Formal Verification Editorial Roles: Action Editor (JMLR), Former Editor (IJCV), Associate Editor-in-Chief (TPAMI) His recent publications explore robust deep learning architectures, formal verification of neural networks, and fairness in multi-source learning environments. Research keywords span neural network design, algorithmic accountability, and structured data modeling. Scientific achievements include: DARPA Disruptive Ideas award (2023) ISTA Alumni Award (2023) He has mentored numerous PhD students including: Bernd Prach (2022 thesis: Robust image classification with 1-Lipschitz networks) Egor Zverev, Nikita Kalinin, Hossein (Qualifying Exam passed 2023-2025) Alex Peste (2023 thesis: Robustness and Fairness in Machine Learning) Mary Phuong (2021 thesis: Underspecification in Deep Learning) Amelie Royer (2020 thesis: Computer Vision applications) Alexander Kolesnikov (2018 thesis: Weakly-Supervised Segmentation) Alex Zimin (2018 thesis: Dependent data learning)

• Medical Image Analysis
• Machine Learning
• Computer Vision
• +4 more

Georg Langs is a PartTime Lecturer at the Faculty of Informatics, Vienna University of Technology , specializing in Medical Image Analysis , Machine Learning , and Computer Vision applications in Radiology and Neuroscience . His research bridges Biomedical Engineering and Pattern Recognition to advance diagnostics and predictive modeling in healthcare. His work focuses on spatiotemporal disease modeling (e.g., bone lesion progression in multiple myeloma), fetal brain connectivity via in-utero fMRI, and automated image segmentation using deep learning and probabilistic frameworks. He has developed cloud-based benchmarking systems for medical image analysis and contributed to functional connectivity mapping in pediatric and fetal neuroscience. Key article trends include AI-driven radiology , dynamic lesion prediction , and reproducibility in fetal imaging . His collaborations span institutions in Austria, the UK, and China. He mentors students in deep learning , longitudinal image analysis , and medical AI through diploma theses.

• Formal Methods
• Cybersecurity
• Software Engineering
• +3 more

Sarah Sophie Sallinger is a PreDoc Researcher at the Institute of Software Technology within the Faculty of Informatics at Technische Universität Wien. Her research focuses on formal methods applied to systems engineering, including cybersecurity, probabilistic systems, and software reliability. She contributes to projects such as ARTIST (2021–2026) and ProbInG (2020–2025). Her work addresses challenges like backdoor detection in software, formalization of Heisenbugs, and controller synthesis for probabilistic hyperproperties. Publications reflect her expertise in formal verification and cybersecurity, with recent work emphasizing practical applications of theoretical frameworks. She supervises student research, including Consistency-based software fault localization with multiple observations (Lukas Graussam, 2024). Contact: Email | Profile

• Software Engineering
• Programming Languages
• Static Analysis
• +4 more

Michael Schröder is a Researcher at TU Wien's Faculty of Informatics, affiliated with the Institute of Software Technology. He holds a BSc and a Dipl.-Ing. (Master's equivalent) in Informatics. His research focuses on software engineering, static analysis, and programming languages, with notable contributions to probabilistic program analysis, security, and empirical studies on developer workflows. Education : Bachelor of Science in Informatics Diplom-Ingenieur (Dipl.-Ing.) in Informatics from TU Wien Research Interests : His work spans static analysis of probabilistic programs, security in software repositories, grammar inference for ad hoc parsers, feature flag dependency analysis in large systems, and empirical studies on command-line tool customization. He has also contributed to medical research in pediatric oncology through a 2019 study on leukemia genetics. Recent Trends in Publications : Recent efforts emphasize language-agnostic static analysis frameworks (2024), lateral movement detection in source code (2023), and grammar inference techniques for parsers (2022). Earlier work includes neural ranking models (2020) and transactional memory systems (2015). Grants & Advising : No explicitly listed grants or student advisement, though recent theses supervised include static grammar inference projects (2025). Labs/Teams : Not explicitly mentioned in available records.

Fotios LygerakisView profile

Researcher

• Representation Learning
• Robotics
• Machine Learning
• +7 more

Fotios Lygerakis is a doctoral student and university assistant at the Department of Cyber-Physical Systems (CPS) at Montanuniversität Leoben, Austria. His research focuses on advancing machine learning and robotics, particularly in representation learning, visuotactile fusion, and reinforcement learning for robotic manipulation. He holds a Diploma in Electrical and Computer Engineering from the Technical University of Crete (2019) and has held roles including teaching assistant at the University of Texas at Arlington, research assistant at Demokritos (Athens), and research intern at Toshiba Research Europe. His research interests span representation learning, multimodal fusion, reinforcement learning, and healthcare robotics. Notable contributions include work on CR-VAE and M2CURL, earning a Best Student Paper Award at the 2024 Ubiquitous Robotics Conference. Lygerakis actively supervises theses in areas like human-robot interaction and self-supervised learning, and teaches courses on machine learning and deep learning. Lygerakis maintains active engagement in the scientific community through reviewing for journals/conferences (e.g., IROS, IJRR), organizing workshops, and leading the Neural Coffee Reading Group. He has presented invited talks at institutions like New York University and Technical University of Crete, and his work has been featured in outlets like Computer Vision News.

• Machine Learning
• Graph Neural Networks
• Differential Privacy
• +3 more

Patrick Indri is a PreDoc Researcher at the Department of Informatics, Technische Universität Wien , specializing in machine learning and its intersections with formal methods and data privacy. His work focuses on graph neural networks, robustness verification, and temporal logic applications. Education : MSc degree holder Research Interests : Expressive Graph Neural Network architectures for specialized graph types Differential Privacy integration in graph-based learning systems Robustness Certification with probabilistic guarantees Temporal logic applications in Cyber-Physical Systems anomaly detection Key Projects include the StruDL (2023–2027) initiative exploring structured deep learning. His publications demonstrate expertise in both foundational machine learning and applied formal verification techniques.

Thomas GrafView profile

Research Professor

• Macrophage
• Cancer biology
• Reprogramming
• +16 more

Thomas Graf is a Research Professor at the Centre for Genomic Regulation (CRG) in Barcelona, Spain, and an ICREA Research Professor since 2006. His academic career spans multiple prestigious institutions including Albert Einstein College of Medicine, European Molecular Biology Laboratory, and Max Planck Institute. Research focus: Cancer biology, Reprogramming, Genetic engineering, Molecular biology Key contributions: Hematopoietic stem cells, Transdifferentiation, Computational biology His recent publications demonstrate expertise in inverse imaging problems, diffusion models, and biomedical applications spanning cardiology and protein structure analysis. Scientific accolades include the Paul Ehrlich Prize and membership in Academia Europaea. 1989: Paul Ehrlich and Ludwig Darmstaedter Prize 1983: Wilhelm Warner Foundation Prize Other: Leukemia Research Award, German Society for Microbiology and Hygiene Prize, Academia Europaea membership

• Randomized Algorithms
• Networking
• Social Networks
• +7 more

Aravind Srinivasan is a Professor of Computer Science at the University of Maryland, College Park, USA. He holds tenure and has held academic positions at the National University of Singapore and Bell Labs. He completed his B.Tech at IIT Madras and his Ph.D. at Cornell University, with postdoctoral research at the Institute for Advanced Study and DIMACS. His research focuses on randomized algorithms, networking, social networks, combinatorial optimization, and their applications to public health, machine learning, and energy systems. He has published over 115 papers in top journals like Nature and the Journal of the ACM, and his work has received significant recognition, including 1,551 citations for his 2004 Nature paper on disease modeling. Education: B.Tech, Indian Institute of Technology Madras Ph.D., Cornell University Awarded Fellowships from ACM, AAAS, IEEE, and EATCS, he also serves as Editor-in-Chief of the ACM Transactions on Algorithms. His students have secured roles in academia, industry, and government, reflecting his impactful mentorship. His contributions span theoretical computer science and applied domains, with notable work in probabilistic methods, network science, and interdisciplinary applications.

Jerzy TiurynView profile

Professor

• Bioinformatics
• Computational Biology
• Sequence Analysis
• +9 more

Jerzy Tiuryn is a Full Professor at the Faculty of Mathematics, Informatics and Mechanics, University of Warsaw (since 1992), with a PhD in Mathematics from the same university (1975). He served as Vice Dean for Scientific Matters and International Relations (2005-2012) and is actively engaged in editorial roles for journals like Theoretical Computer Science , Information and Computation , and BioMed Research International . Education: PhD in Mathematics (University of Warsaw, 1975). Research Interests: His work spans past contributions in program verification, semantics, typed lambda calculus, and type theory, with current focus on bioinformatics and computational biology. Specific areas include sequence analysis, gene family evolution, protein-protein interaction networks, gene regulatory regions, gene mutations in bacterial drug resistance, and comparative genomics. Trends in Publications: His recent articles emphasize computational methods in bacterial genomics (CAMBerVis, CAMBer), regulatory element prediction (Bi-Billboard), protein interaction mapping via phylogeny, and Bayesian network modeling for gene expression. Themes center on genomic data analysis, evolutionary conservation, and algorithm development for biological systems. Scientific Contributions: Awards and memberships highlight leadership in European and Polish academic networks: Board Member, European Research Consortium for Informatics and Mathematics (ERCIM) (2011) First President, Polish Bioinformatics Society (2008-2010) Founding Member/President, PLERCIM (2007-2012) Member, Academia Europaea (since 1996)

Robert YoungView profile

Professor

• Polymers
• Carbon Nanotubes
• Graphene
• +4 more

Professor Robert Young is a leading academic in Polymer Science and Technology at the University of Manchester, UK. With a distinguished career spanning decades, he has held visiting professorships at The Hong Kong Polytechnic University, King Fahd University of Petroleum and Minerals, and École Polytechnique Fédérale de Lausanne. His research focuses on polymers, nanotechnology, and composite materials. Current: Professor of Polymer Science and Technology, University of Manchester 2015-2017: Visiting Chair Professor, The Hong Kong Polytechnic University 2011-2013: Visiting Professor, King Fahd University of Petroleum and Minerals His work combines experimental and computational approaches to study polymer composites, graphene, and advanced materials characterization techniques like Raman spectroscopy. Recent publications emphasize neuromorphic computing, spiking neural networks, and hardware-aware AI. Professor Young's accolades include the 2019 Platinum Medal from the Institute of Materials, Minerals and Mining, and fellowships at the Royal Society (2013) and Royal Academy of Engineering (2006). He has delivered prestigious lectures including the Cockcroft Lecturer (1996) and Wolfson Research Professor (1992).

• Bayesian Probability Theory
• Uncertainty Quantification
• Machine Learning
• +6 more

Sascha Ranftl is an Erwin Schrödinger Fellow at the Courant Institute of Mathematical Sciences, New York University. Their research spans interdisciplinary domains including Bayesian probability theory, uncertainty quantification, computational engineering, and biomedical applications. Affiliation: Courant Institute, NYU Former Affiliation: Graz University of Technology (Institute of Theoretical Physics & Computational Physics, Graz Center of Computational Engineering) Research Interests: Ranftl's work focuses on integrating Bayesian inference with machine learning and physics-informed modeling to address uncertainty quantification in biomedical simulations, particularly for aortic dissection and impedance cardiography . Their approach harmonizes statistical rigor with engineering pragmatism, emphasizing surrogate models and computational fluid dynamics. Recent Publications: Analyze geometric uncertainties in patient-specific blood vessels, develop physics-consistent neural networks, and apply Bayesian frameworks to biomedical diagnostics and elastoplastic material modeling. Scientific Awards: Erwin Schrödinger Fellowship Contact: ranftl@tugraz.at sranf@duck.com

• Causal Inference
• Machine Learning
• Statistical Modeling
• +4 more

Dr. Lucas Kook is a Researcher at the Department of Statistics and Mathematics, WU Vienna University of Economics and Business. His research focuses on causal inference, machine learning, and statistical modeling, with applications in sports analytics, medical data analysis, and healthcare outcomes. He actively develops R packages such as deeptrafo for neural network-based statistical inference. His work bridges theoretical advancements in distributional regression and practical implementations in healthcare and sports domains. Key research areas include algorithm development for significance testing in supervised learning, causal feature selection, and nonparametric methods for conditional independence. Recent studies explore the efficacy of thrombolytic therapies in retinal artery occlusion and the predictive power of deep learning models in stroke patient outcomes. Collaborations span interdisciplinary fields, emphasizing reproducibility and methodological rigor in simulation studies.